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  Subjects -> ELECTRONICS (Total: 155 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 5)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 7)
Advances in Microelectronic Engineering     Open Access   (Followers: 9)
Advances in Power Electronics     Open Access   (Followers: 18)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 178)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 19)
Annals of Telecommunications     Hybrid Journal   (Followers: 7)
Archives of Electrical Engineering     Open Access   (Followers: 11)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 25)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 30)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 9)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 39)
China Communications     Full-text available via subscription   (Followers: 7)
Circuits and Systems     Open Access   (Followers: 13)
Consumer Electronics Times     Open Access   (Followers: 6)
Control Systems     Hybrid Journal   (Followers: 86)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 68)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 1)
Electronics     Open Access   (Followers: 51)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 8)
Electronics For You     Partially Free   (Followers: 55)
Electronics Letters     Hybrid Journal   (Followers: 23)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 38)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 2)
Energy Storage Materials     Full-text available via subscription   (Followers: 1)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 12)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 7)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 5)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 119)
Giroskopiya i Navigatsiya     Open Access  
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 3)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 48)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 38)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 26)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 7)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 43)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 37)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 45)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 13)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 29)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 11)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 19)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 47)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 5)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 13)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 13)
IET Power Electronics     Hybrid Journal   (Followers: 23)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 15)
IETE Journal of Education     Open Access   (Followers: 3)
IETE Journal of Research     Open Access   (Followers: 8)
IETE Technical Review     Open Access   (Followers: 8)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 27)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 6)
Informatik-Spektrum     Hybrid Journal   (Followers: 1)
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 8)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 15)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 5)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 17)
International Journal of Antennas and Propagation     Open Access   (Followers: 9)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 1)
International Journal of Control     Hybrid Journal   (Followers: 14)
International Journal of Electronics     Hybrid Journal   (Followers: 2)
International Journal of Electronics & Data Communication     Open Access   (Followers: 8)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 10)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal  
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 8)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 6)
International Journal of Nanoscience     Hybrid Journal   (Followers: 2)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 3)
International Journal of Power Electronics     Hybrid Journal   (Followers: 12)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 7)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 5)
International Journal on Communication     Full-text available via subscription   (Followers: 12)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 7)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 6)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 2)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription  
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 14)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 6)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 6)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 5)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 3)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription  
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 111)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 6)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 3)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 9)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 2)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 30)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 7)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
Journal of Sensors     Open Access   (Followers: 18)
Journal of Signal and Information Processing     Open Access   (Followers: 8)
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 14)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 6)
Metrology and Measurement Systems     Open Access   (Followers: 4)
Microelectronics and Solid State Electronics     Open Access   (Followers: 13)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 32)
Nanotechnology, Science and Applications     Open Access   (Followers: 3)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
Open Journal of Antennas and Propagation     Open Access   (Followers: 4)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 12)
Paladyn, Journal of Behavioral Robotics     Open Access  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Patents on Electrical & Electronic Engineering     Full-text available via subscription   (Followers: 5)
Recent Patents on Telecommunications     Full-text available via subscription   (Followers: 2)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 3)
Security and Communication Networks     Hybrid Journal   (Followers: 3)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 46)
Semiconductors and Semimetals     Full-text available via subscription  
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 1)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 53)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 9)
Solid-State Electronics     Hybrid Journal   (Followers: 6)
Superconductor Science and Technology     Hybrid Journal   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 1)
Technical Report Electronics and Computer Engineering     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 5)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 5)
Visión Electrónica : algo más que un estado sólido     Open Access  
Wireless and Mobile Technologies     Open Access   (Followers: 5)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access  

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Journal Cover Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.196]   [H-I: 37]   [46 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by IEEE Homepage  [191 journals]
  • Information for Authors
    • PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Institutional Listings
    • PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Frontcover
    • PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • IEEE Geoscience and Remote Sensing Societys
    • PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the
           2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest
    • Authors: Manuel Campos-Taberner;Adriana Romero-Soriano;Carlo Gatta;Gustau Camps-Valls;Adrien Lagrange;Bertrand Le Saux;Anne Beaupère;Alexandre Boulch;Adrien Chan-Hon-Tong;Stephane Herbin;Hicham Randrianarivo;Marin Ferecatu;Michal Shimoni;Gabriele Moser;Devis Tuia;
      Pages: 5547 - 5559
      Abstract: In this paper, we discuss the scientific outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high-resolution RGB images and a three-dimensional (3-D) LiDAR point cloud. The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this paper, we discuss the scientific results obtained by the winners of the 2-D contest, which studied either the complementarity of RGB and LiDAR with deep neural networks (winning team) or provided a comprehensive benchmarking evaluation of new classification strategies for extremely high-resolution multimodal data (runner-up team). The data and the previously undisclosed ground truth will remain available for the community and can be obtained at http://www.grss-ieee.org/community/technical-committees/data-fusion/2015-ieee-grss-data-fusion-contest/. The 3-D part of the contest is discussed in the Part-B paper [1].
