Fusion of Surface Soil Moisture Data for Spatial Downscaling of Daily Satellite Precipitation Data

Remote sensing satellites provide an effective solution for obtaining large-scale precipitation data. However, the spatial resolution of satellite-based precipitation products is often too coarse for hydrological applications at the regional scale. As a solution, spatial downscaling has been increasingly investigated to increase the spatial resolution of satellite-based precipitation. The selection of effective explanatory variables at fine spatial resolution has become a crucial concern in precipitation downscaling. Generally, surface soil moisture (SSM) has a strong physical relation with precipitation (especially at the regional scale), but this relationship has rarely been considered. In this paper, we proposed to fuse SSM in precipitation downscaling. Specifically, the 3 km SSM data (i.e., SPL2SMAP_S) were incorporated to downscale the 10 km Integrated MultisatellitE Retrievals for Global Precipitation Measurement (GPM IMERG) daily precipitation data to 3 km. Based on case studies in southeastern China, the proposed strategy was compared with the existing scheme fusing digital elevation model (DEM) or normalized difference vegetation index (NDVI) data as an alternative. The results demonstrated that, compared to the original precipitation product, all downscaling results can provide richer spatial details. The proposed scheme outperformed the other schemes, with a correlation coefficient (CC) of 0.63, a root mean square error (RMSE) of 15.7 mm, and a mean absolute error (MAE) of 8.74 mm. Furthermore, the proposed scheme is more sensitive to precipitation events of different intensities. In addition, when the historical precipitation is discontinuous, the advantages of the proposed scheme are more apparent.