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  Subjects -> ELECTRONICS (Total: 186 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 6)
Advances in Electronics     Open Access   (Followers: 90)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Power Electronics     Open Access   (Followers: 35)
Advancing Microelectronics     Hybrid Journal  
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 332)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 26)
Annals of Telecommunications     Hybrid Journal   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 14)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 29)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 38)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 13)
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   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 47)
China Communications     Full-text available via subscription   (Followers: 9)
Chinese Journal of Electronics     Hybrid Journal  
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 289)
ECTI Transactions on Computer and Information Technology (ECTI-CIT)     Open Access  
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access  
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 117)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 97)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 100)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elkha : Jurnal Teknik Elektro     Open Access  
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 55)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage Materials     Full-text available via subscription   (Followers: 3)
EPJ Quantum Technology     Open Access   (Followers: 1)
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 204)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 99)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 80)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 49)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 9)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 72)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 71)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 58)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 26)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 42)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 26)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 76)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access  
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 52)
IET Smart Grid     Open Access  
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 11)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 70)
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 35)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 13)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 10)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 6)
International Journal of Control     Hybrid Journal   (Followers: 11)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 3)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Hybrid Intelligence     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 15)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 8)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 25)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 11)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 32)
Journal of Electrical Bioimpedance     Open Access  
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 7)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 3)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 170)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 7)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 10)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal  
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal  
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 10)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 29)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 26)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 5)
Microelectronics and Solid State Electronics     Open Access   (Followers: 26)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 41)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal   (Followers: 1)
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Journal of Antennas and Propagation     Open Access   (Followers: 9)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 15)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Problemy Peredachi Informatsii     Full-text available via subscription  
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 Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 3)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 9)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 5)
Revue Méditerranéenne des Télécommunications     Open Access  
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 56)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 78)
Solid State Electronics Letters     Open Access  
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 9)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Ural Radio Engineering Journal     Open Access  
Visión Electrónica : algo más que un estado sólido     Open Access   (Followers: 1)
Wireless and Mobile Technologies     Open Access   (Followers: 6)
Wireless Power Transfer     Full-text available via subscription   (Followers: 4)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access  

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Similar Journals
Journal Cover
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Journal Prestige (SJR): 1.547
Citation Impact (citeScore): 4
Number of Followers: 56  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-1404
Published by IEEE Homepage  [191 journals]
  • Frontcover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • IEEE Geoscience and Remote Sensing Society
    • Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • IEEE Geoscience and Remote Sensing Society
    • Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Institutional Listings
    • Abstract: Presents a listing of institutions relevant for this issue of the publication.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Foreword to the Special Issue on IGARSS 2018
    • Authors: José Moreno;José Antonio Sobrino;Gustau Camps-Valls;
      Pages: 2012 - 2014
      Abstract: The papers in this special issue were presented at the 2018 International Geoscience and Remote Sensing Symposium (IGARSS-2018) was held on July 22–27, 2018 in Valencia, Spain.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • NOAA-20 VIIRS DNB Aggregation Mode Change: Prelaunch Efforts and On-Orbit
           Verification/Validation Results
    • Authors: Wenhui Wang;Changyong Cao;
      Pages: 2015 - 2023
      Abstract: The Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the National Oceanic and Atmospheric Administration-20 (NOAA-20, previously named Joint Polar Satellite System-1 or J1) satellite was successfully launched in late 2017, following six years of a successful operation by its predecessor on the Suomi National Polar-Orbiting Partnership (S-NPP) satellite. NOAA-20 VIIRS day/night band (DNB) adopts a new on-board aggregation option (Op21), which is different from S-NPP DNB (using Op32), to mitigate high non-linearity at high scan angles, observed in its radiometric response during prelaunch test. As a result, NOAA-20 VIIRS DNB has a larger scan angle at the end of scan (~60.5°) and exhibits a unique feature, i.e., ~600 km extended Earth view (EV) samples, compared to S-NPP DNB and other VIIRS bands. VIIRS geolocation (GEO) algorithm and geometric calibration parameters were analyzed in-depth and subsequently modified to accommodate the NOAA-20 VIIRS DNB aggregation mode change. The GEO code change was tested using S-NPP data; S-NPP DNB simulated J1 DNB radiance and limited J1 prelaunch test data. After the launch, it was further verified using NOAA-20 VIIRS on-orbit observations. Our results show that the prelaunch VIIRS GEO code change performs well. GEO validation results using nighttime point sources show that NOAA-20 DNB GEO errors are comparable to those for S-NPP DNB over the nominal EV range, with averaged nadir equivalent GEO errors less than 200 m after on-bit updates. Over the extended EV samples (scan angle> 56.06°), the averaged GEO errors are less than 500 m. Moreover, NOAA-20 VIIRS DNB radiometric calibration performance is comparable to S-NPP.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Inter-Comparing SNPP and NOAA-20 CrIS Toward Measurement Consistency and
           Climate Data Records
    • Authors: Likun Wang;Yong Chen;
      Pages: 2024 - 2031
      Abstract: The cross-track infrared sounder (CrIS) is a Fourier transform spectrometer that provides hyperspectral soundings of the atmosphere over three wavelength ranges onboard on both Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. Quantifying the radiometric difference and creating a calibration link between SNPP and NOAA-20 CrIS are crucial for creating CrIS long-term climate data records and establishing the space-based calibration standard. This study explores the inter-comparison strategy to identify the radiometric differences between SNPP and NOAA-20 CrIS along the finest spectral scale, including 1) direct comparison; and 2) the two double difference methods using infrared atmospheric sounding interferometer and radiative transfer calculations as transfer targets. Each method has its own advantages and disadvantages. The preliminary results based on the above methods suggest good agreement generally between SNPP and NOAA-20 CrIS. Some consistent findings are disclosed by the above three methods, including 1) around the CO2 absorption band at 650-750 cm-1 range, a consistent warm bias is found on the level of 0.1 K; 2) at the water vapor absorption band, the two CrIS instruments are well consistent to each other and their BT differences are close to zero lines albeit with spectral variation; 3) the largest BT difference of more than -0.6 K is found at the spectral line transition region (around 2380 cm-1). The root causes still need further investigation.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Azimuth Superresolution of Forward-Looking Radar Imaging Which Relies on
           Linearized Bregman
    • Authors: Qiping Zhang;Yin Zhang;Yulin Huang;Yongchao Zhang;
      Pages: 2032 - 2043
      Abstract: Forward-looking radar plays an important role in many military and civilian fields. However, the problem of low azimuth resolution has restricted its applications seriously. Although many methods have been used to achieve azimuth superresolution, the traditional methods suffer from noise amplification or limited resolution under low signal-to-noise (SNR) condition. In this paper, we proposed a Bayesian deconvolution method which relies on linearized Bregman to achieve azimuth superresolution of forward-looking radar imaging. We first used the complex Gaussian distribution and Laplace distribution to describe the distribution of noise and targets, respectively, and transformed the superresolution problem into a convex optimization problem by maximum a posteriori estimation in the Bayesian framework. Second, linearized Bregman algorithm was used to solve the convex optimization problem. The proposed method introduces the prior information of noise and target, and overcomes the ill-posedness of deconvolution. As a result, the azimuth resolution is remarkably enhanced. Besides, the proposed method has high computational efficiency by linearizing objection function, so it can take both time cost and resolution improvement into consideration. Finally, the superior performance was verified by simulation and experimental data.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Airborne Forward-Looking Radar Super-Resolution Imaging Using Iterative
           Adaptive Approach
    • Authors: Yongchao Zhang;Deqing Mao;Qian Zhang;Yin Zhang;Yulin Huang;Jianyu Yang;
      Pages: 2044 - 2054