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the
           2015 IEEE GRSS Data Fusion Contest—Part B: 3-D Contest
    • Authors: A.-V. Vo;L. Truong-Hong;D. F. Laefer;D. Tiede;S. d’Oleire-Oltmanns;A. Baraldi;M. Shimoni;G. Moser;D. Tuia;
      Pages: 5560 - 5575
      Abstract: In this paper, we report the outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society. As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high resolution RGB images (with a ground sample distance of 5 cm) and a 3-D light detection and ranging point cloud (with a point cloud density of approximatively 65 pts/m$^2$). The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this Part B, we report the results obtained by the winners of the 3-D contest, which explored challenging tasks of road extraction and ISO containers identification, respectively. The 2-D part of the contest and a detailed presentation of the dataset are discussed in Part A.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Optimization of Selected Remote Sensing Algorithms for Many-Core
           Architectures
    • Authors: Lubomir Riha;Jacqueline Le Moigne;Tarek El-Ghazawi;
      Pages: 5576 - 5587
      Abstract: This paper evaluates the potential of embedded graphic processing units (GPU) in the Nvidia's Tegra K1 for onboard processing. The performance is compared to a general purpose multicore central processing unit (CPU), a full-fledge GPU accelerator, and an Intel Xeon Phi coprocessor, for two representative potential applications, wavelet spectral dimension reduction of hyperspectral imagery and automated cloud-cover assessment (ACCA). For these applications, Tegra K1 achieved 51% performance for the ACCA algorithm and 20% performance for the dimension reduction algorithm, as compared to the performance of the high-end eight-core server Intel Xeon CPU which has a 13.5 times higher power consumption. This paper also shows the potential of modern high-performance computing accelerators for algorithms such as the ones for which the paper presents an optimized parallel implementation. The two algorithms that were tested mostly contain spatially localized computations, and one can assume that all image processing algorithms containing localized computations would exhibit similar speed-ups when implemented on these parallel architectures.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • ROSCC: An Efficient Remote Sensing Observation-Sharing Method Based on
           Cloud Computing for Soil Moisture Mapping in Precision Agriculture
    • Authors: Lianjie Zhou;Nengcheng Chen;Zeqiang Chen;Chenjie Xing;
      Pages: 5588 - 5598
      Abstract: The inversion of remote sensing images is crucial for soil moisture mapping in precision agriculture. However, the large size of remote sensing images complicates their management. Therefore, this study proposes a remote sensing observation sharing method based on cloud computing (ROSCC) to enhance remote sensing observation storage, processing, and service capability. The ROSCC framework consists of a cloud computing-enabled sensor observation service, web processing service tier, and a distributed database tier. Using MongoDB as the distributed database and Apache Hadoop as the cloud computing service, this study achieves a high-throughput method for remote sensing observation storage and distribution. The map, reduced algorithms and the table structure design in distributed databases are then explained. Along the Yangtze River, the longest river in China, Hubei Province was selected as the study area to test the proposed framework. Using GF-1 as a data source, an experiment was performed to enhance earth observation data (EOD) storage and achieve large-scale soil moisture mapping. The proposed ROSCC can be applied to enhance EOD sharing in cloud computing context, so as to achieve soil moisture mapping via the modified perpendicular drought index in an efficient way to better serve precision agriculture.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • An Inversion-Based Fusion Method for Inland Water Remote Monitoring
    • Authors: Yulong Guo;Yunmei Li;Li Zhu;Qiao Wang;Heng Lv;Changchun Huang;Yuan Li;
      Pages: 5599 - 5611
      Abstract: Although remote sensing technology has been widely used to monitor inland water bodies, the lack of suitable data with high spatial and spectral resolutions has seriously obstructed the practical development of inland water color remote sensing. An inversion-based fusion (IBF) algorithm is proposed to fuse water color and high-spatial resolution images. The algorithm was applied to two datasets: The Hyperion simulated dataset (dataset #1) and a pair of Environmental Satellite 1 (HJ1, launched by China in 2009) and medium resolution imaging spectrometer images (dataset #2). The fusions are quantitatively and qualitatively compared with three widely used algorithms. The results show that the IBF algorithm performs better using both evaluation indexes and visual comparisons. A discussion of the free parameter window size n and the contribution of the low-resolution image (LRI) to the spatial distribution ($w_{{{rm LRI}}}$) shows that a larger n will result in both greater model errors and better control of geometric errors, whereas $w_{{{rm LRI}}}$ helps stabilize the algorithm. Finally, chlorophyll-a concentration maps are developed from the fusions. The significant advantage of the IBF-derived chlorophyll-a concentration map indicates that the IBF algorithm has the potential to advance the monitoring of optical complex inland water.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Mangrove Response to Environmental Change in Kakadu National Park
    • Authors: Emma Asbridge;Richard M. Lucas;
      Pages: 5612 - 5620
      Abstract: The responses of mangroves to climate-related phenomena are difficult to decipher in many areas as human activities also influence changes in their extent and biophysical properties. In Kakadu National Park (KNP) in Australia's Northern Territory, human disturbance of mangroves is minimal and hence observed changes are more likely to indicate a natural environmental change, including that associated with climatic fluctuation. Using fine ( ${rm{
