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  Subjects -> ELECTRONICS (Total: 207 journals)
Showing 1 - 200 of 277 Journals sorted by number of followers
IEEE Transactions on Aerospace and Electronic Systems     Hybrid Journal   (Followers: 313)
Control Systems     Hybrid Journal   (Followers: 253)
IEEE Transactions on Geoscience and Remote Sensing     Hybrid Journal   (Followers: 201)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 197)
Electronics     Open Access   (Followers: 138)
Advances in Electronics     Open Access   (Followers: 133)
Electronic Design     Partially Free   (Followers: 129)
Electronics For You     Partially Free   (Followers: 128)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 120)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 91)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 89)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 88)
IEEE Transactions on Software Engineering     Hybrid Journal   (Followers: 84)
IEEE Transactions on Industrial Electronics     Hybrid Journal   (Followers: 84)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 81)
IET Power Electronics     Open Access   (Followers: 70)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 67)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 63)
IEEE Embedded Systems Letters     Hybrid Journal   (Followers: 62)
IEEE Transactions on Industry Applications     Hybrid Journal   (Followers: 58)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 53)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 53)
Advances in Power Electronics     Open Access   (Followers: 49)
IEEE Nanotechnology Magazine     Hybrid Journal   (Followers: 45)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 45)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 41)
IEEE Transactions on Biomedical Engineering     Hybrid Journal   (Followers: 35)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 34)
IET Microwaves, Antennas & Propagation     Open Access   (Followers: 34)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 32)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 30)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 28)
Electronics Letters     Open Access   (Followers: 28)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 27)
Microelectronics and Solid State Electronics     Open Access   (Followers: 27)
International Journal of Power Electronics     Hybrid Journal   (Followers: 24)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 24)
Journal of Sensors     Open Access   (Followers: 23)
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 22)
IEEE Reviews in Biomedical Engineering     Hybrid Journal   (Followers: 20)
IEEE/OSA Journal of Optical Communications and Networking     Hybrid Journal   (Followers: 19)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 18)
Journal of Artificial Intelligence     Open Access   (Followers: 18)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 17)
IET Wireless Sensor Systems     Open Access   (Followers: 17)
Circuits and Systems     Open Access   (Followers: 16)
Archives of Electrical Engineering     Open Access   (Followers: 15)
International Journal of Control     Hybrid Journal   (Followers: 14)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 14)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 14)
IEEE Women in Engineering Magazine     Hybrid Journal   (Followers: 13)
Advances in Microelectronic Engineering     Open Access   (Followers: 13)
IEEE Solid-State Circuits Magazine     Hybrid Journal   (Followers: 13)
Machine Learning with Applications     Full-text available via subscription   (Followers: 12)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 12)
IEEE Transactions on Broadcasting     Hybrid Journal   (Followers: 12)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 11)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 11)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 11)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 11)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 11)
Open Journal of Antennas and Propagation     Open Access   (Followers: 10)
Solid-State Electronics     Hybrid Journal   (Followers: 10)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 10)
IETE Journal of Research     Open Access   (Followers: 10)
Batteries     Open Access   (Followers: 9)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 9)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 9)
IETE Technical Review     Open Access   (Followers: 9)
Nature Electronics     Hybrid Journal   (Followers: 9)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 8)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 8)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 8)
China Communications     Full-text available via subscription   (Followers: 8)
Superconductivity     Full-text available via subscription   (Followers: 8)
IEEE Transactions on Autonomous Mental Development     Hybrid Journal   (Followers: 8)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 8)
International Journal of Antennas and Propagation     Open Access   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 8)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 8)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
Power Electronic Devices and Components     Open Access   (Followers: 7)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 7)
Nanotechnology, Science and Applications     Open Access   (Followers: 7)
IEEE Magnetics Letters     Hybrid Journal   (Followers: 7)
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Metrology and Measurement Systems     Open Access   (Followers: 6)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 6)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 6)
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access   (Followers: 6)
International Journal of Electronics     Hybrid Journal   (Followers: 6)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 6)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 6)
Journal of Power Electronics     Hybrid Journal   (Followers: 6)
Annals of Telecommunications     Hybrid Journal   (Followers: 6)
Electronic Markets     Hybrid Journal   (Followers: 6)
Energy Storage Materials     Full-text available via subscription   (Followers: 6)
IEEE Transactions on Services Computing     Hybrid Journal   (Followers: 5)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 5)
Journal of Optoelectronics Engineering     Open Access   (Followers: 5)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 5)
Journal of Field Robotics     Hybrid Journal   (Followers: 5)
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
IEEE Pulse     Hybrid Journal   (Followers: 5)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal   (Followers: 4)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
EPE Journal : European Power Electronics and Drives     Hybrid Journal   (Followers: 4)
Advanced Materials Technologies     Hybrid Journal   (Followers: 4)
Frontiers in Electronics     Open Access   (Followers: 4)
Wireless and Mobile Technologies     Open Access   (Followers: 4)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
IEEE Transactions on Haptics     Hybrid Journal   (Followers: 4)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 4)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 4)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Informatik-Spektrum     Hybrid Journal   (Followers: 3)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 3)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 3)
Advancing Microelectronics     Hybrid Journal   (Followers: 3)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 3)
IETE Journal of Education     Open Access   (Followers: 3)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Sensors International     Open Access   (Followers: 3)
e-Prime : Advances in Electrical Engineering, Electronics and Energy     Open Access   (Followers: 3)
EPJ Quantum Technology     Open Access   (Followers: 3)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 3)
Transactions on Electrical and Electronic Materials     Hybrid Journal   (Followers: 2)
ACS Applied Electronic Materials     Open Access   (Followers: 2)
IET Smart Grid     Open Access   (Followers: 2)
Energy Storage     Hybrid Journal   (Followers: 2)
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 2)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal   (Followers: 2)
Journal of Information and Telecommunication     Open Access   (Followers: 2)
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 2)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 2)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Journal of Nuclear Cardiology     Hybrid Journal   (Followers: 2)
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access   (Followers: 1)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 1)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 1)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Електротехніка і Електромеханіка     Open Access   (Followers: 1)
Open Electrical & Electronic Engineering Journal     Open Access   (Followers: 1)
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal   (Followers: 1)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Transactions on Cryptographic Hardware and Embedded Systems     Open Access   (Followers: 1)
International Journal of Hybrid Intelligence     Hybrid Journal   (Followers: 1)
Ural Radio Engineering Journal     Open Access   (Followers: 1)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Automatika : Journal for Control, Measurement, Electronics, Computing and Communications     Open Access  
npj Flexible Electronics     Open Access  
Elektronika ir Elektortechnika     Open Access  
Emitor : Jurnal Teknik Elektro     Open Access  
IEEE Solid-State Circuits Letters     Hybrid Journal  
IEEE Open Journal of Industry Applications     Open Access  
IEEE Open Journal of the Industrial Electronics Society     Open Access  
IEEE Open Journal of Circuits and Systems     Open Access  
Journal of Electronic Science and Technology     Open Access  
Solid State Electronics Letters     Open Access  
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Journal of Engineered Fibers and Fabrics     Open Access  
Jurnal Teknologi Elektro     Open Access  
IET Nanodielectrics     Open Access  
Elkha : Jurnal Teknik Elektro     Open Access  
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Jurnal Teknik Elektro     Open Access  
IACR Transactions on Symmetric Cryptology     Open Access  
Acta Electronica Malaysia     Open Access  
Bioelectronics in Medicine     Hybrid Journal  
Chinese Journal of Electronics     Open Access  
Problemy Peredachi Informatsii     Full-text available via subscription  
Technical Report Electronics and Computer Engineering     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Visión Electrónica : algo más que un estado sólido     Open Access  
Telematique     Open Access  
International Journal of Nanoscience     Hybrid Journal  
International Journal of High Speed Electronics and Systems     Hybrid Journal  
Semiconductors and Semimetals     Full-text available via subscription  

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Similar Journals
Journal Cover
IEEE Transactions on Geoscience and Remote Sensing
Journal Prestige (SJR): 2.649
Citation Impact (citeScore): 6
Number of Followers: 201  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0196-2892 - ISSN (Online) 1558-0644
Published by IEEE Homepage  [228 journals]
  • 2-D and 3-D Image-Domain Least-Squares Reverse Time Migration Through
           Point Spread Functions and Excitation-Amplitude Imaging Condition

    • Free pre-print version: Loading...

      Authors: Wei Zhang;Jinghuai Gao;
      Pages: 1 - 15
      Abstract: The enormous computational overheads and excessive storage requirements are two obstacles to the data-domain least-squares reverse time migration (RTM) approach for the application of large-scale 3-D seismic data. To alleviate this problem, we have developed an image-domain least-squares RTM (IDLSRTM) approach through point spread functions (PSFs) and excitation-amplitude (EA) imaging condition, denoted as EA-IDLSRTM. The key point is that the EA imaging condition, as a cost-effective and practical imaging condition, is used to reconstruct the RTM image and localized PSFs. There are two benefits to this combination. One is that the EA imaging condition can effectively reconstruct the RTM image and localized PSFs with less computational overhead and storage requirement, relative to the zero-lag cross correlation (CC) imaging condition. Another important benefit is that the redundant source wavelets in both the RTM and PSF images computed by the CC imaging condition can be removed by the EA imaging condition, prior to the image-domain inversion. As a result, the proposed approach can explicitly reduce the condition number of the Hessian matrix used in the conventional IDLSRTM approach, which will produce a less ill-conditioned inverse problem. In addition, we introduce an angle-dependent filter for the attenuation of low-wavenumber artifacts to accelerate the convergence. Several experiments with synthetic and field data demonstrate that the proposed EA-IDLSRTM approach can efficiently and effectively recover the high-resolution and high-fidelity reflectivity image. Meanwhile, EA-IDLSRTM can provide better imaging quality than the conventional IDLSRTM approach in the case of relatively smoothed velocity.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Endfire Synthetic Aperture Radar for a Cryobot for Exploration of Icy
           Moons and Terrestrial Glaciers

    • Free pre-print version: Loading...

      Authors: Omkar Pradhan;Albin J. Gasiewski;
      Pages: 1 - 14
      Abstract: An endfire synthetic aperture radar (SAR) for use on a cylindrical ice-penetrating cryobot is presented. The SAR facilitates obstacle detection and mapping inside ice for subsurface exploration using such a cryobot vehicle. The SAR is comprised of four azimuthally arranged directional log periodic antenna elements flush-mounted onto the cryobot’s surface. Aperture synthesis is facilitated by the downward trajectory of the cryobot as it slowly melts through ice. The radar front end and back end are designed using commercial-off-the-shelf (COTS) components and a customized field-programmable gate array (FPGA)-based digital design for signal generation, reception, and processing. Theoretical analysis of the SAR geometry is presented for the case of a point target in which the maximum likelihood estimation (MLE) approach is used for position estimator development. The novelty of this system lies in the use of pairs of antennas, with fixed baselines between them, to implement multiple coherent monostatic SARs to estimate target position in three dimensions. Specifically, range estimation is implemented by conventional chirp processing, polar (or elevation) angle estimation is achieved by SAR processing, and azimuth angle estimation is facilitated using pairs of four azimuthally arranged antennas that can be analyzed as multiple fixed baseline interferometric radars. Radar signal coherency tests and experimental validation of point target estimation and spatial resolution performed in a laboratory environment using the endfire SAR are presented in this study.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Compact and Free-Floating Satellite MIMO SAR Formations

    • Free pre-print version: Loading...

      Authors: Davide Giudici;Pietro Guccione;Marco Manzoni;Andrea Monti Guarnieri;Fabio Rocca;
      Pages: 1 - 12
      Abstract: We discuss a coherent synthetic aperture radar (SAR) formation where $N$ identical sensors transmit at the same time, code, and frequency. This is a particular multiple-input–multiple-output (MIMO) configuration, where the transmitted waveforms interfere together, resulting in an illumination pattern that randomly changes in space and time. Similar to the single-input–multiple-output (SIMO) formations, the diversity provided by the $N$ receiver phase centers can be used to mitigate this interference and reduce the pulse repetition frequency (PRF) for achieving large swath coverage. The good point, in the MIMO case, is that the signal-to-noise ratio (SNR) gain of the system increases, theoretically, with the square of the number of elements. However, residual spurious sidelobes may appear as ghosts of the multiple illuminators. In practice, the power gain is to be optimized, together with ambiguity rejection, sidelobes, and azimuth resolution. The actual performances achievable by these formations in terms of impulse response function (IRF), SNR, and sensitivity to the precise positioning of the sensors are discussed theoretically and based on simulations.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Scientific Products From the First Radar in a CubeSat (RainCube):
           Deconvolution, Cross-Validation, and Retrievals

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      Authors: Ousmane O. Sy;Simone Tanelli;Stephen L. Durden;Eva Peral;Gian-Franco Sacco;Nacer E. Chahat;Svetla Hristova-Veleva;Andrew J. Heymsfield;Aaron Bansemer;Brian Knosp;Gregg Dobrowalski;Peggy P. Li;Quoc Vu;
      Pages: 1 - 20
      Abstract: RainCube (Radar In a CubeSat), developed by the Jet Propulsion Laboratory (JPL) and launched in 2018, was a technology demonstration supported by NASA. RainCube’s radar is the first spaceborne profiling radar fitting on a platform as small as a 6U ( $10times 20times 30,,mathrm {cm^{3}}$ ) CubeSat. This article shows how, despite its smaller size compared to traditional spaceborne radars, RainCube was able to measure clouds and precipitation in the mid-latitude and intertropical regions. Moreover, since RainCube’s measurements are oversampled in the along-track (AT) direction, the horizontal resolution can be enhanced by a robust Wiener deconvolution algorithm. After more than two and a half years of operation, the RainCube mission came to an end on 24 December 2020. The collected record of Ka-band radar profiles compares favorably to collocated measurements from other ground-based and spaceborne radars both radiometrically and geophysically. The examples of multiradar collocations also provide some insights into the potential of constellations of spaceborne radars to study clouds and storms.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Calibrating the Haiyang-2A Calibration Microwave Radiometer When the
           18.7-GHz Band Fails

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      Authors: Zhilu Wu;Yanxiong Liu;Yang Liu;Jungang Wang;Xiufeng He;Wenxue Xu;Maorong Ge;
      Pages: 1 - 10
      Abstract: The wet tropospheric correction (WTC) retrieved from the onboard calibration microwave radiometer (CMR) of Haiyang-2A (HY-2A) is critical in monitoring the global sea level. However, the CMR WTC became significantly biased from June 2017 due to the failure of the 18.7-GHz band, which caused massive errors in the sea surface height (SSH) measurements. We investigate the accuracy of the CMR WTC derived from the two remaining bands to address this problem. A comprehensive evaluation using multisource data demonstrates that the dual-band + backscattering coefficient (BC) algorithm achieves comparable accuracy to the three-band algorithm, and it does not suffer from any large errors when the equipment works well. Hence, we calibrated the HY-2A CMR data with the dual-band + BC algorithm when the 18.7-GHz band failed, and the accuracy of the CMR WTC is improved from 2.34 to 1.39 cm compared with European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 data. In addition, the SSH measurements are improved significantly by a maximum of 2 cm in mean value using the dual-band + BC WTC during the failure period of HY-2A CMR. Compared with Jason-3 SSH measurements, the HY-2A with dual-band + BC shows a slightly larger difference than HY-2A with three-band by 0.1 cm in rms. This method prolongs the operational lifetime of the HY-2A CMR and could be used in the reprocessing of HY-2A observations.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • MAP-Net: SAR and Optical Image Matching via Image-Based Convolutional
           Network With Attention Mechanism and Spatial Pyramid Aggregated Pooling

    • Free pre-print version: Loading...

      Authors: Song Cui;Ailong Ma;Liangpei Zhang;Miaozhong Xu;Yanfei Zhong;
      Pages: 1 - 13
      Abstract: The complementarity of synthetic aperture radar (SAR) and optical images allows remote sensing observations to “see” unprecedented discoveries. Image matching plays a fundamental role in the fusion and application of SAR and optical images. However, both the geometric imaging pattern and the physical radiation mechanism of these two sensors are significantly different, so that the images show complex geometric distortion and nonlinear radiation differences. This phenomenon brings great challenges to image matching, which neither the handcrafted descriptors nor the deep learning-based methods have adequately addressed. In this article, a novel image-based matching method for SAR to optical images via an image-based convolutional network with spatial pyramid aggregated pooling (SPAP) and an attention mechanism is proposed, namely MAP-Net. The original image is embedded through the convolutional neural network to generate the feature map. Through the information extraction and abstraction of the original imagery, the embedded features containing the high-level semantic information are more robust to the geometric distortion and radiation variation among the different modal images, which is beneficial to the matching of cross-modal images. The adoption of the SPAP module makes the network more capable of integrating global and local contextual information. The attention block weights the dense features generated from the network to extract the key features that are invariant, distinguishable, repeatable, and suitable for the image matching task. In the experiments, five sets of multisource and multiresolution SAR and optical images with wide and varied ground coverage were used to evaluate the accuracy of MAP-Net, compared to both handcrafted and deep learning-based methods. The experimental results show that the MAP-Net method is superior to the current state-of-the-art image matching methods for SAR to optical images.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Ocean Color Hyperspectral Remote Sensing With High Resolution and Low
           Latency—The HYPSO-1 CubeSat Mission

    • Free pre-print version: Loading...

      Authors: Mariusz E. Grøtte;Roger Birkeland;Evelyn Honoré-Livermore;Sivert Bakken;Joseph L. Garrett;Elizabeth F. Prentice;Fred Sigernes;Milica Orlandić;J. Tommy Gravdahl;Tor A. Johansen;
      Pages: 1 - 19
      Abstract: Sporadic ocean color events with characteristic spectra, in particular algal blooms, call for quick delivery of high-resolution remote sensing data for further analysis. Motivated by this, we present the mission design for HYPerspectral Smallsat for Ocean observation (HYPSO-1), a 6U CubeSat at 500 km orbital altitude hosting a custom-built pushbroom hyperspectral imager with wavelengths 387–801 nm at 3.33 nm bandpass and a swath width of 70 km. The imager’s expected signal-to-noise ratio is characterized for typical open ocean water-leaving radiance which can be flexibly increased by binning pixels. Using geometric principles, the satellite shall execute a slew maneuver during a scan to induce greater overlap in the pixels with a goal to enable better than 100 m spatial resolution. Since high-dimensional hyperspectral data need to be transmitted over limited space-to-ground communications, we have designed a modular FPGA-based onboard image processing architecture that significantly reduces the data size without losing important spatial-spectral information. We justify the concept with a simulated scenario where HYPSO-1 first collects numerous hyperspectral images of a 40 km by 40 km coastal area in Norway and aims to immediately transfer these to nearby ground stations. Using CCSDS123 lossless compression, it takes about one orbital revolution to obtain the complete data product when considering overhead in satellite bus communications and less than 10 min without the overhead. It is shown that even better latency can be achieved with more advanced onboard processing algorithms.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Relation-Augmented Embedded Graph Attention Network for Remote Sensing
           Object Detection

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      Authors: Shu Tian;Lihong Kang;Xiangwei Xing;Jing Tian;Chunzhuo Fan;Ye Zhang;
      Pages: 1 - 18
      Abstract: Multiclass geospatial object detection in high spatial resolution remote sensing imagery (HSRI) is still a challenging task. The main reason is that the objects in HRSI are location-variable and semantic-confusable, which results in the difficulties in differentiating the complicated spatial patterns and deriving the implicitly semantic labels among different categories of objects. In this article, we propose a relation-augmented embedded graph attention network (EGAT), which enables the full exploitation of the underlying spatial and semantic relations among objects for improving the detection performance. Specifically, we first construct two sets of spatial and semantic graphs of objects–objects for object relations modeling. Second, a Siamese architecture-based embedding spatial and semantic graph attention network is designed for relations reasoning, which is implemented by introducing the long short-term memory (LSTM) mechanism into the EGAT, for learning the relations among different categories of intraobjects and interobjects. Driven by the spatial and semantic LSTM, the EGAT-LSTM can adaptively focus on the critical information of reason graphs for spatial–semantic correlation discrimination in the embedding non-Euclidean feature space. By this way, the EGAT-LSTM can effectively capture the global and local spatial–semantic relationships of objects–objects, and then produce relations-augmented features for improving the performance of object detection. We conduct comprehensive experiments on three public datasets for multiclass geospatial object detection. Our method achieves state-of-the-art performance, which demonstrates the superiority and effectiveness of the proposed method.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • CubeSat Altimeter Constellation Systems: Performance Analysis and
           Methodology

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      Authors: Yuanhao Li;Peter Hoogeboom;Paco López Dekker;Sung-Hoon Mok;Jian Guo;Christopher Buck;
      Pages: 1 - 19
      Abstract: Multiple CubeSat altimeters can work independently or corporately to form altimeter constellations. Different configurations of the constellations can acquire distinguished advantages: improved spatial/temporal sampling and high cross-track resolution, which will be helpful for observations of oceanic small-scale structures and weather forecasting. Compared to single conventional altimeters, CubeSat altimeter constellations may achieve better performances with lower costs. To fully understand these systems, this article focuses on the performance analysis and methodology for CubeSat altimeter constellations. Besides the typical analyses of the resolution, revisit, and absolute sea surface height (SSH) accuracy, the performance analysis was conducted by considering the characteristics of multiple measurements provided by CubeSat altimeter constellations. Local and global spatial sampling performances are investigated for various constellations and compared by sampling density and swath size. Moreover, relative SSH accuracy is introduced and evaluated based on the spatial structure functions of errors to effectively evaluate the measurement performance. Related system requirements on power, delta-v, etc., to achieve the performance are also discussed, which ensures that the analysis fits the boundary conditions of implementation. Finally, different concepts of the CubeSat altimeter constellations are compared, where their limitations and possible solutions are also discussed.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Deconvolution of SNPP VIIRS Solar Diffuser Bidirectional Reflectance
           Distribution Function On-Orbit Change Factor

    • Free pre-print version: Loading...

      Authors: Ning Lei;Xiaoxiong Xiong;
      Pages: 1 - 9
      Abstract: The earth-observing visible infrared imaging radiometer suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) satellite regularly calibrates its reflective solar bands (RSBs), primarily through observing an onboard sunlit solar diffuser (SD). The on-orbit change of the value of the SD bidirectional reflectance distribution function (BRDF) is quantified by a numerical factor, called the H-factor, and is determined by the onboard SD stability monitor (SDSM). Because the spectral response function of an SDSM detector spreads in wavelength, the directly measured H-factor is the true H-factor convolved with the spectral response function. To find the true H-factor, we use the traditional direct method and an innovative iterative approach to separately deconvolve the measured H-factor. Our iterative approach relies on two properties of the SDSM detector spectral response function: the central peak width is narrow enough so that the H-factor does not change much over the peak width, and the dominance of the spectral response function’s integral with respect to the wavelength over the width. The iterative approach is more accurate, of a smaller noise impact, much more flexible in terms of interpolation and extrapolation of functional values, and faster. We have used deconvolved H-factors to calibrate the NASA SNPP VIIRS RSB Collections 1 and 2 Level-1B products.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Ocean Wave Inversion Based on Airborne IRA Images

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      Authors: Daozhong Sun;Yanmin Zhang;Yunhua Wang;Ge Chen;Hanwei Sun;Lei Yang;Yining Bai;Fangjie Yu;Chaofang Zhao;
      Pages: 1 - 13
      Abstract: The interferometric radar altimeter (IRA) is one of the main payloads of the “Guanlan” ocean science satellite proposed by the National Laboratory for Marine Science and Technology of China. To evaluate the effectiveness and accuracy of the IRA in retrieving the ocean dynamic parameters, such as sea surface height (SSH), ocean wave spectrum, and wind speed, two airborne IRA experiments were carried out at Qingdao Xiaomaidao (XMD) sea area on March 31, 2019, and Rizhao sea area on November 16, 2020. In the present work, wave-induced sea surface elevation (SSE) and its spectrum have been retrieved based on the interferograms acquired by the airborne IRA. To suppress the random phase noise, a mean filtering algorithm has been used in the multilook process of calculating the complex IRA images. The results show that the size of the filter window has a significant effect on the retrieved SSE. If the size of the filter window along THE range direction is too large, the flat earth would cause the spectral density of the retrieved ocean wave to be higher. In addition, the comparisons of the retrieved spectra with the buoy measurements demonstrate that the swell can be well-retrieved by IRA images at low sea-state conditions with significant wave height (SWH) less than 0.7 m. However, for wind wave, because of the effect of the velocity bunching along the azimuth direction, the wind wave spectrum can be extracted only when it propagates approximately along the ground-range direction of the IRA images.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • NOAA-20 VIIRS Relative Spectral Response Effects on Solar Diffuser
           Degradation and On-Orbit Radiometric Calibration

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      Authors: Taeyoung Choi;Changyong Cao;
      Pages: 1 - 7
      Abstract: The Visible Infrared Imaging Radiometer Suite (VIIRS) on the National Oceanic and Atmospheric Administration-20 (NOAA-20) satellite performs on-orbit radiometric calibration based on regular solar diffuser (SD) observations illuminated by the Sun at the termination point near the South Pole. Due to exposure to the ultraviolet portion of the solar irradiance spectrum, the SD bidirectional reflectance distribution function (BRDF) has been degrading over time. The SD degradation (called H-factor) was measured by the on-board calibrator called the SD stability monitor (SDSM). Nevertheless, over two years of operation, there have been systematic on-orbit calibration differences between the SD-based and independent moon-based calibration results. In this study, the NOAA VIIRS team used a surface roughness Rayleigh scattering (SRRS) model as a baseline SD degradation, simulated on-orbit center wavelength-based approach of SD degradation, and a new SDSM relative spectral response (RSR)-dependent SD degradation estimation method to evaluate the degradation. There were time-dependent growing differences between the SDSM RSR-applied H-factors and center wavelength interpolated H-factors especially in the short wavelength detectors (SDSM detector 1–4). The NOAA-20 SD-based calibration coefficients (SD F-factors) were reprocessed using the RSR-applied H-factors, and the new SD F-factors show similar long-term trends compared with the independent monthly lunar F-factors. The newly processed SD F-factor suggested that the NOAA-20 VIIRS detectors in the reflective solar bands (M1–M11 and I1–I3) showed very stable responses within 0.5% level over the two years of on-orbit operation.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Improving Detection of a Portable NQR System for Humanitarian Demining
           Using Machine Learning

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      Authors: Yui Otagaki;Jamie Barras;Panagiotis Kosmas;
      Pages: 1 - 11
      Abstract: This article presents an approach to enhance the detection of buried landmines by applying machine learning (ML) to signals from a nuclear quadrupole resonance (NQR) system. This custom-made, low-cost, and portable NQR system has been developed for deployment in humanitarian demining, where strong radio frequency (RF) interference and a low signal-to-noise ratio (SNR) are important challenges for accurate detection. To tackle these problems, we have applied and tested various ML techniques to NQR signals acquired by our system in laboratory experiments with the explosive Research Department X (RDX). Results suggest that ML methods can indeed improve the detection accuracy of the NQR device, and this is confirmed further using data from field trials with our device. Importantly, the trained classifiers can be implemented with our device’s field-programmable gate array (FPGA) architecture and can run with little time penalty compared with simpler but less-efficient fast Fourier transform (FFT)-based energy detection methods.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • On-Orbit Calibration and Performance of NOAA-20 VIIRS Reflective Solar
           Bands

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      Authors: Kevin Twedt;Ning Lei;Xiaoxiong Xiong;Amit Angal;Sherry Li;Tiejun Chang;Junqiang Sun;
      Pages: 1 - 13
      Abstract: The NOAA-20 (N20) satellite was launched on November 18, 2017 carrying the second Visible Infrared Imaging Radiometer Suite (VIIRS) instrument. Immediately following the launch, the VIIRS passed a series of intensive calibration and validation tests, after which regular calibration and operation activities have continued successfully for more than three years. The production of NASA Collection 2 Level 1B (C2 L1B) for N20 VIIRS began in summer 2019. In this article, we evaluate the early mission performance of the N20 VIIRS reflective solar bands (RSB) covering the first three full years of operation. The calibrated RSB gains are calculated primarily from the onboard solar diffuser (SD) and used in generating the C2 L1B reflectance and radiance products. We also show the on-orbit performance of the instrument noise, signal-to-noise ratio (SNR), and a reflectance uncertainty assessment. Comparisons are made to the first three years of operation of the first VIIRS instrument, aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. We evaluate the long-term stability of the calibrated N20 RSB reflectance product by looking at the long-term trends of lunar observations and data from the pseudo-invariant Libya 4 desert site. The N20 RSB have had excellent early mission performance, with changes in the gain of less than 0.5% in the first three years across all detectors, stable L1B reflectance, and very stable values of detector SNR and reflectance uncertainty.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • System Concepts and Potential Applications of a Tri-Beam Spaceborne SAR
           Mission

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      Authors: Fengming Hu;Fengli Xue;Feng Xu;
      Pages: 1 - 14
      Abstract: Multitemporal synthetic aperture radar interferometry (MT-InSAR), capable of detecting both surface deformation and elevation with high precision, is used for many applications in earth observation. Conventional synthetic aperture radar (SAR) missions with a single beam only detect deformation along the line of sight (LOS) and relative elevation due to the undetermined model of phase wrapping. In a multisatellite SAR mission, measurements from different SAR geometry improve the sensitivity of the detectable deformation, especially to the deformation along the north–south (N-S) direction. However, it is difficult to combine the measurements from varying viewing angles since the absolute phase cannot be reconstructed without a ground control point. In this article, a tri-beam SAR system is introduced to detect 3-D deformation and derive multiview 3-D surface model from a single spaceborne platform. The accuracy of the 3-D deformation from the tri-beam SAR is exploited with varying squint and incident angles to obtain the optimal parameters of the three beams. Then a multidimensional coherent scattering model is used to simulate the multitemporal SAR data with different viewing angles. Regarding the tri-beam SAR, potential applications in earth observation including 3-D deformation monitoring, geodetic stereo SAR, and multiview 3-D forest reconstruction are investigated subsequently. The results of this study indicate that the tri-beam SAR is able to measure 3-D deformation and reconstruct 3-D surface model without ground control point.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Regional CubeSat Constellation Design to Monitor Hurricanes

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      Authors: Pardhasai Chadalavada;Atri Dutta;
      Pages: 1 - 8
      Abstract: State-of-the-art weather forecasting systems depend on a variety of data collected by airborne, orbiting, and ground sensors. Regional CubeSat constellations have the potential to improve hurricane forecasting by collecting sensor data over data-starved oceanic regions. Even in regions where strong terrestrial sensor networks exist, constellation sensor data can help reduce forecasting model errors. To this end, the article considers the problem of designing a low-earth orbit CubeSat constellation that meets given resolution requirements over a region of interest. We propose a novel optimization framework that uses the concept of satellite coverage maps to determine the number of satellites and constellation pattern. Numerical simulations are presented for asymmetric constellation design that can provide sensor data over important geographical regions within a specified repeated time window.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Acquisition of Moon Measurements by Earth Orbiting Sensors for Lunar
           Calibration

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      Authors: Thomas C. Stone;
      Pages: 1 - 6
      Abstract: The reflected light from the Moon can be utilized as a reference for radiometric calibration by employing a model to generate reference values corresponding to the Moon observations made by instruments. Using a calibration target that is outside the atmosphere provides a distinct advantage for space-based instruments; however, the lunar irradiance sensed by satellite instruments naturally changes as the host spacecraft traverses its orbit. This article presents a study of the potential impact on lunar radiometric measurements due to their acquisition from an orbiting platform. A simulation of a Sun-synchronous orbit was coupled to the U.S. Geological Survey (USGS) lunar model to generate predicted irradiances for points along orbit passes through several lunations. These irradiance values exhibit variations tied to the spacecraft motion, arising primarily from changes in the Moon-sensor distance and the phase angle. The two effects are similar in overall magnitude, but their respective contributions depend on the time of month and the orbit. Relative changes in irradiance mostly fall within an envelope of ±0.006% per second, except at the smallest phase angles. These studies enable planning space-based Moon observations to minimize the change in the target irradiance, an important consideration for measurements acquired for radiometric characterization of the Moon.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • High-Spatial-Resolution Nighttime Light Dataset Acquisition Based on
           Volunteered Passenger Aircraft Remote Sensing

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      Authors: Cuiling Liu;Qiandi Tang;Yaping Xu;Chisheng Wang;Shuying Wang;Hao Wang;Weidong Li;Hongxing Cui;Qin Zhang;Qingquan Li;
      Pages: 1 - 17
      Abstract: Remotely sensed nighttime light (NTL) data provides an opportunity to observe human economic activities at night. However, existing NTL remote sensing data does not sufficiently reflect human activities at a fine resolution. In this study, we obtain a set of NTL data comprising volunteered passenger aircraft nighttime remote sensing (VPAN-RS) imagery by adopting a passenger plane as the remote sensing platform and using low-cost portable photography equipment as sensors. This method demonstrates the advantages of VPAN-RS data in high spatial resolution, frequent revisits, and low cost, which can supplement existing NTL data. The dataset acquired in this study covers 16 cities in China and one city in Japan, with a spatial resolution up to meter level. The data acquisition method and processing workflow are introduced in this article. Quality assessment shows that the reprojection error of the dataset falls within the range from 5–10 pixels, and the geometric error can be within 15 m. Via a comparison to reference data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor of the Suomi-National Polar-Orbiting Partnership (NPP) polar-orbiting satellite, we found that the VPAN-RS data provides more details of city lighting. Additionally, the radiance of the VPAN-RS data suitably fits that of monthly composite NPP/VIIRS data. We further outline an application example of VPAN-RS nighttime imagery, examine the unique challenges associated with VPAN-RS data with a focus on the influence of the image acquisition method on the data quality, and provide an outlook for the future of VPAN-RS data. We conclude that VPAN-RS NTL images are an effective data source for nighttime earth observations and have the potential for various applications.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Post-Processing Synchronized Bistatic Radar for Long Offset Glacier
           Sounding

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      Authors: Nicole L. Bienert;Dustin M. Schroeder;Sean T. Peters;Emma J. MacKie;Eliza J. Dawson;Matthew R. Siegfried;Rohan Sanda;Poul Christoffersen;
      Pages: 1 - 17
      Abstract: Radar tomography of glaciers promises to improve imaging and estimates of subsurface ice-sheet structures and properties, including temperature distributions, basal materials, ice fabric, and englacial water content. However, bistatic radar data with long (i.e., larger than the ice thickness) walk-away surveys are required to constrain high-fidelity tomographic inversions. These long-offset data have proven difficult to collect due to the hardware complexity of existing synchronization techniques. Therefore, we remove the hardware complexity required for real-time synchronization by synchronizing in postprocessing. Our technique transforms an Autonomous phase-sensitive Radio Echo Sounder (ApRES) system and a software-defined radio receiver into a coherent bistatic radar capable of recovering basal echoes at long offsets. We validated our system at Whillans Ice Stream, West Antarctica, with a walk-away survey up to 1300 m (797 m thick) and at Store Glacier, Greenland, up to 1450 m (1028 m thick). At both field sites, we measured the basal echo at angles beyond the point of total internal reflection (TIR), whose previous literature had set as a hard physical limit. We support our experimental results with high-frequency structure simulation, which shows that ground-based radar systems capture evanescent waves and are not hindered by TIR. Our analysis and experiments demonstrate a system capable of executing wide-angle bistatic radar surveys for improved geometric and radiometric resolution of inversions for englacial and subglacial properties.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Monitoring the Performance of HY-2B and Jason-2/3 Sea Surface Height via
           the China Altimetry Calibration Cooperation Plan

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      Authors: Lei Yang;Yongsheng Xu;Mingsen Lin;Chaofei Ma;Stelios P. Mertikas;Wei Hu;Zhiyong Wang;Bo Mu;Xinghua Zhou;
      Pages: 1 - 13
      Abstract: Calibration and validation (Cal/Val) of the sea surface height as measured by satellite radar altimeters is essential to understand altimeter biases, observation trends, and instrument aging. It also supports the long-term stability of the produced climate change records of sea level as determined by altimetry. In this article, we report the calibration of HY-2B and Jason-2/3 using the established research infrastructure and data sharing initiative introduced by the Altimetry Calibration Cooperation Plan (ACCP) of China. Currently, three ACCP calibration sites encompass the Wanshan Islands, and two national oceanic sites are located along the China coastline. For each Cal/Val site, the components of the facilities—the geodetic, sea level, and Global Navigation Satellite System (GNSS) infrastructure—and the followed monitoring procedures and calibration methods are described. The HY-2B performance was primarily evaluated using about two years data, which indicated a mean bias of −0.2 ± 4.2 cm. Confidence in the results is strong, because the HY-2B biases were cross compared and confirmed by all the three independent sites and the three satellite ground tracks. Compared with its predecessor HY-2A, HY-2B shows very stable observations with no linear drift at present. In addition, Jason-2 and Jason-3 were mainly assessed using Qianliyan site. Our results indicate that the Jason-3 sea-surface height bias is approximately 2–3 cm smaller than that of Jason-2 and that the long-term stability of Jason-2/3 shows no significant trend, which in good agreement with the international dedicated sites. The instrument noises of Jason-2/3 and HY-2B were estimated based on the ACCP sites. The results show that the instrument noise in the previous literature is underestimated. This was also consolidated by the result from wavenumber spectrum and global crossover point analysis. The code and -anshan data used in these Cal/Val experiments are publicly available to facilitate further work in this domain (https://github.com/GenericAltimetryTools/CalAlti).
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Orbit Design for a Satellite Swarm-Based Motion Induced Synthetic Aperture
           Radiometer (MISAR) in Low-Earth Orbit for Earth Observation Applications

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      Authors: Mark Lützner;Thomas Jagdhuber;Adriano Camps;Hyuk Park;Markus Peichl;Roger Förstner;Matthias Jirousek;
      Pages: 1 - 16
      Abstract: Soil Moisture and Ocean Salinity mapping by Earth observation satellites has contributed significantly toward a better understanding of the Earth system, such as its hydrosphere or climate. Nevertheless, an increased spatial resolution below 10 km with a radiometric resolution in the range of 2 K–3 K of radiometric data could yield a more complete picture of global hydrological processes and climate change. Operational radiometers, such as SMOS, have already approached prohibitive sizes for spacecraft due to the required large antenna apertures. Therefore, radiometer concepts based on a large number of satellites flying in close proximity (swarms) have been proposed as a possible solution. This article investigates the orbit mechanics of placing a satellite swarm-based motion induced synthetic aperture radiometer (MISAR) in low Earth orbit for Earth observation applications. The aperture synthesis antenna array is formed by a large number of individual antennas on autonomously controlled nanosatellites (deputies) and a correlator antenna in the Y-configuration carried by a chief satellite. The proposed design methodology is based on the optimization of satellite positions within a plane and the subsequent translation of coordinates into initial conditions for general circular orbits (GCOs). This enables a more computationally efficient orbit optimization and ensures the time invariance of the antenna array response. Based on this methodology, simulations have been performed with swarms consisting of up to 96 satellites. Simulations show that the spatial resolution of an aperture synthesis radiometer can be increased to less than 10 km for applications where the requirements on radiometric sensitivity are more relaxed ( $Delta Tsim 3$ K).
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Evolving Fusion-Based Visibility Restoration Model for Hazy Remote Sensing
           Images Using Dynamic Differential Evolution