      Abstract: Airborne forward-looking radar (AFLR) imaging has raised many concerns in fields of Earth observation, independent of weather and daytime. Constrained by imaging principles, conventional high-resolution radar imaging techniques such as synthetic aperture radar (SAR) and Doppler beam sharpening (DBS) are incapable of AFLR imaging. The real aperture radar (RAR) can obtain AFLR images using a scanning antenna, but suffers from coarse cross-range resolution. Recently, there has been much attention paid to the iterative adaptive approach (IAA), which draws from the benefits of RAR imaging and provides improved cross-range resolution. However, earlier work on the IAA imposed a convolution model on the received azimuth echo, neglecting the effect of the Doppler phase. This model mismatch degrades the imaging performance for moving platforms. To settle this problem, this paper first establishes a Doppler-convolution model of AFLR imaging, where both Doppler phase and antenna convolution effects are considered, allowing more accurate reconstruction when applying the IAA to formulate a super-resolution image. Then, a data-depended approach for Doppler centroid estimation is proposed to circumvent the problem of low estimation precision using platform motion parameters delivered by navigational devices mounted on the radar platform. Simulation results demonstrate that the proposed implementation of the IAA based on the Doppler-convolution model and Doppler centroid estimation can overcome the deficiencies of the SAR and DBS techniques in the forward-looking imaging direction, and present a noticeably superior performance as compared with conventional AFLR imaging methods.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Passive Radar Imaging by Filling Gaps Between ISDB Digital TV Channels
    • Authors: Weike Feng;Jean-Michel Friedt;Grigory Cherniak;Motoyuki Sato;
      Pages: 2055 - 2068
      Abstract: Integrated Services Digital Broadcasting-Terrestrial/Satellite (ISDB-T/S) signal, used in Japan as the digital television (TV) broadcasting standard, is exploited in this paper for passive bistatic radar two-dimensional imaging of stationary targets. A multifunctional system is developed with commercial-off-the-shelf antennas, low-noise block downconverters, and amplifiers. Several practical issues of using ISDB-T/S signals for imaging are highlighted. Ambiguity function analysis, accurate time delay estimation, inverse filtering, and local frequency correction are carried out. Multiple TV channels are combined to improve the range resolution. In order to reduce the artifacts caused by the frequency gaps among multiple TV channels, two low-rank matrix-completion-based algorithms are proposed. Experiment results with different targets validate the performance of the designed system and the proposed algorithms.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Persistent Scatterer Density by Image Resolution and Terrain Type
    • Authors: Stacey Huang;Howard A. Zebker;
      Pages: 2069 - 2079
      Abstract: Persistent scatterer interferometry is a powerful time-series technique which uses the most temporally stable pixels (denoted persistent scatterers, or PS), to enable measurement of deformation in decorrelation-prone data sets. System performance depends heavily on the density of identified PS, which is influenced by two factors: image resolution and terrain type. In this work, we establish a quantitative link between PS density and these factors. First, we present a simple theoretical framework for predicting PS density by estimating the change in the pixel signal-to-clutter ratio (SCR) as a function of bandwidth for several different terrain types. Then, we analyze the behavior of PS density for three terrain types at different image resolutions. The model agrees with empirical results within 50% error, and rather closer for the high SCR points that form the desired network of PS points. Additionally, we find that the probability density functions of PS occurrence with respect to SCR for each region are approximately independent of system bandwidth. Thus, the increase in PS density is roughly proportional to increased bandwidth due to a higher pixel density in finer resolution images. We note that there is a slight increase in PS detectability with increasing bandwidth beyond the bandwidth scaling, but the gain is small compared to the bandwidth factor. These results form a model with a more quantitative understanding of the relationship between PS density and by extension, PS system performance, and image resolution and terrain.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Hybrid Polarimetric GPR Calibration and Elongated Object Orientation
           Estimation
    • Authors: Hai Liu;Xiaoyun Huang;Feng Han;Jie Cui;Billie F. Spencer;Xiongyao Xie;
      Pages: 2080 - 2087
      Abstract: Ground penetrating radar (GPR) has been widely applied to the detection of subsurface elongated targets, such as underground pipes, concrete rebars, and subsurface fractures. The orientation angle of a subsurface elongated target can hardly be delineated by a commercial single-polarization GPR system. In this paper, a hybrid dual-polarimetric GPR system, which consists of a circularly polarized transmitting antenna and two linearly polarized receiving antenna, is employed to detect buried elongated objects. A polarimetric calibration experiment using a gridded trihedral is carried out to correct the imbalances and cross talk between the two receiving channels. A full-polarimetric scattering matrix is extracted from the double-channel GPR signals reflected from a buried elongated object. An improved Alford rotation method is proposed to estimate the orientation angle of the elongated object from the extracted scattering matrix, and its accuracy is validated by a numerical test. A laboratory experiment was further conducted to detect five metal rebar buried in dry sand at different orientation angle relative to the GPR scan direction. The maximum relative error of the estimated angles of the buried rebars in the migrated GPR images is less than 5%. It is concluded that radar polarimetry can provide not only richer information than single-polarization GPR, but also a reliable approach for orientation estimation of a subsurface elongated object.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Unbiased Seamless SAR Image Change Detection Based on Normalized
           Compression Distance
    • Authors: Mihai Coca;Andrei Anghel;Mihai Datcu;
      Pages: 2088 - 2096
      Abstract: Land cover changes may have very different nature, e.g., vegetation development, soil erosion, variation of humidity, or damage of buildings, only to enumerate few cases. In addition, synthetic aperture radar (SAR) observations are a doppelganger of the scene, imaging the scene signature rather than the scene itself. To overcome these challenges, SAR change detection methods generally adapt to the particular situations. We present seamless methods based on normalized compression distance (NCD) estimation. NCD is a similarity metric applied directly to the data, thus with no biases induced by feature estimators or classifiers. Since the diversity of changes is huge and extremely hard to derive typical classes, we introduce paradigm based both on an unsupervised and a supervised method. The change detection procedure mainly consists in dividing image dataset in patches, computing a collection of similarities for pairs of tiles formed differently in each case, and usage of this collection in unsupervised and supervised forms to generate a change map. Both the threshold based histogram, unsupervised method, and the k-NN classifier algorithm, supervised method, have a distinct flow to obtain the change map. To use the NCD operator according to our proposed methods, a speckle resistance test is involved. The experimental results for the two methodologies are computed using two TerraSAR-X images over Sendai and surrounding areas, Japan.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Refining the Interior Orientation of a Hyperspectral Frame Camera With
           Preliminary Bands Co-Registration
    • Authors: Antonio Maria Garcia Tommaselli;Lucas Dias Santos;Raquel Alves de Oliveira;Adilson Berveglieri;Nilton Nobuhiro Imai;Eija Honkavaara;
      Pages: 2097 - 2106
      Abstract: Lightweight hyperspectral sensors carried by unmanned aerial vehicles (UAVs) are becoming powerful remote sensing tools for several applications, for example, forestry and agriculture. Sequential frame acquisition by scanning the spectral bands with tunable Fabry-Pérot interferometer (FPI) is one of the technologies suitable for these applications. The accurate co-registration of the individual bands to produce a hypercube and the bundle adjustment of all bands are still challenging tasks. Because of the geometry and internal optical components of this kind of camera, modeling of the interior geometry of the image bands requires more than a single set of interior orientation parameters (IOP). This paper developed a new method that applies a preliminary two-dimensional (2-D) geometric transformation to co-register all bands, based on projective parameters estimated during the calibration process. This preprocessing avoids the use of several sets of IOPs, simplifying the computation of image orientation with bundle adjustment. Experiments using a close range calibration setup and a UAV-based aerial image block showed that the new method was effective and improved the accuracy of the three-dimensional (3-D) point determination. Accuracy of one times ground sample distance (GSD) in horizontal coordinates and 1.2 GSD in height coordinate was achieved in the bundle adjustment using a single set of IOPs.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Fast and Efficient Limited Data Hyperspectral Remote Sensing Image
           Classification via GMM-Based Synthetic Samples
    • Authors: AmirAbbas Davari;Hasan Can Özkan;Andreas Maier;Christian Riess;
      Pages: 2107 - 2120
      Abstract: In hyperspectral remote sensing (HSRS), feature data can potentially become very high dimensional. At the same time, manual labeling of that data is an expensive task. As a consequence of these two factors, one of the core challenges is to perform multi-class classification using only relatively few training data points. In this work, we investigate the classification performance with limited training data. First, we revisit the optimization of the internal parameters of a classifier in the context of limited training data. Second, we report an interesting alternative to parameter optimization: classification performance can also be considerably increased by adding synthetic GMM data to the feature space while using a classifier with unoptimized parameters. Third, we show that using variational expectation maximization, we can achieve a much faster convergence in fitting the GMM on the data. In our experiments, we show that the addition of synthetic samples leads to comparable, and, in some cases, even higher classification performance than that for a properly tuned classifier on limited training data. One advantage of the proposed framework is that the reported performance improvements are achieved by a quite simple model. Another advantage is that this approach is computationally much more efficient than classifier parameter optimization and conventional expectation maximization.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Multilabel Annotation of Multispectral Remote Sensing Images using
           Error-Correcting Output Codes and Most Ambiguous Examples
    • Authors: Anamaria Radoi;Mihai Datcu;
      Pages: 2121 - 2134
      Abstract: This paper presents a novel framework for multilabel classification of multispectral remote sensing images using error-correcting output codes. Starting with a set of primary class labels, the proposed framework consists in transforming the multiclass problem into multiple binary learning subtasks. The distributed output representations of these binary learners are then transformed into primary class labels. In order to train robust binary classifiers on a reduced annotated dataset, the learning process is iterative and involves determining most ambiguous examples, which are included in the training set at each iteration. As part of the semantic image recognition process, two categories of high-level image representations are proposed for the feature extraction part. First, deep convolutional neural networks are used to form high-level representations of the images. Second, we test our classification framework with a bag-of-visual words model based on the scale invariant feature transform, used in combination with color descriptors. In the first case, we propose the usage of pretrained state-of-the-art deep learning models that cancel the need to estimate model parameters of complex architectures, whereas, in the second case, a dictionary of visual words must be determined from the training set. Experiments are conducted on GeoEye-1 and Sentinel-2 images and the results show the effectiveness of the proposed approach toward a multilabel classification, when compared to other methods.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Multitask Learning-Based Reliability Analysis for Hyperspectral Target
           Detection
