Soil moisture retrieval in plateau areas based on Sentinel-1 and Landsat-8 remote sensing data

Soil moisture is a crucial parameter for agricultural production, water resources management, global climate, et al. Vegetation and roughness, as two key influencing factors, are the focus of research. Synthetic aperture radar is an important tool of obtaining moisture parameter. This article conducts research on the following aspects. First, the overall backscattering coefficient is obtained based on Sentinel-1 radar data, and the backscattering coefficient of vegetation is separated using the water cloud model based on three Landsat-8 vegetation indices (i.e., NDVI, NDWI, and MSAVI). Then, the Dobson model combined with the advanced integral equation model (AIEM) was used to establish a backscattering coefficient table lacking surface roughness, and the minimum cost function was used to obtain the optimal roughness parameters. Finally, the least squares method is used to determine the coefficients of the inverted soil moisture empirical equation. The experimental results show that in the plateau area, the model inversion results are in good agreement with the ground measurement results. Among them, the inversion results using the normalized water index (NDWI2) input water cloud model combined with the optimal roughness are the best. The combined coefficient reaches 0.8402, and the root mean square error is 0.02732cm3/cm3.