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      Authors: Dilbag Singh;Manjit Kaur;Mohamed Yaseen Jabarulla;Vijay Kumar;Heung-No Lee;
      Pages: 1 - 14
      Abstract: Remote sensing images taken during poor environmental conditions are degraded by the scattering of atmospheric particles, which affects the performance of many imaging systems. Hence, an efficient visibility restoration model is required to remove haze from distorted images. However, the design of visibility restoration models is an ill-posed problem as the physical information, such as depth information and attenuation model, is usually unknown. The physical parameters computed using existing models, such as dark channel prior and gradient channel prior, are not accurate, especially for images with large haze gradients. Therefore, in this article, an evolving visibility restoration model is proposed for remote sensing images. Initially, the fusion-based transmission map is computed from the foreground and sky regions. The transmission map is further improved by designing a hybrid constraint-based variational model. Finally, a dynamic differential evolution is utilized to optimize the control parameters of the proposed model. The proposed model is validated on 50 synthetic benchmarks and 50 real-life remote sensing images. For comparative analysis, ten well-known restoration models are also considered. The comparative analysis demonstrates that the proposed model outperforms the existing restoration models.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Radar-Based Human Activity Recognition Combining Range–Time–Doppler
           Maps and Range-Distributed-Convolutional Neural Networks

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      Authors: Won-Yeol Kim;Dong-Hoan Seo;
      Pages: 1 - 11
      Abstract: Recently, radar-based human activity recognition (HAR) has attracted the attention of researchers as it has been proven that a deep learning (DL) model can be automatically determined by learning a radar dataset. However, unlike general optical image data, the process of collecting and labeling radar data for training the DL model requires considerable manpower and costs. Therefore, an approach that can learn the maximum number of features from a limited radar dataset is essential. Moreover, even if the DL models are trained using a dataset obtained from multiple geometries, performance can be degraded for geometries that are unknown to the trained model. Therefore, we propose a novel radar-based HAR combining a range–time–Doppler (RTD) map and a range-distributed-convolutional neural network (RD-CNN). Unlike the time–Doppler (TD) map, which is mainly used for radar-based HAR, the proposed RTD map provides several human activity-related features by extending the TD map to three dimensions according to the range. The proposed RD-CNN is a new DL model that performs HAR by using the RD layer to extract Doppler features, excluding the range information of RTD map. To verify the performance of the proposed model, experiments were conducted by using the University of Glasgow “radar signatures of human activities,” which is an open dataset for radar-based HAR research. The comparison results of the proposed model and CNNs with the same number of parameters demonstrated a higher recognition accuracy and a lower recognition error even for unknown geometries in the training dataset.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • An Improved Blackbody Calibration Cadence for CYGNSS

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      Authors: C. E. Powell;Christopher S. Ruf;Anthony Russel;
      Pages: 1 - 7
      Abstract: An improved blackbody calibration procedure is developed, implemented, and tested for the cyclone global navigation satellite system (CYGNSS). Previously, CYGNSS calibrated its receivers once every minute to account for temperature-induced gain fluctuations. The time spent making calibration measurements limited the duty cycle of wind-speed measurements to approximately 90%. The analysis presented here shows that the 1-min cadence was overly conservative and can be increased to once every 10 min with minimal impact to data quality, thereby improving the wind-speed duty cycle to 98%. A permanent change to the blackbody cadence was made for the complete eight-satellite constellation during July 27, 2021–August 3, 2021, and subsequent analysis verifies that the new cadence improves duty cycle without impacting science data quality, as expected.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Winner Takes All: A Superpixel Aided Voting Algorithm for Training
           Unsupervised PolSAR CNN Classifiers

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      Authors: Yixin Zuo;Jiayi Guo;Yueting Zhang;Yuxin Hu;Bin Lei;Xiaolan Qiu;Chibiao Ding;
      Pages: 1 - 19
      Abstract: Unsupervised methods play an essential role in polarimetric synthetic aperture radar (PolSAR) image classification, where labeled data are difficult to obtain. However, there is still a large gap between existing unsupervised learning methods and supervised learning methods. Without the semantic constraints of labeled data, pixels within the same category are often misclassified into different categories, leaving the output to be messy. To address the previous issue, this article proposes a fully unsupervised pipeline for training convolutional neural networks (CNNs). The pipeline combines low-level superpixels and high-level CNN semantic features for high-quality pseudolabel generation. It effectively eliminates the misclassified pixels by voting within the superpixel blob while preserving the sharpness of edges. With the training process of the model, the quality of the generated labels is getting improved. Experiments on airborne [experimental airborne SAR system from Germany (ESAR)/airborne synthetic aperture radar from America (AIRSAR)] and spaceborne (RadarSat2) PolSAR images prove the effectiveness of the proposed method (measured with overall accuracy (OA), average accuracy (AA), and Kappa metrics). Our method outperforms the previous unsupervised methods (H/alpha-Wishart, SM-Wishart, FDD-H, DEC, and VQC-CAE) with a large margin and even has comparable performance to the supervised CNN model [fully CNN (FCN)].
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Landsat 9 Thermal Infrared Sensor 2 On-Orbit Calibration and Initial
           Performance

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      Authors: Aaron Pearlman;Boryana Efremova;Matthew Montanaro;Allen Lunsford;Dennis Reuter;Joel McCorkel;
      Pages: 1 - 8
      Abstract: The Thermal Infrared Sensor 2 (TIRS-2) on Landsat 9 (L9) was launched on September 27, 2021, and underwent a variety of tests during its commissioning phase to establish its postlaunch performance. We report on the calibration updates performed to maintain its calibration and generate high-quality imagery. This is done by transferring the SI-traceable prelaunch calibration to on-orbit while accounting for changes in the TIRS-2 response as detected through on-board calibrator observations. Additional empirical corrections were implemented to mitigate image striping observed on-orbit. The detector arrays were monitored through its commissioning phase to ensure that stable detectors were chosen for operations. TIRS-2 has demonstrated ~0.025% instability over its orbit, ~80-mK noise equivalent delta temperature (NEdT), and an absolute radiometric uncertainty
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Design of Constellations for GNSS Reflectometry Mission Using the
           Multiobjective Evolutionary Algorithms

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      Authors: Xiaohua Xu;Zhanghai Ju;Jia Luo;
      Pages: 1 - 15
      Abstract: In this simulation study, the operational GNSS satellites of global positioning system (GPS), Galileo, global navigation satellite system (GLONASS), and BeiDou navigation satellite system (BDS), which are currently in service, are used to transmit signals for global navigation satellite system reflectometry (GNSS-R) measurement. low Earth orbit (LEO) constellations composed of 8, 16, and 24 satellites and with two different patterns, the 2-D-lattice flower constellation (2-D-LFC) and the 3-D-lattice flower constellation (3-D-LFC), are designed considering the tradeoff among three objectives, namely the visited coverage (VC), the revisited coverage (RC) and the total cost of the constellation. Two multiobjective evolutionary algorithms (MOEAs), the nondominated sorting genetic algorithm II (NSGA-II) and the multiobjective evolutionary algorithm based on decomposition (MOEA/D), are applied to solve this multiobjective optimization problem (MOP). The optimal constellations meeting the best tradeoff for the three objectives are picked out, and the distributions of the reflected points observed by them are presented and compared. It is found that NSGA-II generally performs better with respect to the convergence and the diversity of the Pareto solutions. The optimal tradeoff constellations are generally with inclinations of around 67° to 77° and orbital altitudes of nearly 1000 km. For certain number of satellites, the latitudinal and longitudinal distributions of the number of the reflected points observed by the optimal 2-D-LFC and 3-D-LFC are highly similar to each other. Moreover, with the resolution of $0.25^{circ },,times ,,0.25^{circ }$ , the VCs of the optimal 8-satellite and 16-satellite 3-D-LFCs reach 58.30% and 79.59%, respectively, and the optimal 24-satellite 2-D-LFC and 3-D-LFC can achieve an average revi-it time of about 11.0 and 10.2 h, respectively.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Novel FDTD-Based 3-D RTM Imaging Method for GPR Working on Dispersive
           Medium

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      Authors: Yuxuan Wu;Feng Shen;Minghao Zhang;Yongfei Miao;Tong Wan;Dingjie Xu;
      Pages: 1 - 13
      Abstract: Nowadays, benefiting from its strong capability of nondestructive detection, the ground-penetrating radar (GPR) has been applied to detect and reconstruct underground targets and has drawn lots of attention both in military and civilian fields. However, in the processing of GPR imaging, due to the dispersion errors caused by random distribution of various particles in soil, conventional imaging methods have disadvantages of low signal-noise ratio (SNR), low resolution, and unbalanced amplitude. In this article, to achieve high resolution and high veracity on underground targets’ 3-D reconstruction, we improved the conventional reverse time migration (RTM) algorithm in perspective of medium constitutive relationship. Besides, we extended the improved RTM method in 3-D environments and reconstructed the 3-D structure of several underground targets. To make the RTM algorithm suitable for stepped frequency continuous wave (SFCW) GPR system, we generated three excitation signal models and analyzed the effect of different excitation signals on imaging performance. Finally, through the quantitative analysis of simulation and on-vehicle experimental results, we found that the 3-D images generated by improved RTM method had higher resolution, smaller measurement error, and higher veracity than those of conventional RTM method.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Investigating the Potential Accuracy of Spaceborne Solar-Induced
           Chlorophyll Fluorescence Retrieval for 12 Capable Satellites Based on
           Simulation Data

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      Authors: Chu Zou;Liangyun Liu;Shanshan Du;Xinjie Liu;
      Pages: 1 - 13
      Abstract: Remote sensing of solar-induced chlorophyll fluorescence (SIF) has been widely investigated with satellites/sensors covering the spectral range of SIF emission with fine spectral resolutions (SRs). The potential precision of SIF retrievals is limited by instruments’ spectral specifications. Here, the influence of spectral characteristics (SR, signal-to-noise ratio (SNR), and spectral coverage) on data-driven SIF retrieval was assessed using simulations, and the SIF retrieval capability of the obsolete, in-orbit, and planned satellites was evaluated from the spectral perspective. As a result, the 757–759- and 735–758-nm fitting windows (FWs) were found to be optimal for far-red SIF retrieval on satellites with fine (< 0.1 nm) and moderate (0.1–0.5 nm) SR. The 682–697-nm FW was found to perform better for red SIF retrievals. For far-red SIF retrievals, Orbiting Carbon Observatory 2 (OCO-2) and TanSat were found to have the best SIF retrieval performance, with a root-mean-square-error (RMSE $^{ast}$ ) of lower than 0. 25 mW $cdot ,,text{m}^{-2},,cdot $ sr $^{-1},,cdot $ nm-1, followed by satellites with moderate SRs (0.1–0.5 nm) such as CO2 monitoring (CO2M), TROPOspheric Monitoring Instrument (TROPOMI), and Global Ozone Monitoring Experiment 2 (GOME-2) (RMSE $^{ast} < 0.5$ mW $cdot ,,text{m}^{-2},,cdot $ sr $^{-1},,cdot $ nm-1). The high SNR of SCanning Imaging Absorption Spe-tro Meter for Atmospheric CHartographY (SCIAMACHY) did not improve the far-red SIF retrieval greatly (RMSE $^{ast} =0.47$ mW $cdot ,,text{m}^{-2},,cdot $ sr $^{-1},,cdot $ nm-1) but obtained a low RMSE $^{ast} $ at the red band (0.43 mW $cdot ,,text{m}^{-2},,cdot $ sr $^{-1},,cdot $ nm-1). The highest red SIF retrieval potential was found on TROPOMI, with an RMSE $^{ast} $ of 0.41 mW $cdot ,,text{m}^{-2},,cdot $ sr $^{-1},,cdot $ nm-1. Tropospheric Emissions Monitoring of Pollution (TEMPO) and FLuorescence Explorer (FLEX) gave poor performance (RMSE $^{ast}>0.9$ mW $cdot ,,text{m}^{-2},,cdot $ sr $^{-1},,cdot $ nm-1) with their current spectral specifications. Improvement of the spectral characteristics is still needed to obtain precise SIF retrievals.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • 3-D Time-Domain Airborne EM Inversion for a Topographic Earth

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      Authors: Yanfu Qi;Xiu Li;Changchun Yin;Huaiyuan Li;Zhipeng Qi;Jianmei Zhou;Yunhe Liu;Xiuyan Ren;
      Pages: 1 - 13
      Abstract: The topography has serious effects on time-domain airborne electromagnetic (AEM) signal, and the EM responses resulted from the topography frequently overwhelm those from the underground abnormal bodies. This brings big challenges to the traditional AEM interpretations based on a flat ground model. In this article, we develop a 3-D AEM inversion algorithm for a topographic earth model. The time-domain finite-element algorithm based on unstructured mesh is used to model the AEM responses. The tetrahedral grids provide the flexibility to fit the rugged topography. Furthermore, we adopt the Gauss–Newton method for our inversion of time-domain AEM data. In the forward modeling and the calculation of Jacobian matrix, we introduce an unstructured local mesh and decouple the meshes for forward modeling and inversion to improve the computational efficiency. For that purpose, we first set up an unstructured inversion mesh and then extract those cells corresponding to the sensitive area of AEM system for each survey station from the inversion mesh and construct a local forward mesh with the extracted cells as the core. After that, we take advantage of the spatial relationship between the local forward meshes and the inversion ones to set up the global sensitivity matrix for Gauss–Newton inversion. We test the effectiveness of our algorithm by applying our 3-D inversion code to both synthetic and survey data. The numerical experiments show that the Earth topography can have big influence on AEM inversions, and ignoring the topography can create serious distortion to AEM inversion results.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Responses of the Very Low Frequency Transmitter Signals During the Solar
           Eclipse on December 26, 2019 Over a North–South Propagation Path

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      Authors: Xudong Gu;Rui Peng;Shiwei Wang;Binbin Ni;Fan Luo;Guangjian Li;Zhipeng Li;
      Pages: 1 - 7
      Abstract: During the solar eclipse, the perturbation of ionospheric D layer changes the characteristics of the Earth’s Ionospheric Waveguide (EIWG) on which the very low frequency (VLF, 3–30 kHz) wave propagation depends. Therefore, the amplitude and phase of the VLF signal transmitted through the waveguide will be abnormal. In this article, based on the VLF transmitter signals observed in Suizhou (31.57°N, 113.32°E) during the total solar eclipse on December 26, 2019 and the days before and after, the variation characteristics of VLF transmitter signals along the north–south propagation path are analyzed in detail. Responses of the amplitude and phase of the signal during the solar eclipse are closely related to the solar obscuring rate. There is a positive correlation between the signal fluctuation and the solar obscuring rate, and the peak time of the two has a delay of ~5 min. By adopting the amplitude and phase of the observed signals and performing the Long Wavelength Propagation Capability (LWPC) propagation simulations, the electron density of the ionosphere over the propagation path is calculated. The results show that the electron density profile above the path during the solar eclipse changes significantly. The electron density decreases with a maximum drop of ~53.5% at the 70 km height, and the reflection height of the signal increases correspondingly. The obtained results are useful to better understand the propagation characteristics of VLF transmitter waves and the corresponding response features of the ionospheric D layer to solar radiation flux variations, especially during the solar eclipse.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Few-Shot SAR Target Classification via Metalearning

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      Authors: Kun Fu;Tengfei Zhang;Yue Zhang;Zhirui Wang;Xian Sun;
      Pages: 1 - 14
      Abstract: The state-of-the-art deep neural networks have made a great breakthrough in remote sensing image classification. However, the heavy dependence on large-scale data sets limits the application of the deep learning to synthetic aperture radar (SAR) automatic target recognition (ATR) field where the target sample set is generally small. In this work, a metalearning framework named MSAR, consisting of a metalearner and a base-learner, is proposed to solve the sample restriction problem, which can learn a good initialization as well as a proper update strategy. After training, MSAR can implement fast adaptation with a few training images on new tasks. To the best of our knowledge, this is the first study to solve a few-shot SAR target classification via metalearning. In particular, the few-task problem is defined by analyzing the effect of available training classes on the performance of metalearning models. In order to reduce the metalearning difficulties caused by the few-task problem, three transfer-learning methods are employed, which can leverage the prior knowledge from the pretraining phase. Besides, we design a hard task mining method for effective metalearning. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set, a specialized data set named NIST-SAR is devised to train and evaluate the proposed method. The experiments on NIST-SAR have shown that the proposed method yields better performances with the largest absolute improvements of 1.7% and 2.3% for 1-shot and 5-shot, respectively, over the next best, which indicates that the proposed method is promising and metalearning is a feasible solution for few-shot SAR ATR.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Simulation of Pol-SAR Imaging and Data Analysis of Mini-RF Observation
           From the Lunar Surface

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      Authors: Niutao Liu;Ya-Qiu Jin;
      Pages: 1 - 11
      Abstract: High circular polarization ratio (CPR) characteristics were found in permanently shaded regions (PSRs) near the lunar poles. High CPR was regarded as a water ice index. The compact-polarimetric (CP) miniature radio frequency (Mini-RF) radar transmits left-circularly polarized signals and receives horizontally polarized ( $S_{text {HL}}$ ) and vertically-polarized ( $S_{text {VL}}$ ) echoes from the lunar surface. Statistics of the CPR data show its relations with the relative phase ( $delta$ ) between $S_{text {HL}} $ and $S_{text {VL}} $ and the degree of polarization ( $m$ ) but few interpretations were provided. The average CPR data reach the maximum and minimum at $delta =pm 90^{circ }$ , respectively. As $m$ becomes very small, the CPR approaches 1. It has been found that CPR is also affected by surface roughness and incidence angle of radar waves. The CPR is now expressed in CP mode to explain the Mini-RF observation. Full-polarimetric radar echoes and CP parameters of the lunar surface are numerically simulated using the bidirectional analytic ray-tracing method. Single-bounce and multiple-bounce scattering components are included in the simulation. Radar images of the lunar crater are simulated with the digital elevation model (DEM) data. The $H-alpha $ decomposition derived from the full-polarimetric simulation -s presented to analyze $delta $ and $m$ . Simulated radar images with different surface roughness are analyzed statistically to study the functional dependences of $delta $ , ${m}$ , and CPR on incidence angle and roughness. Relationships among $delta $ , $m$ , and CPR are used to analyze the effects of incidence angle, roughness, TiO2, and rock abundance on the scattering components. The CPR, $m$ , and $delta $ of PSR craters of different ages are compared with those of nonpolar craters. The results indicate that the CPR, $m$ , and $delta $ are unlikely to be unambiguous evidence of water ice.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • 3-D Object Imaging Method With Electromagnetic Vortex

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      Authors: Jianqiu Wang;Kang Liu;Hongyan Liu;Kaicheng Cao;Yongqiang Cheng;Hongqiang Wang;
      Pages: 1 - 12
      Abstract: The electromagnetic (EM) vortex imaging has demonstrated superior performance in target detection and imaging with azimuthal super-resolution. However, the restricted elevation resolution degrades the acquisition of target spatial information, which limits the development of the radar imaging technology based on orbital angular momentum (OAM). This article offers a solution to achieve the 3-D EM vortex imaging, by effectively utilizing relative motion between radar and target in the line-of-sight (LOS) direction. First, the forward-looking radar imaging scenario is presented, the 3-D echo model is derived, and the characteristics are analyzed as well. Second, the imaging method, based on the back-projection (BP) and spectrum estimation method, is proposed to obtain the target’s 3-D focused image. Furthermore, the influence factors about the elevation resolution are analyzed by the point spread function (PSF). Finally, simulations are carried out to verify the effectiveness of the theoretical analyses.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Time Series X- and Ku-Band Ground-Based Synthetic Aperture Radar
           Observation of Snow-Covered Soil and Its Electromagnetic Modeling

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      Authors: Chuan Xiong;Jiancheng Shi;Jinmei Pan;Haokui Xu;Tao Che;Tianjie Zhao;Yan Ren;Deyuan Geng;Tao Chen;Kaiwen Jiang;Peng Feng;
      Pages: 1 - 13
      Abstract: The snow water equivalent (SWE, a measurement of the amount of water contained in snow packs) is an important variable in earth systems. Microwave remote sensing provides a possible solution for estimating the SWE globally. To support radar SWE retrieval, the snow backscattering theory needs to be studied; the forward simulation model needs to be validated against natural snow observations. In this study, a one-winter experiment to observe the time series backscattering coefficient of snow-covered bare soil is reported. This is the first long time series snow-covered soil backscattering experiment that was measured by an imaging radar. The backscattering coefficient was observed at three frequencies covering the X-band and dual-Ku bands, which are of great interest to the snow remote sensing community and are used for SWE estimation in mountains. The calibration of the synthetic aperture radar (SAR) system was conducted manually and carefully to ensure high-quality radar observation data. The observations from our experiment show that in general, the time series backscattering signature of snow-covered terrain is mainly driven by soil freezing, snow grain size growth, and snow accumulation processes. The time series observations for dry snow are modeled by backscattering models with model inputs directly calculated from field measurements. Our simulation results indicate that the time series radar backscattering at three frequencies and four polarizations can be simulated with high accuracy, including the cross-polarization channels. This study provides some key understanding of the time series signature of radar backscattering from snow and provides some key implications for SWE retrieval from radar observations.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Radar Backscattering Over Sea Surface Oil Emulsions: Simulation and
           Observation

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      Authors: Tingyu Meng;Xiaofeng Yang;Kun-Shan Chen;Ferdinando Nunziata;Dengfeng Xie;Andrea Buono;
      Pages: 1 - 14
      Abstract: Oils floating on the sea surface can be observed as “dark” patches on radar images since the backscattered signals from the contaminated area are reduced in two dominant ways. First, oil slicks could damp short gravity and capillary waves on the sea surface responsible for backscattering energy. Second, the oil-covered sea surface permittivity decreases significantly if the oil film is sufficiently thick or mixed with seawater, i.e., oil emulsion. In this article, the geometry of the oil-covered sea surface is accounted for by the damping of sea waves, which is described by the model of local balance (MLB) combined with the sea wave spectrum. The radar backscattering is predicted by the advanced integral equation method (AIEM) model. The reflection coefficients are calculated based on a layered-medium model to analyze the impact of oil thickness and emulsions on the radar scattering. Numerical simulations demonstrate that: 1) the sensitivity to oil thickness and water content of the oil spills increases when the radar frequency increases; 2) the backscattering signals exhibit a nonlinear behavior with respect to oil thickness; and 3) high wind speed can generally narrow the difference between the radar backscattering from the clean and oil-covered sea surface, while the incidence angle has little effect. Numerical simulations are then compared with the multifrequency synthetic aperture radar observations acquired during the Gulf of Mexico Deepwater Horizon (DWH) oil spill accident and the 2011 Norwegian Clean Seas Association for Operating Companies (NOFO) oil-on-water exercise. Comparison results show that it is possible to estimate the oil thickness at reasonably good accuracy.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Decorrelation of the Near-Specular Land Scattering in Bistatic Radar
           Systems

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      Authors: Davide Comite;Nazzareno Pierdicca;
      Pages: 1 - 13
      Abstract: Signal fluctuations at the receiving antenna have been studied from decades by the radar community, especially to understand the decorrelation of the scattering in radar interferometry. This was done assuming uncorrelated point-like scatterers, leading to a simple model for the geometric decorrelation. In this case, the scattering is certainly incoherent. The quasi-specular reflections gathered under the illumination of signals of opportunity can exhibit significant temporal fluctuations. They are related to the statistical features of the surface roughness and can be observed even in almost flat regions, where a predominant coherent reflection could be expected. The presence of gentle undulations, however, i.e., those showed by surfaces having variations of the profiles comparable with the wavelength over the vertical scale, but much longer over the horizontal one, can determine transition regions where the scattering is neither coherent nor completely incoherent. In these conditions, the nature of the fluctuations of the scattering is not well understood and it requires additional studies. A discussion about the dominance of coherent or incoherent reflection in the Global Navigation Satellite System Reflectometry (GNSS-R) community is presently ongoing. To describe the nature of the scattering, and to understand the decorrelation of the near-specular components in GNSS-R, we propose a numerical study of the field collected by a moving airborne receiver based on the Kirchhoff approximation. Our study demonstrates that the near-specular scattering collected over representative natural landscapes by a GNSS-R receiver is partially coherent and essentially incoherent in most cases. Its correlation time is a function of the roughness parameters, namely standard deviation and correlation length, as well as of the system parameters (i.e., spatial resolution and height). The analysis can provide useful information for the interpretation of GNSS data, which present intrins-c variability that can significantly affect the retrieval of the relevant bio-geophysical parameters.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Rough Surface Scattering Model Comparisons for Radar Altimetry of Sea Ice

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      Authors: Jeffrey D. Ouellette;Tanish P. Himani;Elizabeth M. Twarog;Li Li;
      Pages: 1 - 9
      Abstract: Currently, sea-ice thickness is often estimated using microwave radar altimetry. It is well known that radar measurements are highly dependent on the roughness characteristics of the observed scene and can have a profound effect on altimetry-based thickness estimates. This simulation-based study highlights the importance of sea-ice roughness parameterization in the context of radar altimetry. This work provides a new sea-ice roughness parameterization in the form of an elevation spectrum developed from field campaign data. The formulation of this elevation spectrum is partly based on the generalized power law. Simulations have been developed to predict electromagnetic scattering from surfaces described by the new spectrum, with a focus on the X-band (10 GHz) scattering with nadir incidence. In this work, both the analytical small slope approximation and numerically exact Method of Moments are applied to various sea-ice surface spectra, representing a wide range of surface roughness conditions. The results show that the normalized radar cross section of the sea-ice interface varies significantly depending on the surface elevation spectrum and the statistics of the rough interface. Comparisons with numerically exact methods show that analytical approximations to rough surface scattering work reasonably well for the cases considered here, except at large scattering angles.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Radio Interferometer Observations and Analysis of an Energetic In-Cloud
           Pulse Based on Ensemble Empirical Mode Decomposition

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      Authors: Xiangpeng Fan;Paul R. Krehbiel;Julia N. Tilles;Mark A. Stanley;Seda Senay;Harald E. Edens;William Rison;Yijun Zhang;
      Pages: 1 - 17
      Abstract: Based on the ensemble empirical mode decomposition (EEMD) method, a DAF method for signal construction is proposed that repeatedly decomposes (D) the signal, amplifies (A) the local signal characteristics, and then filters (F) the signal. This method is used to decompose and reconstruct the electric field waveform (called a sferic) of an energetic in-cloud pulse (EIP) with a 247-kA peak current detected by a fast antenna (FA). Based on synchronous sub-microsecond very high-frequency (VHF, 14–88 MHz) radio interferometer (INTF) observations and observed downward fast positive and upward fast negative breakdowns, which occurred simultaneously with the EIP, the EIP sferic is decomposed by the DAF method in 11 steps into two independent sferics: a smoother filtered EIP sferic and an embedded narrow bipolar-like event (NBE). It is verified that strong VHF radiation is generated by the NBE-like event, rather than being associated with the smooth EIP sferic. The analysis, decomposition, and reconstruction of the correlated signals by the EEMD-based DAF method proposed in this article support the idea that the large-amplitude EIP sferic was generated by relativistic discharge responsible for an accompanying terrestrial gamma-ray flash (TGF) rather than by streamer or leader activity.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • 3-D Model-Based Inversion Using Supervised Descent Method for
           Aspect-Limited Microwave Data of Metallic Targets

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      Authors: Zekui Jia;Rui Guo;Maokun Li;Guojun Wang;Zhiqu Liu;Yun Shao;
      Pages: 1 - 10
      Abstract: In this work, we apply the supervised descent method (SDM) to 3-D limited data inversion. In the measurement of the far field, the scattered field in 3-D measurement is small in magnitude and easily polluted by noise. Combining with limited observed data, the inversion problem is highly nonunique and ill-posed. To mitigate the ill-posedness, the model-based inversion is adopted by describing metallic targets based on prior information. Then, a series of generic descent directions is learned in the training stage iteratively using SDM. During inversion, the values of these model-based parameters are reconstructed directly using the learned directions. This approach is validated using both synthetic and experimental data. All simulations and experiments are conducted in a monostatic measurement setup to match real measurement conditions. The results show that by choosing proper prior information, the model-based SDM inversion can effectively compensate for the lack of data and achieve decent accuracy.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Scattering From Fractal Surfaces Based on Decomposition and Reconstruction
           Theorem

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      Authors: Ming Li;Ling Tong;Yiwen Zhou;Yu Li;Xun Yang;
      Pages: 1 - 12
      Abstract: A decomposition and reconstruction theorem (DRT) is introduced to advance computation and provide physical understanding for the scattering from fractal surfaces (FPs). The profile of FP is decomposed into test profiles (TPs), and the scattering of FP is reconstructed using the scattering of TPs to enhance the computational efficiency. A new method that applies DRT on the extended boundary condition method combined with the truncated singular value decomposition technique (TEBCM) is presented and referred to as TEBCM-DRT. The method of TEBCM-DRT is employed to solve the scattering from realistic soil surfaces, and the results are validated against other scattering models. Moreover, the efficiency of TEBCM-DRT and its validity range are investigated. The result shows that TEBCM-DRT improves computational efficiency to $1.95times 10^{5}$ and $7.35times 10^{2}$ times, respectively, compared to TEBCM and the conventional method of moments for a wide range of roughness. In addition, TEBCM-DRT indicates that the amplitude and direction of propagation of scattering modes are dependent on deterministic TPs. This relationship benefits for obtaining the accurate height of an arbitrary point on the profile from bistatic scattering coefficients.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Effects of Rough Hail Scattering on Polarimetric Variables

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      Authors: Djordje Mirkovic;Dusan S. Zrnic;Valery Melnikov;Pengfei Zhang;
      Pages: 1 - 14
      Abstract: We use a commercially available full-wave electromagnetic (EM) tool to model scattering off rough hail. Through modeling various axis ratios and surface roughness, we systematically evaluate their effect on the polarimetric variables used for hail size discrimination. Our results produce the differential reflectivity and the copolar correlation coefficient typically not achieved using forward operators. We compare our results with available information in the literature. The inclusion of the additional shape parameter brings new insights into problems associated with the polarimetric variable for hailstone size gauging. Finally, using dual-polarization weather radars’ (WSR-88Ds’) observations of a hail storm on two opposite sides, we hypothesize that the source of negative differential reflectivity is wet oblate hail in the resonant size range.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Statistical Global Investigation of Pre-Earthquake Anomalous Geomagnetic
           Diurnal Variation Using Superposed Epoch Analysis

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      Authors: Khairul Adib Yusof;Mardina Abdullah;Nurul Shazana Abdul Hamid;Suaidi Ahadi;Essam Ghamry;
      Pages: 1 - 13
      Abstract: Recent studies reported that the diurnal variation of the geomagnetic field exhibited anomalous characteristics several months before earthquakes (EQs). However, the statistical significance of such anomalies detected using the diurnal variation range ratio (DVRR) method still requires verification as only a limited number of EQs occurring in certain seismoactive zones were included. In this study, the DVRR method was employed in conjunction with the superposed epoch analysis (SEA) to perform a large-scale study. A total of 157 EQs that occurred between 2000 and 2019 were investigated using vast geomagnetic field data collected from 92 ground-based magnetometer stations spread across multiple global regions. The case study conducted on the May 13, 2011, M6.1 Honshu (Japan) EQ showed that the diurnal variation of the vertical geomagnetic component disappeared around a week before the EQ. The case study also demonstrated the effectiveness of the DVRR method in minimizing solar disturbance influences, further proving that the observed anomalies were not caused by the Sun. The subsequent SEA revealed statistically significant increases in anomaly counts before the occurrence of EQs for all three geomagnetic components. The number of anomalies gradually increased approaching the day of EQs with temporal lags between the components. Furthermore, a control group analysis confirmed the findings by demonstrating that the increases were not coincidental and several explanations have been suggested, focusing primarily on the emergence and generation mechanisms of the anomalies. Results from this study showed that the anomalies were significant and were potentially precursors to the succeeding EQs.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Hybrid Loop-Tree FEBI Method for Low-Frequency Well Logging of 3-D
           Structures in Layered Media

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      Authors: Yang Zhong;Hanming Wang;Wei-Feng Huang;Wenhao Xu;Jiaqi Xu;Junwen Dai;Qing Huo Liu;
      Pages: 1 - 9
      Abstract: Electromagnetic simulation plays an essential role in formation conductivity determination by electromagnetic well-logging tools, especially when the nonvertical borehole and invasion zones are present in a layered earth. A hybrid finite-element boundary integral (FEBI) method is suitable for such well-logging simulations. The usage of layered medium Green’s functions allows the simulation background to be a planar stratified medium to characterize the earth formation. However, conventional FEBI using the Rao–Wilton–Glisson (RWG) basis function produces inaccurate results due to the low-frequency breakdown of the boundary integral equation solution. The new contribution of this work is to apply the loop-tree (LT) basis functions in the FEBI method to reduce the numerical error at low frequencies. Such an LT-FEBI method can handle all specific requirements of well-logging simulations at low frequencies. A direct solver is applied to make it more efficient to obtain logging curves with multiple source locations. The LT-FEBI simulation results are validated in several numerical examples, including a challenging well-logging model with a deviated borehole and invasion zones in the Oklahoma formation having 28 layers.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Fast Full-Wave Electromagnetic Inverse Scattering Based on Scalable
           Cascaded Convolutional Neural Networks

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      Authors: Kuiwen Xu;Chen Zhang;Xiuzhu Ye;Rencheng Song;
      Pages: 1 - 11
      Abstract: The end-to-end scalable cascaded convolutional neural networks (SC-CNNs) are proposed to solve inverse scattering problems (ISPs), and the high-resolution image can be directly obtained from the scattered field with the guiding by multiresolution labels in the cascaded blocks. To alleviate the difficulty of solving the ISPs via a full-wave way, the proposed SC-CNNs are physically decomposed into two parts, i.e., the linear transformation and the multiresolution imaging networks. The first part is composed of one CNN block and is used to mimic the linear transformation [e.g., backpropagation (BP)] from scattered field to the preliminary image, whereas the second part consists of a few cascaded CNN blocks to realize the reconstruction from the rough image to high-resolution image. With more high-frequency components incorporating into the multiresolution labels, the cascaded networks can be guided through those labels, avoiding black-box operations and enhancing the physical meaning and interpretability. The proposed SC-CNNs are verified by both the synthetic and experimental examples and it is proved that better performance can be achieved in terms of both inversion accuracy and efficiency compared to the BP-Unet and direct inversion scheme (DIS).
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Pattern Synthesis Algorithm for Range Ambiguity Suppression in the LT-1
           Mission via Sequential Convex Optimizations

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      Authors: Ce Yang;Naiming Ou;Yunkai Deng;Dacheng Liu;Yanyan Zhang;Nan Wang;Robert Wang;
      Pages: 1 - 13
      Abstract: The innovative spaceborne Earth observation mission LuTan-1 (LT-1) deploys advanced full polarimetric L-band synthetic aperture radar (SAR) to obtain high-precision, multidimensional ground feature information. As the quad-pol SAR system, LT-1 suffers from strong ambiguities that degrade the quality of the observation products. Antenna pattern synthesis algorithm can suppress the range ambiguities without the increase of the azimuth ambiguities and the system complexity and therefore has great potential to improve the overall ambiguity performance of the quad-pol SAR system. However, the previous research on this kind of method is generally limited to specific situations and lacks of the analysis of actual measurement results. Based on the application requirements of LT-1, this article proposes a novel pattern synthesis algorithm that suppresses the range ambiguities via sequential convex optimizations. In simulation and comparison, the proposed algorithm effectively suppresses the strong co-polarized range ambiguities and shows the flexibility, efficiency, and stability that are significantly better than the previous algorithms. What is more, the analysis of practical performance loss is performed based on the measurement result of the LT-1 antenna, and the practicality and validity of the algorithm are strongly verified.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Theoretical Study on Recognition of Icy Road Surface Condition by
           Low-Terahertz Frequencies

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      Authors: Xiangzhu Meng;Peian Li;Yuning Hu;Fei Song;Jianjun Ma;
      Pages: 1 - 9
      Abstract: Recognition of road surface conditions should always be at the forefront of intelligent transportation systems for the enhancement of transportation safety and efficiency. When road surfaces are covered by ice or snow, accident rate would increase due to the reduction of road surface roughness and also friction between tire and road. High-resolution recognition of natural and man-made surfaces has been proved to be achievable by employing radars operating at low-terahertz (low-THz) frequencies. In this work, we present theoretical investigations on surface condition recognition of an icy road by employing low-THz frequencies. A theoretical model combining integral equation method (IEM), radiative transfer equation (RTE), and Rayleigh scattering theory is developed. Good agreement between the calculation results and measured data confirms the applicability of low-THz frequencies for the evaluation of icy road surface in winter. The influence of carrier frequency, ambient temperature, impurities inside the ice layer, and frozen soil surface conditions on the efficiency of this method is presented and discussed.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Analysis of Aeromagnetic Swing Noise and Corresponding Compensation Method

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      Authors: Diankun Zhang;Xiaojun Liu;Xiaodong Qu;Wanhua Zhu;Ling Huang;Guangyou Fang;
      Pages: 1 - 10
      Abstract: Aeromagnetic noise compensation is a vital part of aerial survey measurement, and its compensation effect directly determines the quality of aeromagnetic survey data. At present, the commonly used compensation model is the T-L model, and the least squares method is used to solve for the coefficients. However, the noise source modeled in the T-L model is incomplete. Since the tail boom cannot be completely rigid, tail-boom swing is an unavoidable problem in aeromagnetic measurement. This kind of swing is the most obvious when the aircraft is maneuvering, and it will significantly interfere with the measurement data of the sensor. In this article, two causes of the swing noise are analyzed, and the nonlinear relationship between the swing displacement and the noise is derived. Since it is difficult to express the nonlinear relationship with mathematical forms to compensate for the aeromagnetic data, we propose a new compensation method that uses a 1-D convolutional neural network to perform secondary compensation on the data already compensated by the T-L model in order to remove the effect of tail-boom swing. The flight experiment data show that the proposed method can significantly improve the quality of aeromagnetic data. Compared with the T-L method, the improve ratio is increased by 60%–100%. It shows that the proposed method has a remarkable compensation effect for aeromagnetic noise.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Surface Magnetic Resonance Sounding Using Electrical Source for Subsurface
           Aquifer Modeling

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      Authors: Xiaoxue Lin;Xing Gao;Ling Wan;Tingting Lin;
      Pages: 1 - 10
      Abstract: Surface magnetic resonance sounding (SMRS) is a unique geophysical method that can directly track and quantify groundwater using the remote sensing technique. The conventional SMRS used a closed coil as the magnetic field source. In the field measurement, a large-size coil is laid on the ground or even multiple coils are set for array detection. This reduces the detection efficiency and consumes labor inevitably. The current study proposes an electrical source (ES), a new mode for exciting the groundwater. It is a long wire placed on the ground and connected to the Earth by two grounding electrodes. The ES has the advantages of labor-saving, time-saving, and better environmental adaptability. Moreover, the magnetic field generated by the ES can transmit farther than the traditional magnetic source. Based on this, we matched different receivers for the ES and simulated the kernel, the resolution, and the signal with different configurations. The results show that using the long grounding wire as both the electrical transmitter and receiver can obtain higher signal amplitude and better resolution than the traditional magnetic configuration. In addition, it has the ability to break through the detection depth of the conventional method.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • 2-D Pixel-Based Inversion for Simultaneous Reconstruction of Resistivity
           and Dielectric Constant From Electromagnetic Logging-While-Drilling
           Measurements

