Downscaling of Remote Sensing Soil Moisture Products Based on TVDI in Complex Terrain Areas

Abstract

Soil Moisture (SM) is an important parameter to describe water cycle, land condition and soil biology. The spatial resolution of SM data acquired by satellites remote sensing is relatively low, so downscaling techniques are used to obtain high-resolution SM data to meet practice demand. However, there is few downscaling algorithm for SM in complex terrain areas. Temperature Vegetation Drought Index (TVDI) is an intermediate variable between Normalized Vegetation Index (NDVI) and Land Surface Temperature (LST) and TDVI is expected to retrieve SM precisely. This study applies NDVI and LST products of the Moderate Resolution Imaging Spectroradiometer (MODIS), and SM products (in 36 km) of Soil Moisture Active Passive (SMAP) to obtain high-resolution (1 km) SM for Sichuan-Chongqing region. The generated high-resolution results are verified by comparing with the Global Land Surface Data Assimilation System (GLDAS) SM products. Simulation results indicate that the proposed method is effective in complex terrain areas.