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 version 3.0 combines the previous two versions, and corrects and optimizes in some areas.

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.

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.

The SMC dataset present a high spatial resolution monthly soil moisture raster dataset over China, spanning form the year 2002 to 2018, and the data are presented over a grid with 0.05°. The data set consists of satellite-based AMSR-E/2 and SMOS SM, and base on a spatially weighted decomposition (SWD) model, using the TVDI calculated from MODIS LST/NDVI data as a weighting factor, downscaling to obtain fine spatial resolution SM. Overall, new datasets were strongly correlated with in-situ observations (correlation coefficient R: 0.82, 0.88, and 0.9 and unbiased root mean square error (ubRMSE): 0.051, 0.048 and 0.042 m³m^-3 on monthly, seasonal and annual scales, respectively). The dataset unprecedentedly long-term high spatial resolution offers important advantages for drought monitoring and its assessment at district and river basin level climate change in China.