    • Authors: Yuxiang Zhang;Ke Wu;Bo Du;Xiangyun Hu;
      Pages: 2135 - 2147
      Abstract: Hyperspectral images contain abundant spectral information, which provide great potential for target detection. However, it also introduces a critical spectral variability problem for hyperspectral target detection, which makes the hyperspectral target detection much difficult than the classical spectral match issue. Many traditional detection methods have been proposed to deal with the spectral variability. However, these algorithms are still highly susceptible to the target spectral variability. The single input restriction and the inherent spectral characteristics mining problem are the main issues with these methods. The multitask learning (MTL) technique may have the potential to solve the above hyperspectral target detection issues since it can extract the inherent similarity and difference within multiple priori target spectra to learn a robust target spectral signature. This paper proposed a MTL-based reliability analysis method for hyperspectral target detection (MultiRely). This approach: 1) utilizes multiple priori target spectra to better represent the target spectral characteristics and construct multiple related detection tasks; 2) takes full advantage of the multitask learning technique to explore the spectral similarity and difference between multiple priori target spectra; 3) and applies the reliability analysis to obtain a reliable target spectrum in order to alleviate the target spectral variability. Experiments on two real hyperspectral datasets and one synthetic hyperspectral dataset illustrated the effectiveness of the proposed algorithm compared to the state-of-the-art detectors.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Abundance Estimation Using Discontinuity Preserving and Sparsity-Induced
           Priors
    • Authors: Jignesh R. Patel;Manjunath V. Joshi;Jignesh S. Bhatt;
      Pages: 2148 - 2158
      Abstract: Abundance estimation is used to infer the proportions of endmembers with the given endmember signatures and reflectance value at each location. In this paper, we propose a two-phase iterative approach to estimate the abundances (fractions) of materials (endmembers) from the pixels of hyperspectral images (HSIs) by using the energy minimization framework. A linear mixture model is used to define the data term. We observe that abundance maps have homogeneous regions with limited discontinuity, and they exhibit spatial redundancy. Hence, we use inhomogeneous Gaussian Markov random field (IGMRF) and sparsity-induced priors as the regularization terms. While the IGMRF prior captures the smoothness and preserves discontinuities among abundance values, the sparsity-induced prior accounts for redundancy. We calculate the IGMRF parameters at every pixel location and learn a dictionary and the sparse representation for abundances using the initial estimate in phase 1, while the final abundance maps are estimated in phase 2. In order to learn the sparsity, we use the approach based on K-singular value decomposition. Both the IGMRF and sparseness parameters are initialized using an initial estimate of abundances and refined using the two-phase iterative approach. The experiments are conducted on synthetic hyperspectral HSIs with different noise levels, as well as on two real HSIs. The results are qualitatively and quantitatively compared with state-of-the-art approaches. Experimental results demonstrate the effectiveness of the proposed approach.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest
           for the Classification of Imbalanced Hyperspectral Data
    • Authors: Wei Feng;Gabriel Dauphin;Wenjiang Huang;Yinghui Quan;Wenxing Bao;Mingquan Wu;Qiang Li;
      Pages: 2159 - 2169
      Abstract: Rotation forest (RoF) is a powerful ensemble classifier and has attracted substantial attention due to its performance in hyperspectral data classification. Multi-class imbalance learning is one of the biggest challenges in machine learning and remote sensing. The standard technique for constructing RoF ensemble tends to increase the overall accuracy; RoF has difficulty to sufficiently recognize the minority class. This paper proposes a novel dynamic SMOTE (synthetic minority oversampling technique)-based RoF algorithm for the multi-class imbalance problem. The main idea of the proposed method is to dynamically balance the class distribution before building each rotation decision tree. A resampling rate is set in each iteration (ranging from 10% in the first iteration to 100% in the last) and this ratio defines the number of minority class instances randomly resampled (with replacement) from the original dataset in each iteration. The rest of the minority class instances are generated by the SMOTE method. The reported results on three real hyperspectral datasets show that the proposed method can get better performance than random forest, RoF, and some popular data sampling methods.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • A Laboratory-Created Dataset With Ground Truth for Hyperspectral Unmixing
           Evaluation
    • Authors: Min Zhao;Jie Chen;Zhe He;
      Pages: 2170 - 2183
      Abstract: Spectral unmixing is an important and challenging problem in hyperspectral data processing. This topic has been extensively studied and a variety of unmixing algorithms have been proposed in the literature. However, the lack of publicly available datasets with ground truth makes it difficult to evaluate and compare the performance of unmixing algorithms in a quantitative and objective manner. Most of the existing works rely on the use of numerical synthetic data and an intuitive inspection of the results of real data. To alleviate this dilemma, in this study, we design several experimental scenes in our laboratory, including printed checkerboards, mixed quartz sands, and reflection with a vertical board. A dataset is then created by imaging these scenes with the hyperspectral camera in our laboratory, providing 36 mixtures with more than 130 000 pixels with 256 wavelength bands ranging from 400 to 1000 nm. The experimental settings are strictly controlled so that pure material spectral signatures and material compositions are known. To the best of our knowledge, this dataset is the first publicly available dataset created in a systematic manner with ground truth for spectral unmixing. Some typical linear and nonlinear unmixing algorithms are also tested with this dataset and lead to meaningful results.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Sparse-SpatialCEM for Hyperspectral Target Detection
    • Authors: Xiaoli Yang;Jie Chen;Zhe He;
      Pages: 2184 - 2195
      Abstract: The constrained energy minimization (CEM) algorithm is widely used for target detection in hyperspectral imagery. This method, as well as most target detection algorithms, focuses on the use of spectral information and neglects the spatial information embedded in images. In real hyperspectral images, it is usual that targets of interest only occupy a minor portion of the pixels, and an object may consist of multiple consecutive pixels in space. Considering these facts, we propose a novel constrained detection algorithm, referred to as Sparse-SpatialCEM, to simultaneously force the sparsity and spatial correlation of the detection output via proper regularizations. Several algorithms, including the CEM, SparseCEM, and constrained magnitude minimization algorithms, are limiting cases of the proposed framework. The formulated problems are solved by using the alternating direction method of multipliers. We validate the proposed algorithms and illustrate its advantages via both synthetic and real hyperspectral data.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Agricultural Monitoring, an Automatic Procedure for Crop Mapping and Yield
           Estimation: The Great Rift Valley of Kenya Case
    • Authors: Roberto Luciani;Giovanni Laneve;Munzer JahJah;
      Pages: 2196 - 2208
      Abstract: Agricultural activities conducted in the Great Rift Valley of Kenya show a significant decline of productivity levels. This phenomenon is mainly related to limited availability of water resources, lack of supporting irrigation, and harvesting techniques ineffectiveness. Production risks reduction is closely related with a better use of water resources and a better understanding of the effects resulting from the multiple interactions between climate, agricultural vegetation, soil type, and crops management techniques. In this paper, a remote and automatic agricultural monitoring system is presented as an effective alternative to the most traditional in situ measurements and observations. We investigated the use of phenological information extracted from satellite imagery combined with crop calendar and supported by agro-ecological zoning (AEZ) in accurate crop classification and monitoring. Vegetation indices extracted from Landsat 8 imagery are capable to track the vegetation development through the year, then phenological profiles can be extracted and implemented into a multitemporal automatic classification process to detect agricultural areas and to discriminate among different crop species. The phenological profiles extracted by satellite imagery are compared with crop calendar data compiled by FAO for the area of interest. The classification procedure is supported by AEZs based on crop modeling and environmental matching procedures in order to identify crop-specific environmental limitations under assumed levels of inputs and management conditions. The FAO crop water productivity model AquaCrop is calibrated for wheat and maize yield mapping in the central highland of Kenya, handling both environmental and phenological data. The combined use of phenological data and AEZs results in a robust methodology with a classification overall accuracy of 91.35%. A good model performance is obtained relative to yield predictions, with R of 0.69 and 0.72.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Urban Extent Extraction Combining Sentinel Data in the Optical and
           Microwave Range
    • Authors: Gianni Cristian Iannelli;Paolo Gamba;
      Pages: 2209 - 2216
      Abstract: This paper illustrates a fusion approach to jointly exploit Sentinel-1 (S1) and Sentinel-2 (S2) data to detect urban areas. The proposed procedure is designed to automatically and effectively exploit the specific characteristics of synthetic aperture radar (SAR) and multispectral data, so that it can be safely applied to different urban environments with satisfying results. To this aim, it starts from two previously developed algorithms for urban extent extraction designed for multispectral or SAR spaceborne data. They are first adapted to use S1 and S2 datasets, and then coupled to a novel procedure that merges the two processing chains with the goal to exploit the spectral properties of man-made materials as well as the double bounce backscatter effect that is common in built-up environments. Experimental results in urban areas all around the world show that the proposed approach is successful at combining S1 and S2 information by reducing the errors of the two original techniques.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land
           Cover Classification
    • Authors: Patrick Helber;Benjamin Bischke;Andreas Dengel;Damian Borth;
      Pages: 2217 - 2226
      Abstract: In this paper, we present a patch-based land use and land cover classification approach using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible, and are provided in the earth observation program Copernicus. We present a novel dataset, based on these images that covers 13 spectral bands and is comprised of ten classes with a total of 27 000 labeled and geo-referenced images. Benchmarks are provided for this novel dataset with its spectral bands using state-of-the-art deep convolutional neural networks. An overall classification accuracy of 98.57% was achieved with the proposed novel dataset. The resulting classification system opens a gate toward a number of earth observation applications. We demonstrate how this classification system can be used for detecting land use and land cover changes, and how it can assist in improving geographical maps. The geo-referenced dataset EuroSAT is made publicly available at https://github.com/phelber/eurosat.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Analysis of CYGNSS Data for Soil Moisture Retrieval
    • Authors: Maria Paola Clarizia;Nazzareno Pierdicca;Fabiano Costantini;Nicolas Floury;
      Pages: 2227 - 2235