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      Authors: Li Yan;Shubin Zeng;Jiefu Chen;
      Pages: 1 - 14
      Abstract: The dielectric effects are significant in certain circumstances in hydrocarbon exploration and production. Neglecting these effects can result in an inaccurate description of subsurface structure in terms of resistivity estimation and boundary location. However, almost all existing research has ignored the dielectric effects in the inversion process for ultradeep electromagnetic logging-while-drilling (LWD) measurements. As a complement, this article aims to investigate the impact of the dielectric constant on the inversion performance. In this study, we present the possible causes of the appearance of large dielectric constant in formations and discuss the influence of dielectric constant on one set of measurements via the sensitivity study. Besides, a 2-D pixel-based inversion algorithm is introduced for simultaneous determination of resistivity and dielectric constant. Also, the gradient of measurements with respect to interested parameters using the adjoint method is provided and validated. In addition, the capability of the inversion approach is demonstrated with several numerical examples. It is found that accounting for the dielectric constant in the inversion can help to improve the interpretation of electromagnetic measurements in some scenarios, thus enhancing the understanding of the subsurface structures.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • 3-D Voxel-Based Reconstruction of Multiple Objects Buried in Layered Media
           by VBIM Hybridized With Unsupervised Machine Learning

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      Authors: Jiawen Li;Yanjin Chen;Jianliang Zhuo;Feng Han;
      Pages: 1 - 12
      Abstract: This article presents a novel hybrid electromagnetic inversion method. The traditional 3-D variational Born iterative method (VBIM) is combined with the unsupervised machine-learning expectation maximization (EM). In each iteration, VBIM first outputs the pseudo-randomly distributed model parameters in all discretized cells in the inversion domain. Then the EM algorithm is used to classify them and estimate the mean model parameter values of each homogeneous scatterer or subscatterer supposing that the reconstructed model parameters in all cells comply with the Gaussian mixture model (GMM). At last, partial cells in the inversion domain classified as “background” will be removed and the unknowns in the next VBIM iteration are reduced. This process is implemented iteratively until no “background” cell can be removed anymore and the data misfit between the measured scattered field and reconstructed field reaches the stop criterion. Finally, the mean value of the model parameter estimated by EM is mandatorily assigned for each homogeneous scatterer or subscatterer. Numerical examples show that the proposed hybrid method works efficiently for the reconstruction of isotropic, anisotropic, homogeneous, or inhomogeneous scatterers. It also has a certain antinoise ability.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • The Use of GPR and Microwave Tomography for the Assessment of the Internal
           Structure of Hollow Trees

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      Authors: Fabio Tosti;Gianluca Gennarelli;Livia Lantini;Ilaria Catapano;Francesco Soldovieri;Iraklis Giannakis;Amir M. Alani;
      Pages: 1 - 14
      Abstract: Internal decays in trees can rapidly escalate into a full decomposition of the inner structural layer, i.e., the “heartwood” layer, due to the action of aggressive diseases and fungal infections. This process leads to the formation of big cavities and hollows, which remain surrounded by the sapwood layer only. Estimating the thickness of the sapwood layer with a high degree of accuracy is therefore crucial for correct assessment of the structural integrity of hollow trees, as well as an extremely challenging task. In this context, ground-penetrating radar (GPR) has proven effective in providing details of the internal structure of trees. Nevertheless, the existing GPR processing methods still offer limited information on their internal configuration. This study investigates the effectiveness of GPR enhanced by a microwave tomography inversion approach in the assessment of hollow trees. To this aim, a living hollow tree was investigated by performing a set of pseudocircular scans along the bark perimeter with a hand-held common-offset GPR system. The tree was then felled, and sections were cut for testing purposes. A dedicated data processing framework was developed and tested through numerical simulations of hollow tree sections. The internal structure of the real trunk was therefore reconstructed via a tomographic imaging approach and the outcomes were quantitatively analyzed by way of comparison with the real sections’ main geometric features. The tomographic approach has proven very accurate in locating the sapwood–cavity interface and in the evaluation of the sapwood layer thickness, with a centimeter prediction accuracy.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • ISAR Imaging Analysis of a Hypersonic Vehicle Covered With Plasma Sheath

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      Authors: Bian Zheng;Li Jiangting;Guo Lixin;
      Pages: 1 - 13
      Abstract: In this article, a hypersonic target electromagnetic (EM) scattering echo model combined with the inhomogeneous zonal medium model (IZMM) and the classical scattering center model (SCM) is proposed with a distributed satelliteborne array radar as the detection platform. A parallel physical optics (PO) method is used for multiview inverse synthetic aperture radar (ISAR) imaging of a moving hypersonic target covered with plasma sheath based on the analysis of high-resolution range profile in the S-X ultrawideband range, reconstructing 2-D EM scattering echo data (the target) and motion compensation. The results show that the surface of the inhomogeneous plasma sheath flow field is an excitation layer with random and irregular fluctuation characteristics, which increases the false scattering centroid of the 1-D range profile of the hypersonic target and can interfere with and disrupt the radar localization of the target along the radial direction. In addition, shallow scattering of EM waves occurs in the plasma sheath, and the average signal intensity of the target can gradually reduce from 0.5 $times ,,10^{-5}$ at 60 km and 20 Ma to 0.1 $times ,,10^{-5}$ at 30 km and 20 Ma, with a fivefold weakening of the overall scattered echo signal. In particular, the faster the hypersonic target travels at 30-km altitude, the weaker the imaged scattered echo signal becomes, with the average intensity of the imaged signal weakening by approximately threefold from 15 to 25 Ma. This study provides considerable technical support and data assurance to establish a synthetic aperture radar (SAR) automatic target recognition (ATR) database, and the findings of this study can be used as a reference for fine-structure feature analysis of hypersonic targets for feature extraction and the classification and identification of targets.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Characterization of Hydraulic Fractures With Triaxial Electromagnetic
           Induction and Sector Coil Rotation Measurement

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      Authors: Ying Zhai;Dejun Liu;David K. Potter;Yang Li;
      Pages: 1 - 11
      Abstract: The characterization of hydraulic fractures is crucial for fracturing evaluation and strategy optimization. Low-frequency electromagnetic triaxial induction measurement is a promising candidate in hydraulic fracture characterization. However, it is difficult to accurately monitor fracture shape, orientation, and the multistage fracture network distribution. A novel method that combines triaxial induction measurement with sector-shaped coils axial rotation measurement (TIM-SCARM) is proposed to characterize hydraulic fractures. It is implemented by using the finite element method with transition boundary conditions (FEM-TBCs), which approximates the thin fracture as a surface to enhance the computational efficiency. The study focuses on quantitative analysis of conductivity, cross-sectional shape, half-length, and orientation of hydraulic fractures to assess their effects on specific configurations of the TIM-SCARM. Furthermore, the correlations between multicomponent signals and fracture characteristics are investigated. Numerical results indicate that the coaxial component signal in SCARM can distinguish the cross-sectional shape and orientation. The cross-polarized component signals provide important features of tilted fractures, and the 3-D signal obtained by instrument rotation could determine the spatial distribution of fracture networks. Therefore, measurements that integrate the multicomponent signals and axial information improve fracture geometry evaluation.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Modeling of Surface Roughness With an Anisotropic Power-Law Spectrum and
           Its Applications to Radar Backscattering From Soil Surfaces

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      Authors: Ying Yang;Kun-Shan Chen;Xiuyi Zhao;
      Pages: 1 - 11
      Abstract: We present a generalized power-law roughness spectrum to account for the spatial anisotropy effects on the radar scattering of a rough surface, where both the correlation anisotropy and the scaling anisotropy are accounted for. The spatial anisotropy is essential to correctly interpret the radar scattering from an agriculture field where both plow and sow are practiced. We investigate the dependence of the backscattering coefficient on the correlation anisotropy and the scaling anisotropy through a model simulation. A drastic change in backscattering strength is observed due to the anisotropy. The correlation anisotropy and the scaling anisotropy generate similar backscattering angular behavior, implying that in the context of spatial anisotropy, merely using correlation length in scattering modeling is insufficient. Equivalently, the correlation length retrieved from the backscattering coefficients perhaps is not unique. Fair use of the generalized anisotropic power-law roughness spectrum in conjunction with the scattering model is illustrated by comparing the backscattering coefficients with experimental measurements. However, the anisotropy complicates the roughness description in terms of surface parameters retrieval because we can generate similar backscattering angular patterns by combining different correlation anisotropy and scaling anisotropy. When the soil moisture is of primary interest, a more suitable radar observation geometry to minimize the spatial anisotropy influence is desirable.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Canopy Radiative Transfer Model Considering Leaf Dorsoventrality

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      Authors: Hanyu Shi;Zhiqiang Xiao;
      Pages: 1 - 11
      Abstract: Adaxial and abaxial leaf surfaces have different anatomical structures and varied chemical compositions. This asymmetry between leaf sides dictates that the spectral properties (reflectance, transmittance, and emissivity) of adaxial and abaxial surfaces are not identical. Laboratory measurements have demonstrated that this difference is significant over certain wavelengths. However, the influences of leaf dorsoventrality on such parameters (e.g., reflectance and brightness temperature) at the canopy scale have received little attention from the remote sensing community. One of the reasons is the lack of canopy radiative transfer models that can handle leaf dorsiventral properties. Although 3-D ray-tracing- or Monte Carlo-based models can achieve this, they are too complex to be implemented for massive tasks due to their low computational efficiency. Instead, they usually serve as benchmarks to evaluate other models. This study develops a unified optical–thermal canopy radiative transfer model considering leaf dorsiventral properties. It is based on the 1-D scattering by arbitrary inclined leaves (SAIL) model and, thus, has excellent efficiency and is easy to use. Evaluation of the proposed model by comparing it with the 3-D ray-tracing discrete anisotropic radiative transfer (DART) model shows that it is consistent with DART, with normalized root mean square errors (NRMSEs) of 0.013 and 0.005 within a reflective (for reflectance) and an emissive [for directional brightness temperature (DBT)] bands, respectively. Preliminary analyses ignoring leaf dorsoventrality within the optical and thermal spectral ranges by using the measured leaf spectra demonstrated that it induced significant errors in canopy reflectance (up to 40%) and DBT (up to 0.2 K) estimates under certain wavelengths. However, it should be noted that the influences of leaf dorsoventrality are determined by leaf adaxial/abaxial spectra, which still need to be explor-d due to limited measurements, especially under the thermal infrared bands.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Locally Perturbed Inaccessible Rough Surface Profile Reconstruction via
           Phaseless Scattered Field Data

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      Authors: Ahmet Sefer;
      Pages: 1 - 8
      Abstract: This work addresses an iteration scheme to observe the image of an inaccessible rough surface profile from the intensity of scattered field data. The solution of the problem is based on the integral equations written on the rough surface profile. By virtue of the surface integral equations, the phaseless scattered field is represented by a nonlinear ill-posed integral operator, which is linearized by Newton’s method and regularized via Tikhonov. The surface image is reconstructed iteratively in the least-squares sense by utilizing spline-type basis functions. A detailed numerical assessment is provided, showing that the algorithm is very effective and promising.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • RanPaste: Paste Consistency and Pseudo Label for Semisupervised Remote
           Sensing Image Semantic Segmentation

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      Authors: Jia-Xin Wang;Si-Bao Chen;Chris H. Q. Ding;Jin Tang;Bin Luo;
      Pages: 1 - 16
      Abstract: With the development of deep learning, remote sensing (RS) image segmentation has been applied with marked success. However, in the process of model training, the large number of labeled images required more expensive annotation. A key challenge is how to make full use of extensive unlabeled images available to improve the segmentation model. In this article, we propose a semisupervised remote sensing image semantic segmentation method defined as RanPaste, which combines labeled images with unlabeled images to improve segmentation performance. First, we obtain pseudo label by randomly pasting part of the ground truth label into the predicted segmentation map. Then, we combine the labeled and unlabeled images to generate rough predictions after strong augmentation. Finally, by using the semisupervised loss, we achieve better performance on remote sensing image segmentation. Our method combines consistency regularization and pseudo label and then utilizes thresholds to gradually improve the model performance. RanPaste enables the model to learn more underlying information in the unlabeled data. Experimental results on six datasets show that RanPaste can learn more latent information from unlabeled data to improve segmentation performance. Besides, our approach achieves better segmentation results on different network structures and datasets.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Reconstruction of Subsurface Objects by LSM and FWI From Limited-Aperture
           Electromagnetic Data

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      Authors: Miao Zhong;Yanjin Chen;Jiawen Li;Feng Han;
      Pages: 1 - 11
      Abstract: This article presents a hybrid 3-D electromagnetic (EM) full-wave inversion (FWI) method for the reconstruction of subsurface objects illuminated by an antenna array with the limited aperture. The 3-D linear sampling method (LSM) is first used to qualitatively reconstruct the rough shapes and locations of the subsurface objects. Then, the 3-D convolutional neural network (CNN) U-Net is used to further refine the images of the unknown objects. Finally, the Born iterative method (BIM) is implemented to quantitatively invert for the dielectric parameters of subsurface inhomogeneous objects or multiple homogeneous objects in the restricted image regions. Numerical simulations show that, compared with the pure FWI method BIM, the proposed hybrid method can reconstruct subsurface 3-D objects from limited-aperture EM data with both higher accuracy and lower computational cost. In addition, the proposed hybrid method also shows a strong antinoise ability for the reconstruction of multiple subsurface objects.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Accurate Iterative Inverse Scattering Methods Based on Finite-Difference
           Frequency-Domain Inversion

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      Authors: Teng-Fei Wei;Xiao-Hua Wang;Ping-Ping Ding;Bing-Zhong Wang;
      Pages: 1 - 10
      Abstract: Two accurate finite-difference frequency-domain (FDFD)-based iterative inverse methods, referred to as FDFD-based iterative method (FIM) and distorted FDFD-based iterative method (DFIM), are proposed for electromagnetic inverse scattering. They can be considered as FDFD-based versions of BIM and DBIM and inherit the advantage of the traditional direct FDFD inversion method, i.e., there is no need for Green’s function of complex background, especially for the inhomogeneous medium. The difference is that full-wave consideration is implemented in the proposed methods to build an iterative framework. Then, the scattered field and object parameter profile are updated in each iteration. Using the high-order components in iteration formulation, both the methods are able to reconstruct high-contrast objects and provide accurate reconstruction results. Compared with other full-wave methods based on differential equation, the two methods also have a distinct characteristic that the inversion accuracy is not limited by extra constraints. The difference between the two proposed methods is whether the background coefficient matrix is updated. Generally, DFIM has faster convergence than FIM due to the updated background coefficient matrix in the iterations. To demonstrate the effectiveness of both the methods, several typical 2-D experiments are conducted. The results show that the proposed methods could achieve good accuracy and high imaging quality.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Application of Optimal Control to Inversion of Self-Potential Data: Theory
           and Synthetic Examples

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      Authors: Mikhail S. Malovichko;Andrey V. Tarasov;Nikolay B. Yavich;Konstantin V. Titov;
      Pages: 1 - 11
      Abstract: Last decades, there has been an increased interest in the use of the self-potential (SP) method in hydrogeophysics. In response to this strong interest, we develop a novel approach to the inversion of SP data. Mathematically, the SP inverse problem is the source identification problem for the Poisson equation. Our approach substantially differs from the standard regularization approach, which explicitly includes the forward-problem operator into the cost functional. We formulated the inverse problem is as an optimal control (OC) problem and then translate it into a variational system. The system is approximated in suitable finite-element (FE) spaces giving rise to an algebraic problem with the saddle point structure. In contrast to the standard approach, which leads to a dense linear system, our method results in a system with a sparse block matrix. It can be efficiently solved by either direct sparse solvers or preconditioned iterative solvers. In this article, we present the formulation of the problem and its FE approximation. We discuss the iterative solution and preconditioning strategies. Our software implementation is based on an industrial FE package. We also present a numerical experiment with node-based linear FEs on tetrahedral grids. Our results suggest that the proposed approach may serve as a rapid and reliable tool for large-scale SP inverse problems. Moreover, the same technique can easily be extended to a wide range of geophysical linear inverse problems, such as inversions of magnetic and gravity data.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Allometric Vegetation Modeling and SAR Image Simulation for Polarimetry
           and Interferometry

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      Authors: Fengli Xue;Feng Xu;
      Pages: 1 - 15
      Abstract: This article establishes a low Degree-of-Freedom allometric vegetation model based on the biomass and allometry database (BAAD). It consists of a 5-bit species encoding scheme and a five-parameter relationship derived from the BAAD data records. Combined with an open-source fractal tree generation engine, it can produce realistic tree samples of a large variety of species. A coherent electromagnetic scattering calculation method is developed for the vegetation model where the generalized Rayleigh–Gans (GRG) approximation and the infinite cylinder approximation are used to calculate the scattering matrix of leaves and branches/trunk. Four-path multiple scattering mechanisms between vegetation and the ground are considered, and attenuations through vegetation canopy are also considered. The scattering model is validated against numerical methods. In addition, an end-to-end simulation tool is developed. An optical image is used to extract individual trees with center positions and crown diameters. The rest parameters are generated using the derived allometry model. A virtual 3-D scene with vegetation on a digital elevation map (DEM) can be generated, and SAR images can be simulated. Several case studies are carried out for both polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR) and multitemporal interferometric SAR (InSAR). One case of the Mount Fuji area is simulated and validated against ALOS-2 data and demonstrates an average scattering coefficient error of less than 3 dB. Additional cases of Southwest China, Hainan Island, and the Great Khingan Mountain demonstrate the feasibility of the proposed simulation scheme for various application scenarios.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Electromagnetic Finite-Element Modeling of Induction Effects for Buried
           Objects in Magnetic Soils

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      Authors: Mohamed H. Elbadry;Josh Wetherington;Mohammed A. Zikry;
      Pages: 1 - 8
      Abstract: A frequency-based finite element (FE) framework has been developed to predict and understand the response of an electromagnetic induction (EMI) sensor due to buried targets. The EMI sensor is used to detect buried targets in magnetic nonconducting soils. The framework was verified with an analytical model that utilizes dipole approximations. The framework was then used to predict the electromagnetic (EM) response due to interrelated stimuli and properties. The results indicate that the sensor was not sensitive to small variations (0–200 mm) in the standoff height and lateral positions, and only showed a significant change in the response due to stand-off variations that were greater than 200 mm. This low sensitivity to minor variations in standoff height and lateral position signify that there are critical distances related to the EM response of buried objects. The response to different target conductivities and permeabilities was also investigated for steel and aluminum targets. The lower conductivity steel targets had EM responses, where the inductive limit was reached at higher frequencies than the higher conductivity aluminum targets. Variations in target permeabilities for steel showed that as permeabilities increased, the frequencies at which the inductive limit was reached also increased. This verified predictive approach can provide a methodology to characterize the EM response of buried objects for a broad class of buried object EM properties, geometries, and input stimuli.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • 3-D Large-Scale TEM Modeling Using Restarting Polynomial Krylov Method

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      Authors: Jianmei Zhou;Kailiang Lu;Xiu Li;Wentao Liu;Zhipeng Qi;Yanfu Qi;
      Pages: 1 - 10
      Abstract: The transient electromagnetic (TEM) method is widely used in near-surface geophysical prospecting. The high-precision forward and inversion of large-scale complex models is a challenging problem. It is difficult to solve the large-scale problems using the direct solvers due to the large memory requirements. In this article, we present a restarting polynomial Krylov method for modeling 3-D large-scale TEM responses. The mimetic finite volume method is carried out for spatial discretization. The step-off TEM response then can be expressed as a matrix exponential function. The restarting polynomial Krylov method is used to solve the matrix exponential function. For a given restart subspace dimension, the residual is used to obtain the forward response at any time that meets the given accuracy. This method does not need to solve large-scale linear equations. Furthermore, the memory usage is mainly determined by the number of spatial discrete grids and the restart subspace order. The numerical experiments of large-scale model demonstrated that the method is accurate and uses limited memory.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Fundamental Definition of Two-Stream Approximation for Radiative Transfer
           in Scattering Atmosphere

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      Authors: Qiu Yin;Ci Song;
      Pages: 1 - 14
      Abstract: The two-stream approximation (TSA) is a primary method for tackling radiative transfer in a scattering atmosphere, which has wide applications in radiation balance evaluation, atmospheric simulation, and remote sensing data assimilation. This article defines fundamental TSA (F-TSA) by introducing the TSA conversion function, which reveals the causal characteristics of various existing specific TSA (S-TSA) models. The unified diffuse irradiance equations and their solutions are obtained based on F-TSA. The properties of the conversion function and the coefficients of irradiance equations are analyzed. It is proven that the weighted average of different conversion functions is also a conversion function. The specified conversion functions and scattering phase function treatments of six existing and four newly proposed S-TSA models are described. These S-TSA models and their typical combinations are evaluated and compared for incident lights from different directions and atmospheric layers with different optical depths. The main results are: 1) the accuracy of an S-TSA model depends on which one of transmissivity and reflectivity is concerned, and is affected by multiple factors such as the specified conversion function, the phase function processing, the single scattering albedo, the optical depth, and the direction of incident light; 2) the proper combination of different S-TSA models can improve the TSA accuracy significantly, and both the delta function and phase function B models combined with the modified Eddington model are recommended; and 3) applying proper combined S-TSA models instead of the delta transformed S-TSA models to calculate radiation flux may be beneficial.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Detection and Localization of Buried Pipelines Using a 3-D Multistatic
           Imaging Radar

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      Authors: Abdulrahman Aljurbua;Kamal Sarabandi;
      Pages: 1 - 10
      Abstract: Subsurface pipeline detection and localization is an important problem in gas and oil transport as well as in construction and excavation missions. A 3-D subsurface multistatic imaging radar with a novel focusing algorithm for pipelines is demonstrated in this article. The traditional back-projection (BP) algorithm, which is suitable for discrete scatterers, cannot be used for extended scatterers such as pipelines whose scattering phase centers are dependent on the relative positions of the transmitter and receiver. To circumvent this problem, a new imaging algorithm that tracks the scattering phase centers in a way that is specific to pipelines is presented and shown to provide significantly better imaging performance. An analytical solution for pipeline scattering is used to derive the algorithm, and justifications for the simplifying assumptions made are provided. The algorithm is tested by applying it to realistic lossy sand simulations data as well as experimental measurements data obtained by a portable vector network analyzer (VNA). Both simulation and measurement results demonstrate the ability of the algorithm to detect and localize metallic as well as dielectric pipes.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Short-Range FMCW Radar-Based Approach for Multi-Target Human-Vehicle
           Detection

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      Authors: Emanuele Tavanti;Ali Rizik;Alessandro Fedeli;Daniele D. Caviglia;Andrea Randazzo;
      Pages: 1 - 16
      Abstract: In this article, a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets crossing a monitored area is proposed. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by low-cost off-the-shelf components, i.e., a 24 GHz frequency-modulated continuous wave (FMCW) radar module and a Raspberry Pi mini-PC. The developed method is based on an ad hoc processing chain to accomplish the automatic target recognition (ATR) task, which consists of blocks performing clutter and leakage removal with an infinite impulse response (IIR) filter, clustering with a density-based spatial clustering of applications with noise (DBSCAN) approach, tracking using a Benedict-Bordner $alpha $ - $beta $ filter, features extraction, and finally classification of targets by means of a $k$ -nearest neighbor ( $k$ -NN) algorithm. The approach is validated in real experimental scenarios, showing its capabilities in correctly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks).
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Simulation and Analysis of Bistatic Radar Scattering From Oil-Covered Sea
           Surface

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      Authors: Tingyu Meng;Kun-Shan Chen;Xiaofeng Yang;Ferdinando Nunziata;Dengfeng Xie;Andrea Buono;
      Pages: 1 - 15
      Abstract: In this study, the bistatic radar scattering coefficients related to an oil-covered sea surface are predicted by modeling both the oil damping effect on surface roughness–through the advanced integral equation method–and the oil modification on the dielectric properties of the scattering surface. The bistatic scattering is analyzed in the whole upper scattering space under different radar frequencies, incidence angles, wind speeds, and oil thicknesses. Numerical predictions show that the scattering energy of an oil-covered sea surface is generally higher in the forward scattering zone than that in the backward one. In addition, the oil damping effect is the main mechanism ruling the scattering behavior in the backward region. The information related to the bistatic scattering geometry is also explored to retrieve oil thickness, representing one of the key parameters for radar-based marine oil pollution observation. A new index is proposed to quantify the sensitivity of bistatic scattering coefficients to oil thickness in different cases: single-polarization features, dual co-polarization features, namely, the polarization ratio (PR) and the normalized polarization difference index (NPDI), and dual-angular scattering features. Numerical results show that the bistatic scattering coefficients result in an enhanced sensitivity to oil thickness with respect to the monostatic case. The single HH-polarized scattering coefficients show better oil thickness sensitivity in the backward region, while the VV-polarized ones are more sensitive to oil thickness in the forward region. The combination of dual-polarized scattering coefficients significantly improves the oil thickness sensitivity compared to single-polarization radar observations, especially in the forward region. PR outperforms NPDI, even though the latter can suppress the effect of wind speed. The combination of dual-angular observations can significantly increase its sensitivi-y of oil thickness in the backward region but at the expense of reduced sensitivity in the forward region.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Coherent Spatially Varying Bidirectional Scattering Distribution Function
           of Rough Surface

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      Authors: Xu Zhang;Feng Xu;
      Pages: 1 - 17
      Abstract: High-resolution synthetic aperture radar (SAR) as an imaging device becomes more and more like a “camera” at the microwave frequency band. How different objects or object surfaces may visually appear in SAR images becomes an interesting research topic. Inspired by the bidirectional reflectance distribution function (BRDF) models employed in computer graphics (CGs), this article proposes the coherent spatially varying bidirectional scattering distribution function (CSVBSDF) for characterizing the electromagnetic scattering and SAR imaging behavior of surfaces. The CSVBSDF establishes a mapping function from observation parameters and surface local parameters to multidimensional measurements. In this article, CSVBSDF of the randomly rough surface is derived via adapting the integral equation method (IEM) to finite-size pixel cells under the plane wave and tapered wave incidence, respectively. It is then validated against the numerical beam simulation method (BSM) in the SAR image domain. A ground-based rail SAR and a 3-D laser scanner are used to measure the SAR image and the corresponding 3-D geometry of a real ground surface. Surface-local parameters, such as the local slope and roughness, are estimated from the measured 3-D geometry and then fed into the CSVBSDF model to produce a synthetic SAR image. Comparison against the real SAR image preliminarily demonstrates the efficacy of the proposed CSVBSDF model.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Computation of Backscattered Fields in Polarimetric SAR Imaging Simulation
           of Complex Targets

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      Authors: Cheng-Yen Chiang;Kun-Shan Chen;Ying Yang;Suyun Wang;
      Pages: 1 - 13
      Abstract: This article presents the computation of the backscattered field in simulation of synthetic aperture radar (SAR) raw data. We develop an improved Kirchhoff approximation to estimate the surface fields induced by the incident waves. The improved Kirchhoff approximation is a second-order iterative solution of the integral equations governing the targets’ electric and magnetic current densities. In computing the second-order re-radiated fields, we applied the shoot-bouncing-ray (SBR) technique to enhance propagation path tracking efficiency. The geometrical theory of diffraction (GTD) was employed to account for the diffraction fields from the edges and wedges, which constitute the total scattered fields collected by SAR in synthetic aperture. As the SAR moves along the azimuth direction, the backscattered signal computation is repeated as the antenna beam crosses the targets. This procedure demands heavy computational resources but requires no priori assumptions of radar cross-sections (RCSs) nor speckle statistics. The raw data is generated as an output signal of the SAR system response into which the backscattered signal is input. We chose three types of the target to demonstrate our approach to confirm the effectiveness. The first set of targets are three dihedral reflectors – two of them were rotated to constitute three unique scattering matrices. The results show that the polarimetric information, both relative amplitudes and phases, are well–preserved. The second target is a rough surface with exponential power spectral density and Gaussian height probability density. We examined image speckle statistics of multi-looking image. The third target type is an electrically large cargo ship (container) sitting over a sea surface. Polarimetric analysis via the Pauli and Y4R decompositions reveals that the polarimetric features are well preserved. Simulation results demonstrate that the present approach is fully -oherent in streamlining the data flow from backscattered field to complex single look images within the SAR imaging scene.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • 2-D Mirrored Aperture Synthesis With Four Tilted Planar Reflectors

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      Authors: Zhenyu Lei;Haofeng Dou;Qingxia Li;Hao Li;Liangbing Chen;Ke Chen;Liangqi Gui;Guanghui Zhao;Zhiwei Chen;Chengwang Xiao;Yuhang Huang;
      Pages: 1 - 13
      Abstract: Setting two reflectors perpendicular to each other and an array produces mirrored aperture synthesis (MAS), which obtains a higher spatial resolution than aperture synthesis (AS) for the same antenna array. The proposal of the MAS theory and the experimental verification suggest that the spatial resolution can be improved by setting the reflector. This article proposes a method in which four tilted planar reflectors are set around an antenna array. This method is named 2-D mirrored AS with four tilted planar reflectors (2-D MAS-T). Because 2-D MAS-T uses more reflectors than MAS, the spatial resolution is further improved. The principle of 2-D MAS-T with an antenna array is given in this article. The relationship between the size of the reflectors, the spatial resolution, and the field of view (FOV) is obtained. The simulation and experimental results demonstrate that 2-D MAS-T can achieve a higher spatial resolution with the same antenna array than 2-D AS and 2-D MAS.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Facet-Based Hybrid Method for Electromagnetic Scattering From Shallow
           Water Waves Modulated by Submarine Topography

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      Authors: Dongfang Li;Zhiqin Zhao;
      Pages: 1 - 14
      Abstract: This article presents a mechanism model of synthetic aperture radar (SAR) remote sensing imaging of submarine topography in shallow areas. According to the shallow water effect and complex geographical factors, a 3-D geometric model of multiscale wave is generated by combining sea spectrum separation and phase superposition methods. It has the advantage of reflecting the random fluctuation characteristics of waves and the refraction phenomenon influenced by submarine topography. In addition, the modulation relationship among submarine topography, tidal current, and sea surface microroughness is established. This constitutes hydrodynamic modulation. The facet-based hybrid method that combines the geometry optics (GO) and the integral equation method (IEM) is adopted to solve the electromagnetic (EM) scattering of super electrically large sea surface. This contains tilt modulation. The effectiveness of the proposed model is demonstrated through the comparisons with the measured results. The influence of the parameters of radar, sea state, water depth, and tidal current direction on the scattering intensity of sea surface is studied as well. It is revealed that the shallower the water depth, the more significant the hydrodynamic and tilt effect. In addition, the velocity bunching (VB) modulation is a special modulation method in SAR imaging. According to the simulated results, the contributions of tilt, hydrodynamic, and VB modulation in SAR imaging mechanism are compared. The VB is the main factor, hydrodynamic is second, and tilt is the least.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Estimating the Parameters of the Spatial Autocorrelation of Rainfall
           Fields by Measurements From Commercial Microwave Links

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      Authors: Adam Eshel;Pinhas Alpert;Hagit Messer;
      Pages: 1 - 11
      Abstract: The spatial structure of rain fields is important to the understanding of their effects on ground-level aspects, such as runoff generation, and is considered crucial information for the accurate reconstruction of these fields. It is commonly characterized by a simplified spatial autocorrelation function (ACF). The near-ground ACF, and—particularly—its decorrelation distance, is evaluated from point measurements (rain gauges and distrometers). However, the spatial representation of such measurements is limited and therefore rarely sufficient for reliable ACF estimation. The emerging use of commercial microwave links (CMLs) for near-ground rain retrieval, and their spatial abundance, suggests using them for ACF estimation. In this study, we propose a method for extracting spatial features of a rain field, and in particular its decorrelation distance, from CML measurements. When sampled by path integration, the rain measurements acquire a distortion as a result of the averaging of a once fluctuating signal, where extreme rain intensities are being smeared. When evaluating the AFC from CMLs’ measurements, this effect needs to be compensated for. We propose methods for retrieving the original parameters characterizing the AFC and validate them on semisynthetic simulated data, based on actual rain events. The error was found to be 5%.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Validation of a Pseudospectral Time-Domain (PSTD) Planetary Radar Sounding
           Simulator With SHARAD Radar Sounding Data

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      Authors: Yang Lei;Maria Carmela Raguso;Marco Mastrogiuseppe;Charles Elachi;Mark S. Haynes;
      Pages: 1 - 15
      Abstract: In a recent study, a 2-D pseudospectral time-domain (PSTD) full-wave simulator was developed and demonstrated to be capable of efficiently solving large-scale low-frequency (e.g., HF) electromagnetic scattering problems, for example, on the application of radar sounding simulations of planetary clutter and subsurfaces. In this article, the 2-D PSTD simulator is applied to simulate a domain as large as 4000 $lambda $ (along-track) $times 1666.67,,lambda $ (cross-track) $times 33.33,,lambda $ (depth) with $lambda =15$ m at an HF frequency of 20 MHz. To accomplish the goal, the simulator is further improved to efficiently model/simulate large cross-track slices of dielectric scenes by allowing nonuniform grid sampling in horizontal (lateral) and vertical directions, and the cross-track results are then stitched together along the track to form the simulated radargram. By combining the SHAllow RADar (SHARAD) viewing geometry and Mars Orbital Laser Altimeter (MOLA) digital elevation model (DEM), we simulate SHARAD returns at three different sites on Mars: one at the North Pole and two at Oxia Planum. At all three sites, the PSTD simulated radargrams are compared with measured SHARAD radargrams. Through power-level calibration and reference time adjustment, the PSTD simulated power estimates are further validated by comparing with real power observations from SHARAD with a 5-dB uncertainty and Pearson correlation coefficient of 0.3–0.4 (a $p$ -value on the order of $10^{-9}$ ), which justifies the use of the 2-D PS-D simulator for emulating surface clutter in planetary radar sounding. This simulator is open source and can be easily modified to support radar sounding simulations in support of other planetary missions with radar sounding instruments.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Ratio Between Discrete IAR Frequencies From Observations in the Solar
           Cycle 24

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      Authors: Alexander S. Potapov;Anatol V. Guglielmi;Boris I. Klain;
      Pages: 1 - 5
      Abstract: Two types of standing Alfvén waves can exist in the ionospheric Alfvén resonator (IAR). The first of them (type 1) corresponds to the case when an antinode of oscillations of the magnetic field of a standing Alfvén wave occurs at the lower boundary of the resonator. The second type corresponds to the case when a node of magnetic field oscillations occurs at the lower boundary. The article proposes an original technique that makes it possible to experimentally determine which of the two indicated types is observed in the experiment. The methodology is based on the idea that in the first case the harmonics of oscillations are odd, i.e., are multiples of 3, 5, 7, and 9, while in the second case the harmonics are multiples of the fundamental frequency: 2, 3, 4, 5, and so on (type 2 of standing waves). Using ground-based observations of the IAR emissions at obs. Mondy from 2009 to 2019, we have shown that events with an odd ratio of harmonics are most often observed.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Analysis of Imageless Ground Scene Classification Using a Millimeter-Wave
           Dynamic Antenna Array

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      Authors: Daniel Chen;Jeffrey A. Nanzer;
      Pages: 1 - 10
      Abstract: We present an analysis of the capability for imageless ground scene classification using a subset of the Fourier domain information obtained with a rotationally dynamic millimeter-wave antenna array. The concept is based on the detection of signal artifacts generated by artificial objects in a scene, which manifests in the Fourier, or spatial frequency, domain. Man-made, artificial structures, such as buildings and roads, are generally characterized by sharp edges, which generate spatial frequency responses that are confined to a narrow angular range but extend over a broad spatial frequency bandwidth. These artifacts can be detected by generating a ring-shaped filter in the Fourier domain, which can be obtained through the novel design of a linear antenna array with rotational dynamics. We discuss the design of a millimeter-wave linear dynamic array for generating ring-filters and analyze the ability of such an array to classify ground scenes containing artificial structures from those without when mounted on an aerial platform, such as a drone. We compare ring filter designs and explore the use of a heuristic classifier and the K-nearest neighbor (K-NN) classifier on a large dataset of microwave ground scenes obtained from a database. Using a single ring filter that can be implemented with a two-element antenna array, small classification errors of 0.6%–3.2% were observed. Implementing multiple filters in a linear array consisting of four elements reduced the error to 0.3%.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Magnetic Viscosity Effect in Magnetic-Source Time-Domain Electromagnetic
           Surveys

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      Authors: Xuejiao Zhao;Huaishi Liu;Yanqi Wu;Jun Lin;Yanju Ji;
      Pages: 1 - 11
      Abstract: The effect of magnetic viscosity (MV) has continued to be observed with the development and application of the time-domain electromagnetic method (TDEM). Field and laboratory data show that the MV effect is characterized by a −1 ± 0.4 scope power-law delay in the late stage of electromagnetic response. Research on the MV effect can improve the detection accuracy of TDEM and assist in prospecting for ferromagnetic minerals. Most of the studies are based on the Chikazumi susceptibility model and the 1-D modeling method. However, the late-stage electromagnetic response shows a −1 power-law delay, which is inconsistent with the measured data. The log-uniform distribution of relaxation time $tau $ in the Chikazumi model is not always appropriate. This study considers the Cole–Cole susceptibility model with a log-normal distribution of relaxation time. The 3-D modeling method of the MV effect is raised based on the rational function approximation algorithm and recursive convolution technique; the control equations and iterative process were adjusted based on finite-different time-domain (FDTD) method. The effectiveness was verified via half-space and layered models; the effects of susceptibility parameters on the response were clarified; moreover, the MV effect of the 3-D anomalous model was analyzed. Our method can model the fractional propagation process of the MV effect more efficiently and help to improve the prospecting accuracy of the TDEM method under complex magnetic geological conditions.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Rough-Surface Polarimetry in Companion SAR Missions

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      Authors: Lorenzo Iannini;Davide Comite;Nazzareno Pierdicca;Paco Lopez-Dekker;
      Pages: 1 - 15
      Abstract: Bistatic scattering from rough surfaces is typically approached through the analysis of the scattered field in the conventional H and V polarization basis, which coincides with the zenith and azimuth unit vectors in a spherical reference frame. This study delves into the impacts of different choices of the transmit and receive linear basis on the performance and design of a synthetic aperture radar (SAR) mission receive-only companion. This article formalizes the rotation of the scattered wave orientation at the antenna axes of the companion with respect to the transmitted one and introduces a novel set of linear polarizations, named principal polarizations, in transmit and receive, deemed more suited to represent the scattering mechanisms of rough surfaces. Such a set is defined by the polarization bases that maximize the radar cross section. It is shown that the theoretical estimates from the proposed geometrical framework provide a good agreement with analytical and numerical simulations, performed considering state-of-the-art numerical solutions. In addition, this article promotes the hypothesis that a bistatic radar configuration, defined through the conventional H and V linear basis, presents a strong similarity, from a target information retrieval standpoint, to a monostatic compact $varphi $ -pol mode, i.e., with the transmission of a linear polarization rotated by an angle $varphi $ . The rotation $varphi $ varies over the swath and as a function of satellite separation. For baselines of 250–300 km, such as those envisioned by the European Space Agency (ESA) Harmony Earth Explorer candidate, and for steep incidence angles, an equivalent $pi /8$ -pol -an be achieved for rough surfaces.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Phaseless Extended Rytov Approximation for Strongly Scattering Low-Loss
           Media and Its Application to Indoor Imaging

