Soil moisture is an important parameter of ecological environment and an important part of water cycle.The retrieval of surface soil moisture based on multi-source remote sensing data is a hotspot and trend in recent years.As a new generation of Sentinel satellites, the Sentinel-1 SAR data combined with the Sentinel-2 optical data have broad application prospects.Taking Salamanca, Spain as the research area, a BP neural network soil moisture retrieval model is constructed by combining the Sentinel-1 backscatter coefficient and incidence angle information, the vegetation index extracted from the Sentinel-2 optical data, and the ground observation data, and the model is applied to retrieve the soil moisture in the area.Finally, the model retrieval results are tested and evaluated.Results show that:(1) Based on the Sentinel-1 satellite VV and VH polarization radar backscatter coefficients and radar incidence angles and the Sentinel-2 vegetation index data, the BP neural network soil moisture retrieval model can realize high-precision retrieval of soil moisture in Salamanca area;(2) In the joint retrieval of soil moisture of optical and microwave data in vegetation coveragearea, the NDVI, NDWI1 and NDWI2 indices from the Sentinel-2 can be used to weaken the influence of vegetation on soil moisture retrieval, but the NDWI1 based on SWRI1 band can obtain more accurate soil moisture retrieval results (RMSE=0.049 cm³/cm³, ubRMSE=0.048 cm³/cm³, Bias=0.008 cm³/cm³, r=0.681);(3) Comparing with the Sentinel-1 VH polarization model, the Sentinel-1 VV polarization model shows greater advantages in soil moisture, indicating that the Sentinel-1 VV polarization model is more suitable for soil moisture retrieval.