Zhao, JF; Wang, YP; Xu, JW; Xie, HF; Sun, S (2020). Soil Moisture Assessment Based on Multi-Source Remotely Sensed Data in the Huaihe River Basin, China. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 56(5), 935-949.

The reliable estimation of soil moisture on spatial and temporal scales is of fundamental importance in improving the impact assessment of drought and flood on plant productivity and enhancing our understanding of the link between the water and biogeochemical cycles. Currently, remotely sensed data can offer a chance to improve spatial variability, especially in environments with scarce ground-based data. To obtain soil moisture information with high resolution and high precision in such mountainous areas as the Huaihe River Basin, we combined the advantages of MODerate-resolution Imaging Spectroradiometer (MODIS) optical remote sensing sensor that has high spatial resolution and sensitive characteristics to surface covering type and Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) passive microwave sensor that has high temporal resolution and the characteristics of small interference by clouds. Five years of AMSR-E and MODIS data (2006-2010) were fused based on the wavelet transform (WT) method. The soil moisture in the Huaihe River Basin was extracted using these fused remotely sensed data. The results showed that the soil moisture from AMSR-E and MODIS fusion data could capture soil moisture dynamics well. The accuracies of soil moisture from AMSR-E and MODIS fusion data based on WT were better than those from single remote sensing data. The soil moisture from MODIS and AMSR-E fusion data was more sensitive to the season, especially in spring, summer, and autumn. The accuracy of soil moisture from MODIS and AMSR-E fusion data in time and space varied in the Huaihe Basin. From the time series, high accuracies of soil moisture were observed in the spring of 2009 and in the spring and winter of 2010. From the spatial series, high accuracies of soil moisture were found in Zhumadian and Bozhou stations, and the worst accuracies were observed in Huaiyin and Shangqiu stations, which were primarily related to some factors such as local topography, vegetation coverage, and precipitation. These findings will provide interesting insights that can be useful for developing measures to prevent drought and flood disasters on a regional scale in China.