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Estimation of Vegetation Parameters of Water Cloud Model for Global Soil
           Moisture Retrieval Using Time-Series L-Band Aquarius Observations
    • Authors: Chenzhou Liu;Jiancheng Shi;
      Pages: 5621 - 5633
      Abstract: Using Aquarius middle beam scatterometer observations, the vegetation parameters of the water cloud model at large scale are estimated and applied to global soil moisture retrieval. Vegetation backscattering is derived using two models: Oh model is used to describe the scattering from bare soil surface, while the water cloud model is implemented to account for the effect of vegetation canopy. The vegetation parameters are estimated by minimizing the deviations between the Aquarius scatterometer observations and backscatter coefficients simulated by the water cloud model. The RMSE is less than 2 dB for both copolarizations and correlation is strong ( $rm{CC> 0.6}$) in most areas. The vegetation parameters were used to retrieve global soil moisture from Aquarius radar data. The comparisons with the Aquarius soil moisture product derived from the Aquarius radiometer observations show low ubRMSE ( $rm{0.06 cm}^{3}rm{/ cm}^{3}$) and strong correlation ( $rm{CC> 0.6}$) in most parts of the world. The impact of errors in input parameters of the water cloud model on the vegetation parameter estimation was assessed by using a Monte–Carlo simulation. The algorithm converges to the true values of the parameters when the input data is noise-free or only the radar measurement error is introduced. It was found that the errors in vegetation parameter are sensitive to the errors in input soil moisture. The errors in two vegetation parameters counteract each other to decrease the error of backscattering simulation. This study demonstrates that the water cloud model could be applied to global scatterometer observations to retrieve soil moisture if the vegetation parameters are appropriately set.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Assimilation of H-SAF Soil Moisture Products for Flash Flood Early Warning
           Systems. Case Study: Mediterranean Catchments
    • Authors: Luca Cenci;Paola Laiolo;Simone Gabellani;Lorenzo Campo;Francesco Silvestro;Fabio Delogu;Giorgio Boni;Roberto Rudari;
      Pages: 5634 - 5646
      Abstract: A reliable estimation of soil moisture conditions is fundamental for rivers’ discharge predictions, especially in small catchments where flash floods occur. In this context, microwave remote sensing can be exploited to estimate soil moisture at large scale. These estimates can be used to enhance the predictions of hydrological models using data assimilation techniques. Flash flood early warning systems can, thus, be improved. This study tested the effect of the assimilation of three different ASCAT-derived soil moisture products, processed and distributed within the EUMETSAT H-SAF framework (SM-OBS-1, SM-OBS-2, SM-DAS-2), into a distributed physically based hydrological model (Continuum). The study areas were three Italian catchments, representative of the typical Mediterranean small basins prone to flash floods. The products were first preprocessed in order to be comparable with the model soil moisture state estimate. Subsequently, they were assimilated using three Nudging-based techniques. Then, observed discharges were compared with the modeled one in order to understand the impact of the assimilation. The analysis was executed for a multiyear period ranging from July 2012 to June 2014 in order to test the assimilation algorithms for operational purposes in real-cases scenarios. Findings showed that the assimilation of H-SAF soil moisture products with simple preprocessing and assimilation techniques can enhance discharge predictions; the improvements significantly affect high flows. Although SM-OBS-2 and SM-DAS-1 are added-value products with respect to SM-OBS-1 (respectively, higher spatial and temporal resolution), they may not necessarily perform better. The impact of the assimilation strongly relies on the permanent catchment characteristics (e.g., topography, hydrography, land cover).
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Coherence Optimization and Its Limitations for Deformation Monitoring in
           Dynamic Agricultural Environments
    • Authors: Jeanine Engelbrecht;Michael R. Inggs;
      Pages: 5647 - 5654
      Abstract: Differential interferometry techniques are well known for its ability to provide centimeter to millimeter scale deformation measurements. However, in natural and agricultural areas, the presence of vegetation and the evolution of the land surface introduce a phase noise component which limits successful interferometric measurement. This paper aims to address the known limitations of traditional dInSAR in the presence of disturbances to reflected signals due to agricultural activities by testing the polInSAR technique for its ability to increase interferometric coherence and to detect surface movement in the areas of interest. Both fully polarimetric RADARSAT-2 and ALOS PALSAR data were subject to coherence optimization using the multiple scattering mechanism (MSM) approach. For C-band RADARSAT-2 data, coherence optimization resulted in a statistically significant increase in interferometric coherence. However, the spatial heterogeneity of the scattering process and how it changes over time caused random phase changes associated with temporal baseline effects and the evolution of the land surface. These effects could not be removed from C-band interferograms using the MSM approach. Therefore, coherence optimization resulted in an increase in the random speckle in interferograms reducing the ability to derive high confidence interferometric measurements, indicating a drawback in the MSM approach to coherence optimization. On the other hand, coherence optimization on L-band data demonstrated an increase in the spatial homogeneity of the speckle noise suggesting that the MSM approach to coherence optimization on L-band data may be more successful in enhancing the ability to extract deformation measurements in dynamic agricultural regions. In general, a good agreement in deformation measurements derived from dInSAR and polInSAR techniques was observed.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Spatial Differentiation of Arable Land and Permanent Grassland to Improve
