The dual-polarized Sentinel-1 synthetic aperture radar (SAR), which performs C-band SAR imaging day and night regardless of the weather, offers new opportunities for wetland cover monitoring in regions frequently covered by cloud. In this study, a decision tree (DT) classifier was applied to investigate the utility of Sentinel-1 for wetland classification in Poyang Lake, China. Six land cover classes were identified: water body, bare land, aquatic vegetation, cropland, forest, and urban area surrounding the lake. Two types of features were extracted from the dual-polarized SAR data, namely, the backscattering coefficients and the polarimetric decomposition components. Then, the DT classifier was trained and applied with backscattering features, polarimetric features, or a combination of the two features. The overall accuracy of all the classifiers was over 90% for the different feature combinations (98.02, 90.63, and 98.59%) for the classes in the lake area, compared with less than 78% (74.84, 63.01, and 77.29%) when the classes surrounding the lake were also considered, which demonstrates the potential of Sentinel-1 SAR data for wetland monitoring. The accuracy of different feature combinations increased in the following order: polarimetric features < backscattering features < combination of polarimetric and backscattering features. The artificial neural network, naïve Bayes, random forest, and adaptive boost algorithms were compared for the case of using backscattering features, and the adaptive boost algorithm showed lower performance than the other three algorithms.