      Abstract: Data from the CYGNSS mission, originally conceived to monitor tropical cyclones, are being investigated here for land applications as well. In this paper, a methodology for soil moisture (SM) retrieval from CYGNSS data is presented. The approach derives Level 3 gridded daily SM estimations, over the latitudinal band covered by CYGNSS, at a resolution of 36 km × 36 km, using the CYGNSS reflectivity over land, coupled with ancillary vegetation and roughness information from the SMAP mission. The results are compared globally with SM measurements from SMAP, which are assumed to be ground truth, showing a good agreement, and a global root-mean-square difference of 0.07 cm3/cm3. A more extensive comparison is performed over two test regions-Texas in the United States and New South Wales in Australia-where reference data from SMAP are complemented with measurements from the SMOS mission. The results over both regions are generally consistent with the global results, and a good agreement is observed between CYGNSS and reference SM measurements from SMAP and SMOS. The study demonstrates that SM can be successfully retrieved from the CYGNSS mission on a global scale and using ancillary information about the overlying vegetation and the characteristics of the soil. The results open up further future perspectives for global, high-resolution SM products from spaceborne Global Navigation Satellite System-Reflectometry data.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Diurnal Changes in Leaf Photochemical Reflectance Index in Two Evergreen
           Forest Canopies
    • Authors: Matti Mõttus;Luiz Aragão;Jaana Bäck;Rocío Hernández-Clemente;Eduardo Eiji Maeda;Vincent Markiet;Caroline Nichol;Raimundo Cosme de Oliveira;Natalia Restrepo-Coupe;
      Pages: 2236 - 2243
      Abstract: The spectral properties of plant leaves relate to the state of their photosynthetic apparatus and the surrounding environment. An example is the well known photosynthetic downregulation, active on the time scale from minutes to hours, caused by reversible changes in the xanthophyll cycle pigments. These changes affect leaf spectral absorption and are frequently quantified using the photochemical reflectance index (PRI). This index can be used to remotely monitor the photosynthetic status of vegetation, and allows for a global satellite-based measurement of photosynthesis. Such earth observation satellites in near-polar orbits usually cover the same geographical location at the same local solar time at regular intervals. To facilitate the interpretation of these instantaneous remote PRI measurements and upscale them temporally, we measured the daily course of leaf PRI in two evergreen biomes-a European boreal forest and an Amazon rainforest. The daily course of PRI was different for the two locations: At the Amazonian forest, the PRI of Manilkara elata leaves was correlated with the average photosynthetic photon flux density (PPFD) (R2 = 0.59, p
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Synergetic Exploitation of the Sentinel-2 Missions for Validating the
           Sentinel-3 Ocean and Land Color Instrument Terrestrial Chlorophyll Index
           Over a Vineyard Dominated Mediterranean Environment
    • Authors: Luke A. Brown;Jadunandan Dash;Antonio L. Lidón;Ernesto Lopez-Baeza;Steffen Dransfeld;
      Pages: 2244 - 2251
      Abstract: Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 missions. To ensure its utility in a wide range of scientific and operational applications, validation efforts are required. In the past, direct validation has been constrained by the need for costly airborne hyperspectral data acquisitions, due to the lack of freely available high spatial resolution imagery incorporating appropriate spectral bands. The Multispectral Instrument (MSI) on-board the Sentinel-2 missions now offers a promising alternative. We explored the synergetic use of MSI data for validation of the OLCI Terrestrial Chlorophyll Index (OTCI) over the Valencia Anchor Station, a large agricultural site in the Valencian Community, Spain. Using empirical and machine learning techniques applied to MSI data, in situ measurements were upscaled to the moderate spatial resolution of the OTCI. An RMSECV of 0.09 g·m-2 (NRMSECV = 20.93%) was achieved, highlighting the valuable information MSI data can provide when used in synergy with OLCI data for land product validation. Good agreement between the OTCI and upscaled in situ measurements was observed (r = 0.77, p
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Net Surface Shortwave Radiation Retrieval Using Random Forest Method With
           MODIS/AQUA Data
    • Authors: Wangmin Ying;Hua Wu;Zhao-Liang Li;
      Pages: 2252 - 2259
      Abstract: The net surface shortwave radiation (NSSR) at the Earth's surface drives evapotranspiration, photosynthesis, and other physical and biological processes. The primary objective of this study is to estimate NSSR in all sky conditions by using narrowband data of the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the AQUA satellite. The random forest (RF) machine learning method for retrieving NSSR was developed with MODerate resolution atmospheric TRANsmission model (MODTRAN 5) simulated data. The bias, root mean square error (RMSE), and R2 for the training dataset of the model are 0.04 W m-2, 2.03 W m-2, and 1.00, respectively; for testing data, these values are 0.53 W m-2, 5.50 W m-2, and 1.00, respectively. Note that the proposed method is better than the traditional method (RMSE 7.29 W m-2) with MODTRAN data, and the sky conditions (clear and cloudy) do not need to be distinguished in the RF method. Seven in situ measurements of the Surface Radiation (SURFRAD) observation network were used to validate the estimated NSSR with MODIS/AQUA data using the proposed RF method, and the bias, RMSE, and R2 of the comparison are -8.4 W m-2, 76.8 W m-2, and 0.91, respectively. Approximately 70% of the absolute difference of all the samples is below 50 W m-2. Considering its concise process and relatively improved accuracy, both in regard to model development and validation, it can be concluded that the retrieval of NSSR with RF will be an efficient and feasible method in the future.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Evaluation of Near-Surface Air Temperature From Reanalysis Over the United
           States and Ukraine: Application to Winter Wheat Yield Forecasting
    • Authors: Andrés E. Santamaría-Artigas;Belen Franch;Pierre Guillevic;Jean-Claude Roger;Eric F. Vermote;Sergii Skakun;
      Pages: 2260 - 2269
      Abstract: In this paper, we evaluate the near-surface air temperature datasets from the ERA-Interim (ERAI), Japanese 55-Year Reanalysis, Modern-Era Retrospective Analysis for Research and Applications Version 2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well (r2> 0.95) and that more modern reanalysis, such as ERAI, had lower errors [root-mean-square difference (RMSD) ~0.9 °C than the older, lower resolution datasets, such as NCEP1 (RMSD ~2.4 °C). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated growing degree day (GDD) during the growing season to estimate the peak normalized difference vegetation index signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • On Bird Species Diversity and Remote Sensing—Utilizing Lidar and
           Hyperspectral Data to Assess the Role of Vegetation Structure and Foliage
           Characteristics as Drivers of Avian Diversity
    • Authors: Markus Melin;Ross A. Hill;Paul E. Bellamy;Shelley A. Hinsley;
      Pages: 2270 - 2278
      Abstract: Avian diversity has long been used as a surrogate for overall diversity. In forest ecosystems, it has been assumed that vegetation structure, composition, and condition have a significant impact on avian diversity. Today, these features can be evaluated via remote sensing. This study examined how structure metrics from lidar data and narrowband indices from hyperspectral data relate with avian diversity. This was assessed in four deciduous-dominated woods with differing age and structure set in an agricultural matrix in eastern England. The woods were delineated into cells within which metrics of avian diversity and remote sensing based predictors were calculated. Best subset regression was used to obtain best lidar models, hyperspectral models, and finally, the best models combining variables from both data sets. The aims were not only to examine the drivers of avian diversity, but to assess the capabilities of the two remote sensing techniques for the task. The amount of understorey vegetation was the best single predictor, followed by foliage height diversity, reflectance at 830 nm, anthocyanin reflectance index 1, and Vogelmann red edge index 2. This showed the significance of the full vertical profile of vegetation, the condition of the upper canopy, and potentially tree species composition. The results thus agree with the role that vegetation structure, condition, and floristics are assumed to have for diversity. However, the inclusion of hyperspectral data resulted in such minor improvements to models that its collection for these purposes should be assessed critically.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Road Segmentation of Unmanned Aerial Vehicle Remote Sensing Images Using
           Adversarial Network With Multiscale Context Aggregation
    • Authors: Yuxia Li;Bo Peng;Lei He;Kunlong Fan;Ling Tong;
      Pages: 2279 - 2287
      Abstract: Semantic segmentation using adversarial networks has proved to be effective in image processing fields. However, two problems need to be solved in the field of the road segmentation of unmanned aerial vehicle (UAV) remote sensing images. One is the occupied proportion of road area in UAV remote sensing images; the other is that the constant size of convolutional kernel cannot deal with multiscale feature very well. To solve these two problems, this paper proposed a road segmentation model that combined the adversarial networks with multiscale context aggregation. First, the output feature-maps of three scales (0.5n, 1n, 2n) were obtained, based on an end-to-end training from image segmentation network. Second, after the convolution and deconvolution operations, the processed images were unified to the size scale of original images. Third, with the pixel-by-pixel addition method, the three scales of image feature (0.5n, 1n, 2n) were merged together, then inputted into the discriminative network. Finally, the errors were obtained and propagated backwards compared with the label, and then the parameters of a generative network and a discriminative network could be updated. Further, the segmented results were compared with those from normal adversarial networks, Linknet and D-linknet, and were developed with the morphological operation. The research results show that the proposed model can improve the precision of road segmentation from UAV images with multiscale context aggregation and the regularization property of adversarial networks.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Flood Area Detection Using PALSAR-2 Amplitude and Coherence Data: The Case
           of the 2015 Heavy Rainfall in Japan
    • Authors: Masato Ohki;Takeo Tadono;Takuya Itoh;Keiko Ishii;Tsutomu Yamanokuchi;Manabu Watanabe;Masanobu Shimada;
      Pages: 2288 - 2298
      Abstract: Rapid detection of flood areas in all weather conditions is needed for monitoring and mitigating flood disasters. In this paper, we investigated flood area detection using L-band synthetic aperture radar data acquired by the Phased-Array-type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard the Advanced Land Observing Satellite-2 during the 2015 heavy rainfall disaster in Kanto and Tohoku areas of Japan. This paper comprehensively compared various observation conditions of PALSAR-2, such as incidence angles, resolutions, and polarizations. We successfully detected open-water flood and built-up area flood by thresholding amplitude and interferometric coherence images. On the basis of our results, we conclude that incidence angles of approximately 20-40°, linear polarization or surface scattering component (HH, VV, and HH + VV), and high resolution are the preferable conditions for flood area detection by PALSAR-2. The optimum threshold resulted in the following flood area detection accuracies: Kappa coefficient (κ) = 0.583 in open water and κ = 0.429 in built-up area. These results successfully demonstrate the feasibility of rapid flood monitoring using PALSAR-2 data.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS
           Land Surface Temperature
    • Authors: Wan Li;Li Ni;Zhao-Liang Li;Si-Bo Duan;Hua Wu;
      Pages: 2299 - 2307
