A fine-resolution soil moisture dataset for China in 2002~2018
Authors/Creators
- 1. School of Physics and Electronic-Engineerring, Ningxia University; School of Earth Sciences and Engineering, Hohai University
- 2. School of Physics and Electronic-Engineerring, Ningxia University; Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences
- 3. School of Surveying and Geo-Informatics, Shandong Jianzhu University
- 4. National Space Science Center, Chinese Academy of Sciences; State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University
- 5. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University
- 6. Civil and Environmental Engineering, University of Connecticut
- 7. School of Earth and Space Sciences, Peking University
- 8. School of Physics and Electronic-Engineerring, Ningxia University
Description
Soil moisture is one of the key parameters for flood forecast, drought detection, crop yield estimation, weather prediction and hydrological modeling. Passive microwave remote sensing technology can quickly obtain soil moisture over large areas, but the coarse spatial resolution imposes great limitations. In order to improve the temporal and spatial resolution of soil moisture products, we built a spatial weight decomposition model to improve the resolution of soil moisture products. The validation and application analysis indicate that new product can meet application needs. SMC versions 2.0 is on the basis of 1.0 is a blend of precipitation data and an improved global remote-sensing-based surface soil moisture (RSSSM) dataset by https://doi.pangaea.de/10.1594/PANGAEA.912597 for further improvement.
Files
SMC_V2.0.zip
Files
(487.4 MB)
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