           a Land Management Model for Nutrient Balancing
    • Authors: Marta Gómez Giménez;Raniero Della Peruta;Rogier de Jong;Armin Keller;Michael E. Schaepman;
      Pages: 5655 - 5665
      Abstract: Agroecosystems play an important role in providing economic and ecosystem services, which directly impact society. Inappropriate land use and unsustainable agricultural management with associated nutrient cycles can jeopardize important soil functions such as food production, livestock feeding, and conservation of biodiversity. The objective of this study was to integrate remotely sensed land cover information into a regional land management model (LMM) to improve the assessment of spatially explicit nutrient balances for agroecosystems. Remotely sensed data and an optimized parameter set contributed to an improved LMM output, allowing for a better land allocation within the model. The best input parameter combination was based on two different land cover classifications with overall accuracies of 98%, improving the land allocation performance compared with using nonspatially explicit input. We conclude that the combined use of remote sensing data and the LMM has the potential to provide valuable guidance for farm practices. It further helps to generate a spatial description of farm-level nutrient balance, a crucial ability when choosing policy options related to sustainable management of agricultural soils.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Soil Parameter Retrievals Over Bare Agricultural Fields Using Multiangular
           RADARSAT-2 Dataset
    • Authors: Hongquan Wang;Sophie Allain-Bailhache;Stéphane Méric;Eric Pottier;
      Pages: 5666 - 5676
      Abstract: The objective of this study is to evaluate the potential of multiangular SAR data at C-band for soil parameters’ discrimination over bare agricultural fields. In order to exploit the incidence angle diversity to enhance the soil parameter retrievals, the conventional multiangular roughness descriptor $Delta_{HH}$ formulated originally as the backscattering difference between two specific incidence angles is adapted to take into account a general incidence angle effect. Moreover, a new angular coherence SAR descriptor $gamma_{HH}$ is proposed by using a pair of low and high incidence angle SAR data. The adapted $Delta_{HH}$ and the proposed $gamma_{HH}$ are applied to the RADARSAT-2 data with three different incidence angles. The results indicate that the proposed $gamma_{HH}$ is more sensitive to surface roughness than the adapted $Delta_{HH}$. Thus, the $gamma_{HH}$ is selected to retrieve the surface roughness, and then the retrieved surface roughness is substituted into a low incidence angle data to retrieve the soil moisture. The RMSE of 6.1–8.5 ${text{m}}^3/{text{m}}^3$ is obtained for soil moisture retrieval.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • A Range Grating Lobes Suppression Method for Stepped-Frequency SAR Imagery
    • Authors: Zegang Ding;Yansu Guo;Wenbin Gao;Qi Kang;Tao Zeng;Teng Long;
      Pages: 5677 - 5687
      Abstract: Stepped-frequency synthetic aperture radar (SAR) system errors induce the periodic magnitude error and phase error (MEPE) in the synthesized wideband spectrum. The periodic MEPE results in periodic grating lobes in the high range resolution profile, which degrade the SAR image quality seriously. This paper presents a data-driven grating lobes suppression (GLS) method, named MEPE-GLS, to suppress grating lobes by estimating and compensating the periodic MEPE. Based on the dominant targets’ image domain data, MEPE-GLS estimates the phase error and magnitude error by the weighted maximum-likelihood estimation and weighted least-squares estimation, respectively. In order to improve the signal-to-clutter-and-noise ratio (SCNR) of the data employed in the MEPE estimation, MEPE-GLS applies the discrete windows to selected targets to remove clutters between two adjacent grating lobes. Compared with conventional GLS methods, MEPE-GLS removes grating lobes effectively while at the same time preserves the original image information, and has no restriction on hardware, scenes, and spectrum reconstruction methods. Simulation and real data experiments validate the effectiveness and robustness of MEPE-GLS.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • An ISAR Imaging Algorithm for Maneuvering Targets With Low SNR Based on
           Parameter Estimation of Multicomponent Quadratic FM Signals and Nonuniform
           FFT
    • Authors: Dong Li;Xiaogang Gui;Hongqing Liu;Jia Su;Han Xiong;
      Pages: 5688 - 5702
      Abstract: The inverse synthetic aperture radar (ISAR) imaging for maneuvering targets has always been a challenging task due to the time-varying Doppler parameter, especially in the low signal-to-noise ratio (SNR) environment. In this paper, a novel ISAR imaging algorithm for maneuvering targets in the low SNR environment based on the parameter estimation approach is presented. First, the received signals of the ISAR in a range bin are modeled as a multicomponent quadratic frequency-modulated (QFM) signal after the migration compensation. Then, to estimate the parameters of the QFM signal, two cubic phase functions (CPFs), namely coherently integrated generalized CPF (CIGCPF) and coherently integrated CPF (CICPF), are developed. The CIGCPF and CICPF are simple and only require the fast Fourier transform (FFT) and the nonuniform FFT (NUFFT). Due to the usage of the NUFFT, the computational cost is reduced, and the searching procedure is unnecessary for the nonuniformly spaced signal. Thanks to the coherent integrations and NUFFT, the CIGCPF and CICPF, which demonstrate the excellent noise-tolerant performance and reduce the error propagation effect, are efficient and suitable for the multicomponent QFM signals in the low SNR environment. Finally, several simulation examples, analyses of the noise-tolerant performance of the proposed method, and ISAR imaging results of the simulated data demonstrate the effectiveness of the proposed method.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • A New Geometry Enforcing Variational Model for Pan-Sharpening