      Abstract: Land surface temperature (LST) is described as one of the most important environmental parameters of the land surface biophysical process. Commonly, the remote-sensed LST products yield a tradeoff between high temporal and high spatial resolution. Thus, many downscaling algorithms have been proposed to address this issue. Recently, downscaling with machine learning algorithms, including artificial neural networks (ANNs), support vector machine (SVM), and random forest (RF), etc., have gained more recognition with fast operation and high computing precision. This paper intends to make a comparison between machine learning algorithms to downscale the LST product of the moderate-resolution imaging spectroradiometer from 990 to 90 m, and downscaling results would be validated by the resampled LST product of the advanced spaceborne thermal emission and reflection radiometer. The results are further compared with the classical algorithm-thermal sharpening algorithm (TsHARP), using images derived from two representatives kind of areas of Beijing city. The result shows that: 1) all machine learning algorithms produce higher accuracy than TsHARP; 2) the performance of TsHARP on urban area is unsatisfactory than rural because of the weak indication of impervious surface by normalized difference vegetation index, however, machine learning algorithms get the desired results on both two areas, in which ANN and RF models perform well with high accuracy and fast arithmetic, SVM also gets a good result but there is a smoothing effect on land surface; and 3) additionally, machine learning algorithms are promising to achieve a universal framework which can downscale LST for any area within the training data from long spatiotemporal sequences.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • An Improved Computational Geometry Method for Obtaining Accurate Remotely
           Sensed Products via Convex Hulls With Dynamic Weights: A Case Study With
           Leaf Area Index
    • Authors: Hong Chen;Hua Wu;Zhao-Liang Li;Jienan Tu;
      Pages: 2308 - 2319
      Abstract: Most retrieval functions used in remote sensing assume that the land surface is homogeneous. When those functions are used at a coarse spatial resolution for heterogeneous surfaces, scale effects might appear. This paper tries to develop an improved computational geometry method (ICGM) upscaling model that takes into consideration the actual distribution of surface measurements by using dynamic weights for the upper and lower envelopes of a convex hull. By aggregating to a series of simulated data at coarse spatial resolution, the weight coefficients can be determined via a least square method. To evaluate the proposed upscaling model, the leaf area index (LAI) is used as an example. The results for three sites with different degrees of heterogeneity show that the ICGM upscaling model can effectively correct for the scale effects of the LAI, and in most cases, achieve an accuracy that is comparable to that of traditional upscaling models. The relative error of the estimated LAI for the selected sites decreases from 3.35%, 11.01%, and 19.62% to an average of 0.28%, 1.48%, and 5.16%, respectively, at kilometer scale. A determination of whether retrieval functions are continuous or derivable is no longer required. Furthermore, there is no need to rely upon synchronous high spatial resolution data. Because the weight coefficients vary little at different scales, those coefficients are thought to be insensitive to different scales and can be taken as constants for a given study site. This study indicates that the proposed method is promising and feasible even for a heterogeneous landscape.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Modeling of Mixed-Pixel Clumping Index From Remote Sensing Data and Its
           Evaluation
    • Authors: Qingmiao Ma;Yingjie Li;Jing Li;Qinhuo Liu;
      Pages: 2320 - 2331
      Abstract: The clumping index (CI) is a canopy structure parameter that describes the dispersion or grouping of leaves. Previously, it has been estimated based on the normalized difference between hotspot and darkspot (NDHD), which is derived from multi-angle remote sensing data. However, currently it is impossible to derive CI from NDHD for a large area at a spatial resolution finer than 275 m since such fine multi-angle data are unavailable. In this study, an algorithm of the mixed-pixel clumping index (MPCI) was implemented, and an MPCI map of China's landmass at 1 km resolution was derived from the HJ-1A/1B data at 30 m resolution. The MPCI map was compared with the previous NDHD CI derived from the moderate resolution imaging spectroradiometer (MODIS). The correlation of these two datasets was greater than 0.9, and the mean bias was approximately 0.1. Indirectly, the MPCI map was applied to an effective leaf area index (LAI) product to derive true LAI. Using the MODIS LAI product as a reference, we found that the coefficient of determination was improved from 0.72 to 0.80, and the root mean squared error was reduced from 0.53 to 0.35 m2/m2 after the effective LAI is corrected by this MPCI map, suggesting that this MPCI map is comparable to the NDHD CI. Although our algorithm is currently tested at 1 km resolution, potentially, it can be applied to higher spatial resolution than 275 m for mapping LAI and carbon cycle modeling before these multi-angle data at higher resolution are available.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Land Surface Temperature Retrieval by LANDSAT 8 Thermal Band: Applications
           of Laboratory and Field Measurements
    • Authors: Pâmela Suélen Käfer;Silvia Beatriz Alves Rolim;María Luján Iglesias;Nájila Souza da Rocha;Lucas Ribeiro Diaz;
      Pages: 2332 - 2341
      Abstract: Land surface temperature (LST) plays an important role in a wide variety of scientific studies. Several methodologies to retrieve LST and correct the atmospheric effects for thermal infrared satellite imagery have been developed and all of them require prior knowledge of the land surface emissivity (LSE). The techniques developed for LSE and LST retrieval need to be validated with field measurements. However, in situ measurements are a challenge, being essential to investigate the particularities of each instrument to verify the best approach to collect data and validate the algorithms. Fourier Transform Infrared (FT-IR) spectrometer has been widely used to obtain emissivity of different targets and calculate temperature. The instrument may be used to validate remote sensing data. Moreover, FT-IR allows to collect both emissivity and temperature at the laboratory, thus being an alternative to field validation. We investigated the emissivity dependence of the temperature. In addition, we evaluated the possibility of replacing in situ measurements by laboratory-controlled measurements. We have chosen two single-channel methods to calculate LST and perform the analysis in a Landsat 8 image. We also performed field measurements at the same time as the satellite overpass. FT-IR showed great potential to validate remotely sensed data. However, the instrument requires some time to acquire stability and attention in the calibration process. Laboratory measurements can replace field data, producing approximately 2% of the difference in the LSE. Both single-channel methods provide good accuracy for LST retrieval. Nonetheless, the improved single-channel has superior performance for the study area conditions.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • The Addition of Temperature to the TSS-RESTREND Methodology Significantly
           Improves the Detection of Dryland Degradation
    • Authors: Arden L. Burrell;Jason P. Evans;Yi Liu;
      Pages: 2342 - 2348
      Abstract: Cold drylands make up 20% of the world's water-limited regions. This paper presents a modification to the Time Series Segmented-RESidual TRENDs (TSS-RESTRENDs) method that allows for the use of temperature as an additional explanatory variable along with precipitation. TSS-RESTREND was performed over Mongolia, both with and without temperature. The addition of temperature reduced the number of pixels that fail the significance tests built into the TSS-RESTREND method from 17% to below 5%. It also improved the statistical confidence in almost all areas. Furthermore, the direction of change is consistent with previous findings that looked at vegetation trends over the same study region. When applied to all of the world's drylands, the inclusion of temperature improved the fit of the vegetation-climate relationship that underpins TSS-RESTREND in 98.8% of areas. The largest improvements to the fit were observed in both the cold drylands of central Asia and North America and the hot drylands of southern Australia. Including temperature also reduced the fraction of global vegetation change that could be attributed to neither climate nor land use from 25.5% to 15.5%.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Improving SEVIRI-Based Hotspots Detection by Using Multiple Simultaneous
           Observations
    • Authors: Giovanni Laneve;Giancarlo Santilli;Roberto Luciani;
      Pages: 2349 - 2356
      Abstract: Geostationary satellites like meteosat second generation (MSG) allow the detection and monitoring of thermal anomalies (wild fires and volcanic eruptions) with a refresh frequency ranging from 5 to 15 min. Such a frequency meets the requirements of the institutions involved in monitoring and containing the fire events and could provide information on the temporal behavior of the fire (through fire radiative power) and the spatial distribution of the events with the related hazard for the population and infrastructure when more occurrences are simultaneously present. A limitation of the operational applicability of this tool is currently represented by the low spatial resolution of the MSG/SEVIRI sensor ranging from 3 km at the equator to 4.5 km at Mediterranean latitudes. The limitations related to the sensitivity of the geostationary sensor to fire sizes have been, at least in part, overcome by introducing specific algorithms. However, the reduced accuracy in the geographic localization of the fire, which can, in principle, occupy any position in an area of about 16 km2 (at Mediterranean latitudes), makes this information not very interesting for the institutions involved in firefighting. This paper analyzes the feasibility of improving the localization of the thermal anomalies (hotspots) by combining images acquired simultaneously from different MSG satellites located at different longitudes. In particular, we combine the images acquired by MSG-9 located at long. 9.0°, MSG-10 located at 0.0° and MSG-8 located at long. 41.5°. The results confirm the possibility of improving the accuracy of the detection by exploiting the observation of the events from different positions in space.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Evaluation of Three Satellite-Based Precipitation Products Over the Lower
           Mekong River Basin Using Rain Gauge Observations and Hydrological Modeling
           
    • Authors: Yishan Li;Wei Wang;Hui Lu;Sothea Khem;Kun Yang;Xiaomeng Huang;
      Pages: 2357 - 2373
      Abstract: Satellite-based precipitation products (SPPs) have great potential in water-related applications, especially in ungauged/poor-gauged basins. Three SPPs, namely integrated multisatellite retrieval for the global precipitation measurement (GPM) mission, tropical rainfall measuring mission multisatellite precipitation analysis version 7, and precipitation estimation from remotely sensed information using artificial neural networks-climate data record, were evaluated over the lower Mekong river basin (LMB) from January 4, 2014 to February 28, 2017 at daily and monthly scales. Daily rainfall data collected from 119 rain gauges in the LMB were used to conduct a pixel-point comparison. Daily discharge observations at six stream gauges, together with a well-calibrated distributed hydrological model, were used to evaluate the hydrological utilities of the three SPP s. The results convey that: integrated multisatellite retrieval for the GPM mission shows more stable and precise estimation of precipitation in pixel-point comparisons (for both all rainfall events and only heavy rain events) than the other two SPP s; precipitation estimation from remotely sensed information using artificial neural networks-climate data record overestimates the rainfall amounts in LMB seriously by 17%; and integrated multisatellite retrieval for the GPM mission performs better than other two SPP s when forcing hydrological model to simulate discharges with more stable and accurate discharge results (daily Nash-Sutcliffe efficiency coefficient larger than 0.73 and monthly Nash-Sutcliffe efficiency coefficient larger than 0.84).