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      Authors: Amartansh Dubey;Samruddhi Deshmukh;Li Pan;Xudong Chen;Ross Murch;
      Pages: 1 - 17
      Abstract: Imaging objects with high relative permittivity and large electrical size remains a challenging problem in the field of inverse scattering. In this work, we present a phaseless inverse scattering method that can accurately reconstruct objects even with these attributes. The novelty of the approach is that it uses a high-frequency approximation for waves passing through lossy media to provide corrections to the conventional Rytov approximation (RA). We refer to this technique as the extended phaseless Rytov approximation for low-loss media (xPRA-LM). Simulation and experimental results are provided for RF indoor imaging using phaseless measurements acquired from 2.4-GHz-based WiFi nodes. We demonstrate that the approach provides accurate reconstruction of objects up to relative permittivities of $15+1.5j$ for object sizes greater than 30 wavelengths. Even at higher relative permittivities of up to $epsilon _{r}=77+ 7j$ , object shape reconstruction remains accurate; however, the reconstruction amplitude is less accurate. To the best of our knowledge, xPRA-LM is the first linear phaseless inverse scattering approximation with such a large validity range and can be used to achieve the potential of RF and microwave imaging in applications such as indoor RF imaging.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Linear Current Sensing for Detecting and Locating Underground Structures

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      Authors: Fridon Shubitidze;Benjamin E. Barrowes;Tornike Shubitidze;Lee J. Perren;
      Pages: 1 - 15
      Abstract: This article introduces a new triaxial magnetic field gradiometer to detect, locate, and map underground metallic utilities, such as a thin wires, railroad tracks, and pipes. The system couples a primary electric field along target’s elongation to maximize excited linear electric (i.e., non-solenoidal) currents and uses magnetic field induction sensing (i.e., Faradays induction law) to measure secondary magnetic fields. First, theoretical and numerical studies are presented for the broadband electromagnetic response from long thin wires buried in a conducting soil at varying depths. Second, numerical calculations are validated against experimental data. Favorable comparisons between model and measured data are illustrated for wires subjected to both plane wave excitation, originating from a 1.4-MHz amplitude modulation (AM) station as well as local excitation. Third, forward and inverse electromagnetic models are applied to electromagnetic gradiometer (EMG) field data to extract the wire’s burial depths from different survey track data, showing good agreement for wire depths from 0 to ~6 m. Finally, multiwire location and orientation are extracted from the triaxial vector gradiometer dataset. These results illustrate the applicability of linear current sensing for detecting, locating, and mapping deep underground metallic infrastructures, such as wires, railroad tracks, and pipes.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Decorrelation of the Near-Specular Scattering in GNSS Reflectometry From
           Space

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      Authors: Davide Comite;Nazzareno Pierdicca;
      Pages: 1 - 13
      Abstract: To understand the temporal decorrelation of the near-specular component of land-scattered signals in global navigation satellite system reflectometry (GNSS-R) and describe the nature of the scattering considering spaceborne receivers at arbitrary altitudes, we propose here an analytical solution of the covariance of the field under the Kirchhoff approximation. Both cases of infinite illumination and finite illumination on the ground are studied. Surfaces with gentle undulations are considered, i.e., those having small slopes and showing slow variations of the profiles over the horizontal scale. This allows for investigating scattered fields that can be neither coherent nor completely incoherent over land surfaces that are nearly flat. In a recent work from the authors, an extensive numerical evaluation of the decorrelation of the near-specular land scattering was presented. The phenomenology of the problem was studied and discussed numerically, solving, for airborne receivers, the relevant scattering integral, both as a function of the geometry of the system and the statistical parameters of the illuminated surface. Such numerical results are used here to validate the proposed closed-form formulation. It is demonstrated how the near-specular scattering, collected over land targets by a GNSS-R receiver from space, decorrelates as a function of the receiver movement and the statistical parameters describing the illuminated surface (namely, height standard deviation and correlation length). The proposed analysis provides information of interest for the design of future GNSS-R missions. The interpretation of GNSS-R data from space, which typically shows strong fluctuations, can also be supported by this approximated analytical study.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Loop Array Antenna in a Borehole for Directivity to a Horizontally
           Polarized Wave

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      Authors: Satoshi Ebihara;Masayuki Matsumoto;
      Pages: 1 - 10
      Abstract: We propose a directive antenna for borehole radar using a horizontally polarized wave in a vertical borehole. This antenna is an array of several horizontal loop antennas, arranged vertically. When a plane wave is incident on the antenna, the differences in arrival times at the array elements enable us to estimate the direction of arrival (DOA). We present a simple model representing the arrival time difference and propose an algorithm for DOA estimation. Furthermore, we synthesized the array signal by analyzing the electromagnetic field using the method of moments (MoM) and applied the proposed algorithm to the signal. We found that mutual coupling between antenna elements affects the DOA estimation and that a space of about 3 cm between antenna elements prevents mutual coupling. Using the MoM analysis, we simulated a cross-hole measurement to demonstrate the antenna’s ability. We carried out field experiments in wet soil to examine the antenna’s ability. The proposed loop array antenna in a water-filled borehole received a direct wave from a source in another borehole. After applying the proposed algorithm to the measured data, we found that we could estimate the DOAs to the source with an error of less than 15° in the azimuth angle and 10° in the elevation angle.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • The Effect of Dielectric Relaxation Processes on the Complex Dielectric
           Permittivity of Soils at Frequencies From 10 kHz to 8 GHz—Part I:
           Experimental

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      Authors: Pavel P. Bobrov;Tatyana A. Belyaeva;Elena S. Kroshka;Olga V. Rodionova;
      Pages: 1 - 9
      Abstract: This is the first of two articles that present experimental spectra of six soil samples with varying clay contents ranging from 0% to 55% and organic carbon levels ranging from 0% to 3.9%, measured at a small step of moisture change at a temperature of 25 °C. The study was carried out using a method that enables the measurements of the same sample over a wide frequency range of 1 kHz–8.5 GHz and in some cases up to 20 GHz. The relative effective complex permittivity (RCP) is strongly influenced by dielectric relaxation processes due to the Maxwell–Wagner (MW) effect in the frequency range of 10 kHz–8.5 GHz as demonstrated. These processes are aided by the presence of clay in the soil. Up to frequencies of 4–5 GHz, these processes have a weak influence, mainly on the imaginary part of the RCP. This explains why in the dielectric models of Dobson and Mironov, where relaxation processes are ignored, free and physical bound water has high specific conductivity. We demonstrated that organic carbon, even at low content, reduces the real and imaginary parts of the RCP when all other factors are equal. Part II will present the results of using the Debye and Cole–Cole formulas to model relaxation processes.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • GNSS-R Snow Depth Inversion Based on Variational Mode Decomposition With
           Multi-GNSS Constellations

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      Authors: Yuan Hu;Xintai Yuan;Wei Liu;Jens Wickert;Zhihao Jiang;
      Pages: 1 - 12
      Abstract: Snow depth monitoring is meaningful for climate analysis, hydrological research, and snow disaster prevention. Global navigation satellite system-reflectometry (GNSS-R) technology uses the relationship between the modulation frequency of the signal-to-noise ratio (SNR) and reflector height to monitor snow depth. Existing research on single constellation has made good progress and is gradually developing toward multiconstellation combined inversion. Aiming at the accuracy of snow depth inversion, this article introduces the variational mode decomposition (VMD) algorithm with the characteristics of an adaptive high-pass filter to detrend the SNR data. The experimental results of KIRU station and P351 station show that the VMD algorithm is suitable for different constellations and has better signal separation effect. The snow depth inversion results for both stations are in high agreement with the in situ snow depths provided by the Swedish Meteorological and Hydrological Institute (SMHI) and the SNOTEL network. The root-mean-square error (RMSE) of the inversion results is reduced by 20%–40% compared to the least-squares fitting (LSF) algorithm, and the correlation coefficients are also greatly improved. Moreover, considering that there is no overlap between the climate station and the inversion area, this article introduces the maximum spectral amplitude as another reference data source and obtains basically consistent experimental conclusions. On this basis, the maximum spectral amplitude is used as the input variable of the entropy method, and the feasibility of the combination strategy is studied. The results show that the combined strategy reduces a little inversion error and improves the temporal resolution of snow depth monitoring. It is of great significance for more accurate and rapid monitoring of snow depth changes and disaster warnings and provides an important reference for further -esearch on the GNSS-R technology.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Underground Cavity Detection Through Spectral Distortion of a GPR Signal

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      Authors: Caleb Leibowitz;Anthony J. Weiss;
      Pages: 1 - 8
      Abstract: We present a novel method to detect underground cavities using cross-borehole ground-penetrating radar (GPR). We model the propagation of a GPR signal across a cavity and find that the spectrum of the signal will be distorted in a specific low-pass manner. Comparing the spectra of received signals with the predicted spectrum using a hypothesis-testing approach allows us to detect cavities with high probability. The proposed method is validated on both simulated and real measurements. Our approach generally remains effective even when conventional methods fail; our method can also be combined with conventional methods to more successfully distinguish between cavities and geologic clutter.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • HCNNet: A Hybrid Convolutional Neural Network for Spatiotemporal Image
           Fusion

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      Authors: Zhuangshan Zhu;Yuxiang Tao;Xiaobo Luo;
      Pages: 1 - 16
      Abstract: In recent years, leaps and bounds have developed spatiotemporal fusion (STF) methods for remote sensing (RS) images based on deep learning. However, most existing methods use 2-D convolution (Conv) to explore features. 3-D Conv can explore time-dimensional features, but it requires more memory footprint and is rarely used. In addition, the current STF methods based on convolutional neural networks (CNNs) are mainly the following two: 1) use 2-D Conv to extract features from multiple bands of the input image together and fuse the features to predict the multiband image directly and 2) use 2-D Conv to extract features from individual bands of the image, predict the reflectance data of individual bands, and finally stack the predicted individual bands directly to synthesize the multiband image. The former method does not sufficiently consider the spectral and reflectance differences between different bands, and the latter does not consider the similarity of spatial structures between adjacent bands and the spectral correlation. To solve these problems, we propose a 2-D/3-D hybrid CNN called HCNNet, in which the 2D-CNN branch extracts the spatial information features of single-band image, and the 3D-CNN branch extracts spatiotemporal features of single-band images. After fusing the features of the dual branches, we introduce neighboring band features to share spatial information so that the information is complementary to obtain single-band features and images, and finally stack each single-band image to generate multiband images. Visual assessment and metric evaluation of the three publicly available datasets showed that our method predicted better images compared with the five methods.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Ambiguity Clutter Suppression via Pseudorandom Pulse Repetition Interval
           for Airborne Radar System

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      Authors: Yukai Kong;Xianxiang Yu;Tao Fan;Guolong Cui;Lingjiang Kong;
      Pages: 1 - 13
      Abstract: Ambiguity clutter for airborne radar systems is usually caused by a uniform pulse repetition interval (UPRI) waveform, which significantly degrades target detection and location performance. To address this issue, this article proposes an ambiguity clutter suppression method resorting to a pseudorandom pulse repetition interval (PrPRI) waveform. First, an airborne radar clutter model accounting for multiple range rings with PrPRI waveform is developed. Next, a nonuniform coherent processing framework is introduced to eliminate the clutter folded in the range and Doppler domain. In particular, the reasons for the enlargement of the area free of clutter corresponding to PrPRI mode are analyzed, as well as the maximum unambiguous Doppler frequency, maximum unambiguous range, and design principles for PRI are derived. Finally, numerical examples are designed to verify that the nonuniform coherent processing framework can enlarge the clutter-free area and achieve unambiguity target detection and location.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • An Analytical Formulation of the Correlation of GNSS-R Signals

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      Authors: Gerardo Di Martino;Alessio Di Simone;Antonio Iodice;
      Pages: 1 - 13
      Abstract: We present an analytical formulation of the correlation coefficient of the electromagnetic fields scattered at near-specular direction by a rough or gently undulating surface and measured at two spatially separated positions occupied by a moving receiver at slightly different times. This allows us obtaining an explicit expression of the correlation time of the received signal in terms of radar and surface parameters. This work provides a contribution to the discussion, currently ongoing in the Global Navigation Satellite System Reflectometry (GNSS-R) scientific community, about the behavior of received signal fluctuations, especially when surface profile variations are such that the scattering is neither coherent nor completely incoherent. The scattering surface is here modeled as randomly rough, and the Kirchhoff approximation (KA) or the first-order small slope approximation (SSA1) is employed to compute the scattered field. In fact, the expression of the correlation coefficient is the same for both approximations. The obtained closed-form expression shows that as the surface correlation length increases, the degree of coherence smoothly increases from the value obtained with the expression already available in the literature for very rough surfaces to a value close to unity for gently undulating surfaces. The obtained behavior of correlation time as a function of surface parameters, system resolution, and observation geometry is in agreement with numerical simulations available in the literature. In general, obtained analytical results are in agreement with the observed behavior of GNSS-R signals over flat land surfaces.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Modeling Bistatic Coherent Scattering From Multilayered Rough Surface
           Using Its Effective Dielectric Constant at P- and L-Bands

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      Authors: Ming Li;Ling Tong;Yiwen Zhou;Brandon O’Dell;
      Pages: 1 - 15
      Abstract: This article proposes a closed-form asymptotic solution for the bistatic coherent scattering of a multilayered rough surface structure based upon its effective dielectric constant (EDC) in specular direction. The EDC is modeled by establishing an equivalence of the coherent scattering between the multilayered rough surface structure and a half-space homogeneous medium. The scattering solution is then solved using the scalar Kirchhoff approximation (SKA) method. This new method is referred to as the SKA-EDC method, and it is applied to analyze the sensitivity of the EDC and coherent reflectivity to bare soils with realistic parameters at P- and L-bands. The result indicates that EDC can give different responses to the soil moisture variations with respect to the coherent reflectivity, enabling the potentials of root-zone soil moisture retrieval. At incidence angle smaller than 35°, EDC gives the same value for both polarizations, and the coherent reflectivity can show a significant response to soil at depths
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Analysis of Swarm Satellite Magnetic Field Data for the 2015 Mw 7.8 Nepal
           Earthquake Based on Nonnegative Tensor Decomposition

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      Authors: Mengxuan Fan;Kaiguang Zhu;Angelo De Santis;Dedalo Marchetti;Gianfranco Cianchini;Alessandro Piscini;Xiaodan He;Jiami Wen;Ting Wang;Yiqun Zhang;Yuqi Cheng;
      Pages: 1 - 19
      Abstract: A nonnegative tensor decomposition (NTD) approach has been developed to analyze the ionospheric magnetic field data of the Swarm Alpha and Charlie satellites for the Mw7.8 2015 Nepal earthquake. All available satellite data were analyzed regardless of geomagnetic activity. We used the amplitude time–frequency spectra of the two-satellite data to build third-order tensors and decomposed them into three components. One of these components seems to be more affected by seismicity. In particular, the cumulative number of anomalous tracks of this component displays accelerated growth that conforms to a sigmoid fit from 60 to 40 days before the mainshock. Subsequently, until ten days before the earthquake, it shows a weak accelerating trend that obeys a power-law behavior and then resumes linear growth after the mainshock. Moreover, the cumulative anomaly was indicated not to be caused by geomagnetic activity, solar activity, or other nonseismic factors. An investigation of the foreshocks around the epicenter reveals that the cumulative Benioff strain also exhibited two accelerated growths before the mainshock, which is consistent with the cumulative result of ionospheric anomalies. In the first acceleration stage, seismicity appeared in the region surrounding the epicenter, and most of the ionospheric anomalies were offset away from the epicenter. During the second acceleration stage, some foreshocks occurred closer to or on the mainshock fault, and ionospheric anomalies also appeared near two faults around the epicenter. Furthermore, the correspondence between the ionospheric anomalies and the anomalies in different geolayers can be explained by the lithosphere–atmosphere–ionosphere coupling model.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Analysis of Low-Frequency Drone-Borne GPR for Root-Zone Soil Electrical
           Conductivity Characterization

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      Authors: Kaijun Wu;Sébastien Lambot;
      Pages: 1 - 13
      Abstract: In this study, we analyzed low-frequency drone-borne ground-penetrating radar (GPR) and full-wave inversion for soil electrical conductivity mapping. Indeed, in the lowest GPR frequency ranges, the soil surface reflexion coefficient depends more on the soil electrical conductivity than on its permittivity. Numerical experiments were conducted within the frequency range 15–45 MHz to analyze parameter sensitivities, the well-posedness of the inverse problem as well as the depth of sensitivity. The results show that the soil surface reflexion is significantly more sensitive to the soil electrical conductivity than the soil permittivity. Therefore, the conductivity can be retrieved using full-wave inversion within this frequency range, with a characterization depth varying from 0.5 to 1 m, depending on the soil properties. Yet, the permittivity also affects the results and should be accounted for in the inversion strategy. Field measurements were performed using low-frequency drone-borne radar with a 5-m half-wave dipole antenna, and electromagnetic induction (EMI) measurements with different depth sensitivities were conducted for comparison. Kriging interpolation was used to get maps from measurement points. The soil conductivity maps obtained by the proposed GPR and EMI are compliant in terms of absolute values and spatial patterns. This study demonstrated the capacity of low-frequency drone-borne GPR for fast, field-scale soil electrical conductivity mapping.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Wave Parameter Inversion With Coherent Microwave Radar Using Spectral
           Proper Orthogonal Decomposition

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      Authors: Jiangheng He;Zezong Chen;Chen Zhao;Xi Chen;Yunyu Wei;Chunyang Zhang;
      Pages: 1 - 11
      Abstract: Coherent microwave radar processes sea echoes by using a direct relationship between wave orbital velocity and wave spectrum instead of backscattered intensity to obtain wave parameter. However, the measurements of wave orbital velocity are susceptible to ocean conditions (e.g., low sea state and interference), and thus the method’s performance is often uneven under the mixed conditions. To solve this problem, a novel method for wave parameter inversion based on spectral proper orthogonal decomposition (SPOD) is proposed. The spatial–temporal series of the wave orbital velocities are first processed using SPOD, and a series of expansion coefficients, modes, and eigenvalues can be obtained. Later, the ocean conditions are classified according to the distribution of the eigenvalues, and the truncated frequencies and modes are used to remove nonwave contributions. Finally, wave parameter inversion is performed using the reconstructed spatial–temporal velocities. The proposed method is validated by data from two ocean observation experiments, including coherent S-band radar wave echoes and buoy measurements. The results indicate that the proposed method can discard nonwave contribution and obtain wave parameters in good agreement with buoy measurements regardless of low sea state or mixed ocean conditions.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Full Waveform Inversion of Transient Electromagnetic Data in the Time
           Domain

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      Authors: Junjie Xue;Jiulong Cheng;Leiv-J. Gelius;Xin Wu;Yang Zhao;
      Pages: 1 - 8
      Abstract: Quasi-seismic imaging is a popular form of electromagnetic (EM) imaging technology. In most cases, it is considered that it will yield more obvious layered characteristics if the EM response is transformed into the virtual wave domain. However, the time profile of the virtual wave cannot effectively show the underground EM structure before migration. In this article, we make an improvement to process the virtual wave, by applying the full waveform inversion (FWI) method, with the steps detailed as follows: 1) we use the truncated singular value decomposition (TSVD) method to transform the transient EM response to the virtual wave field; 2) using the FDTD method, we calculate the virtual wave field according to the resistivity model, then use the shearlet transform (ShT) to remove the direct wave; and 3) we use a least square equation between the virtual wave and transformed virtual wave to express the problem of FWI. Next, we apply one of the steepest descent methods—the Marquardt approach—to obtain the optimal underground conductivity information. Finally, we use the synthetic and field data to show the accuracy and rationality of the method proposed in this article.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Soil Moisture Profile Retrievals Using Reflection of Multifrequency
           Electromagnetic Signals

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      Authors: Alexander G. Voronovich;Richard J. Lataitis;
      Pages: 1 - 10
      Abstract: A method for retrieving soil moisture (SM) profiles using multifrequency, ground-reflected electromagnetic (EM) waves is proposed. In a previous publication, a retrieval technique was developed, which used variations in the angular dependence of the reflectivity to infer the SM profile. In this follow-on work a more practical approach based on the frequency dependence of the modulus of the reflection coefficient for a single incidence angle is extended and its feasibility demonstrated. Two possible approaches are considered: the direct retrieval of the SM profile and the retrieval of the dielectric constant (DC) profile. The former immediately yields the parameter of interest, however, it requires a soil dielectric model linking the DC of the soil to its water content. Such a model, which depends on the type of soil, may not be immediately available. The latter does not require a linking model, but by comparing measured profiles of the DC under dry and wet conditions, the SM profile can be estimated. Both approaches are considered in this article and their feasibility investigated with the help of numerical simulations in the presence of multiplicative noise in the data. For the case of a direct retrieval of the SM profile, a representative soil dielectric model was used. The retrieval procedure was simplified and made more robust and the frequency independence assumption of the DC in the earlier work was removed.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Derivation and Validation of Three-Dimensional Microwave Imaging Using a
           W-Band MIMO Radar

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      Authors: Sumin Kim;Jeongbae Kim;Chaerin Chung;Min-Ho Ka;
      Pages: 1 - 16
      Abstract: This study proposes a method for synthesizing 3-D microwave images using a multiple-input–multiple-output (MIMO) radar. Recent studies on MIMO radar technology have reported promising hardware that can be used in radar imaging. In particular, the increased amount of data acquired from MIMO radar is effective for 3-D radar imaging. Despite the demand for using 3-D radar imaging, the underlying technical challenges have not been addressed. Although the array synthetic aperture radar method is promising, realizing a multichannel radar with numerous channels requires significant effort in terms of hardware. Therefore, an MIMO radar can be used to effectively increase the number of channels with reduced hardware. Studies on beat frequency division (BFD) frequency-modulated continuous wave (FMCW) radars show that the simultaneously transmitted MIMO signal is suitable for radar imaging applications without hindering the along-track imaging performances. In this study, the signal of a BFD FMCW radar mounted on a movable platform is modeled according to the imaging geometry, and a fast Fourier transform-based imaging algorithm is derived to efficiently process the multichannel data. This algorithm is applied to the simulated radar data to verify the effectiveness of the proposed method. The method is further assessed by applying the imaging algorithm to the radar data acquired from a functional W-band BFD FMCW radar hardware. The radar transceiver was mounted on a movable table, and the data are measured at an outdoor experiment site. The results verify that the proposed method can be used for various 3-D radar imaging applications.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Multiple Buried Target Reconstruction Using a Multiscale Hybrid of
           Diffraction Tomography and CMA-ES Optimization

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      Authors: Maryam Hajebi;Ahmad Hoorfar;
      Pages: 1 - 13
      Abstract: In this article, a hierarchical stochastic optimization algorithm for profiling of multiple high-contrast buried objects in large investigation domains (IDs) is presented. As this problem is highly nonlinear and ill-posed, a combination of different profiling modalities is required to tackle the challenges. First, an initialization step using the qualitative diffraction tomography (DT) method is performed to not only limit the ID to scatterers’ locations but also obtain an approximation of their dielectric permittivity range. Then, an algorithm that combines the iterative multiscaling approach (IMSA) with the reconstruction capabilities of covariance matrix adaptation evolution strategy (CMA-ES) is implemented. IMSA is a multistep strategy, which starts with coarse meshes in lower frequencies and, then, step by step, tightens the ID to the newly found domain of scatterers and uses finer meshes for partitioning them. This procedure enhances the resolution without increasing the number of unknowns. In each step, the inversion process is executed using the global optimization technique of CMA-ES. The proposed technique uses the full advantages of global optimization technique and at the same time, by executing it on a multiscaling scheme and using the initializing step, reduces the number of unknowns, the degree of freedom in the search space, and the required measured data. The numerical assessments for various scenarios are performed, which clearly shows an acceptable dielectric profile retrieval, even for inhomogeneous dielectric distributions or noisy measurements. Moreover, a comparison between CMA-ES and other global optimization algorithms is performed, which reveals the outperformance of CMA-ES in these scenarios.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Hybrid Microwave Imaging of 3-D Objects Using LSM and BIM Aided by a CNN
           U-Net

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      Authors: Feng Han;Miao Zhong;Junjie Fei;
      Pages: 1 - 9
      Abstract: This article presents an efficient and accurate 3-D quantitative hybrid microwave imaging (MWI) method. The linear sampling method (LSM) is first carried out to quickly find the approximate shapes and locations of the unknown objects in the imaging domain based on the scattered field data recorded by receivers which are placed in the far-field zone and wrap the domain. Then the full-wave inversion (FWI) is implemented in a downsized domain which tightly encloses the unknown objects instead of in the whole domain through the Born iterative method (BIM) to quantitatively retrieve the dielectric model parameters of the objects. Because the LSM fails to obtain the sufficiently accurate shapes of the unknown objects, a trained 3-D convolutional neural network (CNN) U-Net is inserted between the LSM imager and the BIM solver to further refine the obtained shapes of LSM, which is expected to aid the following FWI. The proposed hybrid method is validated via the quantitative imaging of both inhomogeneous isotropic scatterers and multiple homogeneous anisotropic scatterers. It is shown that the hybrid method can achieve both higher reconstruction accuracy and lower computational cost compared with direct BIM inversion. Meanwhile, its antinoise ability is also tested.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Statistics of the Stokes Parameters for the Signal Scattered by Layered
           Structures With an Arbitrary Number of Slightly Rough Interfaces

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      Authors: Richard Dusséaux;Saddek Afifi;
      Pages: 1 - 12
      Abstract: Horizontally stratified structures are commonly used to represent naturally occurring structures, such as soils. The electromagnetic signature of such a medium illuminated by radar and the polarization state of the scattered wave are fully determined by the knowledge of the four Stokes parameters. In this article, we determine the statistics of the four Stokes parameters for the signal scattered by layered structures with an arbitrary number of slightly rough interfaces. The rough interfaces are realizations of second-order stationary centered Gaussian stochastic processes and the layered structure is illuminated by an elliptically polarized monochromatic wave. The zenithal and azimuthal components of the far scattered electric field are derived from the first-order small perturbation method. The derivation leads to a multivariate Gaussian model for the underlying complex scattered amplitudes, and we establish the closed-form expressions of the probability density function, the cumulative density function, and the first- and second-order moments for the four Stokes parameters. For an observation direction outside the incidence plane, we establish the condition on the incidence wave parameters for which the zenithal and azimuthal components are uncorrelated. For an air/snow cover/frozen soil/unfrozen soil structure, we analyze the marginal probabilities and validate the theory by comparison with Monte Carlo simulations. More generally, when the two complex components of the field scattered by the illuminated zone are Gaussian random variables, these statistics offer possibilities for in-depth investigating the polarization of scattering processes from random media.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • High-Resolution Microwave Photonic Radar With Sparse Stepped Frequency
           Chirp Signals

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      Authors: Cong Ma;Yue Yang;Fengting Cao;Xiangchuan Wang;Xi Liu;Chenkai Meng;Jiangtao Zhang;Shilong Pan;
      Pages: 1 - 10
      Abstract: High-resolution radar requires broadband signal generation and processing, which challenges the state-of-the-art electronics. In contrast, microwave photonic technologies featuring wide bandwidth and flexible frequency are effective for broadband microwave signal generation and processing to improve radar detection performance. However, the bandwidth is practically limited because the broadband radar with a continuous spectrum signal is always susceptible to interference from other electromagnetic applications operating at the coincident frequency band. Here, we propose a microwave photonic radar with sparse stepped frequency chirp (SSFC) signals to break through the bandwidth limitation, achieving ultrahigh-resolution detection with enhanced anti-interference ability. The SSFC signal with an ultrawide bandwidth is generated by recirculating frequency-shifting a narrowband chirp signal. Microwave photonic dechirping of SSFC signals and compressive reconstructions of dechirped signals are performed to extract target information fast and precisely. In the experiment, we conducted a microwave photonic radar based on an SSFC signal spanning a frequency range of 18 GHz but actually occupying a 4.5-GHz effective spectrum, successfully distinguishing two simulated point targets with a distance of 8.3 mm, and achieving the ranging error within $pm 225~mu text{m}$ . In addition, high-precision vibration monitoring and high-resolution two-dimensional imaging capabilities are validated by a simple pendulum detection and an inverse synthetic aperture radar (ISAR) experiment.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Inverse Scattering by Perfectly Electric Conducting (PEC) Rough Surfaces:
           An Equivalent Model With Line Sources

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      Authors: Ahmet Sefer;Ali Yapar;
      Pages: 1 - 9
      Abstract: This article presents a new method for the reconstruction of the perfectly electric conducting (PEC) rough surface profiles by utilizing electromagnetic waves. The inaccessible rough surface is illuminated by a tapered plane electromagnetic wave, and the scattered field data are measured on a certain number of points above the surface under test. The method for the inverse electromagnetic imaging problem is based on a special representation of the scattered field in terms of a finite number of fictitious discrete line sources located along a plane below the rough surface. The current densities of these fictitious sources are obtained through the regularized solution of an ill-posed problem. Then, it is shown that the image of the rough surface can be directly retrieved by seeking the points in the space where the tangential component of the total electric field vanishes. Alternatively, a much more rigorous iterative method based on a regularized Newton algorithm is also presented. A comprehensive numerical analysis is provided to demonstrate the feasibility of the presented approach. In this context, the quantitative successes of both approaches are interpreted by considering a very sensitive $ell _{2}$ -norm-based error function between the actual and the reconstructed surface profiles. Regarding different scattering scenarios taken into account, the error values obtained for satisfactory reconstructions are generally in the range of 10%–30% for both methods. It is also shown that the presented algorithms are capable of reconstructing the rough surfaces, which oscillate for every $lambda $ horizontally and have a peak-to-peak variation of $0.5lambda $ at most.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • An Electromagnetic Simulator for Sentinel-3 SAR Altimeter Waveforms Over
           Land—Part I: Bare Soil

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      Authors: Giuseppina De Felice Proia;Marco Restano;Davide Comite;Maria Paola Clarizia;Jérôme Benveniste;Nazzareno Pierdicca;Leila Guerriero;
      Pages: 1 - 11
      Abstract: ALtimetry for BIOMass (ALBIOM) is a Permanent Open Call Project funded by the European Space Agency (ESA) to explore the possibility of forest biomass retrieval by using Copernicus Sentinel-3 (S-3) Synthetic aperture Radar ALtimeter (SRAL) in low- and high-resolution mode at Ku- and C-bands. It represents an original work in the research of new techniques for vegetation observation using altimetry data. Because of the complexity of the land surfaces, no algorithm has been developed for a specific retracking of the altimetric land waveform. This calls for the development of a model able to reproduce the acquisition system and the target scattering phenomena to simulate the interaction of the radar pulse with the land. In this first work, we present the electromagnetic simulator of S-3 SRAL altimeter measurements over bare soil scenarios realized through a modification of the Soil and Vegetation Reflection Simulator (SAVERS) simulator developed by the team for Global Navigation Satellite System Reflectometry (GNSS-R) over land. The impact of topography has also been taken into account. We demonstrate that SAVERS for S-3 SRAL proved its capability to reproduce altimeter waveforms’ main attributes for both flat and topography scenarios.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • An Electromagnetic Simulator for Sentinel-3 SAR Altimeter Waveforms Over
           Land—Part II: Forests

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      Authors: Giuseppina De Felice Proia;Marco Restano;Davide Comite;Maria Paola Clarizia;Jérôme Benveniste;Nazzareno Pierdicca;Leila Guerriero;
      Pages: 1 - 10
      Abstract: Forests play a crucial role in the climate change mitigation by acting as sinks for carbon and, consequently, reducing the CO2 concentration in the atmosphere and slowing global warming. For this reason, above ground biomass (AGB) estimation is essential for effectively monitoring forest health around the globe. Although remote sensing-based forest AGB quantification can be pursued in different ways, in this work, we discuss a new technique for vegetation observation through the use of altimetry data that have been introduced by the ESA-funded ALtimetry for BIOMass (ALBIOM) project. ALBIOM investigates the possibility of retrieving forest biomass through Copernicus Sentinel-3 Synthetic Aperture Radar Altimeter (SRAL) measurements at the Ku- and C-bands in low- and high-resolution modes. To reach this goal, a simulator able to reproduce the altimeter acquisition system and the scattering phenomena that occur in the interaction of the radar altimeter pulse with vegetated surfaces has been developed. The Tor Vergata Vegetation Scattering Model (TOVSM) developed at Tor Vergata University has been exploited to simulate the contribution from the vegetation volume via the modeling of the backscattering of forest canopy through a discrete scatterer representation. A modification of the Soil And Vegetation Reflection Simulator (SAVERS) developed by the team for Global Navigation Satellite System Reflectometry over land has also been taken into account to simulate the soil contribution.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Intercomparison of Electromagnetic Scattering Models for Delay-Doppler
           Maps Along a CYGNSS Land Track With Topography

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      Authors: James D. Campbell;Ruzbeh Akbar;Alexandra Bringer;Davide Comite;Laura Dente;Scott T. Gleason;Leila Guerriero;Erik Hodges;Joel T. Johnson;Seung-Bum Kim;Amer Melebari;Nazzareno Pierdicca;Christopher S. Ruf;Leung Tsang;Tianlin Wang;Haokui Xu;Jiyue Zhu;Mahta Moghaddam;
      Pages: 1 - 13
      Abstract: A comparison of three different electromagnetic scattering models for land surface delay-Doppler maps (DDMs) obtained from global navigation satellite system reflectometry (GNSS-R) along a Cyclone Global Navigation Satellite System (CYGNSS) track in the San Luis Valley, Colorado, USA, is presented. The three models are the analytical Kirchhoff solutions (AKS), the Soil And VEgetation Reflection Simulator (SAVERS), and the improved geometrical optics with topography (IGOT). Common inputs to the three models were defined by using field samples of soil moisture and texture, soil surface roughness measurements, and a digital elevation model (DEM). The resulting peak reflectivity profiles of the models and the CYGNSS data all had a range of 10 dB along the selected track, mainly due to the influence of topography. The reflectivities obtained from all three models agreed with one another within 2.4 dB along the full length of the track. The models also showed general agreement with the corresponding CYGNSS data, although the modeled profiles were higher than CYGNSS Science Data Record Version 3.1 by an average of 5 dB and also smoother. Additional characterization of fine-scale surface roughness is identified as an area for future work to improve model fidelity. An intercomparison of DDM structure for three selected acquisitions is also provided.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Wavelet-Based Compressive Deep Learning Scheme for Inverse Scattering
           Problems

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      Authors: Zheng Zong;Yusong Wang;Zhun Wei;
      Pages: 1 - 11
      Abstract: Recently, physics-assisted deep learning schemes (DLSs) have demonstrated the state-of-the-art performance for solving inverse scattering problems (ISPs). However, most learning approaches typically require a high-computational overhead and a big memory footprint, which prohibits further applications. In this work, a wavelet-based compressive scheme (WCS) is proposed in solving ISPs, where the multisubspace information is explored by wavelet bases and branched between each encoder and decoder path. It is shown that the proposed WCS can be simply adapted to commonly used DLSs, such as the back-propagation scheme (BPS) and the dominant current scheme (DCS), to reduce the computational and storage load. Specifically, benefiting from compressive and multiresolution properties of wavelet and with the help of the factorized convolution method, more than 99.7% trainable weights are reduced in both illustrated back-propagation (BP)-WCS and dominant current (DC)-WCS, whereas the performance deterioration is limited around 1% in terms of traditional BPS and DCS. Extensive numerical and experimental tests are conducted for quantitative validations. Comparisons are also made among UNet, a well-known compressive method (Mobile-UNet), and the proposed method. It is expected that the suggested compression technique would find its applications on deep learning-based electromagnetic inverse problems under source-limited scenarios.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Unrolled Optimization With Deep Learning-Based Priors for Phaseless
           Inverse Scattering Problems

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      Authors: Samruddhi Deshmukh;Amartansh Dubey;Ross Murch;
      Pages: 1 - 14
      Abstract: Inverse scattering problems (ISPs), such as those in electromagnetic imaging using phaseless data (PD-ISPs), involve imaging objects using phaseless measurements of wave scattering. Such inverse problems can be highly nonlinear and ill-posed under extremely strong scattering conditions such as when the objects have very high permittivity or are large in size. In this work, we propose an end-to-end reconstruction framework using unrolled optimization with deep priors to solve PD-ISPs under very strong scattering conditions. We incorporate an approximate linear-physics-based model into our optimization framework along with a deep learning (DL)-based prior and solve the resulting problem using an iterative algorithm which is unfolded into a deep network. This network not only learns data-driven regularization but also overcomes the shortcomings of approximate linear models and learns nonlinear features. More important, unlike the existing PD-ISP methods, the proposed framework learns optimum values of all the tunable parameters (including multiple regularization parameters) as a part of the framework. Results from simulations and experiments are shown for the use case of indoor imaging using 2.4-GHz phaseless Wi-Fi measurements, where the objects exhibit extremely strong scattering and low absorption. Results show that the proposed framework outperforms the existing model-driven and data-driven techniques by a significant margin and provides up to 20 times higher validity range.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • SOM-Net: Unrolling the Subspace-Based Optimization for Solving Full-Wave
           Inverse Scattering Problems

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      Authors: Yu Liu;Hao Zhao;Rencheng Song;Xudong Chen;Chang Li;Xun Chen;
      Pages: 1 - 15
      Abstract: In this article, an unrolling algorithm of the iterative subspace-based optimization method (SOM) is proposed for solving full-wave inverse scattering problems (ISPs). The unrolling network, named SOM-Net, inherently embeds the Lippmann–Schwinger physical model into the design of network structures. The SOM-Net takes the deterministic induced current and the raw permittivity image obtained from backpropagation (BP) as the input. It then updates the induced current and the permittivity successively in subnetwork blocks of the SOM-Net by imitating iterations of the SOM. The final output of the SOM-Net is the full predicted induced current, from which the scattered field and the permittivity image can also be deduced analytically. The parameters of the SOM-Net are optimized in a supervised manner with the total loss to simultaneously ensure the consistency of the induced current, the scattered field, and the permittivity in the governing equations. Numerical tests on both synthetic and experimental data verify the superior performance of the proposed SOM-Net over typical ones. The results on challenging examples, such as scatterers with tough profiles or high permittivity, demonstrate the good generalization ability of the SOM-Net. With the use of deep unrolling technology, this work builds a bridge between traditional iterative methods and deep learning methods for solving ISPs.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • The Relationship Between “Large” Early/Fast VLF Events and
           Modal Propagation Nulls