    • Authors: Pengfei Liu;Liang Xiao;Songze Tang;
      Pages: 5726 - 5739
      Abstract: In this paper, a new variational method for pan-sharpening is proposed to obtain a high-resolution multispectral (MS) image from a low-resolution MS image and a high-resolution panchromatic (PAN) image. In addition to using the data generative fidelity term and wavelet-based spectral information preserving term, we also associate the Hessian structural information of the PAN image with the desired pan-sharpened MS image to enforce geometry correspondence in the fusion process. More specifically, we introduce a new geometry enforcing term called “vectorial Hessian feature consistence” and combine it with the data generative fidelity term and wavelet-based spectral information preserving term to form an unified variational model for pan-sharpening. Then, the optimal solution of the proposed variational pan-sharpening model is effectively obtained using the fast iterative shrinkage thresholding algorithm (FISTA) method. In addition to well preserving spectral information, our algorithm is also able to eliminate some undesired blocky or blurry artifacts by incorporating the curvature information. Experimental results demonstrate that the proposed method outperforms various well-known pan-sharpening methods in terms of both excellent spatial and spectral qualities.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Fusion of Panchromatic and Multispectral Images via Coupled Sparse
           Non-Negative Matrix Factorization
    • Authors: Kai Zhang;Min Wang;Shuyuan Yang;Yinghui Xing;Rong Qu;
      Pages: 5740 - 5747
      Abstract: In this paper, we construct a new coupled sparse non-negative matrix factorization (CSNMF) model for the fusion of panchromatic (PAN) and multispectral (MS) images. Two CSNMFs are developed for a joint sparse representation of MS and PAN images. Moreover, a sequential iterative algorithm is proposed to simultaneously find the solution to CSNMF. Because learned dictionaries can reveal the latent structure of images in spatial and spectral domains, the fused high-resolution MS images can be calculated by multiplying the dictionary of PAN image and the sparse coefficients of MS images. Some experiments are taken on simulated and real QuickBird data, and the results show that CSNMF outperforms its counterparts in both visual quality and numerical guidelines.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • A Spatial Gaussian Mixture Model for Optical Remote Sensing Image
           Clustering
    • Authors: Bei Zhao;Yanfei Zhong;Ailong Ma;Liangpei Zhang;
      Pages: 5748 - 5759
      Abstract: Clustering has always been one of the most challenging tasks in optical remote-sensing image (ORSI) processing, as a result of the intrinsic complexity of the distribution of the ground objects. The Gaussian mixture model (GMM), as a traditional, effective clustering method, has been widely applied. However, the traditional model does not take the spatial information into consideration. To solve the problem, a new model named the spatial Gaussian mixture model (SGMM) is proposed for ORSI clustering. The SGMM can incorporate the spatial information by generating spatial windows around pixels. An estimation algorithm based on expectation–maximization (EM) is also developed to estimate the parameters of the SGMM. The relationships between the SGMM/GMM and the SGMM/probabilistic latent semantic analysis (PLSA) are analyzed theoretically. The proposed SGMM can be considered to be an extension of the GMM and a continuous version of PLSA. In addition, two methods based on the SGMM are proposed to infer the cluster labels of the pixels. One method is based on the maximum likelihood rule, and is called SGMM-MLR, while the other method combines the SGMM and conditional random fields (CRF), and is called SGMM-CRF. The experimental results with three remote-sensing images show that the proposed clustering method based on the SGMM can improve the performance of clustering for ORSIs, compared to $k$-means, fuzzy c-means (FCM), and the GMM. It is also able to acquire a better performance than the latest cluster methods with spatial information, such as kernel weighted fuzzy local information c-means (KWFLICM), and the GMM coupled with CRF.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Attitude Jitter Detection Based on Remotely Sensed Images and Dense Ground
           Controls: A Case Study for Chinese ZY-3 Satellite
    • Authors: Shijie Liu;Xiaohua Tong;Fengxiang Wang;Wenzheng Sun;Chengcheng Guo;Zhen Ye;Yanmin Jin;Huan Xie;Peng Chen;
      Pages: 5760 - 5766
      Abstract: Satellite platform vibration induced by the onboard dynamic components and exterior perturbation deteriorates platform stability and causes attitude jitter, resulting in image distortion and geometric accuracy degradation. This paper presents an attitude jitter detection method based on images and dense ground controls, which requires neither high-performance attitude measurement devices nor specific sensor configuration like parallax observation. Attitude variations will result in image space discrepancies at control points, from which the attitude jitter can be retrieved. The method was validated by a case study with ZY-3 satellite, which is the first civilian high-resolution stereo mapping satellite in China. The experimental results show that an almost constant jitter frequency of about 0.65 Hz was detected, which was consistent with the direct attitude observations. The photogrammetric method is theoretically capable of detecting attitude jitter up to half of the image line scanning rate according to Shannon's sampling theorem, which is far beyond the detectable range by direct attitude observations at a frequency usually not higher than 4 Hz. Currently, the attitude jitter of the roll angle and the pitch angle can be effectively detected and accurately estimated for the nadir camera. The pitch angle jitter is also well estimable for the forward and the backward cameras. With approximation, the roll angle jitter for the two off-nadir cameras can also be estimated, though with some deviation caused by the influence of the yaw angle jitter.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Compensation for Distortion of Basic Satellite Images Based on Rational
