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Reviewer Board

Members of the reviewer board are selected from all Remote Sensing reviewers for regularly providing timely high quality reports on submitted manuscripts. Responsibilities of reviewers are available here.

Members

1. Cooperative Institute for Research in Environmental Sciences (CIRES), NOAA National Centers for Environmental Information, Formerly NOAA’s National Geophysical Data Center, Boulder, CO 80305, USA
2. Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA
Interests: remote sensing; high performance computing; hyperspectral imaging; image processing; geoinformatics; pattern recognition and artificial intelligence
School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: thermal infrared remote sensing; UAV remote sensing; urban remote sensing; land surface temperature; hydrological process; climate change.
5825 University Research Court, Suite 4001, University of Maryland, College Park, MD 20740-3823, USA
Interests: atmospheric physics and environment; remote sensing
Department of Geoscience and Remote Sensing, Delft University of Technology, 2628 CN Delft, The Netherlands
Interests: shadow detection; 3D City Model; change detection; dense image matching; machine learning; deep learning; LiDAR; vhr images
ASRC Federal Data Solutions, Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA
Interests: remote sensing; land cover mapping; machine learning; time series analysis
Department of Ecosystem Science and Management, Texas A&M University, College Station, TX 77450, USA
Interests: hyper point cloud (HPC); HPC-based intensity surface; percentile height; gridding; full waveform lidar; Tree Segmentation; vegetation structure
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
Interests: bio-optical properties; ocean color remote sensing; phytoplankton size structure; primary production; phytoplankton carbon
School of Environment and Surveying, China University of Mining and Technology, Xuzhou, China
Interests: remote sensing; deep learning; GIS; geo-semantic
IM Systems Group, Inc., Rockville, MD 20852, USA
Interests: image processing; remote sensing; global climate change; Earth Energy Budget
School of Environmental Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China
Interests: atmospheric chemistry; trace gases; remote sensing; modeling
MIT Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Interests: Urban Complex Systems; urban heat islands; 3D solar city; spatiotemporal data modeling; spatial database
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: soil moisture; evapotranspiration; river; lake; hydrology; remote sensing

Website
School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China
Interests: remote sensing of vegetation; global change ecology; vegetation-environment interactions

Website
Department of Information Engineering-DII, Marche Polytechnic University, Ancona I-60131, Italy
Interests: artificial intelligence techniques in remote sensing; deep learning; machine learning; computer vision; 3D modeling; UAV and satellite remote sensing; LiDAR; GIS; land use/land cover mapping; land use/land cover changes; precision agriculture
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
Interests: image processing; remote sensing; machine learning; computer vision

Website
Arkansas Forest Resources Center, University of Arkansas Agricultural Experiment Station, University of Arkansas, Monticello, AR 71655, USA
Interests: soil science; pedology; land evaluation; remote sensing; geographic information system; land cover land use classification
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