Daily soil moisture mapping at 1 km resolution based on SMAP data for desertification areas in northern China
Pinzeng Rao,Yicheng Wang,Fang Wang,Yang Liu,Xiaoya Wang,and Zhu Wang
Pinzeng Rao
State Key Laboratory of Hydroscience and Engineering, Department of
Hydraulic Engineering, Tsinghua University, Beijing 100084, China
State Key Laboratory of Simulation and Regulation of Water Cycle in
River Basin, China Institute of Water Resources and Hydropower Research,
Beijing 100038, China
Yicheng Wang
State Key Laboratory of Simulation and Regulation of Water Cycle in
River Basin, China Institute of Water Resources and Hydropower Research,
Beijing 100038, China
State Key Laboratory of Simulation and Regulation of Water Cycle in
River Basin, China Institute of Water Resources and Hydropower Research,
Beijing 100038, China
Yang Liu
State Key Laboratory of Simulation and Regulation of Water Cycle in
River Basin, China Institute of Water Resources and Hydropower Research,
Beijing 100038, China
Xiaoya Wang
State Key Laboratory of Remote Sensing Science, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Zhu Wang
State Key Laboratory of Simulation and Regulation of Water Cycle in
River Basin, China Institute of Water Resources and Hydropower Research,
Beijing 100038, China
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It is urgent to obtain accurate soil moisture (SM) with high temporal and spatial resolution for areas affected by desertification in northern China. A combination of multiple machine learning methods, including multiple linear regression, support vector regression, artificial neural networks, random forest and extreme gradient boosting, has been applied to downscale the 36 km SMAP SM products and produce higher-spatial-resolution SM data based on related surface variables.
It is urgent to obtain accurate soil moisture (SM) with high temporal and spatial resolution for...