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Impervious Surface Estimation From Optical and Polarimetric SAR Data Using
           Small-Patched Deep Convolutional Networks: A Comparative Study
    • Authors: Hongsheng Zhang;Luoma Wan;Ting Wang;Yinyi Lin;Hui Lin;Zezhong Zheng;
      Pages: 2374 - 2387
      Abstract: Incorporating optical and polarimetric synthetic-aperture radar (SAR) data to estimate impervious surface is useful but challenging due to their different geometric imaging mechanism and the high diversity of urban land covers. The recent development of deep convolutional networks (DCN) opens a promising opportunity by automatically extracting the deep features from both data sets. In this study, a small-patched DCN (SDCN) was designed to estimate the impervious surface from optical and SAR data. Benchmark methods, e.g., GoogLeNet, VGG16, ResNet50, and the support vector machine were employed for comparison. Two study sites in the most complex metropolitan of China, the Guangdong-Hong Kong-Macau Greater Bay Area, were selected to assess the proposed method. Experimental results indicated the effectiveness of proposed SDCN with a better accuracy outperforming other benchmark methods. Furthermore, we found that 60%-80% of training samples performed comparably with the whole training set, indicating that a large number of training samples may not be necessary in all cases, depending on the settings of some factors (e.g., number of epochs). Generally, SDCN appears more suitable than other methods in terms of combining the optical and SAR data and improved the accuracy of estimating impervious surface.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Estimation and Multiscale Transformation of Aboveground Biomass: An
           HGSU-Oriented Approach Based on Multisource Data
    • Authors: Yingkun Du;Jing Wang;Zhengjun Liu;Yifan Lin;
      Pages: 2388 - 2396
      Abstract: Research works on aboveground biomass (AGB) estimation have received attention for a long time. However, most existing research works were based on pixels at respective scale, and relatively few studies focused on estimation and multiscale transformation of AGB. We, therefore, developed an innovative object-oriented approach to estimate and transform AGB under multiple scales. First, AGB-correlated spectral, structural, and geographic indicators were derived from multisource data. Subsequently, multiresolution segmentation technology was performed to produce homogeneous geography and spectrum units (HGSUs) at different scales. Finally, AGB at each scale was retrieved based on HGSUs and Random Forest (RF) algorithm. Besides, the utilities of nonspectral variables in modeling were further evaluated. Results showed that the HGSU-oriented approach was effective and advantageous to achieve the AGB estimation and multiscale transformation based only on the same dataset with few user-defined parameters. Structural and geographic variables, especially soil type, vegetation species, and CHM, played important roles in modeling, while the contribution of spectral variables decreased with the increasing scale in general. The HGSUs combined multiple pieces of information such as spectra, texture, vegetation height, soil type, slope, elevation, and land use, and provided a more detailed segmentation, a faster stability speed with increasing regression trees, and a higher accuracy than those based on common image objects segmented only by spectral indicators. Results also evidenced that the RF regression model had the capability to ingest mixed data. This study supplemented the existing AGB estimation research works especially for shorter vegetation in coastal areas (relative to forests), and the proposed approach was promising in larger regional scales.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • A Systematic Study of Synthetic Aperture Radar Interferograms Produced
           From ALOS-2 Data for Large Global Earthquakes From 2014 to 2016
    • Authors: Yu Morishita;
      Pages: 2397 - 2408
      Abstract: Many studies have used Advanced Land Observing Satellite 2 (ALOS-2) synthetic aperture radar (SAR) interferograms to make remarkable advances toward understanding and assessing seismic hazards. Next-generation satellites will make abundant L-band SAR data available in the near future, enabling even more progress in earthquake research and disaster response. Because a deep understanding of the performance capabilities and limitations of the only existing L-band satellite, ALOS-2, is crucial for planning future L-band SAR missions, this study produced SAR interferograms using ALOS-2 data for large global earthquakes that occurred between August 2014 and December 2016. All of the interferograms produced (49 from 30 seismic events) exhibited adequate coherence even in densely vegetated areas, where C-band interferograms tend to be unreliable because of severe decorrelation. Interferograms for 23 of the 30 seismic events successfully captured significant coseismic deformation signals. These results indicated that ALOS-2 can be leveraged to detect deformations with a high spatial resolution and a high precision unavailable from other instruments, particularly in tropical areas. In addition to the high coherence and high spatial resolution, ALOS-2 features such as left-looking and rapid emergency observations are also advantageous for earthquake research and disaster response. Although current baseline conditions are not always desirable for ALOS-2 interferograms because of limited observation resources, planned L-band missions (e.g., ALOS-4 and NASA-ISRO SAR), which will offer much wider coverage and higher observation frequency, are expected to improve this situation.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Retrieving Atmospheric and Land Surface Parameters From At-Sensor Thermal
           Infrared Hyperspectral Data With Artificial Neural Network
    • Authors: Mengshuo Chen;Li Ni;Xiaoguang Jiang;Hua Wu;
      Pages: 2409 - 2416
      Abstract: The radiances observed by satellites are influenced by both land surface and atmospheric parameters, and it is difficult to retrieve these parameters simultaneously from multispectral measurements with high accuracies. Even though several methods have been proposed, they focus on the retrieval of land surface or atmospheric parameters separately. Generally, these atmospheric parameters are atmospheric water vapor and temperature profiles. Thus, this study aims to establish a back propagation (BP) artificial neural network (ANN) to retrieve land surface emissivity (LSE), land surface temperature (LST), atmospheric transmittance, upward radiance, and downward radiance simultaneously from the hyperspectral thermal infrared (TIR) data, suitable for various air mass types and surface conditions. The principle component analysis technique is first used to compress and remove noise from the data. The evaluation of the ANN using the simulated data without noise indicated that the root mean square error (RMSE) of LST is approximately 0.643 K; the RMSEs of emissivity, transmittance, upward, and downward radiance are approximately 0.0046, 0.005, 0.72, and 2.95 K, respectively. When applied on the simulated data containing noise, the errors of LST, LSE, transmittance, upward, and downward radiance are 1.26, 0.01, 0.01, 1.54, and 4.57 K, respectively. When applied on the real atmospheric infrared sounder data, the retrieved accuracies become worse because of various unstudied reasons. However, the results show that the proposed ANN is promising in retrieving the land surface and atmospheric parameters simultaneously. Because of its simplicity, the proposed ANN can be used to produce preliminary results employed as the first estimates for physics-based retrieval methods.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Improving Impervious Surface Extraction With Shadow-Based Sparse
           Representation From Optical, SAR, and LiDAR Data
    • Authors: Yinyi Lin;Hongsheng Zhang;Gang Li;Ting Wang;Luoma Wan;Hui Lin;
      Pages: 2417 - 2428
      Abstract: Numerous studies on environmental modeling and ecological process lay emphasis on the fundamental information of the impervious surface area (ISA). However, accurate ISA extraction from high-resolution satellite images remains challenging due to both the high diversity of land covers and shadow effects from tall buildings and trees. To address the problem, a discriminative Optical-SAR-LiDAR dictionary sparse representation classification (OSLD-SRC) method was proposed using high-resolution WorldView-2, GeoEye-1, TerraSAR-X, and airborne LiDAR data. First, it used multisource data and fuzzy samples by low-pass filtering (LPF) to solve the problem of roads and buildings misclassification; second, it learned the Optical-SAR-LiDAR dictionary for nonshadow and shadow classes, and then used discriminative sparse coding method for classification to reduce the shadow effects and improve the ISA extraction accuracy. Experimental results demonstrated the effectiveness of the proposed method.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Aerosol Plume Characterization From Multitemporal Hyperspectral Analysis
    • Authors: Pierre-Yves Foucher;Philippe Déliot;Laurent Poutier;Olivier Duclaux;Valentin Raffort;Yelva Roustan;Brice Temime-Roussel;Amandine Durand;Henri Wortham;
      Pages: 2429 - 2438
      Abstract: In this paper, we focus on airborne hyperspectral imaging methodology to characterize particulate matter (PM) near industrial emission sources. Two short-term intensive campaigns were carried out in the vicinity of a refinery in the south of France, in September 2015 and February 2016. Different protocols of in situ PM measurements were performed, at stack measurements (flow rate and offline chemical analysis) and online measurement at the refinery border (size distribution, concentration, and chemistry of aerosols). A multitemporal methodology to retrieve aerosol type, to map the aerosol concentration, and to quantify mass flow rate from airborne hyperspectral data is described in this paper. This method applied to the refinery detected plume from the main stack yields a black carbon to sulfate ratio of 10/90 in mass inside the plume, with an average size distribution smaller than 100 nm. These results are in a good agreement with the online analysis of aerosols at the refinery border. The resulting quantitative map with a metric spatial resolution leads to an estimated flow rate of about 1 g/s and is in a good agreement with in situ stack measurements and modeling.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Validation of the LaSRC Cloud Detection Algorithm for Landsat 8 Images
    • Authors: Sergii Skakun;Eric F. Vermote;Jean-Claude Roger;Christopher O. Justice;Jeffrey G. Masek;
      Pages: 2439 - 2446
      Abstract: This study aims at validating the cloud mask produced by the land surface reflectance code (LaSRC) for Landsat 8 data. To detect clouds in optical satellite imagery, LaSRC uses quality assurance (QA) layers, which are produced during the atmospheric correction process. The QA layers include a “cloud mask,” which is based on the estimation of a residual metric showing the quality of aerosol inversion, and “high aerosol,” which shows the impact of aerosols on the derived surface reflectance. Validation is performed using the “L8 Biome” cloud validation dataset, which is produced by the US Geological Survey, and consists of 96 Landsat 8 scenes distributed globally over 12 different biomes. We show that the LaSRC cloud detection algorithm reliably identifies thick clouds with commission and omission errors less than 4%. Large cloud overdetection errors occur for thin clouds, which is due to the subjectivity of defining and extracting thin clouds in the reference dataset. We conclude this paper with recommendations on using the LaSRC QA layers, and give suggestions on reducing subjectivity, when generating cloud validation datasets.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • FLEX: A Parametric Study of Its Tandem Formation With Sentinel-3