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      Authors: Hunter Burch;Robert C. Moore;
      Pages: 1 - 7
      Abstract: Early/fast very low-frequency (VLF) events evidence the direct impulsive coupling of lightning energy to the overlying mesosphere and lower ionosphere, and they are readily detected as rapid (6 dB) early/fast events have not yet been satisfactorily simulated, and past work has suggested that large amplitude early/fast VLF events may only be detected when the receiver is located in a VLF propagation “null.” VLF propagation “nulls” result from the destructive interference of propagating waveguide modes, and they would result in deceptively large relative changes in field amplitude by reducing the magnitude of the ambient field, rather than by increasing the magnitude of the scattered field. In this article, we present a new method to quantify the degree of destructive modal interference at the location of the VLF receiver, and we apply this method to analyze the impact of modal interference on the detected amplitude changes associated with 235 early/fast VLF events occurring over the Midwestern United States on 21 September 2016. No meaningful dependence on destructive modal interference is detected. We conclude that contemporary models of lightning-ionosphere interactions and/or VLF scattering require modification to accurately reproduce observations of large early/fast VLF events.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Rapid Surrogate Modeling of Electromagnetic Data in Frequency Domain Using
           Neural Operator

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      Authors: Zhong Peng;Bo Yang;Yixian Xu;Feng Wang;Lian Liu;Yi Zhang;
      Pages: 1 - 12
      Abstract: The efficiency of solving geophysical inverse problem largely relies on the efficiency of solving the corresponding forward problem. As for electromagnetic (EM) data forward modeling in frequency domain, the conventional numerical methods, e.g., finite difference method (FDM), discretize the governing equations resulting in a large linear system which is usually expensive to solve. Meanwhile, for inversion iteration, we normally do not need to solve the forward problem in high precision. Thus, a rapid surrogate modeling approach which uses the neural network is promising for replacing the forward modeling module in the inversion scheme. Here, we proposed an algorithm which uses the neural operator to solve the EM data modeling problem in the frequency domain. To develop a surrogate model for EM data forward problem, we introduce an extended Fourier neural operator (EFNO) that enables the calculation at least 100 times faster than the conventional FDM solver while maintaining good precision. Moreover, by adding a subnetwork the proposed neural operator has good generalization which has the capacity of predicting solution at any site locations and frequencies. Due to the discretization invariance of Fourier neural operator, the neural operator trained on coarse grids can easily transfer to fine grids with only retraining part of parameters, resulting in a super-resolution prediction capability. We test our proposed method with 2-D and 3-D magnetotelluric (MT) data modeling problems, demonstrating that the EFNO has great potentials for severing as a general rapid surrogate forward solver in EM data inversion scheme.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • An Iterative Domain Decomposition Technique Based on Subspace-Based
           Optimization Method for Solving Highly Nonlinear Inverse Problem

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      Authors: Yuyue Zhang;Tiantian Yin;Zhiqin Zhao;Zaiping Nie;Xudong Chen;
      Pages: 1 - 13
      Abstract: Due to the strong nonlinearity, it is always a challenge to reconstruct strong scatterers with high contrasts and/or large dimensions. This article proposes an iterative domain decomposition technique (IDDT) based on the framework of the subspace-based optimization method (SOM) to solve highly nonlinear inverse scattering problems (ISPs). This method takes advantage of the fact that the reduction of unknowns can reduce the nonlinearity of ISPs and different parts of scatterers have different effects on scattered fields. In the inversion procedure, the domain of scatterers (DoS) is obtained by refining the domain of interests (DoI) first. Then, the DoS is divided into two subdomains according to their contributions to scattered fields: the dominant subdomain and the subordinate subdomain. The induced current of the subordinate subdomain is approximated by its deterministic part. Therefore, only the induced current of the dominant subdomain needs to be reconstructed, greatly reducing the dimensions of the solution domain. Then the properties of the entire DoS are retrieved with the properties of the dominant subdomain as initial guesses. This technique can be used repeatedly to improve the reconstruction quality. Compared with the original SOM, this method can reduce the nonlinearity of ISPs and reconstruct stronger scatterers with better reconstruction qualities and less computation loads. The feasibility and efficiency of IDDT-SOM are discussed from the perspective of the relative distribution of induced current by numerical and experimental examples.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • RED-PAN: Real-Time Earthquake Detection and Phase-Picking With Multitask
           Attention Network

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      Authors: Wu-Yu Liao;En-Jui Lee;Da-Yi Chen;Po Chen;Dawei Mu;Yih-Min Wu;
      Pages: 1 - 11
      Abstract: In this article, we show that the real-time earthquake detection and phase picking with multitask attention network (RED-PAN) can carry out earthquake detection and seismic phase picking on real-time and continuous data with appropriate data augmentation. Goal-oriented data augmentations materialize the capability of RED-PAN. Mosaic waveform augmentation (MWA) synthesizes data conditioned by superimposed earthquake waveforms, marching MWA (MMWA) extends MWA to allow the dynamic input of seismograms, and earthquake early warning augmentation (EEWA) enables to identify $P$ arrivals using the early part of $P$ -wave waveforms. For stable $P$ and $S$ arrival probability distribution functions (pdfs) of continuous recordings, we use the median values of phase predictions at each time point until the model scans through, which we term the seismogram-tracking median filter (STMF). For real-time $P$ arrival detection, we use a threshold (0.3) on the real-time $P$ arrival pdf as the trigger criterion. We examined our proposed strategy in different application scenarios. For the dataset of the fixed-length samples, our RED-PAN(60 s) model performs similar to EQTransformer (EqT) on the STanford EArthquake Dataset (STEAD) and outperforms the Taiwan dataset. For continuous data examination of the 2019 Ridgecrest earthquake sequence, the number of earthquake waveforms detected by our RED-PAN(60 s) model is 2.7 times the number of EqT under the same receptive field (60-s-long seismogram). In the application of earthquake early warning (EEW), our RED-P-N(60 s) model only requires the $P$ -wave waveform about 0.13 s long from the $P$ -alert and 0.09 s long from the Taiwan Strong Motion Instrumentation Program (TSMIP) network. The source code is available at https://github.com/tso1257771/RED-PAN.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Elastic Wave Modeling With High-Order Temporal and Spatial Accuracies by a
           Selectively Modified and Linearly Optimized Staggered-Grid
           Finite-Difference Scheme

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      Authors: Hongyu Zhou;Yang Liu;Jing Wang;
      Pages: 1 - 22
      Abstract: High-order staggered-grid finite-difference (SFD) schemes are preferred in elastic wave simulation for geophysical problems because they decrease the accumulation of error from grid dispersion. However, most SFD approaches reach high-order spatial but limited temporal accuracy. To tackle the issue, we develop a novel temporal and spatial high-accuracy elastic SFD scheme by selectively modifying the spatial operators of the original SFD stencil. This modification has three main advantages. First, it facilitates the design of a new SFD stencil with temporal and spatial accuracies to arbitrary even-order by a Taylor-series expansion method. Second, it helps boost the accuracy further by implementing a linear optimization method. Third, the new selectively modified SFD (SMSFD) stencil needs fewer float-point operations (FPOs) than the existing temporal high-order SFD stencil. We compare our new SMSFD scheme with spatial high-order and temporal–spatial high-order SFD schemes and show that our new elastic SMSFD scheme possesses better accuracy and stability and requires fewer FPOs than these methods.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Single-Image Super-Resolution for Remote Sensing Images Using a Deep
           

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      Authors: Yadong Li;Sébastien Mavromatis;Feng Zhang;Zhenhong Du;Jean Sequeira;Zhongyi Wang;Xianwei Zhao;Renyi Liu;
      Pages: 1 - 24
      Abstract: Super-resolution (SR) technology is an important way to improve spatial resolution under the condition of sensor hardware limitations. With the development of deep learning (DL), some DL-based SR models have achieved state-of-the-art performance, especially the convolutional neural network (CNN). However, considering that remote sensing images usually contain a variety of ground scenes and objects with different scales, orientations, and spectral characteristics, previous works usually treat important and unnecessary features equally or only apply different weights in the local receptive field, which ignores long-range dependencies; it is still a challenging task to exploit features on different levels and reconstruct images with realistic details. To address these problems, an attention-based generative adversarial network (SRAGAN) is proposed in this article, which applies both local and global attention mechanisms. Specifically, we apply local attention in the SR model to focus on structural components of the earth’s surface that require more attention, and global attention is used to capture long-range interdependencies in the channel and spatial dimensions to further refine details. To optimize the adversarial learning process, we also use local and global attentions in the discriminator model to enhance the discriminative ability and apply the gradient penalty in the form of hinge loss and loss function that combines $L1$ pixel loss, $L1$ perceptual loss, and relativistic adversarial loss to promote rich details. The experiments show that SRAGAN can achieve performance improvements and reconstruct better details compared with current state-of-the-art SR methods. A series of ablation investigations and model analyses validate the efficiency and effectiveness of our method.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Modular Remote Sensing Big Data Framework

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      Authors: Chen Xu;Xiaoping Du;Xiangtao Fan;Zhenzhen Yan;Xujie Kang;Junjie Zhu;Zhongyang Hu;
      Pages: 1 - 11
      Abstract: Today, remote sensing (RS) data are already regarded as “big data.” Developments in computer science have made it possible to explore the potential treasure within remote sensing big data, but only limited remote sensing research has made use of big data technology due to gaps in techniques between big data and remote sensing. In this research, we analyzed the full processing flow of remote sensing big data from the perspective of both computer science and remote sensing science and proposed a modular framework. Computation ready data (CRD), a dynamic data type for computation based on analysis ready data (ARD), is proposed to connect the two main modules of the framework, the data module and computation module. Compared with existing research, the proposed framework classifies and abstracts the key technical and research points of the processing of remote sensing big data as replaceable modules and bridges them through an open organization. Subsequently, we built a prototype platform with open-source technologies and carried out three experiments to validate the feasibility and advantages of the framework, namely normalized difference vegetation index (NDVI) production, water body change detection, and land use classification. Results indicate that this framework can greatly reduce experimental costs for remote sensing researchers. While the proposed framework has proven flexible and practical, further research is needed for the technical implementation of certain modules to achieve the original intention of the framework.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Spatial-Temporal Feature-Based Detection Framework for Infrared Dim
           Small Target

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      Authors: Jinming Du;Huanzhang Lu;Luping Zhang;Moufa Hu;Sheng Chen;Yingjie Deng;Xinglin Shen;Yu Zhang;
      Pages: 1 - 12
      Abstract: The detection of infrared small targets under low signal-to-clutter ratio (SCR) and complex background conditions has been a challenging and popular research topic. In this article, a spatial-temporal feature-based detection framework is proposed. First, several factors, such as the infrared target’s small sample, the sensitive size, and the usual sample selection strategy, that affect the detection of small targets are analyzed. In addition, the small intersection over union (IOU) strategy, which helps to solve the false convergence and sample misjudgment problem, is proposed. Second, aiming at the difficulties due to the target’s dim information and complex background, the interframe energy accumulation (IFEA) enhancement mechanism-based end-to-end spatial-temporal feature extraction and target detection framework is proposed. This framework helps to enhance the target’s energy, suppress the strong spatially nonstationary clutter, and detect dim small targets. Experimental results show that using the small IOU strategy and IFEA mechanism, the proposed multiple frame-based detection framework performs better than some popular deep learning (DL)-based detection algorithms.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A GPU-Accelerated Framework for Simulating LiDAR Scanning

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      Authors: Alfonso López;Carlos J. Ogayar;Juan M. Jurado;Francisco R. Feito;
      Pages: 1 - 18
      Abstract: In this work, we present an efficient graphics processing unit (GPU)-based light detection and ranging (LiDAR) scanner simulator. Laser-based scanning is a useful tool for applications ranging from reverse engineering or quality control at an object scale to large-scale environmental monitoring or topographic mapping. Beyond that, other specific applications require a large amount of LiDAR data during development, such as autonomous driving. Unfortunately, it is not easy to get a sufficient amount of ground-truth data due to time constraints and available resources. However, LiDAR simulation can generate classified data at a reduced cost. We propose a parameterized LiDAR to emulate a wide range of sensor models from airborne to terrestrial scanning. OpenGL’s compute shaders are used to massively generate beams emitted by the virtual LiDAR sensors and solve their collision with the surrounding environment even with multiple returns. Our work is mainly intended for the rapid generation of datasets for neural networks, consisting of hundreds of millions of points. The conducted tests show that the proposed approach outperforms a sequential LiDAR scanning. Its capabilities for generating huge labeled datasets have also been shown to improve previous studies.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Large-Window Curvature Computations for High-Resolution Digital Elevation
           Models

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      Authors: Anne M. Denton;Rahul Gomes;David M. Schwartz;David W. Franzen;
      Pages: 1 - 20
      Abstract: With the increasing availability of high-resolution digital elevation model (DEM) data, a need has emerged for new processing techniques. Topographic variables, such as slope and curvature, are relevant on length scales far larger than the pixel resolution of modern DEM datasets. An approach for computing slope and curvature is proposed that uses standard regression coefficients over large windows while generating output on the full resolution of the original data, without adding substantially to the computation time. In the proposed window-aggregation approach, aggregates for fitting a quadratic function are computed iteratively from the DEM data in a process that scales logarithmically with the window size. It is shown that the window-aggregation algorithm produces the results of much higher quality than the two-step process of applying neighborhood operations such as focal statistics followed by small-window topographic computations, at comparable computational cost.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Scalable Building Height Estimation From Street Scene Images

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      Authors: Yunxiang Zhao;Jianzhong Qi;Flip Korn;Xiangyu Wang;
      Pages: 1 - 18
      Abstract: Building height estimation plays an essential role in many applications such as 3-D city rendering, urban planning, and navigation. Recently, a new building height estimation method was proposed using street scene images and 2-D maps, which is more scalable than traditional methods that use high-resolution optical images, radar, or light detection and ranging (LiDAR) data, which are proprietary or expensive to obtain. The method needs to detect building rooflines to compute building height via the pinhole camera model. We observe that this method has limitations in handling complex street scene images where buildings occlude each other or are blocked by other objects such as trees since rooflines can be difficult to locate. To address these limitations, we propose a robust building height estimation method that computes building height simultaneously from street scene images with an orientation along the street and images facing the building with an upward-looking view. We first detect roofline candidates from both types of images. Then, we use a deep neural network called RoofNet to classify and filter these candidates and select the best candidate via an entropy-based ranking algorithm. When the true roofline is identified, we compute building height via the pinhole camera model. Experimental results show that the proposed RoofNet model yields a higher accuracy on building corner and roofline candidate filtering compared with state-of-the-art open-set classifiers. Our overall building height estimation method outperforms the baseline by up to 11.9% in accuracy and achieves 92.8% in height estimation error within 4 m on the collected dataset.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Removing Atmospheric Turbulence Effects Via Geometric Distortion and Blur
           Representation

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      Authors: Xia Hua;Chao Pan;Yu Shi;Jianguo Liu;Hanyu Hong;
      Pages: 1 - 13
      Abstract: Removing the geometric distortion and space-time-varying blur caused by atmospheric turbulence from a given image sequence remains a challenge. Since geometric distortion and blur are two different kinds of distortions and interact with each other in the process of image restoration, it is difficult to extract the features that are useful to the restoration process when the images experience multiple distortions. In this article, we propose a new scheme based on geometric distortion and blur representation. The blur invariants and maximum gradient are used to represent the geometric distortion and sharpness of an image frame, respectively. The proposed scheme consists of three parts. First, two fast frame selection algorithms based on independent evaluations of the sharpness and geometric distortion are proposed to subsample a sharp subsequence and obtain a reference image. Next, to suppress the geometric distortion, a moment-blur-invariant-based method is presented to estimate the deformation vector between two degraded frames, and the selected sharp frames are registered to the reference image. Finally, a blind deconvolution method is applied to deblur the fused image, generating a final restoration result. Various experimental results show that the proposed method can effectively alleviate distortion and blur, as well as significantly improve the visual quality of real atmospheric turbulence-degraded images.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Evaluation of the Impacts of Rain Gauge Density and Distribution on
           Gauge-Satellite Merged Precipitation Estimates

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      Authors: Yuanyuan Chen;Jingfeng Huang;Xiaodong Song;Huayang Wen;Haiyu Song;
      Pages: 1 - 18
      Abstract: The capacity of combined gauge-satellite precipitation estimates largely depends on the characteristics of the input data such as the number, location and reliability of rain gauges, and satellite-derived precipitation quality. The objective of this study is to examine the influence of rain gauge network configuration including density and spatial distribution on the performance of the gauge-satellite merging estimation at monthly and ten-day temporal scales. Dense rain gauge observations and satellite-derived precipitation data (i.e., TMPA 3B42 Version 7 and Version 06 IMERG Final Run) in two provinces of China are used. A two-stage downscaling-integration approach is applied in the gauge-satellite precipitation estimation. Various scenarios of rain gauge density and combination are designed and their corresponding merged precipitation estimates are evaluated using statistical indices. The merged results using the TMPA and IMERG precipitation product, respectively, are compared. The results show that: 1) the influence of rain gauge network configuration on the gauge-satellite merged precipitation estimates gradually decreases with the increase in rain gauge density, and the gauge-satellite merged precipitation estimates are more sensitive to the rain gauge network density in wet season and ten-day temporal scale than in dry season and monthly scale, respectively and 2) the merged precipitation estimation using the IMERG precipitation data generally outperforms the estimation using TMPA precipitation data in the low gauge density scenarios, and the gap decreases with the increase in the rain gauge network density. In the areas with sparse rain gauges, improving the quality of satellite precipitation data would significantly improve the performance of the gauge-satellite merging estimation.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Satellite-Derived Aerosol Optical Depth Fusion Combining Active and
           Passive Remote Sensing Based on Bayesian Maximum Entropy

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      Authors: Xinghui Xia;Bin Zhao;Tianhao Zhang;Luyao Wang;Yu Gu;Kuo-Nan Liou;Feiyue Mao;Boming Liu;Yanchen Bo;Yusi Huang;Jiadan Dong;Wei Gong;Zhongmin Zhu;
      Pages: 1 - 13
      Abstract: Satellite-derived aerosol optical depth (AOD) is an important parameter for studies related to atmospheric environment, climate change, and biogeochemical cycle. Unfortunately, the relatively high data missing ratio of satellite-derived AOD limits the atmosphere-related research and applications to a certain extent. Accordingly, numerous AOD fusion algorithms have been proposed in recent years. However, most of these algorithms focused on merging AOD products from multiple passive sensors, which cannot complementarily recover the AOD missing values due to cloud obscuration and the misidentification between optically thin cloud and aerosols. In order to address these issues, a spatiotemporal AOD fusion framework combining active and passive remote sensing based on Bayesian maximum entropy methodology (AP-BME) is developed to provide satellite-derived AOD data sets with high spatial coverage and good accuracy in large scale. The results demonstrate that AP-BME fusion significantly improves the spatial coverage of AOD, from an averaged spatial completeness of 27.9%–92.8% in the study areas, in which the spatial coverage improves from 91.1% to 92.8% when introducing Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD data sets into the fusion process. Meanwhile, the accuracy of recovered AOD nearly maintains that of the original satellite AOD products, based on evaluation against ground-based Aerosol Robotic Network (AERONET) AOD. Moreover, the efficacy of the active sensor in AOD fusion is discussed through overall accuracy comparison and two case analyses, which shows that the provision of key aerosol information by the active sensor on haze condition or under thin cloud is important for not only restoring the real haze situations but also avoiding AOD overestimation caused by cloud optical depth (COD) contamination in AOD fusion results.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Extending the EOS Long-Term PM2.5 Data Records Since 2013 in China:
           Application to the VIIRS Deep Blue Aerosol Products

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      Authors: Jing Wei;Zhanqing Li;Lin Sun;Wenhao Xue;Zongwei Ma;Lei Liu;Tianyi Fan;Maureen Cribb;
      Pages: 1 - 12
      Abstract: PM2.5 is hazardous to human health, and high-quality data are thus needed on a routine basis. An attempt is made here to improve the accuracy of near-surface PM2.5 estimates using the newly released aerosol product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite with the Deep Blue retrieval algorithm. A high-quality PM2.5 data set is generated at a spatial resolution of 6 km from 2013 to 2018 by applying the space-time extremely randomized trees (STET) model, which also aims to extend the Earth Observing System (EOS) long-term PM2.5 data records in China. The PM2.5 estimates are highly consistent with ground-based measurements, with an out-of-sample cross-validation coefficient of determination (CV-R2) of 0.88, a root-mean-square error (RMSE) of $16.52~mu text{g}/text{m}^{3}$ , and a mean absolute error of $10~mu text{g}/text{m}^{3}$ at the national scale. Spatiotemporal PM2.5 variations at monthly scales are also well captured (e.g., $R^{2} =0.91$ –0.94, RMSE = 5.8– $11.6~mu text{g}/text{m}^{3})$ . PM2.5 varied greatly at regional and seasonal scales across China. Benefiting from emission reduction and air pollution controls, PM2.5 pollution has reduced dramatically in China with an average of $- 5.6~mu text{g}/text{m}^{3}$ /yr−1 during 2013–2018. Significant regional reductions are also seen, in particular, in the Beijing–Tianjin–H-bei region ( $- 6.6~mu text{g}/text{m}^{3}$ /yr−1, $p < 0.001$ ), and the Deltas of Yangtze River ( $- 6.3~mu text{g}/text{m}^{3}$ /yr−1, $p < 0.001$ ) and Pearl River Delta ( $- 4.5~mu text{g}/text{m}^{3}$ /yr−1, $p < 0.001$ ). Our study improved the accuracy of near-surface PM2.5 estimates in terms of their spatiotemporal variations at a relatively long-term record, which is important for future air pollution and health studies in China.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Hourly Rainfall Forecast Model Using Supervised Learning Algorithm

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      Authors: Qingzhi Zhao;Yang Liu;Wanqiang Yao;Yibin Yao;
      Pages: 1 - 9
      Abstract: Previous studies on short-term rainfall forecast using precipitable water vapor (PWV) and meteorological parameters mainly focus on rain occurrence, while the rainfall forecast is rarely investigated. Therefore, an hourly rainfall forecast (HRF) model based on a supervised learning algorithm is proposed in this study to predict rainfall with high accuracy and time resolution. Hourly PWV derived from Global Navigation Satellite System (GNSS) and temperature data are used as input parameters of the HRF model, and a support vector machine is introduced to train the proposed model. In addition, this model also considers the time autocorrelation of rainfall in the previous epoch. Hourly PWV data of 21 GNSS stations and collocated meteorological parameters (temperature and rainfall) for five years in Taiwan Province are selected to validate the proposed model. Internal and external validation experiments have been performed under the cases of slight, moderate, and heavy rainfall. Average root-mean-square error (RMSE) and relative RMSE of the proposed HRF model are 1.36/1.39 mm/h and 1.00/0.67, respectively. In addition, the proposed HRF model is compared with the similar works in previous studies. Compared results reveal the satisfactory performance and superiority of the proposed HRF model in terms of time resolution and forecast accuracy.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Advancing Radar Nowcasting Through Deep Transfer Learning

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      Authors: Lei Han;Yangyang Zhao;Haonan Chen;V. Chandrasekar;
      Pages: 1 - 9
      Abstract: Deep learning is emerging as a powerful tool in scientific applications, such as radar-based convective storm nowcasting. However, it is still a challenge to extend the application of a well-trained deep learning nowcasting model, which demands to incorporate the learned knowledge at a certain location to other locations characterized by different precipitation features. This article designs a transfer learning framework to tackle this problem. A convolutional neural network (CNN)-based nowcasting method is utilized as the benchmark, based on which two transfer learning models are constructed through fine-tune and maximum mean discrepancy (MMD) minimization. The base CNN model is trained using radar data in the source study domain near Beijing, China, whereas the transferred models are applied to the target domain near Guangzhou, China, with only a small amount of data in the target area. The influence of a varying number of target data samples on the nowcasting performance is quantified. The experimental results demonstrate that the deep transfer learning models can improve the nowcasting skills.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Estimation of Polar Depletion Regions by VTEC Contrast and Watershed
           Enhancing

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      Authors: Enrique Monte-Moreno;Manuel Hernandez-Pajares;Haixia Lyu;Heng Yang;Angela Aragon-Angel;
      Pages: 1 - 20
      Abstract: This article presents a method for determining near-Pole ionization depletion regions and troughs from global navigation satellite system (GNSS) vertical total electron content (VTEC) maps. To define the regions, we use Image processing tools, considering the VTEC maps as gray-level images. In this work, watershed segmentation is applied to determine the edges of regions, based on the flow of areas between the lowest ionization zones (< 1%) and the medium ionization regions (> 10%). In order to enhance the contrast between low-ionization regions and those with a higher relative ionization, the histogram of the maps is equalized, which increases the contrast of the VTEC maps, which is the distance between low-ionization regions and those with a higher relative ionization are increased. The use of percentile thresholds makes this method independent of hypotheses about the generation of depletions, their morphology, or the number of depletion regions that can occur on a map. We test the performance by comparing the depletion regions identified in eight events reported in the literature under different conditions of the ionosphere. The events used as reference were measured by satellites, radar, and GNSS. The method that we propose allows for real-time processing of GNSS VTEC maps. The morphology of the detected regions matches the shape that is reported in the literature.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Applying the New MODIS-Based Precipitable Water Vapor Retrieval Algorithm
           Developed in the North Hemisphere to the South Hemisphere

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      Authors: Jia He;Zhizhao Liu;
      Pages: 1 - 12
      Abstract: A new algorithm to retrieve water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) channels using the ensemble-based empirical regression model, which was developed based on the North Hemisphere (western North America) data, was for the first time applied and validated to the South Hemisphere, mainly the Australia and its surrounding regions. By employing the empirical regression algorithm to retrieve water vapor from MODIS Level 1 reflectance data, the wet bias of MODIS product has been significantly reduced. Validation against global positioning system (GPS) water vapor observations over the period January 1, 2017 to December 31, 2019 in and around Australia shows that the root mean square error (RMSE) of water vapor data obtained from MODIS/Terra has reduced by 58.53% from 5.712 to 2.369 mm when using two-channel ratio transmittance and has reduced by 56.14% to 2.505 mm when using three-channel ratio transmittance. For the data obtained from MODIS/Aqua, the RMSE has reduced by 49.17% from 5.170 to 2.628 mm using two-channel ratio transmittance and has reduced by 46.60% to 2.761 mm using three-channel ratio transmittance, respectively. In addition, validations of the retrieved water vapor results over such a large research area (0°-55°S in latitude and 95°-180°E in longitudes) also show no temporal or spatial dependence, implying that the algorithm is homogeneous, accurate, and robust.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Assessments of Doppler Velocity Errors of EarthCARE Cloud Profiling Radar
           Using Global Cloud System Resolving Simulations: Effects of Doppler
           Broadening and Folding

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      Authors: Yuichiro Hagihara;Yuichi Ohno;Hiroaki Horie;Woosub Roh;Masaki Satoh;Takuji Kubota;Riko Oki;
      Pages: 1 - 9
      Abstract: The Earth Clouds, Aerosol, and Radiation Explorer (EarthCARE) is a satellite mission jointly developed by the Japan Aerospace Exploration Agency (JAXA) and the European Space Agency (ESA). One challenging feature of this mission is the observation of Doppler velocity by the Cloud Profiling Radar (EC-CPR). The Doppler measurement accuracy is affected by random errors induced by Doppler broadening due to the finite beamwidth and Doppler folding caused by the finite pulse repetition frequency. We investigated the impact of horizontal (along-track) integration and unfolding methods on the reduction of Doppler errors, in order to improve Doppler data processing in the JAXA standard algorithm. We simulated EC-CPR-observed Doppler velocities from pulse-pair covariances with the latest EC-CPR specifications using the radar reflectivity factor and Doppler velocity fields simulated by a satellite data simulator and a global cloud system resolving simulation. Two representative cases of a cirrus cloud and precipitation were examined. In the cirrus cloud case, the standard deviation of random error was decreased to 0.5 m/s for −10 dB ${Z} _{mathbf {e}}$ after 10-km horizontal integration. In the precipitation case, large falling speeds of precipitation caused Doppler folding errors due to larger Doppler velocities than that in the cirrus cloud case. When ${Z} _{mathbf {e}}$ is larger than −15 dB ${Z} _{mathbf {e}}$ , the standard deviations of random error were less than 1.0 m/s after 10-km horizontal integration and unfolding.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Compressive Sensing-Based 3-D Rain Field Tomographic Reconstruction Using
           Simulated Satellite Signals

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      Authors: Weiwei Jiang;Yafeng Zhan;Shen Xi;Defeng David Huang;Jianhua Lu;
      Pages: 1 - 13
      Abstract: As an alternative to traditional meteorological methods, rain attenuation in satellite-to-Earth microwave communication signals has been used for rainfall reconstruction in recent years. In this article, the existing 2-D rain field reconstruction problem is extended to a 3-D scenario by leveraging the low Earth orbit satellite system. A compressive sensing approach is further proposed to solve the 3-D rain field reconstruction problem. The Starlink system is used as a reference, and two synthetic rain events near the Great Barrier Reef in Australia, which are generated from the weather research and forecasting model, are used to evaluate the reconstruction performance. Simulation results show that the compressive sensing approach performs better than both the traditional least squares and the least absolute shrinkage and selection operator approaches.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Prediction of Vertical Profile of NO₂ Using Deep Multimodal Fusion
           Network Based on the Ground-Based 3-D Remote Sensing

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      Authors: Shulin Zhang;Bo Li;Lei Liu;Qihou Hu;Haoran Liu;Rui Zheng;Yizhi Zhu;Ting Liu;Mingzhai Sun;Cheng Liu;
      Pages: 1 - 13
      Abstract: The vertical distribution profiles of NO2 are essential for understanding the mechanisms, detecting near-surface emissions, and tracking pollutant transportation at high altitude. However, most of the published NO2 studies are based on the surface 2-D measurements. The ground-based 3-D remote-sensing stations were recently built to measure vertical distribution profiles of NO2. However, the stations were spatially sparse due to the high cost and could not make the measurements without sunlight. In this study, we first developed a multimodel fusion network (MF-net) based on the sparse vertical observations from the Jing-Jin-Ji region. We achieved the 3-D profile prediction of NO2 in the range of 39.005–41.405N and 115.005–117.905E with 24-h coverage. The MF-net significantly surpassed the conventional WRF-CHEM model and provided a more accurate evaluation of the NO2 transmission between Beijing and the neighboring cities. Besides, the MF-net covers the monitoring of NO2 to the whole study area and extends the monitoring time to the entire day (24 h), making it serviceable for continuous spatial-temporal estimation of NO2 and its transmission in pollution events. The MF-net provides more robust data support to formulate reasonable and effective pollution prevention and control measures.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Similarities and Differences in Clutter Detection Between Electronic Scans
           and Mechanical Scans With a Polarimetric-Phased Array Radar

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      Authors: Zhe Li;Guifu Zhang;
      Pages: 1 - 9
      Abstract: This article presents similarities and differences in clutter detection between electronic scans and mechanical scans with a cylindrical polarimetric-phased array radar (CPPAR). Theoretical explanations of clutter features in electronic scans that are different from those in mechanical scans are explored and verified by observations with the CPPAR. Clutter detection results with the CPPAR, based on the co polar correlation coefficient, dual-scan cross-correlation coefficient, power ratio, and their combinations in the electronic scan and mechanical scan modes are presented and compared.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Ultrahigh-Resolution (250 m) Regional Surface PM2.5 Concentrations Derived
           First From MODIS Measurements

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      Authors: Jianjun Liu;Fuzhong Weng;Zhanqing Li;
      Pages: 1 - 12
      Abstract: Aerosol optical depth from different satellite sensors are widely used to estimate surface PM2.5 concentrations. However, these products generally have coarse resolutions, limiting the ability to evaluate PM2.5 concentrations in urban regions where the human activities are relatively high. This study first develops an ensemble machine learning approach to produce PM2.5 concentrations with an extremely high spatial resolution of 250 m, based on Moderate Resolution Imaging Spectroradiometer (MODIS) measurements of top-of-atmosphere reflectance and related meteorological variables. The Yangtze River Delta region, with one of the highest levels of PM2.5 pollution in China, is the study region chosen. The model shows a very high and stable performance with a coefficient of determination ( $R^{2}$ ) of 0.90, a root-mean-square error (RMSE) of $12.0~mu text{g}/text{m}^{3}$ , a mean prediction error (MPE) of $7.8~mu text{g}/text{m}^{3}$ , and a mean relative prediction error (RPE) 16.9% for sample-based cross validation. The model can accurately capture the distribution patterns and magnitudes of PM2.5 concentrations over the study region for seasonal mean, daily variations, and different levels of air pollution. The very high resolution of the model has the advantage of capturing the uneven spatial distribution of PM2.5 concentrations at small spatial scales and identifying small areas with very high PM2.5 concentrations, offering a possible approach for locating the sources of PM2.5 emissions. In general, the model developed here estimates very well PM2.5 concentrations at a very high spatial resolution, providing detailed information, us-ful for air-pollution-related studies, as well as pollution monitoring and evaluation by governments, especially in urban and urban-center areas.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Tropical Cyclone Intensity Estimation From Geostationary Satellite Imagery
           Using Deep Convolutional Neural Networks

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      Authors: Chong Wang;Gang Zheng;Xiaofeng Li;Qing Xu;Bin Liu;Jun Zhang;
      Pages: 1 - 16
      Abstract: In this study, a set of deep convolutional neural networks (CNNs) was designed for estimating the intensity of tropical cyclones (TCs) over the Northwest Pacific Ocean from the brightness temperature data observed by the Advanced Himawari Imager onboard the Himawari-8 geostationary satellite. We used 97 TC cases from 2015 to 2018 to train the CNN models. Several models with different inputs and parameters are designed. A comparative study showed that the selection of different infrared (IR) channels has a significant impact on the performance of the TC intensity estimate from the CNN models. Compared with the ground truth Best Track data of the maximum sustained wind speed, with a combination of four channels of data as input, the best multicategory CNN classification model has generated a fairly good accuracy (84.8%) and low root mean square error (RMSE, 5.24 m/s) and mean bias (−2.15 m/s) in TC intensity estimation. Adding attention layers after the input layer in the CNN helps to improve the model accuracy. The model is quite stable even with the influence of image noise. To reduce the side-effect of the very unbalanced distribution of TC category samples, we introduced a focal_loss function into the CNN model. After we transformed the multiclassification problem into a binary classification problem, the accuracy increased to 88.9%, and the RMSE and the mean bias are significantly reduced to 4.62 and −0.76 m/s, respectively. The results show that our CNN models are robust in estimating TC intensity from geostationary satellite images.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Sensing Horizontally Oriented Frozen Particles With Polarimetric Radio
           Occultations Aboard PAZ: Validation Using GMI Coincident Observations and
           Cloudsat a Priori Information

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      Authors: Ramon Padullés;Estel Cardellach;F. Joseph Turk;Chi O. Ao;Manuel de la Torre Juárez;Jie Gong;Dong L. Wu;
      Pages: 1 - 13
      Abstract: The sensitivity of PAZ’s Polarimetric Radio Occultation (PRO) observations to horizontally oriented frozen particles is assessed using coincident measurements from the Global Precipitation Measurement (GPM) core satellite’s radiometer GPM Microwave Imager (GMI) and ancillary information from Cloudsat. The difference between the horizontal and vertical polarizations of the GMI observed radiances at 166 GHz, indicative of horizontally oriented particles, is compared with the PAZ differential phase shift observations. A clear positive trend is observed, exhibiting a good correlation between the two observations. The radiometer absolute observations and polarization differences (PD) are then used to build a lookup table of ice water content (IWC) vertical profiles, using coincident observations between GPM and Cloudsat. The vertical profiles of IWC, along with the information of the radiometric PDs and some simple assumptions, are used to simulate the expected PAZ differential phase shift observations. The validation with the PAZ and GPM coincident measurements shows a high correlation between the simulated and observed differential phase shift, therefore proving the sensitivity of polarimetric radio occultations to horizontally oriented frozen particles. These results demonstrate that PRO can not only sense the precipitation near the surface (the original goal of the mission) but also the frozen particles near and well above the freezing level, providing additional detail of the cloud vertical structure.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Integrated Water Vapor Estimation Through Microwave Propagation
           Measurements: First Experiment on a Ground-to-Ground Radio Link

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      Authors: Francesco Montomoli;Giovanni Macelloni;Luca Facheris;Fabrizio Cuccoli;Samuele Del Bianco;Marco Gai;Ugo Cortesi;Gianluca Di Natale;Alberto Toccafondi;Federico Puggelli;Andrea Antonini;L. Volpi;D. Dei;P. Grandi;F. Mariottini;A. Cucini;
      Pages: 1 - 13
      Abstract: Measurement of water vapor (WV) in the lower troposphere on a continuous temporal basis would improve our knowledge of the atmospheric dynamics and the performance of numerical weather prediction models. In recent studies, a new measurement concept, the normalized differential spectral attenuation (NDSA) approach, was proposed. It is based on measurements of differential attenuation at 18.8 and 19.2 GHz performed along a tropospheric radio link. While NDSA measurement at a fixed elevation angle provides information on integrated WV (IWV), measurements at different elevation angles allow to retrieve the vertical WV content profile. A prototype NDSA demonstrator, which consists of two units, a synthesized transmitter and a software-defined radio receiver, has been designed and implemented. The system was accurately characterized through several laboratory tests, and then a first experimental campaign was conducted at fixed elevation angle along a ground-to-ground radio link. Obtained results confirm the sensitivity of the NDSA measurements to the IWV along such link with a good agreement with the existing ground-based and satellite data products.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Retrieval of Global Carbon Dioxide From TanSat Satellite and Comprehensive
           Validation With TCCON Measurements and Satellite Observations

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      Authors: Xinhua Hong;Peng Zhang;Yanmeng Bi;Cheng Liu;Youwen Sun;Wei Wang;Zeqing Chen;Hao Yin;Chengxin Zhang;Yuan Tian;Jianguo Liu;
      Pages: 1 - 16
      Abstract: To cope with global climate change and monitor global CO2 concentration distribution, the first Chinese carbon dioxide satellite (TanSat) has been successfully launched in December 2016. In this study, we implemented a CO2 retrieval scheme by calibrating the TanSat sun-glint (GL) mode spectra and adapting the Iterative Maximum $A$ Posteriori Differential Optical Absorption Spectroscopy (IMAP-DOAS) algorithm for CO2 spectral retrieval. The global terrestrial CO2 total vertical column density (VCD) and column-averaged dry-air mole fractions of CO2 ( $text{X}_{text {CO2}}$ ) were simultaneously retrieved from TanSat GL spectral observations. Then, a comprehensive verification was performed between TanSat CO2 retrieval and other measurements including Total Carbon Column Observing Network (TCCON), the Japanese Greenhouse gases Observing SATellite (GOSAT), and the US Orbiting Carbon Observatory-2 (OCO-2). Further comparisons between our TanSat CO2 retrieval and ground-based FTIR measurements from TCCON indicated a good correlation with the mean bias of −0.78 ppm, the standard deviation at 1.75 ppm, and the Pearson correlation coefficient of 0.81. In addition, cross-satellite CO2 validations of TanSat with GOSAT and OCO-2 showed consistently spatiotemporal trends for both CO2 VCD and $text{X}_{text {CO2}}$ . In summary, we can conclude that the presented CO2 retrieval scheme has achieved global CO2 retrieval from TanSat GL mode spectra with high precision and accuracy, as suggested by the results of independent ground-based and satellite validations.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Predicting Tropical Cyclogenesis Using a Deep Learning Method From Gridded
           Satellite and ERA5 Reanalysis Data in the Western North Pacific Basin

