Downscaling of FY3B Soil Moisture Based on Land Surface Temperature and Vegetation Data

Soil moisture (SM) is a key variable in the study of hydrology, the environment, meteorology, and other fields. One widely used approach to retrieve soil moisture data is based on satellite remote sensing technology. However, the spatiotemporally continuous soil moisture products retrieved from microwave remote sensing data do not meet the accuracy requirements of flood prediction and irrigation management, due to their coarse spatial resolution. China's Fengyun-3B (FY3B) microwave radiation imager (MWRI) soil moisture product is one of the relatively new passive microwave products. Remotely sensed soil moisture data retrieved by the MWRI onboard the FY3B satellite is currently provided at a 25 km grid resolution. In this study, in terms of the thermal inertia theory, the FY3B soil moisture products were downscaled from 25 km to 1 km based on the North American Land Data Assimilation System (NLDAS) grid (12.5 km). For different ranges of the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR), the relationship of soil moisture and diurnal temperature change from the land surface model of NLDAS could be obtained. The 1 km soil moisture was then computed from this regression model using 1 km LST data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) (1 km), which was then bias-corrected using FY3B 25 km soil moisture data. The algorithm was applied to every FY3B pixel in the Soil Moisture Active Passive Validation Experiment 2015 (SMAPVEX15). The downscaling results were validated using the in-situ soil moisture from SMAPVEX15. The downscaling estimates better characterize the continuity of spatial and temporal aspects and are more consistent with the soil moisture data used for validation.