    • Authors: David Arnas;Pedro Jurado;Itziar Barat;Berthyl Duesmann;Ralf Bock;
      Pages: 2447 - 2452
      Abstract: The Fluorescence Explorer (FLEX) is an Earth observation mission currently in development by the European Space Agency to perform quantitative measurements of the solar induce vegetation fluorescence. As a core of the mission concept, FLEX is planned to be launched by the end of 2023 and shall fly in tandem with one of the Copernicus Sentinel-3 satellites, which is already in orbit. This situation will allow FLEX to benefit from the optical and thermal sensors of Sentinel-3 and provide an integrated package of measurements. This paper presents the preliminary parametric analysis on the along-track dynamic of the tandem flight formation between Sentinel-3 and FLEX. It includes the study of the maneuvering strategy for a master-slave scenario concept, where Sentinel-3 acts as the master of the formation, whereas FLEX is the slave. This control strategy is assessed in terms of safety for both satellites and performance, where it is of interest to maintain the along-track distance between satellites as bounded as possible. Numerical and analytical results of this paper are provided showing that the control strategy proposed is able to fulfill the mission requirements under the expected conditions while providing enough time for the control center to coordinate the orbital maneuvers of Sentinel-3 and FLEX. Additionally, and from the results obtained in this paper, it is concluded that the safest configuration for the mission is locating FLEX ahead of Sentinel-3.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Syntactic Pattern Recognition for Wavelet Clustering in Seismogram
    • Authors: Kou-Yuan Huang;Dar-Ren Leu;
      Pages: 2453 - 2461
      Abstract: In a seismogram, there exist many kinds of wavelets. The reflected wavelet from the gas sand zone has a different shape with other layers. Usually, the information of each wavelet is weak and unknown, and the unsupervised classification method is applied to the clustering of the wavelets. Using the shape structure of the wavelet, syntactic pattern recognition is applied to the clustering. The extracted wavelets can be represented as strings of symbols. Levenshtein distance is used to calculate the distance between the two strings. Bottom-up and top-down hierarchical clustering methods are used in the construction of the dendrogram. The top-down hierarchical clustering by the recursive method is proposed. A new pseudo F-statistics is proposed to decide the optimal number of clusters. From the experimental results in simulated and real seismograms, the wavelets on the gas sand zone can be detected successfully. It can improve the seismic interpretation.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • $_{2}$ +Fluxes+and+Modeled+Partial+Pressure+of+CO $_{2}$ +in+Open+Ocean+of+Bay+of+Bengal&rft.title=Selected+Topics+in+Applied+Earth+Observations+and+Remote+Sensing,+IEEE+Journal+of&rft.issn=1939-1404&rft.date=2019&rft.volume=12&rft.spage=2462&rft.epage=2469&rft.aulast=Mahanta;&rft.aufirst=Abhishek&rft.au=Abhishek+Dixit;Lekshmi+K;Rishikesh+Bharti;Chandan+Mahanta;">Net Sea–Air CO $_{2}$ Fluxes and Modeled Partial Pressure of CO $_{2}$
           in Open Ocean of Bay of Bengal
    • Authors: Abhishek Dixit;Lekshmi K;Rishikesh Bharti;Chandan Mahanta;
      Pages: 2462 - 2469
      Abstract: The key role of oceans in the global climatic system can be accurately quantified by the measurement of CO2 exchange across air-sea interface. The direction and magnitude of this CO2 exchange are mainly governed by the gradient of partial pressure of carbon dioxide (pCO2) at the air-sea interface. The sea surface pCO2 is highly variable, primarily regulated by seasonal sea surface temperature (SST) and CO2 (aqueous) concentration. In this paper, the moored pCO2 observations from autonomous pCO2 system deployed at 15 °N 90 °E in the Bay of Bengal (BOB) were used to derive CO2 fluxes to examine variability over time scale at BOB. An attempt has also been made to develop a model using multiple linear regression (MLR) and support vector regression (SVR) to estimate the sea surface pCO2 from space-based observations of SST and salinity. pCO2 variability was primarily attributed to SST and CO2 concentration changes. Derived fluxes showed that BOB is a net annual source (0.440 g-C/m2/year for year 2014) of CO2 at mooring location with significant seasonality effects. For the estimation of surface pCO2, SVR model showed better results [root mean square error (RMSE) = 7.68 μatm] in comparison to MLR model (RMSE = 12.36 μatm). The seasonal climatological pCO2 maps from September 2011 to February 2018 showed southward increase in pCO2 in all seasons possibly due to southward increase in SST and less influence of river water. These pCO2 maps can further be utilized in deriving CO2 flux maps for BOB.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Airborne Wind Vector Scatterometer for Sea Surface Measurements
    • Authors: Juha Kainulainen;Sampo Salo;Janne Lahtinen;Guifré Molera Calves;Jaakko Seppänen;Jaan Praks;Teemu Hakala;Yuwei Chen;Juha Hyyppä;Martin Unwin;Philip Jales;Gerhard Ressler;Tânia Casal;Josep Rosello;
      Pages: 2470 - 2476
      Abstract: This paper describes the development of an Airborne Wind Vector Scatterometer for low-resolution wind speed measurements. The scatterometer is designed to meet a wind speed accuracy requirement of 1 m/s. In this ad hoc and low-budget project, the development of the instrument exploited some already existing subsystems. The development started from the definition of the scientific system requirements and ended with two experimental flights for wind vector retrieval from three test areas in the Gulf of Finland, the Baltic Sea. One of the flights was planned for a simultaneous overpass with the TDS-1 satellite that was conducting Global Navigations Satellite System - Reflectometry (GNSS-R) measurements. The results confirm the measurement capabilities of the GNSS-R technology and the desired 1 m/s wind speed accuracy of the scatterometer that was developed.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Effect of Faraday Rotation on L-Band Ocean Normalized Radar Cross Section
           and Wind Speed Detection
    • Authors: Osamu Isoguchi;Kenta Ishizuka;Takeo Tadono;Takeshi Motohka;Masanobu Shimada;
      Pages: 2477 - 2485
      Abstract: Observational evidence that L-band backscatter measurements are distorted by Faraday rotation (FR) is presented using match-ups consisting of the phased-array type L-band synthetic aperture radar-2 (PALSAR-2) data, scatterometer winds, and FR estimated from total electron contents (TEC) and magnetic fields. Investigating these data reveals that the PALSAR-2-derived ocean surface HH normalized radar cross section (NRCS) tends to decrease slightly as the FR angle increases and the HV one tends to increase the most. At an incidence angle of 27.5°, the measured HV NRCS increases by about 15 dB or more, whereas the FR angle increases from 0° to 15°. The variation of the PALSAR-2 observations with FR and its incident angle dependence are roughly explained by a model based on the scattering matrix of linearly polarized backscatter signatures. The error of wind speeds estimated by the L-band HH geophysical model function of ocean surface winds from the match-ups shows a tendency to underestimate wind speeds as FR increases. This is explained by the fact that the HH NRCS decreases as FR increases. Meanwhile, it is suggested that because ocean cross-polarized (HV) data with large co- and cross-polarization ratio has strong sensitivity to FR due to the inclusion of the co-polarization component, correction for the influence of the FR angle is essential for accurate ocean wind estimation.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Empirical Characterization of the SMOS Brightness Temperature Bias and
           Uncertainty for Improving Sea Surface Salinity Retrieval
    • Authors: Estrella Olmedo;Verónica González-Gambau;Antonio Turiel;Justino Martínez;Carolina Gabarró;Marcos Portabella;Joaquim Ballabrera-Poy;Manuel Arias;Roberto Sabia;Roger Oliva;
      Pages: 2486 - 2503
      Abstract: After more than eight years of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) acquisitions, an exhaustive, empirical characterization of the biases and uncertainties affecting SMOS brightness temperatures over the ocean is possible. We show that both parameters strongly depend not only on the position in the field of view, but also on the geographical location of the acquisition. Metrics based on the differences between expected and theoretical values of the bias and the uncertainty are developed and used for quantitatively assessing the locations where SMOS errors are currently not accurately characterized. This characterization can be used for the definition of a new empirical SMOS sea surface salinity (SSS) bias correction, a better cost function retrieval, and more accurate filtering criteria, which are expected to lead to a better SMOS SSS Level 2 product. We present a new L2 SMOS SSS product based on the described investigation. The performance of this preliminary product is similar to that of the version v662 of the official L2 SMOS SSS product at medium and low latitudes. However, it provides a better coverage at high latitudes and coastal regions affected by radio frequency interference (RFI), which correspond to those regions where the SMOS errors are currently poorly estimated.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Comparison of ATMS Striping Noise Between NOAA-20 and S-NPP and Noise
           Impact on Warm Core Retrieval of Typhoon Jelawat (2018)
    • Authors: Xiaolei Zou;Xiaoxu Tian;
      Pages: 2504 - 2512
      Abstract: The Advanced Technology Microwave Sounder (ATMS) is onboard both the National Oceanic and Atmospheric Administration (NOAA)-20 and the Suomi National Polar-Orbiting Partnership (S-NPP) satellites. NOAA-20 has the same sun-synchronous orbit as that of the S-NPP, but is 50 min (i.e., half orbit) ahead. The striping noise is found in ATMS brightness temperature observations from both NOAA-20 and S-NPP. In this study, first, a striping noise detection and mitigation algorithm that was previously developed for striping noise mitigation in ATMS observations from S-NPP is adopted to characterize the striping noise in NOAA-20 ATMS brightness temperature measurements. It combines a principal component analysis and an ensemble empirical mode decomposition method. It is found that the magnitudes of both the striping noise and the random noise in NOAA-20 ATMS data are smaller than those in S-NPP ATMS data. Second, global positioning system radio occultation retrieved temperature profiles are used as the training dataset for ATMS hurricane warm core retrievals in order to investigate the impacts of the data noise. Numerical results are demonstrated using the case of Typhoon Jelawat (2018), which rapidly intensified from a Category 1 to a Category 4 super typhoon and weakened back to Category 1 within 24 h. Finally, we show that a half-orbit separation of NOAA-20 from S-NPP enables the rapidly evolving vertical structures of Typhoon Jelawat. This suggests an enhanced tropical cyclone monitoring capability offered by NOAA-20 and S-NPP for this hurricane season and a few following years.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Validation of Sea Surface Wind From Sentinel-1A/B SAR Data in the Coastal
           Regions of the Korean Peninsula
    • Authors: Jae-Cheol Jang;Kyung-Ae Park;Alexis Aurélien Mouche;Bertrand Chapron;Ji-Hyun Lee;