           Function Model
    • Authors: Wenchao Huang;Guo Zhang;Xinming Tang;Deren Li;
      Pages: 5767 - 5775
      Abstract: The rational function model (RFM) along with basic images has been offered as the basic geometric model for further geometric processing. However, it has errors, especially nonlinear distortion errors, caused by inappropriate in-orbit geometric calibration or unreasonable geometric calibration parameters. To eliminate these errors, various RFM compensation methods have been proposed, but their application is restricted by rigorous conditions such as narrow camera field and small attitude errors. This paper proposes a generic method to eliminate nonlinear distortions of the basic satellite images. This method recovers rigorous sensor model (RSM) from RFM, then uses a traditional RSM geometric calibration method to compensate for the nonlinear distortions, and generates a new RFM from the compensated RSM. Experiments using the Gaofen-1 (GF-1) wide-field view-1 (WFV-1) sensor show that the method can raise the orientation accuracy level from several pixels to 1 pixel with ground control points (GCPs), thereby almost eliminating nonlinear distortion.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • A New Algorithm for Bilinear Spectral Unmixing of Hyperspectral Images
           Using Particle Swarm Optimization
    • Authors: Wenfei Luo;Lianru Gao;Antonio Plaza;Andrea Marinoni;Bin Yang;Liang Zhong;Paolo Gamba;Bing Zhang;
      Pages: 5776 - 5790
      Abstract: Spectral unmixing is an important technique for exploiting hyperspectral data. The presence of nonlinear mixing effects poses an important problem when attempting to provide accurate estimates of the abundance fractions of pure spectral components (endmembers) in a scene. This problem complicates the development of algorithms that can address all types of nonlinear mixtures in the scene. In this paper, we develop a new strategy to simultaneously estimate both the endmember signatures and their corresponding abundances using a biswarm particle swarm optimization (BiPSO) bilinear unmixing technique based on Fan's model. Our main motivation in this paper is to explore the potential of the newly proposed bilinear mixture model based on particle swarm optimization (PSO) for nonlinear spectral unmixing purposes. By taking advantage of the learning mechanism provided by PSO, we embed a multiobjective optimization technique into the algorithm to handle the more complex constraints in simplex volume minimization algorithms for spectral unmixing, thus avoiding limitations due to penalty factors. Our experimental results, conducted using both synthetic and real hyperspectral data, demonstrate that the proposed BiPSO algorithm can outperform other traditional spectral unmixing techniques by accounting for nonlinearities in the mixtures present in the scene.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Adaptive Spatial Regularization Sparse Unmixing Strategy Based on Joint
           MAP for Hyperspectral Remote Sensing Imagery
    • Authors: Ruyi Feng;Yanfei Zhong;Liangpei Zhang;
      Pages: 5791 - 5805
      Abstract: Sparse unmixing, as a recently developed spectral unmixing approach, has been successfully applied based on the assumption that the observed image signatures can be expressed in an efficient linear sparse regression with the potentially very large endmember spectral library. To improve the unmixing accuracy, spatial information has been incorporated in the sparse unmixing formulation by adding an appropriate spatial regularization for the hyperspectral remote sensing imagery. However, for the traditional spatial regularization sparse unmixing (SRSU) algorithms, it is a difficult task to set appropriate user-defined regularization parameters in real applications, and this often has a high computational cost. To overcome the difficulty of the regularization parameter selection, the adaptive spatial regularization sparse unmixing (ASRSU) strategy based on the joint maximum a posteriori (JMAP) estimation technique is proposed in this paper. In ASRSU, the SRSU problem is formulated in the framework of JMAP with an appropriate prior model. ASRSU considers the regularization parameters and the abundances jointly by an alternating iterative process, and the relationships between the regularization parameters and the abundances are obtained from the JMAP model. Based on the ASRSU strategy, two ASRSU algorithms are presented: the adaptive total variation spatial regularization sparse unmixing algorithm and the adaptive nonlocal means filtering sparse unmixing algorithm. The experimental results demonstrate that the two proposed ASRSU algorithms based on JMAP can adaptively obtain optimal or near-optimal regularization parameters for the three simulated datasets and the two real hyperspectral remote sensing images.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Approximate Computing of Remotely Sensed Data: SVM Hyperspectral Image
           Classification as a Case Study
    • Authors: Yuanfeng Wu;Xinghua Yang;Antonio Plaza;Fei Qiao;Lianru Gao;Bing Zhang;Yabo Cui;
      Pages: 5806 - 5818
      Abstract: Onboard processing systems are becoming very important in remote sensing data processing. However, a main problem with specialized hardware architectures used for onboard processing is their high power consumption, which limits their exploitation in earth observation missions. In this paper, a novel strategy for approximate computing is proposed for reducing energy consumption in remotely sensed onboard processing tasks. As a case study, the implementation of support vector machine (SVM) hyperspectral image classification is considered by using the proposed approximate computing framework. Experimental results show that the proposed approximate computing scheme achieves up to 70% power savings in the kernel accumulation computation procedure with negligible degradation of classification accuracy as compared to the traditional ripple carry adder (RCA) precise computation. This is an important achievement to meet the restrictions of onboard processing scenarios.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Estimation of 2-D Clutter Maps in Complex Under-Canopy Environments From
