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Soil moisture is an important parameter in the surface process, and it is indispensable in the field of crop growth and drought monitoring. SAR can penetrate clouds and fog to achieve high-resolution observations, so it has great advantages in remote sensing estimation of soil moisture. In this paper, Sentinel-1A radar data and MODIS were used to explore the applicability of soil moisture retrieval in a large area based on the support vector regression (SVR) method. By analyzing the characteristics closely related to soil moisture, the input of the algorithm was determined including VV, VH polarization backscattering coefficient, radar local incident angle (LIA), digital elevation model (DEM), slope (SLP), and normalized vegetation index (NDVI). Then the accuracy of the SVR model constructed with different feature combinations was discussed, and the best performing model was selected to estimate soil moisture in northern and central Anhui province. The results showed that the model with the combination of VV, LIA, NDVI, DEM, and SLP input had the highest accuracy with R2 of 0.9413 and the root mean square error (RMSE) of 0.0085 cm3·cm-3 , in which terrain factor had a greater impact. Finally, the best model was used to achieve a wide range of soil moisture retrieval, and test samples were used to verify the estimation accuracy with R2 of 0.6444 and RMSE of 0.036 cm3·cm-3 . Moreover, the temporal and spatial distribution of the retrieval result is reasonable, which can characterize the distribution difference of a large study area.
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