Soil moisture retrieval in the Tibetan plateau using optical and passive microwave remote sensing data

High-precision soil moisture estimation in Tibetan Plateau plays an essential role in the terrestrial water cycle and its impact on the global weather and climate. The Temperature Vegetation Dryness Index (TVDI) is widely used to estimate soil moisture on a large scale, but the accuracy of the TVDI can be influenced by some factors (eg. the surface temperature and NDVI). The AMSR-E data accurately records the soil moisture information, but the spatial resolution is low. This study aims to develop appropriate methods to estimate the soil moisture with high accuracy over the Tibetan Plateau. Firstly, the NDVI was replaced by MSAVI to correct the saturation characteristic of NDVI, and the terrain-induced variations in the land surface temperature (Ts) were removed using the ASTER GDEM data. Then, a soil moisture estimating model was built from a neural network combining the improved TVDI data, AMSR-E data and TRMM data and applied to obtain the soil moisture of three networks (CAMP/Tibet, Maqu, and Nagqu) in Tibetan Plateau. Finally, the method was validated using in situ soil moisture measurements. The results show that the soil moisture retrieved by the present algorithm has a higher accuracy (RMSE=0. 031 similar to 0. 041 m(3) . m(-3)). This study also has applied the algorithms for the Tibetan Plateau continuous soil moisture spatial distribution retrievals, and the trend of the soil moisture was compared with the trend of measured precipitation. The result showed the trends of the two variables had a strong correlation.