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      Authors: Rui Zhang;Qingshan Liu;Renlong Hang;Guangcan Liu;
      Pages: 1 - 10
      Abstract: This article proposes a deep learning model to predict tropical cyclogenesis (TCG) from gridded satellite and ERA5 reanalysis data in the western North Pacific basin. The proposed model contains two modules. First, convolutional neural network (CNN)-based deep features are extracted for each predictor, and then, the extracted features are fused with two fully connected layers to differentiate and investigate the relationship between predictors and TCG. The experimental data of this study are composed of 3232 developing tropical cluster clouds and 6657 nondeveloping ones; 90% of the collected data are utilized to train the model, and the rest are used to evaluate the trained model. Totally, nine predictors have been considered for the study, and the results show that the brightness temperature (IR), relative vorticity (Vo), and geopotential height (Z) perform better than the other predictors. A combined model with six predictors [IR, Z, RH (relative humidity), Vo, WS10 m (wind speed at the height of ten meters above the surface of the Earth), and mslp (mean sea-level pressure)] achieves the best TCG predicting performance, i.e., 97.1% of developing tropical cyclones are detected at a probability threshold of 0.13 with a false alarm rate of 20.3%. The experimental results demonstrate that the proposed method is superior to the existing methods and also indicate that the fusion of satellite and reanalysis data is a promising method to predict TCG.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Systematic Assessment of MODTRAN Emulators for Atmospheric Correction

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      Authors: Jorge Vicent Servera;Juan Pablo Rivera-Caicedo;Jochem Verrelst;Jordi Muñoz-Marí;Neus Sabater;Béatrice Berthelot;Gustau Camps-Valls;José Moreno;
      Pages: 1 - 17
      Abstract: Atmospheric radiative transfer models (RTMs) simulate the light propagation in the Earth’s atmosphere. With the evolution of RTMs, their increase in complexity makes them impractical in routine processing such as atmospheric correction. To overcome their computational burden, standard practice is to interpolate a multidimensional lookup table (LUT) of prestored simulations. However, accurate interpolation relies on large LUTs, which still implies large computation times for their generation and interpolation. In recent years, emulation has been proposed as an alternative to LUT interpolation. Emulation approximates the RTM outputs by a statistical regression model trained with a low number of RTM runs. However, a concern is whether the emulator reaches sufficient accuracy for atmospheric correction. Therefore, we have performed a systematic assessment of key aspects that impact the precision of emulating MODTRAN: 1) regression algorithm; 2) training database size; 3) dimensionality reduction (DR) method and a number of components; and 4) spectral resolution. The Gaussian processes regression (GPR) was found the most accurate emulator. The principal component analysis remains a robust DR method and nearly 20 components reach sufficient precision. Based on a database of 1000 samples covering a broad range of atmospheric conditions, GPR emulators can reconstruct the simulated spectral data with relative errors below 1% for the 95th percentile. These emulators reduce the processing time from days to minutes, preserving sufficient accuracy for atmospheric correction and providing model uncertainties and derivatives. We provide a set of guidelines and tools to design and generate accurate emulators for satellite data processing applications.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Discrimination of Biomass-Burning Smoke From Clouds Over the Ocean Using
           MODIS Measurements

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      Authors: Qing Wang;Yingcheng Lu;Chuanmin Hu;Yongxiang Hu;Minwei Zhang;Junnan Jiao;Jilian Xiong;Yongxue Liu;Zhenke Zhang;
      Pages: 1 - 10
      Abstract: Smokes from biomass burning can contribute substantial amounts of hazardous substances and carbon to the atmosphere. These substances can be transported seaward and deposited on the ocean surface. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to map the relative smoke concentration over the ocean between November 8 and 11, 2018 from the recent California fires, with the ultimate goal of developing a generally applicable approach to map smokes over oceans. Because both biomass-burning smokes and clouds can produce strong backscattering signals, two key differences are used to separate them: 1) water-vapor absorption in certain wavelengths only occurs in clouds and 2) cumulus and cirrus clouds occur at different altitudes, therefore, bearing different thermal signatures. Based on these observations, a decision-tree method is developed to separate smokes from clouds. First, MODIS top-of-atmosphere (TOA) reflectance at 936 nm is used to detect both clouds and smokes over oceans. Then, brightness temperature derived from the 9730-nm band is used to separate cirrus from others. Finally, a water absorption depth (WAD) index is used to distinguish cumulus clouds from smokes, whose relative concentration in each image pixel is estimated from the MODIS TOA reflectance at 859 nm. Such derived smoke distribution and concentration are validated using concurrent Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) data, which provide the fine mode aerosol optical thickness (AOT) of smokes. Test of the approach over the recent Australia fires shows promising results, suggesting that the approach might be implemented by operational agencies to monitor and quantify smokes from biomass burning on a routine basis.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Examination of Humidity and Ice Supersaturation Profiles Over West
           Antarctica Using Ground-Based G-Band Radiometer Retrievals

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      Authors: Maria P. Cadeddu;Domenico Cimini;Virendra Ghate;Dan Lubin;Andrew M. Vogelmann;Israel Silber;
      Pages: 1 - 16
      Abstract: Humidity profiles retrieved from a ground-based millimeter-wave radiometer located at McMurdo Station, Antarctica, and the West Antarctica Ice Sheet Divide are presented, and their suitability to study the humidity of the polar climate is assessed. The dry conditions of the Antarctic winter and spring are ideal for ground-based millimeter-wave measurements, and the retrievals appear to realistically reproduce the spatial and temporal variabilities of humidity at both sites. The radiometer has the ability to capture the daily variability of very low humidity (0.5–4 g/kg) in the low-to-mid troposphere with an uncertainty of 10%–20% during the Antarctic winter, spring, and summer. Despite the coarse vertical resolution (200–600 m in the first 4 km), the retrievals provide additional information with respect to the European Centre for Medium Range Weather Forecasts (ECMWF) profiles used as background information. The radiometer is also able to realistically identify the location and frequency of supersaturated layers with respect to ice in the mid troposphere. The occurrence of supersaturated layers is correlated with the occurrence of ice clouds identified by a cloud mask. Overall, results show that ground-based microwave and millimeter-wave radiometry is a viable complement to satellite observations to provide continuous information on the thermodynamic state of the low-to-mid troposphere at high latitudes.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Geostationary Hyperspectral Infrared Sounder Channel Selection for
           Capturing Fast-Changing Atmospheric Information

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      Authors: Di Di;Jun Li;Wei Han;Ruoying Yin;
      Pages: 1 - 10
      Abstract: Various methodologies have been developed for selecting a subset of channels from a hyperspectral infrared (IR) sounder for assimilation. The information entropy iterative method was considered optimal for channel selection. However, this method only considers the decrease in uncertainty in the atmospheric state caused by measurements at a single time, without considering the dynamic effect of measurements over a period of time; therefore, it might not be optimal for hyperspectral IR sounders onboard geosynchronous satellites that mainly aim to observe rapidly changing weather events. An alternative channel selection method is developed by adding an $M$ index, which reflects the Jacobian variance over time; the adjusted algorithm is ideal for the Geosynchronous Interferometric Infrared Sounder (GIIRS), which is the first high-spectral-resolution advanced IR sounder onboard a geostationary weather satellite. Comparisons between the conventional algorithm (information entropy iterative method) and the adjusted algorithm show that the channels selected from GIIRS by the adjusted algorithm will have larger brightness temperature diurnal variations and better information content than the conventional algorithm, based on the same background error covariance matrix, the observational error covariance matrix, and the channel blacklist. The adjusted algorithm is able to select the channels for monitoring atmospheric temporal variation while retaining the information content from the conventional method. The 1-D variational (1Dvar) retrieval experiment also verifies the superiority of this adjusted algorithm; it indicates that using the channel selected by the adjusted algorithm could enhance the water vapor profile retrieval accuracy, especially for the lower and middle troposphere atmosphere.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • GNSS-RS Tomography: Retrieval of Tropospheric Water Vapor Fields Using
           GNSS and RS Observations

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      Authors: Wenyuan Zhang;Shubi Zhang;Nan Ding;Lucas Holden;Xiaoming Wang;Nanshan Zheng;
      Pages: 1 - 13
      Abstract: High spatiotemporal resolution atmospheric water vapor can be retrieved using the Global Navigation Satellite System (GNSS) tomography technique, in which the remained ill-posed problem of the tomography system resulting from the acquisition geometry is a vital issue to be addressed. Remote sensing (RS) water vapor data, with high-resolution and global coverage, show great potential for retrieval of slant water vapor (SWV) observations to improve the tomographic geometrical distribution. In this article, we develop a GNSS-RS (GNSS combining RS) tomography model to fully exploit the value of observation signals from GNSS and RS measurements. The two key factors of retrieving the RS SWV are performed by calibrating the original precipitable water vapor (PWV) images and adding the tropospheric horizontal gradients. The results reveal that when introducing the RS SWV observations into the tomography model, the acquisition geometry is significantly improved, with the average rate of voxels crossed by rays from 62% to 95% and the mean number of observation signals from 395 to 508 during the tomographic periods. Independent radiosonde data are used to validate the tomographic water vapor fields. The mean root-mean-square error (RMSE) and bias of the water vapor profiles derived from GNSS-RS solutions are decreased by 28% and 45% with respect to the GNSS-only results, respectively. Such improvements highlight that GNSS-RS troposphere tomography has significant potential to improve the reconstruction of the atmospheric water vapor fields.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Single Scattering Albedo of High Loading Aerosol Estimated Across East
           Asia From S-NPP VIIRS

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      Authors: Fangwen Bao;Tianhai Cheng;Ying Li;Shuaiyi Shi;Hong Guo;Yu Wu;
      Pages: 1 - 11
      Abstract: Single scattering albedo (SSA) is a key variable to describe the aerosol absorption for solar radiation and also a critical metric for the climate impact. However, a great challenge is noticed from most spaceborne algorithms since satellite measured reflectances result from a convolved effect of aerosol loading and absorption, which is hardly differentiated and SSA can only be simply approximated depend on a few aerosol model candidates. This study is intended to propose an algorithm to obtain aerosol SSA indirectly from visible infrared imaging radiometer suite (VIIRS), developing a strategy to characterize the aerosol absorption of polluted plume over East Asia. A new parameterization scheme for aerosol models is proposed by optimizing the independent SSA values to the mixed combination of three basic components. Notable sensitivities mean that the updated aerosol assumptions work efficiently in the lookup table-based algorithm. The algorithm is less affected by the uncertainties of surface definitions and achieves reasonable SSA estimations under higher aerosol loadings conditions (AOD > 0.5). The comparison is encouraging that new SSA results have an expected correlation with those retrieved independently of ground-based sun photometers, especially for high aerosol loading SSA, with better correlations (0.603, 0.571, and 0.473 of $R^{2}$ at 440, 550, and 675 nm, respectively), lower mean biases (0.026, 0.030, and 0.042 at 440, 550, and 675 nm, respectively), and 85% of the retrievals fall within the 5% expected error (EE) envelope. The flexible SSA products will thus be more useful for characterizing aerosol absorption in pollution.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Locally Weighted Neural Network Constrained by Global Training for
           Remote Sensing Estimation of PM₂.₅

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      Authors: Tongwen Li;Huanfeng Shen;Qiangqiang Yuan;Liangpei Zhang;
      Pages: 1 - 13
      Abstract: Fine particulate matter (PM2.5) pollution can cause serious public health problems worldwide. A novel geographically and temporally weighted neural network constrained by global training (GC-GTWNN) is proposed in this article for the remote sensing estimation of surface PM2.5. The global neural network (NN) is trained to learn the overall effect of the influencing variables on surface PM2.5, and the local geographically and temporally weighted NN (GTWNN) addresses the spatiotemporal heterogeneity of the relationship between PM2.5 and the influencing variables. Specifically, a global NN is trained with all samples collected from the entire study domain and period. Then, initialized with the global NN, the GTWNN models are built for each location and time and fine-tuned via spatiotemporally localized samples. Meanwhile, the geographically weighted loss function is designed for GTWNN. The proposed GC-GTWNN modeling is tested with a case study across China, which integrates satellite aerosol optical depth, surface PM2.5 measurements, and auxiliary variables. Cross-validation results indicate that a remarkable improvement is observed from the global NN to GC-GTWNN modeling ( $R^{2}$ value increasing from 0.49 to 0.80), and GC-GTWNN modeling also notably outperforms the conventionally popular PM2.5 estimation models.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Sparse Reconstruction of 3-D Regional Ionospheric Tomography Using Data
           From a Network of GNSS Reference Stations

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      Authors: Yun Sui;Haiyang Fu;Denghui Wang;Feng Xu;Shaojun Feng;Jin Cheng;Ya-Qiu Jin;
      Pages: 1 - 15
      Abstract: 3-D computerized ionospheric tomography (CIT) is an ill-posed problem due to the insufficient amount of observations, it remains challenging for practical applications. In this article, we proposed an ionospheric tomography method that combined data-driven methods with compressed sensing (CS) to deal with the ill-posed problem. First, slant total electron content (STEC) data were extracted by undifferenced and uncombined precise point positioning (UCPPP) with known fixed station coordinates. Second, data-driven methods were adopted to construct the projection matrix from the ionospheric model. Third, compressed sensing was used to derive the sparse solution based on $L_{1}$ norm. The ionospheric tomography can be achieved well by using observations during the shorter time interval and in a sparse receiver distribution based on the property of compressed sensing. Results of experiment based on real Global Positioning System (GPS) observation data verified the effectiveness of the proposed methods. By comparing with the colocated ionosonde, it is found that the CS methods are more consistent with the actual ionospheric fluctuation than the modified constrained algebraic reconstruction technique (CART). In terms of the differential STEC (dSTEC) analysis, the error of the tomography model by Compressed Sensing-Principal Component Analysis (CS-PCA) is less than 0.2 TEC unit (TECU), and the time resolution is 5 min. The UCPPP with constraint by CS-PCA shows the best performance of 12.2%, 40.9% and 0.31% improvement in positioning accuracy, convergence time, and fixed rate over the UCPPP with constraint by modified CART. The proposed data-driven methods may be important for high-resolution 4-D ionospheric tomography in the future.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Continuous Mapping of Broadband VHF Lightning Sources by Real-Valued MUSIC

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      Authors: Jayant Chouragade;Rajesh Kumar Muthu;
      Pages: 1 - 7
      Abstract: To improve the mapping quality of lightning discharge processes at a reduced computational complexity, broadband implementation of the real-valued MUSIC (RV-MUSIC) direction of arrival (DOA) method is proposed and evaluated. A very high frequency (VHF) broadband high-speed continuous acquisition system consisting of three omnidirectional “L” shaped antennae array is implemented to acquire lightning radio emissions in a 20–80-MHz frequency band. The cost function of RV-MUSIC for the broadband lightning signal is derived. Monte-Carlo simulations are carried out to demonstrate functionality and to evaluate the robustness of the proposed method. Further, the proposed method is validated by generating lightning maps using observational data. Simulation and observational results are compared with the conventional incoherent MUSIC (IMUSIC) and interferometric DOA methods. The computational costs of all three methods are derived, and their respective estimated execution times are also presented. Results show that the proposed method can generate lightning maps of similar quality to that of IMUSIC at a significantly lower computational load.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • The First Validation of Sentinel-3 OLCI Integrated Water Vapor Products
           Using Reference GPS Data in Mainland China

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      Authors: Jiafei Xu;Zhizhao Liu;
      Pages: 1 - 17
      Abstract: The integrated water vapor (IWV) products collected from June 1, 2019 to May 31, 2020 from the ocean and land color instrument (OLCI) sensor, onboard the Sentinel-3 satellites, are evaluated against reference water vapor data estimated from ground-based 214 global positioning system (GPS) stations in the Mainland China. This is the first time to thoroughly evaluate the quality of Sentinel-3 OLCI IWV products by in situ GPS-measured IWV data from such a large spatial coverage as China. The validation results show that, under cloud-free conditions, the OLCI IWV measurements agree very well with the ground-based GPS water vapor data, with a root-mean-square error (RMSE) of 3.03 mm for Sentinel-3A satellite and 3.13 mm for Sentinel-3B satellite. The dependence of OLCI IWV on various parameters was also analyzed. Analysis showed that the accuracy of inland OLCI IWV products was superior to that in coastal areas and that OLCI tended to overestimate IWV value in lower elevation and underestimate IWV value in higher elevation. The accuracy of OLCI IWV measurements increased as IWV decreased. Solar zenith angle analysis showed that the OLCI IWV product had a higher accuracy at a larger solar zenith angle. In spring and winter, the OLCI IWV observations had higher accuracy than those in summer and autumn. OLCI IWV tended to underestimate IWV value in most land cover types. Except for the polar climatic zone, the Sentinel-3 OLCI IWV products tended to overestimate IWV value. The validation results against previous studies were also discussed in this work.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • The Inhomogeneity Effect of Sea Salt Aerosols on the TOA Polarized
           Radiance at the Scattering Angles Ranging From 170° to 175°

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      Authors: Meng Li;Lei Bi;Wushao Lin;Fuzhong Weng;Shuangyan He;Xiaoyu Zhang;
      Pages: 1 - 12
      Abstract: Sea salt aerosols are mostly distributed over the oceans and they can significantly affect the atmospheric radiative transfer. This article investigated sea salt aerosol impact on the polarization state of radiance at the top of the atmosphere (TOA) through the use of sea salt particle models. Specifically, six models of sea salt aerosols, including a homogeneous sphere, two super-spheroids, and three inhomogeneous spheres with both spherical and nonspherical cores, were considered and their optical properties were computed using the Lorenz–Mie theory and the invariant imbedding T-matrix method. The polarized radiance at the TOA was simulated by using a vector adding–doubling radiative transfer model. It was demonstrated that the inhomogeneous sphere-modeled TOA polarized radiance had a minimum at the backscattering angles ranging from 170° to 175°, whereas such features disappear when a homogeneous sphere or nonspherical model is used. To prove this effect, the satellite marine aerosol vertical feature mask data from the Cloud Aerosol Light Detection and Ranging (Lidar) and Infrared Pathfinder Satellite Observations (CALIPSO) over global ocean areas were collocated with the measurements from Polarization and Anisotropy of Reflectance for Atmospheric Science Coupled with Observations from a Lidar (PARASOL). It was found that the PARASOL polarized radiance also had negative values at the backscattering angles ranging from 170° to 175°. Thus, the obvious negative polarized radiances at these backscattering angles could be indicative of inhomogeneous sea salt aerosols. Both homogeneous and inhomogeneous sea salt models related to ambient relative humidity (RH) should be considered for accurate radiative transfer simulations.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Integration of Multisource Data to Estimate Downward Longwave Radiation
           Based on Deep Neural Networks

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      Authors: Fuxin Zhu;Xin Li;Jun Qin;Kun Yang;Lan Cuo;Wenjun Tang;Chaopeng Shen;
      Pages: 1 - 15
      Abstract: Downward longwave radiation (DLR) at the surface is a key variable of interest in fields, such as hydrology and climate research. However, existing DLR estimation methods and DLR products are still problematic in terms of both accuracy and spatiotemporal resolution. In this article, we propose a deep convolutional neural network (DCNN)-based method to estimate hourly DLR at 5-km spatial resolution from top of atmosphere (TOA) brightness temperature (BT) of the Himawari-8/Advanced Himawari Imager (AHI) thermal channels, combined with near-surface air temperature and dew point temperature of ERA5 and elevation data. Validation results show that the DCNN-based method outperforms popular random forest and multilayer perceptron-based methods and that our proposed scheme integrating multisource data outperforms that only using remote sensing TOA observations or surface meteorological data. Compared with state-of-the-art CERES-SYN and ERA5-land DLR products, the estimated DLR by our proposed DCNN-based method with physical multisource inputs has higher spatiotemporal resolution and accuracy, with correlation coefficient (CC) of 0.95, root-mean-square error (RMSE) of 17.2 W/m2, and mean bias error (MBE) of −0.8 W/m2 in the testing period on the Tibetan Plateau.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Detecting Multilayer Clouds From the Geostationary Advanced Himawari
           Imager Using Machine Learning Techniques

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      Authors: Zhonghui Tan;Chao Liu;Shuo Ma;Xin Wang;Jian Shang;Jianjie Wang;Weihua Ai;Wei Yan;
      Pages: 1 - 12
      Abstract: This study develops a machine learning (ML)-based multilayer cloud detection algorithm for the passive Advanced Himawari Imager (AHI) aboard the geostationary Himawari-8 satellite. AHI measurements in the 0.64-, 1.6-, 2.3-, 3.9-, 7.3-, 8.6-, 11.2-, and 12.4- $mu text{m}$ channels, their combinations, geolocations, and observational geometries are used as predictors, and collocated active CloudSat and CALIPSO data are used to accurately label multilayer cloud pixels as the reference/truth of the predictand. We develop an ML-based daytime model (ML-Day) that utilizes all the aforementioned predictors and an all-time one (ML-All) that excludes the solar channel-dependent variables. Among four ML algorithms, the random forest (RF) performs slightly better than the artificial neural network, K-nearest neighbor, and support vector machines. By comparing with the merged CloudSat and CALIPSO product, the ML-Day model correctly identifies ~89% single-layer clouds and ~70% multilayer clouds, outperforming the Moderate Resolution Imaging Spectroradiometer (MODIS) operational multilayer cloud product (~80% and ~40% given by Marchant et al.). The success rates of ML-All for single-layer and multilayer clouds also reach ~85% and ~64%, respectively. The misclassification of our algorithm is mostly caused by missing optically thin clouds, a drawback of most radiometers without the 1.38- $mu text{m}$ channel. Furthermore, with multilayer cloud pixels well detected by our algorithm, the AHI operational cloud top height retrievals are found to be larger biased due to multilayer cloud occurrence and-might be improved by considering cloud vertical structures.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Morphology-Based Adaptively Spatio-Temporal Merging Algorithm for
           Optimally Combining Multisource Gridded Precipitation Products With
           Various Resolutions

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      Authors: Siyu Zhu;Ziqiang Ma;Jintao Xu;Kang He;Hui Liu;Qingwen Ji;Guoqiang Tang;Hao Hu;Hao Gao;
      Pages: 1 - 21
      Abstract: Gridded precipitation products with fine resolutions and qualities are of great importance for understanding the global water–carbon-energy cycles at various spatiotemporal scales. Though continuous developments in Satellite Remote Sensing fields have been providing great strengths for measuring the precipitation from space, merging precipitation products from different sources, especially the gauge observations, is still the optimal way for obtaining high-quality precipitation data. Currently, the mainstream merging methods mainly focus on merging the rain rates without the considerations of rain events. In this study, we propose a new assumption that both rain events and rain rates should be considered in the merging procedures rather than only the rain rates. To meet our assumption, a morphology-based adaptive spatio-temporal merging algorithm (MASTMA) for combining various precipitation products is proposed, in which the morphology theory is first introduced to comprehensively consider the influences from both rain events and rain rates. The multisource and multiscale precipitation products including the gauge-based data (CPC-U, 0.5°, daily), the satellite-based data [Global Satellite Mapping of Precipitation by Moving Vector with Kalman (GSMaP-MVK), 0.1°, hourly; integrated multisatellite retrievals for global precipitation measurement late run (IMERG-LR), 0.1°, half-hourly], and the reanalysis data (ERA5-land, 0.1°, hourly), have been comprehensively considered in MASTMA for generating the final estimates (MASTMA-F, 0.1°, hourly) over the southeastern regions of the Mainland China in the periods from 2016 to 2019. The main conclusions include but are not limited to: 1) considerations on rain events contribute significantly to the final merged results, especially when eliminating false extreme values over the regions where precipitation-is greatly overestimated; 2) the MASTMA could optimally integrate the advantages from multisource precipitation products with different resolutions, particularly from the perspective of the spatial distributions; and 3) the final merged estimates using MASTMA outperform the contemporary state-of-the-art precipitation products especially in terms of modified Kling–Gupta Efficiency (mKGE) and critical success index (CSI). Additionally, the results of this study suggest that MASMTA is a new promising merging approach with great robustness and applicability, and has the foreseeable potentials for the operational run to generate the optimal global merged precipitation products.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Data-Driven Cloud Clustering via a Rotationally Invariant Autoencoder

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      Authors: Takuya Kurihana;Elisabeth Moyer;Rebecca Willett;Davis Gilton;Ian Foster;
      Pages: 1 - 25
      Abstract: Advanced satellite-borne remote sensing instruments produce high-resolution multispectral data for much of the globe at a daily cadence. These datasets open up the possibility of improved understanding of cloud dynamics and feedback, which remain the biggest source of uncertainty in global climate model projections. As a step toward answering these questions, we describe an automated rotation-invariant cloud clustering (RICC) method that leverages deep learning autoencoder technology to organize cloud imagery within large datasets in an unsupervised fashion, free from assumptions about predefined classes. We describe both the design and implementation of this method and its evaluation, which uses a sequence of testing protocols to determine whether the resulting clusters: 1) are physically reasonable (i.e., embody scientifically relevant distinctions); 2) capture information on spatial distributions, such as textures; 3) are cohesive and separable in latent space; and 4) are rotationally invariant (i.e., insensitive to the orientation of an image). Results obtained when these evaluation protocols are applied to RICC outputs suggest that the resultant novel cloud clusters capture meaningful aspects of cloud physics, are appropriately spatially coherent, and are invariant to orientations of input images. Our results support the possibility of using an unsupervised data-driven approach for automated clustering and pattern discovery in cloud imagery.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Associations of Hurricane Intensity Changes to Satellite Total Column
           Ozone Structural Changes Within Hurricanes

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      Authors: Lin Lin;Xiaolei Zou;
      Pages: 1 - 7
      Abstract: Hurricane top structures are not well captured by airborne or dropsonde observations. Total column ozone (TCO) observations provided by the Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite are employed in an investigation of hurricane top structures. We show that the OMPS NM TCO data can capture the top structures of Hurricane Maria (2017) over the Atlantic Ocean. An observed local maximum of TCO in the eye region reveals a strong upper tropospheric downward motion that lowers the tropopause above the hurricane eye. A rainband-like distribution of low TCO content reflects strong convection areas where the tropopause is raised and well correlates spatially with the high cloud top regions derived from the S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS). A sixth-order even polynomial fitting is used to reveal the TCO radial structures by introducing two characteristic parameters. One is a radial distance parameter ( $R_{mathrm {TCO}}$ ) representing the spatial range, and the other is an intensity parameter defined as the TCO difference from the hurricane center to 600-km radial distance from the hurricane center ( $Delta {mathrm {TCO}}_{600,{mathrm {km}}} $ ). Based on an analysis of ten hurricanes over the Northern Atlantic Ocean in 2017, we show that before a hurricane reaches its maximum strength, there is always a decrease of $R_{mathrm {TCO}}$ and an increase of $Delta {mathrm {TCO}}_{600,{mathrm {km}}} $ . It is anticipated that more accurate initial hurricanes could be produced if the TCO structures were combined with other surface, near surface, and tropospheric information in vor-ex initialization.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Convective Precipitation Nowcasting Using U-Net Model

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      Authors: Lei Han;He Liang;Haonan Chen;Wei Zhang;Yurong Ge;
      Pages: 1 - 8
      Abstract: Convective precipitation nowcasting remains challenging due to the fast change in convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The nowcasting problem is first transformed into an image-to-image translation problem in deep learning under the U-Net architecture, which is based on convolutional neural networks (CNNs). The input of the model is five consecutive radar images; the output is the predicted radar reflectivity image. The model consists of three operations: upsampling, downsampling, and skip connection. Three methods, U-Net, TREC, and TrajGRU, are used for comparison in the experiments. The experimental results show that both deep learning methods outperform the TREC method, and the CNN-based U-Net can achieve almost the same performance as TrajGRU which is a recurrent neural network (RNN)-based model. With the advantages that U-Net is simple, efficient, easy to understand, and customize, this result shows the great potential of CNN-based models in addressing time-series applications.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary
           Satellite Data: Deep Neural Network, Machine Learning, and Physical Models
           

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      Authors: Jong-Min Yeom;Seungtaek Jeong;Jong-Sung Ha;Kwon-Ho Lee;Chang-Suk Lee;Seonyoung Park;
      Pages: 1 - 12
      Abstract: In this study, a new deep learning method was developed to estimate the spatiotemporal properties of the hourly aerosol optical depth (AOD) because existing physical models are limited in their abilities to separate the reflectance between aerosols and the underlying surface over land, accurately and effectively. By incorporating geostationary ocean color imagery (GOCI), multispectral bands were applied to train data-driven models to estimate the high-spatiotemporal-resolution AOD over Northeast Asia. Physical model and traditional machine learning (ML) models (the random forest (RF) and support vector regression (SVR) models) were compared with the deep neural network (DNN) model to evaluate its accuracy, implementing hold-out validation and $k$ -fold cross-validation approaches. In the statistical results of the hold-out validation, the DNN model showed the higher accuracy (root mean square error (RMSE) = 0.112, mean bias error (MBE) = 0.007, and correlation coefficient $(R) = 0.863$ ) relative to the traditional SVR (RMSE = 0.123, MBE = −0.010, and $R = 0.833$ ) and RF (RMSE = 0.125, MBE = 0.004, and $R = 0.825$ ) models. The DNN model also exhibited the best performance for most statistical metrics among the traditional SVR, RF, and selected physical models (except for the correlation coefficients and index of agreement) in the spatial and temporal cross-validation analyses. Although the DNN model was trained using the match-up dataset between the top of atmosphere (TOA) reflectance from GOCI multispectral bands and AErosol RObotic NETwork measurem-nts, it showed high spatial and temporal generalization performance owing to its deeper and more complicated network structure. Hourly GOCI AOD data obtained using a deep learning approach with high accuracy are expected to be useful for the quantification of aerosol contents and monitoring of diurnal variations in the AOD.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Developing Deep Learning Models for Storm Nowcasting

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      Authors: Joaquin Cuomo;V. Chandrasekar;
      Pages: 1 - 13
      Abstract: Storm nowcasting relies on reasonably fast sampled radar data, and deep learning (DL) can be used to harness this vast amount of data. Despite all the publications on this topic over the past five years, there are still ad hoc assumptions and a lack of standardization. This work addresses aspects that have not yet been analyzed on the development of DL models for nowcasting systems, such as the effects of different history lengths or using non-convex metrics during the training phase. For example, we show that even if the loss function is varied, it does not significantly influence the predictions, and that the number of predicted frames has a significant impact. We used the experiments’ results to propose different models and compare their performance against other DL models. The results show that the proposed models outperform, in many aspects, the existing implementations.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Neural Network-Based Method for Time Series Modeling of 3-D Atmospheric
           Refractivity Using Radio Occultation Measurements

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      Authors: Jonas Nnabuenyi Nzeagwu;Johnson O. Urama;Augustine E. Chukwude;Daniel I. Okoh;
      Pages: 1 - 8
      Abstract: Satellite measurements of the atmosphere by remote sensing techniques are becoming the trend for atmospheric probing. The problem is that such measurements are usually not continuous in time series for given Earth locations. This article presents a neural network-based method for time series modeling of the atmospheric refractivity in 3-D space using radio occultation measurements from the COSMIC mission. The method offers an opportunity to obtain continuous refractivity values in time series for any given Earth location, even if there are no measurements. Time series inputs of year, day of year, and hour of day are used to train the 3-D space measurements of refractivity from both COSMIC-1 and COSMIC-2 missions. The data cover periods from April 2006 to September 2020, and the data for the Nigerian region (and from altitudes 0.1 to 39.9 km) are illustrated. The effectiveness of solar activity indicator [sunspot number (SSN)], as an input for the neural network process, is investigated. The result shows that the SSN was an insignificant input for improving the prediction accuracy of the model. A comparison of the model predictions and the COSMIC measurements shows that there is very good correlation (correlation coefficients are approximately 1.0) between the model predictions and the COSMIC measurements. Typical root-mean-square errors between the model predictions and the COSMIC measurements are about 7.3 N-units on ground, 0.6 N-units at 20 km, and 0.3 N-units at 40 km. The model predictions are demonstrated to vary (in time and space) in patterns that agree with physical measurements of refractivity.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Fostering the Need of L-Band Radiometer for Extreme Oceanic Wind Research

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      Authors: Prashant Kumar;R. M. Gairola;
      Pages: 1 - 6
      Abstract: Ocean surface winds from space-borne radiometers and scatterometers are crucial inputs for numerical models for the operational weather and oceanic sea state forecasts. The rainfall, associated mainly with deep convective clouds, influences the wind retrievals from these sensors during extreme winds conditions (higher than 30 m $text{s}^{-1}$ ), as the signal strongly gets affected by the intervening atmosphere, mainly the precipitation. This study emphasizes the importance of the winds from L-band radiometric measurements from Soil Moisture Active Passive (SMAP) satellite compared to the operational Advanced Scatterometer (ASCAT) (C-band) and SCATSAT-1 (Ku-band) scatterometers, National Centers for Environmental Prediction (NCEP) final analysis, and European Centre for Medium-Range Weather Forecasts reanalysis 5 (ERA5) reanalysis wind products. Investigation at global scale suggests that except SMAP, no other selected data are able to capture wind speed more than 56 m $text{s}^{-1}$ , and large underestimations are found in presently available scatterometers and reanalysis. These high wind speed errors are more prominent when verified with Joint Typhoon Warning Center (JTWC) best track data for global storms. Moreover, the high winds near storms from scatterometers are generally flagged as rainy pixels and not used for operational applications. These limitations of capturing high winds in scatterometers have been addressed here using histogram corrections approach using SMAP retrieved winds. Moreover, the regional model simulations for a case study suggest that the modified scatterometer winds have larger impact on tropical storm prediction mainly on track and intensity.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Preliminary On-Orbit Performance Test of the First Polarimetric
           Synchronization Monitoring Atmospheric Corrector (SMAC) On-Board
           High-Spatial Resolution Satellite Gao Fen Duo Mo (GFDM)

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      Authors: Zhengqiang Li;Weizhen Hou;Zhenwei Qiu;Bangyu Ge;Yanqing Xie;Jin Hong;Yan Ma;Zongren Peng;Wei Fang;Dongying Zhang;Xiaobing Sun;Yanli Qiao;Jing Yu;Wentao Yang;Jun Lin;Zhongzheng Hu;
      Pages: 1 - 14
      Abstract: Obtaining accurate atmospheric parameters, e.g., aerosol optical depth (AOD) and column water vapor (CWV), is important for the quantitative atmospheric correction (AC) of the high-spatial resolution remote sensing images. However, due to the strong temporal and spatial changes of the atmospheric parameters, it will be a challenge to ensure spatiotemporal registration of the satellite images given the AC parameters obtained separately from ground-based or other satellite products, which affects significantly the accuracy of the AC. The China National Space Administration launched a high resolution and multimode imaging satellite [Gao Fen Duo Mo (GFDM)] in July 2020, which has multifunctional observation modes and flexible mobility, with a high-spatial resolution imaging sensor (0.42 m in panchromatic and 1.6 m in multispectrum) and equipped the synchronization monitoring atmospheric corrector (SMAC) sensor. As the first atmospheric corrector with polarization detection capability on-board high-spatial resolution satellite, SMAC is designed to obtain multispectral intensity and polarized data and to retrieve synchronously AC parameters in the same field of view with main sensor. Based on the SMAC in-orbit test data, a lookup table method using the optimized inversion framework and a dual-channel ratio retrieval method are developed to derive AOD and CWV, respectively, in this article. The AOD and CWV results are validated against the AERosol RObotic NETwork (AERONET). The preliminary test of AC performance on the multispectral images of GFDM satellite indicates that SMAC is of great potential to improve the quality of the main sensor’s image.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Reconstructing High-Resolution Ocean Subsurface and Interior Temperature
           and Salinity Anomalies From Satellite Observations

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      Authors: Lingsheng Meng;Chi Yan;Wei Zhuang;Weiwei Zhang;Xupu Geng;Xiao-Hai Yan;
      Pages: 1 - 14
      Abstract: Accurately retrieving ocean interior parameters from remote sensing observations is essential for ocean and climate studies because direct observations are sparse and costly. Furthermore, high-resolution structure of seawater properties is critical for understanding the oceanic processes and changes on multiple scales. Here, we designed a new method based on a deep neural network to retrieve subsurface temperature anomaly (STA) and subsurface salinity anomaly (SSA) in the Pacific Ocean at high (1/4°) and super (1/12°) horizontal resolution. We utilized multisource satellite-observed sea surface data (e.g., sea level, temperature, salinity, and wind vector) as inputs. The results revealed that our model retrieved the high- and super-resolution STA/SSA with high accuracy, and the model was reliable in a wide range of depths (near surface to 4000 m) and times (all months in 2014). Regarding the high-resolution STA (SSA) estimation, the average coefficient of determination ( $R^{2}$ ) was 0.984 (0.966), and the average root-mean-squared error (RMSE) was 0.068 °C (0.016 psu). For the super-resolution STA, the average $R^{2}$ was 0.988 and RMSE was 0.093 °C. Here, we established an effective technique that improved the resolution and accuracy of estimating the ocean interior parameters from satellite observation. The new technique provides some new insights into oceanic observation and dynamics.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Validation of Precipitation Measurements From the Dual-Frequency
           Precipitation Radar Onboard the GPM Core Observatory Using a Polarimetric
           Radar in South China

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      Authors: Hao Huang;Kun Zhao;Peiling Fu;Haonan Chen;Gang Chen;Yu Zhang;
      Pages: 1 - 16
      Abstract: The dual-frequency precipitation radar (DPR) onboard the global precipitation measurement (GPM) satellite provides valuable measurements of precipitation. In this study, the GPM DPR products (version 6) are validated against a ground-based S-band polarimetric radar in South China based on a volume-matching method. Good consistency is found for the reflectivity factor ( $Z$ ) calibration of the two instruments. From the perspective of microphysics, the mass-weighted mean diameter ( $D_{m}$ ) estimates correspond well with those of the ground-based radar in the inner swath of the normal scan (NS); however, underestimation is found for the raindrop number concentration, indicated by the generalized intercept parameter ( $N_{w}$ ), especially for the intense echoes. Thus, the GPM DPR product may fail to depict the microphysical characteristics of small-to-medium raindrops in high concentration for heavy rainfall in South China. This is attributed to the negative $Z$ bias of the DPR caused probably by insufficient correction of attenuation, which also leads to clear underestimation in the liquid water content ( $W$ ) and the rainfall rate ( $R$ ) products for intense echoes. In the outer swath where only single-frequency retrieval is available, overestimation in $D_{m}$ exists regardless of echo intensity level, and more underestimation can be found in $N_{w}$ , $W$ , and $R$ especially for intense echoes. In the selected typhoon and squall line cases, better capability in revealing microphysical properties is also found for the inner swath of the NS. After adjusting the scan mode, the performance of the precipitation products in the outer swath can be improved by dual-frequency retrievals in the future.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Very Short-Term Rainfall Prediction Using Ground Radar Observations and
           Conditional Generative Adversarial Networks