           Airborne Discrete-Return Lidar
    • Authors: Heezin Lee;Michael J. Starek;S. Bruce Blundell;Michael Schwind;Christopher Gard;Harry Puffenberger;
      Pages: 5819 - 5829
      Abstract: Detection of near-ground objects occluded by above-ground vegetation from airborne light detection and ranging (lidar) measurements remains challenging. Our hypothesis is that the probability of obstruction due to objects above ground at any location in the forest environment can be reasonably characterized solely from airborne lidar data. The essence of our approach is to develop a data-driven learning scheme that creates high-resolution two-dimensional (2-D) probability maps for obstruction in the under-canopy environment. These maps contain information about the probabilities of obstruction (clutter map) and lidar undersampling (uncertainty map) in the near-ground space. Airborne and terrestrial lidar data and field survey data collected within the forested mountainous environment of Shenandoah National Park, Virginia, USA are utilized to test and evaluate the proposed approach in this work. A newly developed individual tree detection algorithm is implemented to estimate the undersampled stem contributions to the probability of obstruction. Results show the effectiveness of the tree detection algorithm with an accuracy index (AI) of between 61.5% and 80.7% (tested using field surveys). The estimated clutter maps are compared to the maps created from terrestrial scans (i.e., ground truth) and the results show the root-mean-square error (RMSE) of 0.28, 0.32, and 0.34 at three study sites. The overall framework in deriving near-ground clutter and uncertainty maps from airborne lidar data would be useful information for the prediction of line-of-sight visibility, mobility, and above-ground forest biomass.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • A Lidar-Radar Framework to Assess the Impact of Vertical Forest Structure
           on Interferometric Coherence
    • Authors: Matthew Brolly;Marc Simard;Hao Tang;Ralph O. Dubayah;Justin P. Fisk;
      Pages: 5830 - 5841
      Abstract: In this paper, we present novel modeling approaches to investigate the sensitivity of radar interferometric coherence to variations in the vertical forest canopy profile. We introduce a common framework applicable to model radar microwave extinction and structure from lidar data. To perform this analysis, we make use of interferometric data from the uninhabited aerial vehicle synthetic aperture radar (UAVSAR) L-band radar and full waveform lidar data from laser vegetation imaging sensor (LVIS). The datasets were acquired over the Laurentides Wildlife Reserve Forest, Quebec, Canada. A twofold analysis of the framework to estimate interferometric coherence is undertaken. First, a sensitivity analysis is performed by incorporating lidar waveform Legendre descriptions into two adapted independent polarimetric interferometry models. Second, we examine the effectiveness of using lidar data in this novel way to model radar interferometric coherence. Where appropriate, coherence estimates are obtained using Legendre solutions up to fourth order and at resolutions up to 75 m. The maximum $text{r}^{2}$ values between modeled outputs and observed coherence across hh, vv, and hv polarizations are shown as $0.51 (p < 0.05)$ and $0.76 (p < 0.05)$ at 25 and 75 m pixel resolutions, respectively. The introduction of a common framework to combine lidar and radar enables an estimation of the impact of canopy structure on observed interferometric coherence and provides further insight into the feasibility of assuming uniform microwave extinction rates on different scales through forest canopy. The framework’s potential lies in its use to assess performance of canopy structure estimates fro- future spaceborne radar interferometers in synergy with lidar data.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • On the Contribution of GOCE Satellite-Based GGMs to Improve GNSS/Leveling
           Geoid Heights Determination in Saudi Arabia
    • Authors: Basem Elsaka;Abdulaziz Alothman;Walyeldeen Godah;
      Pages: 5842 - 5850
      Abstract: The global geopotential models (GGMs) derived from the gravity field and steady-state ocean circulation explorer (GOCE) mission provide important information about Earth gravity functionals (e.g., geoid heights, gravity anomalies, and disturbances). Among gravity functionals, we utilize geoid heights which have been determined from several recent GOCE-based GGMs and validate them against 5187 collocated Global Navigation Satellite System (GNSS)/leveling observations over a network of dedicated benchmarks in Saudi Arabia. Our aim is to consider the spectral consistency between GOCE-based GGMs and ground-based data. Accordingly, we incorporate high/very high frequencies of gravity functionals, i.e., the gravity signal beyond the maximum d/o of GOCE-based GGMs, using EGM2008 and a high-resolution digital terrain model based on the Shuttle Radar Topography Mission (SRTM). This investigation indicates that completing the missing high-frequency component of geoid heights in GOCE-based GGMs, using EGM2008 and SRTM data, results in an improvement of about 16% in the reduction in the standard deviation (SD) of the differences. This is provided by DIR_R5 at SH d/o 230, which shows improvement from 37.5 cm, without applying the spectral enhancement method (SEM), compared to 31.4 cm when applying the SEM. Finally, three types of transformation models, namely four-, five-, and seven-parameter transformations, are examined to deal with the data biases and to provide a better fitting of geoid heights obtained from the studied GOCE-based GGMs to those from GNSS/leveling data. These models reveal that the SD of vertical datum over the region of Saudi Arabia is at the level of about 22 cm.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Ionospheric Effects in GNSS-Reflectometry From Space
    • Authors: Adriano Camps;Hyuk Park;Giuseppe Foti;Christine Gommenginger;
      Pages: 5851 - 5861