      Pages: 2513 - 2529
      Abstract: In this study, using in situ measurements at 17 buoy stations off the Korean Peninsula, C-band model (CMOD) functions for Sentinel-1A/B IW mode synthetic aperture radar (SAR) data were validated. In total, 395 Sentinel-1A/B IW mode dual-vertical polarized images were used for collocation with in situ measurements from May 1, 2015, to September 30, 2017, and 807 matchup points were obtained. Prior to the validation, preprocessing such as speckle noise reduction and ship and land masking was completed. The in situ wind speeds were converted to a 10-m neutral wind considering atmospheric stability. High-resolution wind speeds were estimated by using the CMOD functions such as CMOD4, CMOD_IFR2, CMOD5, CMOD5.N, and CMOD5.Na. The root-mean-square errors of each model were less than approximately 1.8 m·s-1 (1.83, 1.82, 1.69, 1.68, and 1.65 m·s-1, respectively). The biases of all models were higher in the western coastal region than those in the eastern coastal region. The results showed the advantages and disadvantages of each model in the estimation of wind speeds in the coastal region around the Korean Peninsula as proposed in a concept of combined errors. The wind speeds derived from the SAR data also presented a tendency for water depth to be overestimated over shallow bathymetry and to be underestimated at high wind speeds. In addition, this study assessed potential sources of wind speed errors such as the effects originating from wind direction input, different platforms of Sentinel-1A and Sentinel-1B and their calibration, and from radar interference or regional oceanic characteristic environments.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Analog Data Assimilation of Along-Track Nadir and Wide-Swath SWOT
           Altimetry Observations in the Western Mediterranean Sea
    • Authors: Manuel Lopez-Radcenco;Ananda Pascual;Laura Gomez-Navarro;Abdeldjalil Aissa-El-Bey;Bertrand Chapron;Ronan Fablet;
      Pages: 2530 - 2540
      Abstract: The growing availability of ocean data brought forth by recent advancements in remote sensing, in situ measurements, and numerical models supports the development of data-driven strategies as a powerful, computationally efficient alternative to model-based approaches for the interpolation of high-resolution, gap-free, regularly gridded sea surface geophysical fields from partial satellite-derived observations. In this paper, we investigate such data-driven strategies for the spatio-temporal interpolation of sea level anomaly (SLA) fields in the Western Mediterranean Sea from satellite-derived altimetry data. We introduce and evaluate the analog data assimilation (AnDA) framework, which exploits patch-based analog forecasting operators within a classic Kalman-based data assimilation scheme. With a view toward the upcoming wide-swath surface water and ocean topography (SWOT) mission, two different types of altimetry data are assimilated: along-track nadir data and wide-swath SWOT altimetry data. Using an observing system simulation experiment, we demonstrate the relevance of AnDA as an improved interpolation method, particularly for mesoscale features in the 20- to 100-km horizontal scale range. Results report an SLA reconstruction RMSE (correlation) improvement of 42% (14%) with respect to optimal interpolation, and show a clear gain when the joint assimilation of SWOT and along-track nadir observations are considered.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Potential of SWOT for Monitoring Water Volumes in Sahelian Ponds and Lakes
    • Authors: Manuela Grippa;Cyprien Rouzies;Sylvain Biancamaria;Denis Blumstein;Jean-Francois Cretaux;Laetitia Gal;Elodie Robert;Marielle Gosset;Laurent Kergoat;
      Pages: 2541 - 2549
      Abstract: Small water bodies play a pivotal role in Sahel, as a critical source of water for livestock and people, and providers of important ecosystems services. Monitoring, modeling, and better understanding their hydrological behavior is, therefore, a key issue. The future Surface Water and Ocean Topography (SWOT) satellite mission will bring a resolution and spatial coverage breakthrough, allowing the estimation of water levels and volumes in small water bodies worldwide. This paper assesses the potential of SWOT for monitoring water volumes in Sahelian ponds and lakes. This is done by analyzing SWOT-like synthetic data produced using a SWOT simulator developed by NASA-JPL. For the Agoufou lake, water levels were retrieved with an accuracy better than 4 cm, while slightly worse results were obtained for the Zalam-Zalam lake, that has a more elongated shape. In addition, data from the global precipitation mission dual-frequency precipitation radar have been also employed to investigate the backscattering coefficient variability in the same radar frequency band (Ka-band) as SWOT. We have found that, in the study region, the contrast between water and land, dependent on soil type, soil moisture, and wind conditions, is sometime quite small which can be challenging for water masks estimation. Overall, the first application of the SWOT simulator over the Sahel has shown the good potential of SWOT for monitoring the seasonal variability of water levels and volumes in this region.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Automated Seasonal Separation of Mine and Non Mine Water Bodies From
           Landsat 8 OLI/TIRS Using Clay Mineral and Iron Oxide Ratio
    • Authors: Jit Mukherjee;Jayanta Mukherjee;Debashish Chakravarty;
      Pages: 2550 - 2556
      Abstract: Opencast mining has huge effects on water pollution for several reasons. Fresh water is heavily used to process ore. Mine effluent and seepage from various mine related areas especially tailing reservoir, increase water pollution immensely. Monitoring and classification of mine water bodies, which have such environmental impacts, have several research challenges. In the past, land cover classification of a mining region detects mine and non mine water bodies simultaneously. Water bodies inside surface mines have different characteristics from other water bodies. In this paper, a novel method has been proposed to differentiate mine and non mine water bodies over the seasons, which does not require to set a threshold value manually. Here, water body regions are detected over the entire scene by any classical water body detection algorithm. Further, each water body is treated independently, and reflectance properties of a bounding box over each water body region are analyzed. In the past, there were efforts to use clay mineral ratio (CLM) to separate mine and non mine water bodies. In this paper, it has been observed that iron oxide ratio (IO) can also separate mine and non mine water bodies. The accuracy is observed to increase, if the difference of CLM and IO is used for segregation. The proposed algorithm separates these regions by taking into account seasonal variations. Means of differences of CLM and IO of each bounding box have been clustered using K-means clustering algorithm. The automation provides precision and recall for mine, and non mine water bodies as [77.83%, 76.55%] and [75.18%, 75.84%], respectively, using ground truths from high-definition Google Earth images.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Investigating Water Variation of Lakes in Tibetan Plateau Using Remote
           Sensed Data Over the Past 20 Years
    • Authors: Yanhong Wu;Mengru Li;Linan Guo;Hongxing Zheng;Hongyuan Zhang;
      Pages: 2557 - 2564
      Abstract: Water storage change of the lakes in the Tibetan Plateau is regarded as one of the most critical regional hydrological consequences owing to climate change. In this study, we investigate the water storage changes in 22 lakes in the Tibetan Plateau based on sequential remote sensed lake area and water level, which are derived from moderate resolution imaging spectroradiometer (MODIS) surface reflectance and Laboratoire D'Etudes en Géophysique et Océanographie Spatiales (LEGOS) altimetry data, respectively. Water storage of the lake is estimated on the basis of the relationship between lake area and water level. The method can be seen as an alternative to the conventional hydrological approaches. The results show that, during 2001-2017, most of the studied lakes in the Tibetan Plateau have shown significant increasing trends in water storage accompanied with larger lake area and higher water level. The changes in lake water storage are found in close relation to variations of climate factors such as precipitation, potential evaporation, and temperature in most lakes. The climate change impacts, however, can be amplified or attenuated by other environmental factors in some lake catchments.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Thermophysical Features of Shallow Lunar Crust Demonstrated by Typical
           Copernican Craters Using CE-2 CELMS Data
    • Authors: Zhiguo Meng;Xiangyue Li;Shengbo Chen;Yongchun Zheng;Jiancheng Shi;Tianxing Wang;Yuanzhi Zhang;Jinsong Ping;Yu Lu;
      Pages: 2565 - 2574
      Abstract: Chang'E lunar microwave sounder (CELMS) data provide a potential way to understand the thermophysical features of the shallow lunar crust. In this study, four typical Copernican craters, including Copernicus, Aristarchus, Tycho, and Jackson, have been selected and their brightness temperature (TB) performances are evaluated with the CE-2 CELMS data. The results are as follows. First, the hot TB anomaly is reunderstood according to the TB behaviors. The cause to the anomaly is still in doubt, and we rule out the previous explanations as rock abundance or topography. Second, the existence of the cold anomaly indicates the shallow lunar crust is likely much colder than what we knew. Third, the TB performances indicate that the shallow lunar crust is likely homogeneous, and the materials here have very low (FeO+TiO2) abundance and thermal inertia. Also, the difference of the TB performances from the crater floors to far distance will provide a new constraint for the cratering formation study.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • MTE Features of Apollo Basin and Its Significance in Understanding the SPA
           Basin
    • Authors: Zhiguo Meng;Yongzhi Wang;Shengbo Chen;Yongchun Zheng;Jiancheng Shi;Tianxing Wang;Yuanzhi Zhang;Jinsong Ping;Lele Hou;
      Pages: 2575 - 2583
      Abstract: Apollo basin is located within the large South Pole- Aitken (SPA) basin. The study on Apollo basin will provide interesting information about the basic geologic issues about the lunar farside. In this paper, the normalized brightness (TB) temperature (nTB) maps and the TB difference (dTB) maps are generated with the Chang'E-2 microwave sounder data to study the microwave thermal emission features of Apollo basin. The results are as follows. First, the mare volcanism in Apollo basin is re-understood according to the nTB performances at noon, and they should be originated from the southern part of the Apollo basin and strongly altered by the later impact ejecta. Second, the nTB maps indicate that there exists a special material from Dryden crater to Chaffee crater, whose thickness is more than 31 cm but less than 76.9 cm. Third, the similar dTB performances at 3.0 GHz indicate the homogeneous regolith thermophysical parameters of Apollo basin in the lateral direction. Fourth, the dTB maps and the discovered cold TB anomaly indicate the homogeneity of the SPA basin at least in the microwave thermophysical parameters. Our study also shows that the scientific study about the lunar surface is not sufficient only by visible data.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
  • Introducing IEEE collabratec
    • Pages: 2584 - 2584
      Abstract: Advertisement, IEEE.
      PubDate: July 2019
      Issue No: Vol. 12, No. 7 (2019)
       
 
 
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