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      Authors: Yerin Kim;Sungwook Hong;
      Pages: 1 - 8
      Abstract: Weather radars play an important role in in situ rainfall monitoring owing to their ability to measure instantaneous rain rates and rainfall distributions. Currently, the Korea Meteorological Administration (KMA) provides instantaneous radar observation data and predictions based on the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) for up to 6 h, for short-term forecasting. This study presents a conditional generative adversarial network (CGAN)-based radar rainfall prediction method for very short-range weather forecasts from 10 min to 4 h. The CGAN-predicted model was trained and tested using KMA’s constant altitude plan position indicator (CAPPI) observation data. The qualitative comparison between the radar observation and the CGAN-predicted rain rates displayed high statistical scores, such as the probability of detection (POD) = 0.8442, false alarm ratio (FAR) = 0.2913, and critical success index (CSI) = 0.6268, in the case of a 1-h prediction for rainfall on September 5, 2019, 15:20 KST. This study demonstrates the capability of the CGAN model for short-term rainfall forecasting. Consequently, the CGAN-generated radar-based rainfall prediction could complement the KMA MAPLE system and be useful in various forecasting applications.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Precipitation Estimates From Commercial Microwave Links: Practical
           Approaches to Wet-Antenna Correction

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      Authors: Jaroslav Pastorek;Martin Fencl;Jörg Rieckermann;Vojtěch Bareš;
      Pages: 1 - 9
      Abstract: An inadequate correction for wet-antenna attenuation (WAA) often causes a notable bias in quantitative precipitation estimates (QPEs) from commercial microwave links (CMLs) limiting the usability of these rainfall data in hydrological applications. This article analyzes how WAA can be corrected without dedicated rainfall monitoring for a set of 16 CMLs. Using data collected over 53 rainfall events, the performance of six empirical WAA models was studied, both when calibrated to rainfall observations from a permanent municipal rain gauge network and when using model parameters from the literature. The transferability of WAA model parameters among CMLs of various characteristics has also been addressed. The results show that high-quality QPEs with a bias below 5% and root-mean-square error (RMSE) of 1 mm/h in the median could be retrieved, even from subkilometer CMLs where WAA is relatively large compared to raindrop attenuation. Models in which WAA is proportional to rainfall intensity provide better WAA estimates than constant and time-dependent models. It is also shown that the parameters of models deriving WAA explicitly from rainfall intensity are independent of CML frequency and path length and, thus, transferable to other locations with CMLs of similar antenna properties.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Satellite Aerosol Retrieval Using Scene Simulation and Deep Belief Network

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      Authors: Chen Jia;Lin Sun;Yunfang Chen;Qinhuo Liu;Huiyong Yu;Wenhua Zhang;
      Pages: 1 - 16
      Abstract: Aerosol satellite remote sensing retrieval is of great importance in the study of climatic and environmental effects. However, due to varied factors affecting the signals reaching satellite sensors, accurate aerosol retrieval remains challenging. Focusing on limitations in the availability of aerosol data from real scenes, we simulated real scenes to obtain sample data to achieve aerosol retrieval using a deep belief network (DBN). The 6S atmospheric radiative transfer model was used to simulate various possible parameters of earth–atmosphere, sun, and sensor in real scenes. A large amount of simulated data was generated and used as sample datasets of DBN training to obtain the aerosol inversion model. Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to perform aerosol optical thickness (AOT) retrieval experiments. Global-scale aerosol retrieval experiments were conducted based on the following three representative regions: 1) Beijing–Tianjin–Hebei region in China; 2) Midwestern and Southern United States; and 3) Central and Western Europe. The retrieval results were verified using Aerosol Robotic Network (AERONET) datasets in comparison with MCD19A2 aerosol products. Five indicators, including mean absolute error (MAE) and within expected error ( ${f} _{=text {EE}}$ ), were used for the evaluation. The evaluation indicators of the proposed method, in which MAEs were 0.0626, 0.0366, and 0.0487, and ${f} _{=text {EE}}$ ’s were 86.28%, 85.21%, and 80.71%, performed better than MCD19A2 in three typical regions. The most significant advantage of the proposed method is that high-precision retrieval of spatially continuous AOT can be achieved using single-temporal satellite imagery data, which is not possible realizing in current aerosol retrieval methods.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Deep Learning for Bias Correction of Satellite Retrievals of Orographic
           Precipitation

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      Authors: Haonan Chen;Luyao Sun;Robert Cifelli;Pingping Xie;
      Pages: 1 - 11
      Abstract: The performance of various composite satellite precipitation products is severely limited by their individual passive microwave (PMW)-based retrieval uncertainties because the PMW sensors have difficulties in resolving heavy rain and/or shallow orographic precipitation systems. Characterizing the error structure of PMW retrievals is crucial to improving precipitation mapping at different space–time scales. To this end, this article introduces a machine learning framework to quantify the uncertainties associated with satellite precipitation products with an emphasis on orographic precipitation. A deep convolutional neural network (CNN) is designed, which utilizes the ground-based Stage IV precipitation estimates as target labels in the training phase, to reduce biases involved in the precipitation product derived using the NOAA/Climate Prediction Center morphing technique (CMORPH). The products before and after bias correction are evaluated using four independent precipitation events over the coastal mountain region in the western United States, and the impact of topography on satellite-based precipitation retrievals is quantified. Experimental results show that the orographic gradients have a strong impact on precipitation retrievals in complex terrain regions. The accuracy of CMORPH is dramatically enhanced after applying the proposed machine learning-based bias correction technique. Using Stage IV data as references, the overall correlation (CC), normalized mean error (NME), and normalized mean absolute error (NMAE) of CMORPH are improved from 0.55, 32%, 63%, to 0.88, −2%, 39%, respectively, after bias correction for the independent case studies presented in this article. Such a machine learning scheme also has great potential for improved fusion of other or future satellite precipitation retrievals.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Cloud Detection Method Using CNN Based on Cascaded Feature Attention and
           Channel Attention

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      Authors: Jing Zhang;Jun Wu;Hui Wang;Yuchen Wang;Yunsong Li;
      Pages: 1 - 17
      Abstract: Cloud detection is of great significance for the subsequent analysis and application of remote-sensing images, and it is a critical part of remote-sensing image preprocessing. In this article, we propose a cloud detection method using convolutional neural networks based on cascaded feature attention and channel attention (CFCA-Net). The CFCA-Net uses cascaded feature attention module (CFAM) to enhance the attention of the network toward important color feature and texture feature. The CFAM cascaded the color feature attention and texture feature attention module in the encoder. The CFAN-Net also uses channel attention to highlight the important information in the channel dimensions. The attention module is based on multi-scale features and uses dilated convolution with different dilation rates to obtain information about multiple receptive fields. Moreover, a loss function combined quadtree and binary cross-entropy (BCE) was also introduced to make the network focus on the edge of cloud area. We validated our CFCA-Net on the Gaofen-1 wide field-of-view (WFV) imagery dataset. The experimental results show that the CFCA-Net performs well under different scenarios, and its overall accuracy reaches 97.55%. Moreover, subjective cloud detection results also prove the effectiveness of our algorithm.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Aerosol Optical Depth Retrieval Over South Asia Using FY-4A/AGRI Data

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      Authors: Yanqing Xie;Zhengqiang Li;Jie Guang;Weizhen Hou;Abdus Salam;Zahir Ali;Li Fang;
      Pages: 1 - 14
      Abstract: The Advanced Geosynchronous Radiation Imager (AGRI) is one of the main imaging sensors onboard the Fengyun-4A (FY-4A) satellite. Because of its high observation frequency, AGRI is suitable for continuous monitoring of atmospheric aerosols. In this study, we propose an aerosol optical depth (AOD) retrieval algorithm called the multichannel (MC) algorithm, which uses four channels (0.65, 0.83, 1.61, and $2.25~mu text{m}$ ) of AGRI. The algorithm assumes that the ratios between surface reflectance of different channels remain unchanged within two weeks, and the ratios are calculated by using Moderate-Resolution Imaging Spectroradiometer (MODIS)-combined AOD data to perform atmospheric correction on AGRI data under low pollution conditions (AOD at 550 nm less than 0.5). Since this algorithm is not developed for specific surface types, AOD retrieval can be achieved over both dark targets and bright surfaces. This algorithm has been applied to aerosol retrieval in South Asia. The accuracy assessment of the AGRI AOD dataset in 2019 and 2020 using the ground-based data from 11 aerosol robotic network (AERONET) sites shows that the AGRI AOD dataset has a high accuracy, and the statistical parameters of AGRI AOD dataset are slightly better than those of MODIS-combined AOD dataset. The root-mean-square error (RMSE), mean absolute error (MAE), relative mean bias (RMB), and percentage of data with errors within the expected error $pm (0.05+0.15 times {{text {AOD}}}_{{text {AERONET}}})$ (EE15) of AGRI AOD dataset are 0.16, 0.12, 0.23, and 63.71%, respectively. The RMSE, MAE, RMB, and EE15 of MODIS-combined AOD dataset are 0.18, 0.13, 0.24, and 61.06%, respectively.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Global Tropical Cyclone Precipitation Estimation via a Multitask
           Convolutional Neural Network Based on HURSAT-B1 Data

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      Authors: Mei Xue;Renlong Hang;Xiao-Tong Yuan;Pengfei Xiao;Qingshan Liu;
      Pages: 1 - 12
      Abstract: Fast and accurate global tropical cyclone (TC) precipitation estimation from satellite observations is still a challenging issue. In this article, we propose an effective model based on a multitask convolutional neural network (CNN) to estimate near-real-time global TC precipitation from HURSAT-B1 data. Our network mainly consists of three modules: the feature extraction module, the wind grade classification module, and the precipitation estimation module. The first module aims at extracting the spatial features of satellite imageries, the second module focuses on classifying the wind grades of the satellite imageries into six categories that are used to assist in estimating TC precipitation, and the third module is to estimate TC precipitation. To evaluate the effectiveness of our proposed model, we compare it with multiple linear regression (MLR) and random forest (RF) models based on integrated multisatellite retrievals for the global precipitation measurement (GPM) mission (IMERG). Besides, four typical TC events are selected to specifically analyze the temporal and spatial distribution of TC precipitation estimation. Experimental results show that the probability of detection and accuracy achieved by our proposed model are 0.68 and 0.81, while the correlation coefficient (CC) and MSE are 0.61 and 7.80, respectively. In terms of the four TC events, our proposed model obtains a more consistent and continuous spatial distribution of precipitation than MLR and RF. More importantly, our proposed model can achieve high spatiotemporal results, which has the potential to serve as an operational algorithm for global TC precipitation estimation.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Adaptive Aerosol Optical Depth Forecasting Model Using GNSS Observation

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      Authors: Qingzhi Zhao;Jing Su;Zufeng Li;Pengfei Yang;Yibin Yao;
      Pages: 1 - 9
      Abstract: As one of the important factors in atmospheric physical and chemical processes, aerosol optical depth (AOD) has an important impact on regional and global climate. Therefore, monitoring and predicting the temporal and spatial changes of AOD is of considerable significance. Existing methods mainly use a large number of meteorological parameters and ground observations to forecast AOD. However, modeling data are numerous and difficult to obtain practically. In this study, an adaptive AOD forecasting (AAF) model is proposed using the zenith total delay (ZTD) derived from global navigation satellite system (GNSS). This model only uses the ZTD as the external input parameter and considers the time autocorrelation of AOD for the previous epoch. In addition, AAF can adaptively adjust the model coefficients and has high accuracy. The AOD data derived from the Second Modern-era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Aerosol Robotic Network in the Beijing–Tianjin–Hebei (BTH, $113^{circ } 27^{prime }$ E– $119^{circ } 50^{prime }$ E, $36^{circ } 05^{prime }$ N– $42^{circ } 40^{prime }$ N) region over the period of 2015–2017 are used to perform the experiment. In addition, ZTD data of 16 GNSS stations in BTH region from the Crustal Movement Observation Network of China are selected to establish the AAF model. Experimental result reveals good performance of the proposed AAF model for internal and external validations. The difference in root mean square (rms), mean absolute error, and Bias of AOD between the AAF model and MERRA-2 are 0.11, 0.08, and 0.03, -espectively. Compared with the existing AOD forecast models, the proposed AAF model is superior in terms of time resolution, rms, and correlation.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Retrieval of Atmospheric Aerosol Optical Depth From AVHRR Over Land With
           Global Coverage Using Machine Learning Method

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      Authors: Xiaoqing Tian;Ling Gao;Jun Li;Lin Chen;Jingjing Ren;Chengcai Li;
      Pages: 1 - 12
      Abstract: Aerosols play an important role in global climate change, which requires long-term data records. Advanced very high-resolution radiometer (AVHRR) provides continuous observations for up to 40 years since 1979, which makes it worthwhile to retrieve aerosol optical depth (AOD) from AVHRR over land. A novel algorithm for retrieving AOD from AVHRR is developed based on the machine learning (ML) method. The AVHRR observations from pathfinder atmospheres–extended (PATMOS-x) Level-2 dataset and corresponding AOD products ( $0.55~mu mathrm {m}$ ) from moderate resolution imaging spectroradiometer (MODIS) in 2014 are used as training data. And AOD products in three years (2015, 2006, and 1998) named AVHRR XGB-AOD were generated for evaluation. Comparisons show that the AVHRR XGB-AOD is consistent with the MODIS AOD with correlation coefficients greater than 0.80 and RMSE less than 0.18 for most months in 2015 and 2006. The temporal and spatial characteristics from AVHRR XGB-AOD are similar to those from the MODIS AOD, but those from the previous AVHRR AOD with deep blue (DB) algorithm are significantly different. Validation with AERONET indicates that more than 68% of the matchups fall within expected error [EE, ±( $0.05,,pm ,,0.25times {mathrm {AOD}}_{mathrm {AERONET}}mathrm {)] }$ in 2015 and 2006, while the fraction is 66% in 1998. Compared to the DB algorithm, the ML-based algorithm performs better in high-AOD conditions over vegetated regions, such as in Southeast Asia, where the DB algorithm significantly underestimates. In low-AOD conditions, the ML-based algorithm performs better over western North America and Australia, where the aerosol composition varies greatly.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Novel Real-Time Error Adjustment Method With Considering Four Factors
           for Correcting Hourly Multi-Satellite Precipitation Estimates

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      Authors: Hanqing Chen;Bin Yong;Jonathan J. Gourley;Debao Wen;Weiqing Qi;Kun Yang;
      Pages: 1 - 11
      Abstract: High-accuracy near-real-time satellite precipitation estimates (SPEs) provide an opportunity for hydrometeorologists to improve the forecasting of extreme events, such as flood, landslide, tropical cyclone, and other extreme events, at the large scale. However, the currently operational near-real-time SPEs still have larger errors and uncertainties. In this study, we found that there exists a clear relationship of spatial plane function (SPF) between retrieval errors of SPEs and four crucial factors including topography, seasonality, climate type, and rain rate. Based on this finding, we proposed a novel error adjustment method to correct the near-real-time hourly global satellite mapping of precipitation (GSMaP-NRT) estimates in real-time. The new satellite precipitation dataset, namely, ILSF-RT, was then inter-compared with the latest near-real-time GSMaP product suite (i.e., GSMaP-NRT and GSMaP-Gauge-NRT). Verification results show that the proposed method can effectively reduce the retrieval errors of GSMaP-NRT for various terrains and rain rates over different seasons and climate-type areas. The new ILSF-RT even exhibits a general improvement over the GSMaP-Gauge-NRT estimates. Furthermore, one important merit of the new method is that it can perform rather well in validation even when not much historical data were applied as training samples in calibration, for example, during the generation of ILSF-RT, only 45 data pairs of satellite retrievals and ground observations were used for winter season over Chinese arid areas. However, the results of bias score show that the current method seems unsuitable to adjust the rainfall events with higher rain rates (>=1 mm hr−1), which needs to be further improved.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Cloud Removal in Remote Sensing Images Using Generative Adversarial
           Networks and SAR-to-Optical Image Translation

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      Authors: Faramarz Naderi Darbaghshahi;Mohammad Reza Mohammadi;Mohsen Soryani;
      Pages: 1 - 9
      Abstract: Satellite images are often contaminated by clouds. Cloud removal has received special attention due to the wide range of satellite image applications. As the clouds thicken, the process of removing them becomes more challenging. In such cases, using auxiliary images, such as near-infrared or synthetic aperture radar (SAR), for reconstructing is common. In this study, we attempt to solve the problem using two generative adversarial networks (GANs): the first translates SAR images to optical images and the second removes clouds using the translated images of prior GAN. Also, we propose dilated residual inception blocks (DRIBs) instead of vanilla U-net in the generator networks and use structural similarity index measure (SSIM) in addition to the L1 loss function. Reducing the number of downsamplings and expanding receptive fields by dilated convolutions increased the quality of output images. We used the SEN1-2 dataset to train and test both GANs, and we made cloudy images by adding synthetic clouds to optical images. In addition, we used the SEN12MS-CR dataset to test network performance to remove real clouds. The restored images are evaluated using PSNR, SSIM, SAM, MAE, RMSE, and $Q$ . We compared the proposed method with state-of-the-art deep learning models and achieved more accurate results in both SAR-to-optical image translation and cloud removal parts.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Extraction of Aerosol Optical Extinction Properties From a Smartphone
           Photograph to Measure Visibility

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      Authors: Shiqi Yao;Bo Huang;
      Pages: 1 - 13
      Abstract: Airborne particulate matter participates in the scattering of light, thus leading to visibility degradation. The widespread use of smartphones provides an opportunity to determine the level of this degradation from a smartphone photograph perspective by extraction of the image features representing aerosol optical extinction properties (AOEPs). This article presents novel algorithms to measure visibility through the extraction of AOEPs (i.e., the local medium transmission rate and the local medium extinction coefficient) from a single photograph. Among them, the transmission rate is derived based on the refined photograph’s dark channel prior and the extinction coefficient from the improved transmission map and depth map. Furthermore, to address the complexity of urban scenes, we draw on visual features manifested on a photograph in various weather conditions, analyze the influence of scene structures on extracted features, and identify the combination of extracted features to improve the estimated AOEPs. We also exploit the suitability of these two AOEPs and develop a method to automatically select an appropriate property from them based on the scene structure of a given photograph. The proposed algorithm is first validated through experiments using public databases. Then, two experiments, one on a city scale and the other on a national scale, are conducted using smartphone photographs crawled from the Internet to evaluate the accuracy of visibility estimation. Experimental results show that the proposed algorithms can estimate the atmospheric visibility in real time; therefore, this study provides an effective method of monitoring environments with crowdsourced data.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Estimation of Tropical Cyclone Intensity Using Synthetic Satellite
           Microwave Temperature Anomaly Structure and a Multifeature Distribution
           Learning Network

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      Authors: Miao Tian;Taidong Zhang;Guanghui Liu;Lin Lin;
      Pages: 1 - 12
      Abstract: Accurate intensity estimation of tropical cyclone (TC) is important and challenging. Currently, no standardized method is available for estimating the TC intensity, usually in terms of the maximum surface wind speed, using satellite remote sensing data. This article proposes a multifeature distribution learning (MFDL) model that uses satellite microwave brightness temperatures to estimate the TC intensity. The MFDL model uses the brightness temperatures of Advanced Technology Microwave Sounder (ATMS) to generate the synthetic temperature anomaly fields of TCs at different pressure levels. These 3-D temperature anomaly data are trained by MFDL to establish the relationship between anomaly fields and TC intensity. MFDL treats the TC intensity estimation as a probability distribution of wind speed, instead of as a regression problem as in the conventional TC intensity estimation methods. In this study, 964 TCs occurred in North Atlantic (NA) and North Pacific (NP) from 2012 to 2019 are used for training and testing. The simulation results suggest that estimation accuracy is greatly improved using the preprocessed 3-D TC anomaly data. The MFDL network achieves the average mean absolute errors (MAEs) of 4.37 m/s for NA TCs and 4.92 m/s for NP TCs in the years 2018 and 2019. The results show that the MFDL network and ATMS temperature-based anomaly can be a promising means for TC intensity estimation in operational weather forecast.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Satellite-Derived Aerosol–Cloud Relationships Under Anthropogenic
           Polluted Conditions of Arabian Sea

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      Authors: Chaluparambil B. Lima;Sakuru V. S. Sai Krishna;Shivali Verma;Sudhakaran S. Prijith;Muvva V. Ramana;
      Pages: 1 - 7
      Abstract: Uncertainty associated with estimating the impact of anthropogenic aerosols on clouds is hindering accurate projections of future climate. Here, we evaluate the effect of aerosols on clouds under anthropogenic polluted conditions and relatively maritime conditions, over the Arabian Sea (AS), under the influence of the same large-scale meteorology and spatiotemporal domains. The relationship between diurnal mean cloud macrophysical properties, namely, cloud fraction (CF) and cloud top temperature (CTT) from geostationary satellite (INSAT-3D), and aerosol properties, from Moderate Resolution Imaging Spectroradiometer (MODIS), is examined for the winter months of 2018. During winter, trade winds transport large quantities of continental aerosols over to the AS, where the semipermanent field of stratocumulus cloud layers exists. We observed distinctively different cloud macrophysical properties over polluted conditions compared to maritime conditions. The strengths of aerosol–cloud interaction on CF and CTT are, respectively, 0.3 and −0.02 for polluted conditions and are 0.1 and −0.005, respectively, for maritime conditions, with an increase in the aerosol index from 0.0 to 0.6. Absorbing aerosol radiative effects at elevated layers over polluted conditions majorly drive the observed changes in cloud properties. Further analysis of Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observations (CALIPSO) profiles illustrates the bimodal structure of cloud occurrence over polluted conditions. Elevated absorbing aerosol layers above stratus clouds enhance low-level cloud cover. Besides, warming due to elevated absorbing aerosols reduces intermingled cumulus cloud layers and, at the same time, enhances the development of growing cloud layers above elevated pollution layers. The observed changes in cloud macrophysical properties in relation to the mutual position of aerosol and cloud layers suggest a more complica-ed cloud regime-dependent process over this region.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A New Cumulative Anomaly-Based Model for the Detection of Heavy
           Precipitation Using GNSS-Derived Tropospheric Products

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      Authors: Haobo Li;Xiaoming Wang;Suelynn Choy;Suqin Wu;Chenhui Jiang;Jinglei Zhang;Cong Qiu;Li Li;Kefei Zhang;
      Pages: 1 - 18
      Abstract: In recent years, tropospheric products obtained from ground-based global navigation satellite system (GNSS) measurements, especially the zenith total delay (ZTD) and precipitable water vapor (PWV) estimates, have advanced their usages in meteorological applications such as the detection of precipitation events. Generally, a cumulative anomaly (CA) time series of any atmospheric variable, which represents the long-term departure of the variable from its “normal” cycle, is widely used for quantitatively estimating the variable’s variations in response to a weather event. In this study, a new cumulative anomaly-based model (NCAM) containing 14 variables, including not only PWV and ZTD values but also their respective six types of derivatives, for detecting heavy precipitation was developed. The 6-h CA time series of the variables were calculated based on the data of hourly precipitation records and time series of ZTD and PWV collected at the co-located HKSC–King’s Park (KP) stations over the eight-year period 2010–2017. The model was evaluated using the 14 variables’ CA time series to detect heavy precipitation events happened in the summer months over the period 2018–2019, and precipitation records in the same period were used as the reference. Results demonstrated that 99.1% of heavy precipitation was correctly detected by the NCAM with a lead time of 2.87 h, and the false alarm ratio (FAR) score resulting from the model was reduced to 22.4%. In addition, two case studies were also conducted to verify the effectiveness of the NCAM. These results all provide a promising direction for the application of using the CA time series of GNSS tropospheric products to the detection of heavy precipitation events.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Combining CALIPSO and AERONET Data to Classify Aerosols Globally

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      Authors: Yingjing Lin;Pengfei Tian;Chenguang Tang;Shuting Pang;Lei Zhang;
      Pages: 1 - 12
      Abstract: Angstrom exponent (AE) and aerosol optical depth (AOD) obtained from the aerosol robotic network (AERONET) and volume depolarization ratio (VDR) obtained from the cloud-aerosol Lidar with orthogonal polarization (CALIOP) from March 2018 to February 2019 were used in our study. Data used in this study are direct observation, avoiding the limitations and uncertainties from the inversion process, and providing accurate information about the aerosol properties. Both instruments were within colocation criteria of a 40-km radius and ±2 h were defined as coincident cases. Six aerosol types were differentiated using the threshold method based on the AE, AOD, and VDR data. Discussion of the aerosol classification yielded the following results: 1) clean marine aerosols were the most abundant and widely distributed aerosols, followed by other types of aerosol (33.2%), polluted dust aerosols (26.8%), natural dust aerosols (2.3%), biomass burning aerosols (1.8%), and clean continental aerosols (1.1%); 2) clean marine aerosols were mainly distributed in North America and Europe, and polluted dust aerosols frequently appeared on the edges or downwind of deserts; and 3) the aerosols controlled by natural conditions (e.g., natural dust aerosols) were sensitive to seasonal variations, whereas those controlled by anthropogenic activities (e.g., polluted dust aerosols) were not. This study provides a new method for the collaborative observation of aerosol types with ground-based and satellite data. It is rare to provide annual global distribution of aerosol types and their seasonal variations; these results provide a reference for understanding the global aerosol distribution status.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Conformal Regressor With Random Forests for Tropical Cyclone Intensity
           Estimation

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      Authors: Pingping Wang;Ping Wang;Di Wang;Bing Xue;
      Pages: 1 - 14
      Abstract: Tropical cyclone (TC) intensity estimation is critical for disaster forecasting and severe weather warning. In recent years, the performance of various TC intensity estimation models has been gradually enhanced, but the accuracy still needs to be improved. In this article, 71 intensity-related features are extracted from satellite infrared images of TCs. These features are grouped by eye features, circle features, texture features, and time-series features. Using the random forest model as the underlying algorithm of conformal prediction (CP), an intensity applicable CP framework is proposed. On the one hand, the proposed network can achieve point estimation of the TC intensity. On the other hand, it is also possible to realize the intensity interval estimation that satisfies a given significance level. In the experiments, the root mean square error of the point estimation algorithm is 7.86 kt (1 kt ≈ 0.51 m/s), and the performance of the proposed algorithm is better than the comparison algorithms. In addition, interval estimation enriches decision-making information. The experimental results show that the proposed model is a competitive and promising method for estimating the TC intensity.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Real-Time Ionospheric Imaging of S₄ Scintillation From Limited Data With
           Parallel Kalman Filters and Smoothness

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      Authors: Alexandra Koulouri;
      Pages: 1 - 12
      Abstract: In this article, we propose a Bayesian framework to create 2-D ionospheric images of high spatio-temporal resolution to monitor ionospheric irregularities as measured by the $S_{4}$ index. Here, we recast the standard Bayesian recursive filtering for a linear Gaussian state-space model, also referred to as the Kalman filter, first by augmenting the (pierce point) observation model with connectivity information stemming from the insight and assumptions/standard modeling about the spatial distribution of the scintillation activity on the ionospheric shell at 350-km altitude. Thus, we achieve to handle the limited spatio-temporal observations. Then, by introducing a set of Kalman filters running in parallel, we mitigate the uncertainty related to a tuning parameter of the proposed augmented model. The output images are a weighted average of the state estimates of the individual filters. We demonstrate our approach by rendering 2-D real-time ionospheric images of $S_{4}$ amplitude scintillation at 350 km over South America with temporal resolution of 1 min. Furthermore, we employ extra $S_{4}$ data that was not used in producing these ionospheric images, to check and verify the ability of our images to predict this extra data in particular ionospheric pierce points. Our results show that in areas with a network of ground receivers with relatively good coverage (e.g., within a couple of kilometers distance) the produced images can provide reliable real-time results. Our proposed algorithmic framework can be readily used to visualize real-time ionospheric images taking as inputs the available scintillation data provided from freely available web-servers.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Considering Various Multimoment Bulk Microphysics Schemes for Simulation
           of Passive Microwave Radiative Signatures

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      Authors: Jiseob Kim;Dong-Bin Shin;Donghyeck Kim;
      Pages: 1 - 15
      Abstract: Passive microwave radiative transfer models are strongly influenced by the cloud and precipitation hydrometeor properties. Particularly, they can sensitively interact with frozen hydrometeors through multiple high-frequency channels. However, frozen hydrometeors are one of the most difficult parameters to comprehend due to the lack of in situ data. Until recently, studies have attempted to describe more reasonable hydrometeor distributions using various microphysics parameterizations coupled with the weather research and forecasting (WRF) models. Herein, we aim to apply the proposed methods to passive microwave radiative transfer simulations. We implemented a passive microwave radiative transfer simulation that considers various microphysical assumptions by creating a new Mie scattering lookup table. Furthermore, we evaluated the bulk microphysics parameterizations [WDM6, Morrison (MORR), Thompson (THOM), and P3 schemes] for the tropical cyclone Krosa (2019) that were observed by the global precipitation measurement microwave imager instrument, specifically concentrating on the rimed and aggregated ice categories (snow, graupel, and P3 ice). Based on the evaluation results, we concluded the following: WDM6 graupel and MORR snow afford excessive scattering signals at 37 GHz. However, at 166 GHz, none of the parameterizations produces sufficient scattering signals for comparison with the observations. The P3 ice affords significantly underestimated scattering signals at 89 GHz and above, despite its sophisticated assumptions. On the contrary, THOM snow affords scattering signals similar to the observations, despite a shape-related error. In summary, this study introduced a method for implementing a microphysical-consistent radiative transfer computation and successfully showed how various microphysical assumptions of clouds can change the passive microwave radiative signatures.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Decomposing Satellite-Based Classification Uncertainties in Large Earth
           Science Datasets

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      Authors: Pedro Ortiz;Marko Orescanin;Veljko Petković;Scott W. Powell;Benjamin Marsh;
      Pages: 1 - 11
      Abstract: Collection of increasingly voluminous multispectral data from multiple instruments with high spatial resolution has posed both an opportunity and a challenge for maximizing their utilization, analysis, and impact. Obtaining accurate estimates of precipitation globally with high temporal resolution is crucial for assessing multiscale hydrologic impacts and providing a constraint for development of numerical models of the atmosphere that provide weather and climate predictions. Precipitation type classification plays an important role in constraining both the inverse problem in satellite precipitation retrievals and latent heat transfer within weather prediction simulations. Precipitation type, however, is often reported deterministically, without uncertainty attached to an estimate. Machine learning techniques are capable of extracting content of interest from large datasets and accurately retrieving discrete and continuous properties of physical systems, but with limited insights to the retrieval components—such as errors and the physical relationship between the observed and retrieved properties. To address this shortcoming, we perform precipitation type classification to introduce a novel tool for decomposing errors of satellite-retrieved products. We use Bayesian neural networks to map global precipitation measurement mission microwave imager observations to dual-frequency precipitation radar-derived precipitation type, which perform comparably to deterministic models, but with the added benefit of providing well-calibrated uncertainties. Through uncertainty decomposition, we demonstrate well-calibrated uncertainties as useful for making decisions concerning high uncertainty predictions, model selection, targeted data analysis, and data collection and processing. Additionally, our Bayesian models enable mathematical confirmation of a data distribution change as the cause for an unacceptable decline in model accuracy.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Observation Capability of a Ground-Based Terahertz Radiometer for Vertical
           Profiles of Oxygen and Water Abundances in Martian Atmosphere

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      Authors: Takayoshi Yamada;Philippe Baron;Lori Neary;Toshiyuki Nishibori;Richard Larsson;Takeshi Kuroda;Frank Daerden;Yasuko Kasai;
      Pages: 1 - 11
      Abstract: We present the expected performance for a ground-based terahertz (THz) radiometer, a plan to be launched on the TERahertz EXplore-1 (TEREX-1) Mars exploration microspacecraft. The small THz passive radiometer has been developed for the TEREX series of future microspacecrafts. This spacecraft is an opportunity for organizations with limited resources and technology to conduct frequent missions to Mars well suited for resource exploration in contrast to all of the current and past Mars missions of large/giant class missions with fully government lead. The observation frequencies of the TEREX-1 radiometer are 474.64–475.64 and 486.64–487.64 GHz with a 100-kHz resolution, and the double-sideband noise temperature less than 3000 K. A theoretical error analysis is performed with the instrument characteristics to assess for the first time up-looking observations of atmospheric oxygen molecules (O2) and water vapor (H2O). Measurement errors for O2 and H2O are 7%–22% and 14%–25% with 8–17- and 5–10-km vertical resolution in the vertical ranges 0–55 and 0–25 km, respectively. TEREX-1 is also capable to measure minor species, O3 and H2O2, with a precision better than 30% within two independent layers. We used the integration time of 1 h for all simulations. Our theoretical simulation showed the instrument characteristics of the TEREX-1 sensor are able to observe vertical profiles of O2 and H2O abundances with the same level of the large class missions.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Low Computational Cost Lightning Mapping Algorithm With a Nonuniform
           L-Shaped Array: Principle and Verification

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      Authors: Huaifei Chen;Weijiang Chen;Yu Wang;Kai Bian;Nianwen Xiang;Kejie Li;Zhu Zhang;
      Pages: 1 - 10
      Abstract: Recently, 2-D multiple signal classification (MUSIC) algorithm was applied to map lighting propagation with improved quality. However, the quality is improved at the cost of increasing the calculation load. To improve the efficiency, this article proposes an improved MUSIC-based lightning mapping algorithm, which transforms the 2-D direction of arrival (DOA) estimation problem for an L-shaped array into 1-D DOA estimation problem for the two arms of the array. Additionally, the lightning mapping array is arranged in a nonuniform way to reduce the risk of trivial ambiguity problem. The performance of the developed lightning mapping system is verified with numerical Monte Carlo simulation and further with real lightning observation results. The simulation results show that the nonuniform array outperforms the uniform array in terms of detection accuracy, spatial resolution, and antiambiguity. The observation results show that the MUSIC-based methods produce better lightning mapping quality compared with the interferometry method (15 777 sources mapped), due to better robustness for low signal-to-noise ratio (SNR) lightning very high-frequency (VHF) signals. Compared with the 2-D MUSIC method (34 620 sources mapped), the proposed low computational cost method (30 461 sources mapped) is proven to produce fairly similar path of lightning leader with significant lower computational cost. Specifically, the proposed method saves about 84.91% of calculation time compared with the 2-D MUSIC method in practice.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Evaluating Precipitable Water Vapor Products From Fengyun-4A
           Meteorological Satellite Using Radiosonde, GNSS, and ERA5 Data

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      Authors: Jingshu Tan;Biyan Chen;Wei Wang;Wenkun Yu;Wujiao Dai;
      Pages: 1 - 12
      Abstract: Precipitable water vapor (PWV) products from the second generation of China’s geostationary meteorological satellite Fengyun-4A (FY-4A) have the advantage of high spatiotemporal resolution and can play an increasingly important role in the study of atmosphere and climate. Using the radiosonde, the global navigation satellite system (GNSS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis 5 (ERA5) reanalysis data, this study presented a comprehensive evaluation of PWV products from FY-4A for a one-year period from January 2019 to January 2020. Results indicated that FY-4A PWV data have a good agreement with radiosonde and GNSS measured ones with the same correlation coefficient of 0.976, and the root mean square errors (RMSEs) are 3.95 and 3.73 mm, respectively. Compared with the radiosonde and GNSS, the FY-4A Advanced Geostationary Radiation Imager (AGRI) was found to underestimate the water vapor during humid conditions when the PWV is greater than 50 mm. The magnitude of underestimation increases with the growth in water vapor content. In terms of spatial variability, the RMSE of FY-4A PWV decreases with the increase in latitude, while the relative RMSE (R-RMSE) displays an opposite pattern. RMSE from the comparison between FY-4A and ERA5 PWV varies from 0 to 6 mm depending upon the location. Statistics showed that 55.08%, 59.79%, and 83.13% RMSE values are less than 4 mm in the evaluation by radiosonde, GNSS, and ERA5, respectively. The seasonal and diurnal variations of RMSE showed that: 1) summer exhibited larger RMSE than winter and 2) daytime obtained slightly worse performance than nighttime.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • LiDAR-Based Remote Sensing of the Vertical Profile of Aerosol Liquid Water
           Content Using a Machine-Learning Model

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      Authors: Tong Wu;Zhanqing Li;Xiaoai Jin;Wei Wang;Hao Wu;Rongmin Ren;Dongmei Zhang;Lu Chen;Yunfei Su;Maureen Cribb;
      Pages: 1 - 10
      Abstract: The aerosol liquid water content (ALWC) dictates the hygroscopicity of aerosol particles. To date, measurements of ALWC have been confined primarily to ground-based observations although vertical profiles of ALWC are crucial for understanding its interactions with meteorology. This study proposes a novel method for deriving profiles of ALWC using data acquired by a Light Detection and Ranging (LiDAR), a microwave radiometer, and a suite of aerosol instruments measuring aerosol physical and chemical properties, deployed during a five-month field experiment in Guangzhou, China. The retrieval approach is based on a machine-learning model named the gradient-boosted decision tree model. The inversion accuracy and stability are assessed through comparisons with ALWC data acquired on the ground and at the top of the Guangzhou tower of 532 m above ground. The agreements are encouraging: with the coefficient of determination $({R} ^{2}) = 0.870$ and root-mean-square error (RMSE) = $3.28 ~mu text{g} ~cdot $ m−3 for all data; ${R} ^{2} = 0.776$ and RMSE = $2.18 ~mu text{g}~ cdot $ m−3 for tower data; and ${R} ^{2} = 0.872$ and RMSE = $4.1~ mu text{g} ~cdot $ m−3 for ground data. From the vertical distribution of the retrieved ALWC in Guangzhou, ALWC is higher in the lower boundary layer, especially when air pollution is severe. The proportion of liquid water in aerosol -articles is closely related to the relative humidity in the environment, which will affect the morphology of aerosol particles (with about every 10% increase in liquid water, the depolarization ratio decreases by 0.02). The model may be of general use for studying air pollution and secondary aerosol generation.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A New Algorithm for Himawari-8 Aerosol Optical Depth Retrieval by
           Integrating Regional PM₂.₅ Concentrations

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      Authors: Weiwei Xu;Wei Wang;Nan Wang;Biyan Chen;
      Pages: 1 - 11
      Abstract: The advanced Himawari imager (AHI) onboard Himawari-8 can provide full-disk observations with high temporal resolution (10 min), which has outstanding advantages for dynamic real-time aerosol monitoring in East Asia. In this study, a new aerosol retrieval algorithm for AHI by integrating regional PM2.5 concentrations (IRPAR) was proposed. The IRPAR algorithm constructed the surface reflectance library by integrating regional PM2.5 levels as a quantitative indicator of atmospheric aerosol loadings. The IRPAR algorithm was used to obtain the aerosol optical depth (AOD) retrievals over Beijing–Tianjin–Hebei (BTH) region from March 2019 to February 2020, and its performance was preliminarily evaluated by aerosol robotic network (AERONET) measurements. The results showed that the IRPAR algorithm was able to obtain more highly accurate AOD retrievals compared to the JAXA L2 algorithm during the autumn in BTH region, with a large $R$ of 0.87 (0.71 for JAXA L2 AOD) and a global climate observing system fraction (GCOSF) percentage of 28% (21% for JAXA L2 AOD). During different daytime hours, the IRPAR AOD showed a stable retrieval performance, while the JAXA L2 AOD exhibited a worst performance from 12:00 to 14:00 Beijing standard time (BST). These results demonstrated that the IRPAR algorithm was relatively less affected by the viewing angle. Future work will require a comprehensive evaluation of the IRPAR algorithm on a larger spatial scale.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Nonlinear Beamforming Based on Group-Sparsities of Periodograms for Phased
           Array Weather Radar