      Abstract: Global navigation satellite systems-reflectometry (GNSS-R) is an emerging technique that uses navigation opportunistic signals as a multistatic radar. Most GNSS systems operate at L-band, which is affected by the ionosphere. At present, there is only a GNSS-R space-borne scatterometer on board the UK TechDemoSat-1, but in late 2016, NASA will launch the CYGNSS constellation, and in 2019, ESA will carry out the GEROS experiment on board the International Space Station. In GNSS-R, reflected signals are typically processed in open loop using a short coherent integration time (∼1 ms), followed by long incoherent averaging (∼1000 times, ∼1 s) to increase the signal-to-noise ratio. In this study, the global ionospheric scintillation model is first used to evaluate the total electron content and the scintillation index S4. The ionospheric scintillation impact is then evaluated as a degradation of the signal-to-noise ratio, which can be used to assess the altimetry and scatterometry performance degradation in a generic GNSS-R mission. Since ionospheric scintillations are mostly produced by a layer of electron density irregularities at ∼350 km height, underneath most LEO satellites, but closer to them than to the Earth's surface, intensity scintillations occur especially in the GNSS transmitter-to-ground transect, therefore, the impact is very similar in conventional and interferometric GNSS-R. Using UK TechDemoSat-1 data, signal-to-noise ratio fluctuations are computed and geo-located, finding that they occur in the open ocean along ∼±20° from the geomagnetic equator where S4 exhibits a maximum, and in low wind speed regions, where reflected signals contain a non-negligible coherent component.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • On the Correlation Between GNSS-R Reflectivity and L-Band Microwave
           Radiometry
    • Authors: Alberto Alonso-Arroyo;Adriano Camps;Alessandra Monerris;Christoph Rüdiger;Jeffrey P. Walker;Raul Onrubia;Jorge Querol;Hyuk Park;Daniel Pascual;
      Pages: 5862 - 5879
      Abstract: This work compares microwave radiometry and global navigation satellite systems-reflectometry (GNSS-R) observations using data gathered from airborne flights conducted for three different soil moisture conditions. Two different regions are analyzed, a crops region and a grassland region. For the crops region, the correlation with the $I/2$ (first Stokes parameter divided by two) was between 0.74 and 0.8 for large incidence angle reflectivity data (30 $^{circ}$–50$^{circ}$ ), while it was between 0.51 and 0.61 for the grassland region and the same incidence angle conditions. For the crops region, the correlation with the $I/2$ was between 0.64 and 0.69 for lower incidence angle reflectivity data ( $
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • An Optimal Weighting Method of Global Positioning System (GPS) Troposphere
           Tomography
    • Authors: Jiming Guo;Fei Yang;Junbo Shi;Chaoqian Xu;
      Pages: 5880 - 5887
      Abstract: The functional model of Global Positioning System (GPS) troposphere tomography consists of three types of equations including the observation equation, the horizontal constraint equation, and the vertical constraint equation. The prerequisite for ensuring the accuracy of troposphere tomography modeling is to determine the optimal weights for the three types of equations. In order to reasonably determine the weights among these equations, this paper proposes an optimal weighting method. Compared to the conventional equal weighting scheme and constant weighting scheme, the method proposed in this paper can adaptively adjust the weights for various equations and enable the posterior unit weight variances for the three types of equations that achieve statistically equal. Numerical results under various weather conditions showed that the proposed method can significantly improve the accuracy of GPS tomography modeling with the GPS PPP-estimated slant tropospheric delay as reference when compared to the other two conventional methods.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Uncertainty Quantification in the Infrared Surface Emissivity Model (ISEM)
    • Authors: Tanvir Islam;Prashant K. Srivastava;George P. Petropoulos;
      Pages: 5888 - 5892
      Abstract: Accurate modeling of surface emissivity is imperative for accurate radiative transfer simulation and forward modeling of satellite radiance observations. The Radiative Transfer for (A)TOVS (RTTOV) fast radiative transfer model uses the Infrared Surface Emissivity Model (ISEM) for the computation of sea surface emissivity in the infrared ( IR) spectrum. However, the model does not incorporate the effect of surface-emitted surface reflected (SESR) radiation and dependence of wind speed in the emissivity calculation. This paper investigates the uncertainty in the ISEM model caused by ignoring the SESR radiation and wind speed effects in the 3 IR bands, 3.7, 11, and 12 μm. First, we develop a new model called Surface E missivity Model in IR with SESR (SEMIS) that takes the SESR radiation and wind speed effects into account. The uncertainty in the ISEM model is then quantified by comparing the ISEM emissivity against SEMIS derived emissivity. The comparison results suggest that two models are in excellent agreement below $sim 60^circ $ emission angle, implying no notable uncertainty in the ISEM model at smaller angles. Nevertheless, uncertainty tends to significantly increase with increasing emission angle above $sim 60^circ $, which is even more notable at high wind speed ( ${rm{sim 15, m/s}}$). Two models are further compared against emissivity measurements from a radiometer. The ISEM model has produced large errors as opposed to the SEMIS.
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Special Issue on Advances in Agro-geoinformatics Research and Application
    • Pages: 5893 - 5893
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Proceedings of the IEEE
    • Pages: 5894 - 5894
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Become a published author in 4 to 6 weeks
    • Pages: 5895 - 5895
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
  • Introducing IEEE collabratec
    • Pages: 5896 - 5896
      PubDate: Dec. 2016
      Issue No: Vol. 9, No. 12 (2016)
       
 
 
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