The accuracy evaluation of SAR-retrieved soil moisture is generally carried out by comparing a limited number of measured soil moisture data with inversion values. In fact, the evaluation results could only reflect inversion accuracy of finite sampling area. This paper presented a method for evaluating reliability of soil moisture retrieved from the whole study area. First, measured soil moisture data and surface roughness data were selected, and then empirical equation of soil moisture retrieval was obtained by multiple regression statistical fitting. Soil moisture inversion was carried out in the whole study area. Then, principal component analysis (PCA) was used to extract principal component from 10 factors (soil moisture, surface temperature, NDVI, soil texture index, topography index, radar incidence angle and b3, b4, b5 and b7 in Landsat TM) affecting soil moisture by choosing from TM and SAR data, and the first three principal components were combined into RGB images. The RGB image containing the first three principal component was segmented using watershed algorithm. A graph of a split target area could be get. At last a data set about regional feature similarity was obtained by calculating mahalanobis distance between 6 dimensional feature vectors of each segmentation region and feature vectors of each quadrat area. These components of region feature vector included soil moisture, land surface temperature, NDVI, soil texture index, surface roughness and radar incidence angle. The reliability of the inversion results was computed based on the data set. The results were validated by using Envisat ASAR (advanced synthetic aperture radar) C-band dual polarization (VV, HH) data and the observed values of ground truth measurements synchronizing with Envisat ASAR. Soil moisture was retrieved, and reliability of inversion results was evaluated. By using measured data of soil moisture in several sampling areas, the accuracy of inversion results was evaluated. By comparing reliability and R2, the results show that reliability of inversion results can effectively reflect the accuracy of soil moisture retrieval.