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      Authors: Daichi Kitahara;Hiroki Kuroda;Akira Hirabayashi;Eiichi Yoshikawa;Hiroshi Kikuchi;Tomoo Ushio;
      Pages: 1 - 19
      Abstract: We propose nonlinear beamforming for phased array weather radars (PAWRs). Conventional beamforming is linear in the sense that a backscattered signal arriving from each elevation is reconstructed by a weighted sum of received signals, which can be seen as a linear transform for the received signals. For distributed targets such as raindrops, however, the number of scatterers is significantly large, differently from the case of point targets that are standard targets in array signal processing. Thus, the spatial resolution of the conventional linear beamforming is limited. To improve the spatial resolution, we exploit two characteristics of a periodogram of each backscattered signal from the distributed targets. The periodogram is a series of the powers of the discrete Fourier transform (DFT) coefficients of each backscattered signal and utilized as a nonparametric estimate of the power spectral density. Since each power spectral density is proportional to the Doppler frequency distribution, 1) major components of the periodogram are concentrated in the vicinity of the mean Doppler frequency and 2) frequency indices of the major components are similar between adjacent elevations. These are expressed as group-sparsities of the DFT coefficient matrix of the backscattered signals, and we propose to reconstruct the signals through convex optimization exploiting the group-sparsities. We consider two optimization problems. One problem roughly evaluates the group-sparsities and is relatively easy to solve. The other evaluates the group-sparsities more accurately but requires more time to solve. Both problems are solved with the alternating direction method of multipliers (ADMM) including nonlinear mappings. Simulations using synthetic and real-world PAWR data show that the proposed method dramatically impr-ves the spatial resolution.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Method of Inversing Dynamic Aerosol Extinction-to-Backscattering Ratio
           Based on Lidar Echo Signal and Ground Aerosol Extinction Coefficient or
           Aerosol Optical Depth

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      Authors: Chuwei Zhang;Jian Wu;Wenxuan Fan;Jingchuan Zhao;Qidong Yang;Jinlin Zha;Deming Zhao;Peishi Sun;
      Pages: 1 - 15
      Abstract: When using the Fernald method to solve the lidar equation, the selection of aerosol extinction-to-backscattering ratio (lidar ratio) is an important error source, which becomes a major factor limiting the accuracy of aerosol extinction profile. Therefore, we proposed a method for dynamic lidar ration inversion, named iteration inverse generation method (IIGM). This method is based on the Fernald method, takes the ground extinction coefficient as the loop iteration constraints, and is combined with the lidar signal to inverse the local dynamic lidar ratio. The local lidar ratio obtained by IIGM is in the range of 10–50 sr. The average relative error between the ground extinction coefficient inversed from dynamic lidar ratio and the measured ground extinction coefficient is 0.0041%. The relative error between the inverse aerosol optical depth (AOD) and the measured AOD is 20.38%. In addition to using the ground extinction coefficient as the iteration constraints of IIGM, AOD is introduced as another optional iterative constraint. The IIGM can achieve accurate inversion of lidar signals in different situations.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Hybrid Method for Fine-Scale Wind Field Retrieval Based on Machine
           Learning and Data Assimilation

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      Authors: Hang Gao;Jie Zhou;Pak-Wai Chan;Kai-Kwong Hon;Jianbing Li;
      Pages: 1 - 12
      Abstract: To better describe the main features of the complex airflow in the atmospheric boundary layer, a hybrid wind field retrieval method based on machine learning (ML) and data assimilation (DA) is proposed. Based on the joint measurement of lidar and in situ measurements, a 3-D variational data assimilation (3DVAR) method is used to retrieve the fine-scale wind field. To address the iterative interpolation problem in the traditional DA methods, this article isolates the interpolation from the optimization and uses the regression methods in ML to estimate the interpolated observations on analysis grids. More specifically, the supervised regression and semisupervised regression are, respectively, used for lidar and in situ observations according to their heterogeneity. Simulation and field measurement results indicate that, compared with the traditional DA methods, the proposed method can better estimate both 2-D and 3-D velocities, by an improvement of more than 42.3% on average.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Cloud Detection and Classification Algorithms for Himawari-8 Imager
           Measurements Based on Deep Learning

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      Authors: Wenwen Li;Feng Zhang;Han Lin;Xiaoran Chen;Jun Li;Wei Han;
      Pages: 1 - 17
      Abstract: A deep-learning-based cloud detection and classification algorithm for advanced Himawari imager (AHI) measurements from the geostationary satellite Himawari-8 has been developed. It is found that a combination of observed radiances and simulated clear-sky radiances can substantially improve cloud phase discrimination, especially for optically thin clouds. Therefore, cloud detection, cloud phase classification, and multilayer cloud detection are obtained simultaneously from multispectral observed radiances and simulated clear-sky radiances using deep neural networks (DNNs). Two DNN models are established for all-day and daytime-only applications, respectively, using active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) merged cloud products from 2016 as reference labels. The independent dataset from 2017 is used to validate the DNN models. It is shown that both the DNN models outperform the official Moderate Resolution Imaging Spectroradiometer (MODIS) and AHI products in cloud detection and phase discrimination, and the enhancement is more significant over land than over water surface. For multilayer cloud detection, the probability of detecting multilayer clouds reaches ~60% for the all-day model and is increased to ~70% for the daytime model, which is substantially better than MODIS and AHI products. In practical cases, multilayer cloud detection by DNN models is more consistent with CPR/CALIOP than two official products. In addition, the DNN models have superior capability in detecting the optically thin cirrus, which is omitted by MODIS and AHI products. Specifically, the cases also demonstrate that the DNN models can provide effective mixed-phase cloud identification. This deep-learning-based algorithm has the potential for measurements from other similar instruments.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • FY4QPE-MSA: An All-Day Near-Real-Time Quantitative Precipitation
           Estimation Framework Based on Multispectral Analysis From AGRI Onboard
           Chinese FY-4 Series Satellites

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      Authors: Ziqiang Ma;Siyu Zhu;Jun Yang;
      Pages: 1 - 15
      Abstract: Accurate and near-real-time rain information at fine scales is critical for forecasting local weather and floods. Traditional classical infrared (IR) cloud-top brightness temperature data alone do not contain sufficient precipitation-related information, and the introduction of visible (VIS) observations limits their applications to daytime. Methods for the efficient and comprehensive utilization of multichannel IR observations for accurately retrieving all-day near-real-time rain rates with consistent high quality warrant further exploration. In this study, we propose an all-day near-real-time quantitative precipitation estimation framework based on multispectral analysis (MSA) from the Advanced Geosynchronous Radiation Imager (AGRI) onboard Chinese FY-4 series satellites; the proposed framework is called FY4QPE- MSA. Multiple IR bands are comprehensively and efficiently considered by adopting the principal component analysis technique to reduce the dimensionality to a few independent features while preserving most of the variations. The main conclusions include, but are not limited to, the following aspects: 1) the MSA from IR channels provides valuable information that facilitates the more accurate delineations of the precipitation occurrences; 2) FY4AQPE -MSA outperforms FY4AQPE- Offical [ $sim 20$ % gain in the Pearson correlation coefficient (CC), $sim 25$ % gain in the root mean square error (RMSE), and $sim 15$ % gain in the critical success index (CSI)], FY4AQPE-Single ( $sim 10$ % gain in CC, $sim 15$
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Applicability of Machine Learning Model to Simulate Atmospheric CO₂
           Variability

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      Authors: Imran A. Girach;M. Ponmalar;S. Murugan;P. Abdul Rahman;S. Suresh Babu;Radhika Ramachandran;
      Pages: 1 - 6
      Abstract: Carbon dioxide (CO2) is the most important greenhouse gas influencing the Earth’s climate; therefore, accurate modeling of its variability has paramount significance. In this regard, we have performed the simulation of CO2 residue (i.e., detrended depersonalized CO2) based on input of meteorological parameters (temperature, humidity, pressure, and wind), El Niño index, sea surface temperature, and normalized difference vegetation index in a machine learning (ML) model. Long-term observations available from the World Data Centre for Greenhouse Gases (WDCGG) and the National Oceanic and Atmospheric Administration (NOAA) have been used for training and validation of ML model. The model successfully reproduced 72% of observed variability in CO2 residue with an error of 0.45 ppmv over Mauna Loa (19.54°N; −155.58°E). The cumulative temperature anomaly is found to play a key role in the simulation of CO2 residue over Mauna Loa. Evaluation reveals a reasonably good agreement between modeled and observed CO2 residue ( $R^{2} = 0.20$ –0.55 and root-mean-square error (RMSE) = 20%–60%) over regional sites and for global mean CO2. However, the model shows a limitation in capturing spikes likely caused by strong local influences. Inclusion of additional input parameters, representing local anthropogenic influences, is recommended to further improve the model performance over regional sites. Our study demonstrates the potential of ML modeling for the simulations of CO2 variability to complement the computationally expensive climate models.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Motivating a Synergistic Mixing-Layer Height Retrieval Method Using
           Backscatter Lidar Returns and Microwave-Radiometer Temperature
           Observations

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      Authors: Marcos P. Araújo da Silva;Francesc Rocadenbosch;Robin L. Tanamachi;Umar Saeed;
      Pages: 1 - 18
      Abstract: Mixing-layer-height (MLH) retrieval methods using backscattered lidar signals from a ceilometer (Jenoptik CHM -15k Nimbus) and temperature profiles from a microwave radiometer (MWR) and Humidity And Temperature PROfiler (HATPRO) radiometer physics GmbH (RPG) are compared in terms of their complementary capabilities and associated uncertainties. The extended Kalman filter (EKF) is used for MLH retrieval from backscattered lidar signals, and the parcel method is used for MLH retrieval from MWR-derived potential-temperature profiles. The two principal sources of uncertainty in ceilometer-based MLH estimates are: 1) incorrect layer attribution ( $sim $ hundreds of meters) and 2) noise-induced errors (about 50 m at $3sigma $ ). MWR MLH uncertainties comprise: 1) the total uncertainty in the retrieved potential temperature profile and 2) ±0.5 K uncertainty in the surface temperature. Ceilometer- and MWR-based MLH estimates are, in turn, compared with reference to MLH estimates from radiosoundings. Twenty-one measurement days from the high definition clouds and precipitation for advancing climate prediction (HD(CP) 2) Observational Prototype Experiment (HOPE) campaign at Jülich, Germany, are considered. It is shown that the MWR can track the full mixed layer (ML) diurnal cycle (i.e., including morning and evening transitions) with height-increasing error bars. The ceilometer-EKF MLH estimates are much smaller errorbars than those from the MWR under the well-developed clear-sky ML, but the ceilometer-EKF is prone to ambiguous tracking some multilayer scenarios (e.g., the residual layer). We, therefore, introduce the synergistic MLH retrieval approach that combines both ceilometer and MWR estimates in order to optimize the benefits of both.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Aerosol Retrieval Algorithm for Sentinel-2 Images Over Complex Urban Areas

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      Authors: Yue Yang;Kangzhuo Yang;Yunping Chen;
      Pages: 1 - 9
      Abstract: High-resolution aerosol retrieval is of great significance for understanding the impact of aerosols on air pollution and climate change. In this study, an algorithm for aerosol retrieval at a spatial resolution of 60 m over complex urban areas is developed using Sentinel-2 images. The proposed algorithm has two assumptions: 1) the blue–red surface reflectance ratio does not change temporally in a single season and 2) surface reflectance over bright areas is invariant over three months. Then, the aerosol optical depth (AOD) is retrieved from the surface reflectance correlations with a combination of temporal signatures over the vegetated areas and bright areas. The aerosol robotic network (AERONET) measurements in Beijing and its surrounding from 2017 to 2019 are collected and used to validate the retrieved 60-m Sentinel-2 AODs; 77% of the retrieved Sentinel-2 AODs fall within the expected error (EE), and the correlation coefficient is of 0.927. MODerate resolution Imaging Spectroradiometer (MODIS) AOD products at 1- and 10-km resolutions (MCD19A2 and MOD04_L2, respectively) are acquired to compare with the retrieved Sentinel-2 AODs. Comparison results show that the retrieved Sentinel-2 AODs are superior to the MOD04_L2 dark target (DT) and MCD19A2 AODs, and slightly better than the MOD04_L2 deep blue (DB), and DT and DB combined (DTBC) AODs. The validation and comparison results indicate that the proposed algorithm is able to describe aerosol distributions at high resolution continuously. However, further work is needed to apply the proposed algorithm on a global scale.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Intercomparison of Total Precipitable Water Derived From COSMIC-2 and
           Three Different Microwave Radiometers Over the Ocean

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      Authors: Shuaimin Wang;Tianhe Xu;Yujing Xu;Chunhua Jiang;Fan Gao;Zhen Zhang;Yuguo Yang;Zhenlong Fang;Huijie Xue;
      Pages: 1 - 10
      Abstract: Total precipitable water (TPW) values derived from Constellation Observing System for Meteorology, Ionosphere and Climate-2 (COSMIC-2) are compared with those derived from Special Sensor Microwave Imager Sounder (SSMIS), Global Precipitation Measurement (GPM) Microwave Imager (GMI), and Advanced Microwave Scanning Radiometer-2 (AMSR-2) over the ocean from October 1, 2019 to February 16, 2020. The overall comparison results indicate that TPW values derived from SSMIS, AMSR-2, and GMI have a good correlation and agreement with COSMIC-2 TPW values with the correlation coefficients greater than 0.99 and root mean square (rms) no greater than 2.7 mm. We compare TPW derived from three different microwave radiometers with COSMIC-2 TPW over the subtropical and tropical oceans. The differences illustrate that TPW values derived from three different microwave radiometers are more consistent with COSMIC-2 TPW values over the subtropical ocean than those over the tropical ocean. In addition, we also analyze the relationship between the TPW retrieval accuracy derived from three different microwave radiometers and environmental factors, including cloud, rain rate, wind speed, and surface temperature. The results indicate that four environmental factors have an important influence on the TPW retrieval from three different microwave radiometers.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Assessing Synergistic Radar and Radiometer Retrievals of Ice Cloud
           Microphysics for the Atmosphere Observing System (AOS) Architecture

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      Authors: Yuli Liu;Gerald G. Mace;Derek J. Posselt;
      Pages: 1 - 14
      Abstract: After exploring numerous observing system designs, the NASA aerosols, clouds, convection, and precipitation (ACCP) study team arrived at the top candidate architecture referred to as the atmosphere observing system (AOS) that is composed of a suite of spaceborne instruments in two orbital inclinations to characterize the complexity of hydrometeors and aerosols in the Earth’s atmosphere. This study proposes hybrid Bayesian retrieval algorithms that combine the Monte Carlo integration (MCI) and cost function optimization approaches to quantitatively evaluate the AOS architecture for skill in constraining the ice cloud microphysical properties. The remote sensor candidates under evaluation include multiple-frequency radars with W-, Ka-, and Ku-band channels and a submillimeter-wave radiometer. Two optimization techniques, the optimal estimation method (OEM) and Markov chain Monte Carlo (MCMC), are developed to maximize the posterior distribution function to retrieve ice cloud microphysical quantities with uncertainty estimates. Observing system simulation experiments are conducted using simulated synergistic radar and radiometer observations to determine the pixel-level retrieval accuracies by comparing the retrieved parameters to the true values. Results demonstrate that the low-frequency Ka-/Ku-band radar observations are complementary to the W-band channel since they provide more constraints on the condensed cloud scenes that are composed of large particles. The brightness temperature measurements exhibit sensitivities to the ice cloud layers with large water content (WC), and the synergistic active and passive observations improve the ice water path retrieval accuracies significantly. The scores measuring how well the AOS architecture satisfies the desired retrieval uncertainties for different ice cloud geophysical variables are also derived.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Assessing Overlapping Cloud Top Heights: An Extrapolation Method and Its
           Performance

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      Authors: Zhonghui Tan;Shuo Ma;Chao Liu;Shiwen Teng;Na Xu;Xiuqing Hu;Peng Zhang;Wei Yan;
      Pages: 1 - 11
      Abstract: Under the assumption that clouds are homogeneous and single-layered (SL), most current operational cloud top height (CTH) products derived from passive radiometers may largely underestimate the CTH of overlapping clouds. This article proposes a statistics-based extrapolation algorithm for retrieving the CTHs of overlapping clouds using only existing cloud property products available for most operational radiometers, and the method is successfully employed for the advanced himawari imager (AHI) observations. Because regional clouds within the same “system” have relatively continuous geometric properties, especially CTH, due to similar atmospheric conditions, upper-layer ice cloud CTHs (ITHs) and lower-layer water cloud CTHs (WTHs) are inferred using the CTH retrievals of well-chosen neighboring SL ice and water clouds, respectively. The proposed algorithm uses the latest machine-learning-based model to reasonably distinguish overlapping clouds from SL clouds, and optimizes the extrapolation by considering three physical constraints on neighboring, cloud phase, and cloud optical thickness (COT). Validated using active observations from CloudSat and cloud-aerosol Lidar and infrared pathfinder satellite observation (CALIPSO), our algorithm improves the AHI CTH mean bias for overlapping clouds from −5.1 to −2.6 km. More importantly, the algorithm provides CTH information of underlying water clouds that are unavailable from existing radiometer-based products. With the simultaneous retrieval of ITH and WTH, this algorithm increases our capability to detect the vertical structures of overlapping clouds and better evaluate the cloud radiative effects (CREs).
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • High-Order Taylor Expansion for Wind Field Retrieval Based on Ground-Based
           Scanning Lidar

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      Authors: Hang Gao;Jie Zhou;Chun Shen;Xuesong Wang;Pak-Wai Chan;Kai-Kwong Hon;Jianbing Li;
      Pages: 1 - 14
      Abstract: The uniform and linear wind models have been commonly used for wind field retrieval in meteorological community. However, the accuracy and robustness of the retrieval results can be quite unsatisfactory due to the mismatch between these models and the real wind distribution, especially under complex wind conditions. In this article, a nonlinear model based on high-order Taylor expansion is proposed to deal with this limitation, and the combination of ridge regression and decomposition-iteration process (denoted as Ridge-DI method) is further introduced to solve the model with high accuracy and robustness. A case study on simulation and field experiment shows that the proposed method with the third-order Taylor expansion can reduce the mean root-mean-square errors (RMSEs) of the retrieved velocities by more than 16.84% in comparison with traditional methods.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Cloud, Atmospheric Radiation and Renewal Energy Application (CARE) Version
           1.0 Cloud Top Property Product From Himawari-8/AHI: Algorithm Development
           and Preliminary Validation

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      Authors: Xu Ri;Gegen Tana;Chong Shi;Takashi Y. Nakajima;Jiancheng Shi;Jun Zhao;Jian Xu;Husi Letu;
      Pages: 1 - 11
      Abstract: Investigations of the effects of clouds on Earth’s radiation budget demand accurate representations of cloud top parameters, which can be efficiently obtained by large-scale satellite remote sensing approaches. However, the insufficient utilization of multiband information is one of the major sources of uncertainty in cloud top products derived from geostationary satellites. In this study, we developed a new algorithm to estimate Cloud, Atmospheric Radiation and renewal Energy application (CARE) version 1.0 cloud top properties [cloud top height (CTH), cloud top pressure (CTP), and cloud top temperature (CTT)]. The algorithm is constructed from ten thermal spectral measurements in Himawari-8 observations by using the random forest (RF) method to comprehensively consider the contribution of each band to the cloud top parameters. We chose the highly accurate Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products in 2018 as the true values. The sensitivity analysis demonstrated that the products can be fully reproduced by using multiple Himawari-8 channels with the addition of the digital elevation model (DEM) data. The validation results of the 2019 CALIOP data confirm that the new algorithm shows an effective performance, with correlation coefficients ( $R$ ) of 0.89, 0.89, and 0.90 for CTH, CTP, and CTT, respectively. Moreover, a significant improvement in the ice cloud estimation is achieved, in which the CTT $R$ value increased from 0.46 to 0.70, as well as an improvement in the sea area, where the CTT $R$ value increased from 0.71 to 0.84 compared with the Himawari-8 products of the Japan Aerospace Exploration Agency (JAXA) P-tree system. The further analyses performed here capture the diurnal cycle of cloud top parame-ers well in different temporal scales over the Asia–Pacific region.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • A Method for Estimating the Background Column Concentration of CO2 Using
           the Lagrangian Approach

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      Authors: Zhipeng Pei;Ge Han;Xin Ma;Tianqi Shi;Wei Gong;
      Pages: 1 - 12
      Abstract: With the rapid growth of greenhouse gas (GHG) monitoring satellites, more and more studies focused on the issue of inversion/optimization of carbon dioxide (CO2) fluxes using satellite-derived XCO2 observations in recent years. A common and critical challenge in this framework is the separation of background and anomalies from XCO2 observations, which directly affect the performance of the CO2 fluxes’ inversion. We proposed a novel method to accurately extract background XCO2 from satellite observations. A series of observing system simulation experiments (OSSEs) were performed to test the performance of the method. We found that the bias and uncertainty of the background concentration are below 0.01 and 0.05 ppm in the given cases, respectively. Based on this method, we selected five overpasses from 2014 to 2016 to demonstrate a regional-scale flux inversion near Riyadh. The comparison with the two previous methods shows that the posterior simulated XCO2 by the method proposed in this article can match better with the observed XCO2 from Orbiting Carbon Observatory-2 (OCO-2).
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • In-Orbit Test of the Polarized Scanning Atmospheric Corrector (PSAC)
           Onboard Chinese Environmental Protection and Disaster Monitoring Satellite
           Constellation HJ-2 A/B

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      Authors: Zhengqiang Li;Yanqing Xie;Weizhen Hou;Zhenhai Liu;Zhaoguang Bai;Jin Hong;Yan Ma;Honglian Huang;Xuefeng Lei;Xiaobing Sun;Xiao Liu;Benyong Yang;Yanli Qiao;Jun Zhu;Qiang Cong;Yang Zheng;Maoxin Song;Peng Zou;Zhongzheng Hu;Jun Lin;Lanlan Fan;
      Pages: 1 - 17
      Abstract: As the successors of the overdue HuanjingJianzai-1 (HJ-1) satellites and new members in Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HuanjingJianZai-2 series satellites (HJ-2 A/B) have been launched on September 27, 2020. Each satellite carries four sensors, including the polarized scanning atmospheric corrector (PSAC), the charge-coupled device (CCD) camera, the hyperspectral imager (HSI), and the infrared spectroradiometer (IRS). Among them, PSAC is mainly used for the monitoring of atmospheric parameters to provide data support for atmospheric environmental monitoring and atmospheric correction of data from other sensors. To test the in-orbit performance of PSAC, we develop the “day-1” aerosol and water vapor retrieval algorithms. The preliminary validation results based on ground-based observations show that the aerosol optical depth (AOD) and columnar water vapor (CWV) datasets developed based on PSAC data have high accuracy and can effectively characterize the temporal trends of AOD and CWV. The accuracy of PSAC AOD dataset is better than the expected error (EE) ±(0.05 + 0.2 * AODAERONET), and the accuracy of PSAC CWV dataset is better than the EE ±(0.05 + 0.15 * CWVAERONET). To eliminate the negative impact of the atmosphere on CCD data and expand its application range, aerosol and water vapor data developed based on PSAC are used for atmospheric correction of CCD data. Compared with L1 CCD data, the texture details and clarity of CCD data after atmospheric correction have been significantly improved.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Spatiotemporal Investigation of Near-Surface CO2 and Its
           Affecting Factors Over Asia

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      Authors: Farhan Mustafa;Lingbing Bu;Qin Wang;Muhammad Shahzaman;Muhammad Bilal;Rana Waqar Aslam;Changzhe Dong;
      Pages: 1 - 16
      Abstract: In this work, we extracted the near-surface CO2 concentration from the Greenhouse gases Observing SATellite (GOSAT) and the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker model datasets for a temporal period of eight years from 2010 to 2017 to study the spatiotemporal distribution of near-surface CO2 and the factors affecting it over five regions of Asia, including Central Asia, East Asia, South Asia, Southeast Asia, and West Asia. The near-surface CO2 datasets from both satellite and model were first validated against the ground-based CO2 observations obtained from the World Data Center for Greenhouse Gases (WDCGG) stations located in Asia to confirm their applicability, and the results showed a good agreement between the datasets with significant correlations. The results from the time-series analyses showed a gradual increase in the near-surface CO2 with significant monthly and seasonal variations over all the regions. To study the factors affecting the spatial distribution of near-surface CO2, we investigated the relationship of near-surface CO2 with the anthropogenic CO2 emissions, terrestrial ecosystem, and winds. The results showed that, over Asia, the anthropogenic CO2 emissions and winds primarily controlled the spatial distribution of near-surface CO2. However, in the areas where anthropogenic emissions were lower, the terrestrial ecosystem and winds affected the near-surface CO2 distribution. To study the factors controlling the temporal distribution of near-surface CO2, the relationship of near-surface CO2 with vegetation, precipitation, and relative humidity was investigated. The results showed an inverse relationship between near-surface CO2 and normalized difference vegetation index (NDVI), precipitation, and relative humidity over monsoon-influenced regions, i.e., East Asia, S-uth Asia, and Southeast Asia. However, a positive relation of near-surface CO2 was observed with precipitation and relative humidity over arid and semiarid regions, i.e., Central Asia and West Asia. The results were also verified by determining the correlations among these variables.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Toward an Improved Global Longwave Downward Radiation Product by Fusing
           Satellite and Reanalysis Data

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      Authors: Shiyao Wang;Tianxing Wang;Wanchun Leng;Gaofeng Wang;Husi Letu;
      Pages: 1 - 16
      Abstract: Surface longwave downward radiation (LWDR) plays an important role in modulating greenhouse effect and climate change. Constructing a global longtime series LWDR dataset is greatly necessary to systematically and in-depth study the LWDR effect on the climate. However, the current multisource LWDR products (satellite and reanalysis) show large differences in terms of both spatiotemporal resolutions and accuracy in various regions. Therefore, it is necessary to fuse multisource datasets to generate more accurate LWDR with high spatiotemporal resolution on a global scale. To this end, a downscaling strategy is first proposed to generate LWDR dataset with 0.25° resolution from CERES-SYN data with 1° scale, by incorporating the land surface temperature (LST), total column water vapor (TCWV), and elevation. Then, a machine learning-based fusion method is provided to generate a global hourly LWDR dataset with a spatial resolution of 0.25° by combining three products (CERES-SYN, ERA5, and GLDAS). Compared with ground measurements, the performance of generated LWDR product reveals that the correlation coefficient ( $R$ ), mean bias error (BIAS), and root-mean-square error (RMSE) were 0.97, −0.95 W/m2, and 22.38 W/m2 over the land and 0.99, −0.88 W/m2, and 10.96 W/m2 over the ocean, respectively. In particular, it shows improved accuracy in the low and middle latitude regions compared with other LWDR products. Considering its better accuracy and higher spatiotemporal resolution, the new LWDR product can provide essential data for deeply understanding the global energy balance and even the global warming. Moreover, the proposed fusion strategy can be enlightening for readers in the fields of multisource data combination and big data analysis.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • MSTCGAN: Multiscale Time Conditional Generative Adversarial Network for
           Long-Term Satellite Image Sequence Prediction

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      Authors: Kuai Dai;Xutao Li;Yunming Ye;Shanshan Feng;Danyu Qin;Rui Ye;
      Pages: 1 - 16
      Abstract: Satellite image sequence prediction is a crucial and challenging task. Previous studies leverage optical flow methods or existing deep learning methods on spatial–temporal sequence models for the task. However, they suffer from either oversimplified model assumptions or blurry predictions and sequential error accumulation issue, for a long-term forecast requirement. In this article, we propose a novel multiscale time conditional generative adversarial network (MSTCGAN). To address the sequential error accumulation issue, MSTCGAN adopts a parallel prediction framework to produce the future image sequences by a one-hot time condition input. In addition, a powerful multiscale generator is designed with the multihead axial attention, which helps to carefully preserve the fine-grained details for appearance consistency. Moreover, we develop a temporal discriminator to address the blurry issue and maintain the motion consistency in prediction. Extensive experiments have been conducted on the FengYun-4A satellite dataset, and the results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art approaches.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Estimating Single-Epoch Integrated Atmospheric Refractivity From InSAR for
           Assimilation in Numerical Weather Models

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      Authors: Gert Mulder;Freek J. van Leijen;Jan Barkmeijer;Siebren de Haan;Ramon F. Hanssen;
      Pages: 1 - 12
      Abstract: Numerical weather prediction (NWP) models are used to predict the weather based on current observations in combination with physical and mathematical models. Yet, they are limited by the spatial density and the accuracy of the available observations. Satellite radar interferometry (InSAR) is known to be extremely sensitive to the 3-D atmospheric refractivity distribution and has a high spatial resolution, providing information that can be used for assimilation in NWP models. However, due to the inherent superposition of two or more atmospheric states, only biased and temporally differenced signals can be retrieved, which can also be contaminated by deformation signals and decorrelation. Here, we present a method to estimate single-epoch absolute atmospheric delays by combining InSAR time series with prior NWP model prediction time series, using a constrained least-squares estimation. We show that this leads to a solution that reliably extracts the single-epoch relative delays from InSAR data and uses prior NWP model data to find the absolute reference for these delays while mitigating long-term deformation and decorrelation signal. This approach leads to repetitive delay updates with a spatial resolution of 500 m, which can be directly assimilated into numerical weather models.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Learning Surface Ozone From Satellite Columns (LESO): A Regional Daily
           Estimation Framework for Surface Ozone Monitoring in China

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      Authors: Songyan Zhu;Jian Xu;Chao Yu;Yapeng Wang;Qiaolin Zeng;Hongmei Wang;Jiancheng Shi;
      Pages: 1 - 11
      Abstract: Continuously monitoring surface ozone (O3) spatial distribution and forecasting its variations are beneficial to improving air quality and ensuring public health in China, although achieving this goal faces challenges from currently available observations and retrieval techniques. Hence, we introduce a coupled surface O3 estimation framework (LESO) to address these challenges by integrating ground-level observing networks and satellite remote sensing. LESO features easy-to-use deep learning algorithms, independence on chemical transportation models (CTMs), and consistent performance using data from different satellites. LESO includes a deep forest 21 (DF21) model to interpolate O3 concentration by learning spatial patterns and a long short-term memory (LSTM) model to forecast O3 concentration by learning data from the past. We used sites of city-level in situ networks as the control sites to manifest short-distance O3 transportation. Satellite-based observations of O3 precursor indicators were incorporated to capture O3 photochemical reactions. DF21 explained a larger fraction of O3 variability (90%) with a mean bias error (MBE) of smaller than $1~mu text {g/m}^{3}$ . We also investigated the impact of the number of training sites on the DF21 performance, which suggested that five training sites could ensure a good DF21 performance for the most areas ( $R^{2}> 0.85$ and bias $< 2~mu text {g/m}^{3}$ ). The forecast O3 concentration via LSTM showed a good and stable agreement ( $R^{2}approx 0.85$ and bias $< 5~mu text {g/m}^{3}$ ) with ground-based measurements for 8-, 24-, 28-, and 72-h time periods, respectively. Overall, LESO aims to bring convenient functionality and reliable surface O3 estimates for broad users.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • An Enhanced Geographically and Temporally Weighted Neural Network for
           Remote Sensing Estimation of Surface Ozone

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      Authors: Tongwen Li;Jingan Wu;Jiajia Chen;Huanfeng Shen;
      Pages: 1 - 13
      Abstract: Surface ozone (O3) pollution is a severe environmental problem that endangers human health. It is necessary to obtain high spatiotemporal resolution O3 data to provide support for pollution monitoring and prevention. For this purpose, this study makes comprehensive use of remote sensing data, reanalysis data, and ground station observations and develops an enhanced geographically and temporally weighted neural network (EGTWNN) model to acquire high spatial and temporal resolutions of O3 data. The EGTWNN model is nested by two neural networks (NNs). The first NN automatically learns the spatiotemporal proximity relationship to obtain spatiotemporal weights for the samples, and the spatiotemporal weights are then inputted into the second NN to conduct weighted modeling of the relationship between O3 and influencing variables. The contribution of the proposed model is that the first NN replaces the traditional empirical weighting method and represents the spatiotemporal proximity relationship more accurately to improve estimation accuracy. Results indicate that the cross-validation $R^{2}$ and the root mean square error (RMSE) of EGTWNN are 0.81 and $21.24 mu text{g}$ /m3, respectively, which are increased by 0.02 and decreased by $sim 1 mu text{g}$ /m3 relative to those of the traditional empirical weighting method-based geographically and temporally weighted NN model. The results also show that, compared with the geographically and temporally weighted regression model, the proposed model achieves superior performance. In addition, the spatiotemporal weights obtained by the first NN of EGTWNN are highly consistent with those obtained by the traditional empirical weighting method, -ndicating that the results of NNs are highly interpretable.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Analysis of the 3-D Evolution Characteristics of Ionospheric Anomalies
           During a Geomagnetic Storm Through Fusion of GNSS and COSMIC-2 Data

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      Authors: Yang Wang;Yibin Yao;Jian Kong;Changzhi Zhai;Xuanxi Chen;Lulu Shan;
      Pages: 1 - 19
      Abstract: To solve the ill-posed and accuracy problems experienced by global navigation satellite system (GNSS) computerized ionosphere tomography (CIT), this study proposes the use of the ionospheric profile data of COSMIC-2 as the initial scale factor to constrain GNSS data. At present, studies are lacking on long-term data volume statistics and accuracy assessment of COSMIC-2 ionospheric profile products. Therefore, we calculated the data volume statistics and assessed the ionospheric quality of the COSMIC-2 data for the whole year of 2020. We used incoherent scattering radar (ISR) and ionosonde data to evaluate the quality of the COSMIC-2 ionospheric profile data. To verify the accuracy and reliability of the CIT algorithm for COSMIC-2 ionosphere profile-constrained GNSS data, the American region was selected. On the plane, the tomographic results were superimposed and compared with the global ionospheric map (GIM). The root mean square (rms) of the vertical total electron content (VTEC) difference in the six periods was 0.68, 0.97, 0.63, 0.86, 0.76, and 0.82 total electron content unit (TECU), respectively. In the vertical direction, the scale factor that was not involved in the CIT was compared with the ratio of total electron contents (TECs) in each layer to the total TEC. The average difference of the ratio factors in the four periods was 3.72%, 2.79%, 1.80%, 3.05%, 1.99%, and 2.37%, respectively. Finally, an intermediate-level geomagnetic storm that occurred on July 25, 2020, was selected for analysis, and the 3-D ionospheric morphological changes and evolution characteristics of the Australian region during this geomagnetic storm were studied.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • Aerosol Optical Depth Retrieval Based on Neural Network Model Using
           Polarized Scanning Atmospheric Corrector (PSAC) Data

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      Authors: Zheng Shi;Zhengqiang Li;Weizhen Hou;Linlu Mei;Lin Sun;Chen Jia;Ying Zhang;Kaitao Li;Hua Xu;Zhenhai Liu;Bangyu Ge;Jin Hong;Yanli Qiao;
      Pages: 1 - 18
      Abstract: As the successors of the Huanjing Jianzai-1 (HJ-1) series satellites in the Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two Huanjing Jianzai-2 (HJ-2) A/B satellites have been successfully launched on September 27, 2020. The polarized scanning atmospheric corrector (PSAC) sensors, onboard the HJ-2 A/B satellites, are served as the synchronously atmospheric correction instrument requiring a high-speed and accurate aerosol optical depth (AOD) algorithm. For this purpose, we proposed a neural network-based AOD retrieval model (named the AODNet) that takes full advantage of the multispectral measurements of PSAC for AOD retrieval at a high speed. The training of AODNet is conducted by the simulated observation data (currently applicable for the China region) from the forward calculation using the radiative transfer model. In this way, the land surface reflectance (LSR) is no need for our well-trained model. It is expected to be one of the effective ways to solve the ill-pose problem in the decoupling of the atmosphere and surface information in AOD retrieval. Either the Sun-sky radiometer Observation NETwork (SONET) AOD or the AErosol RObotic NETwork (AERONET) AOD was used to validate the AODNet AOD. The correlation coefficient is higher than 0.85, and more than 60% of the AODNet AOD can fall into the expected error envelope of ±(0.05+20%). The cross-comparison shows that the AODNet has better accuracy than MODIS dark target (DT) and deep blue (DB) algorithm. The air pollution episode is well characterized by the AODNet AOD using PSAC data.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • PrecipLSTM: A Meteorological Spatiotemporal LSTM for Precipitation
           Nowcasting

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      Authors: Zhifeng Ma;Hao Zhang;Jie Liu;
      Pages: 1 - 8
      Abstract: Accurate and timely nowcasting precipitation has huge social and economic benefits. However, the changes in clouds including expansion, dissipation, and distortion are extremely complex, which exacerbates the difficulty of forecasting. Fortunately, it still follows certain meteorological laws, which can be explored based on spatiotemporal information but are not fully considered by previous models. In this article, we design two modules to focus on these messages based on atmospheric characteristics. Specifically, the spatial local attention memory (SLAM) module combines local attention and memory mechanism to capture the meteorological spatial relationship, while the time difference memory (TDM) module combines differential technology and memory mechanism to capture the meteorological temporal variants. We combine these two modules with PredRNN and propose PrecipLSTM to sufficiently capture the spatiotemporal dependencies of radar data. We do exhaustive experiments with five baselines on four radar datasets. It is verified that PrecipLSTM achieves state-of-the-art results with fewer parameters than the previous state-of-the-art method.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • NLED: Nonlocal Echo Dynamics Network for Radar Echo Extrapolation

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      Authors: Taisong Chen;Qian Li;Xuan Peng;Qi Lv;Jinrui Jing;
      Pages: 1 - 13
      Abstract: Radar echo extrapolation is a common approach to weather nowcasting, which has become a significant support to detect potential disastrous weather a few hours ahead. The dynamics pattern inside an echo intensity sequence is beneficial for echo prediction. However, existing extrapolation methods have limited ability to consider entire time-series echo context in an entire echo sequence from a given historical timestamp to a given future timestamp, thus leading to low long-term extrapolation accuracy. To solve this issue, we introduce spatiotemporal self-attention and propose a deep learning model named the nonlocal echo dynamics (NLED) network to capture the dependencies of the entire time domain. The NLED network has an encoder–decoder architecture for extrapolation. The encoder decomposes historical echoes into features of multiple spatial scales, which makes it better at learning echo dynamics from the global scale to the local scale. The decoder employs nonlocal blocks with sparse self-attention related to echo dynamics to learn correlations in the entire echo event, which is beneficial for predicting long-term echo distributions. Our model is evaluated on radar reflectivity datasets from Shanghai and Hong Kong. The experimental results indicate that the NLED model achieves a more accurate long-term forecast and alleviates the forgetting of stronger echo dynamics, thus validating the effectiveness of entire time-series modeling by the NLED network.
      PubDate: 2022
      Issue No: Vol. 60 (2022)
       
  • REMNet: Recurrent Evolution Memory-Aware Network for Accurate Long-Term
           Weather Radar Echo Extrapolation

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      Authors: Jinrui Jing;Qian Li;Leiming Ma;Lei Chen;Lei Ding;
      Pages: 1 - 13
      Abstract: Weather radar echo extrapolation, which predicts future echoes based on historical observations, is one of the complicated spatial–temporal sequence prediction tasks and plays a prominent role in severe convection