Crop type classification is one of the most significant applications in polarimetric synthetic aperture radar (PolSAR) imagery. As an advanced remote-sensing technique, PolSAR has been proved to provide high-resolution information, including the intensity and polarization of illustrated land surface. However, single-temporal PolSAR data are restricted to provide sufficient information for crop classification and identification. With the increase of number of airborne and spaceborne PolSAR systems, a large number of real PolSAR data are generated, and thus provides opportunities for multi-temporal data analysis. The potential of improving crop classification accuracy by introducing the differential characteristics of H/α parameters for multi-temporal PolSAR images was investigated. Firstly, by analyzing the characteristics of several typical crops in different growing stages, a new parameter was defined for the first time to describe the differential characteristics of H/α distribution. Therefore, a new supervised classification method with the newly defined parameter was proposed to classify different crop types. The main idea of the proposed method was to apply various features of classical H/α parameters to improve the accuracy of crop classification. A validation test for the new approach was performed with Sentinel-1 data sets which were simulated by Radarsat-2 data sets and provided by ESA. The results showed that the mean accuracy of the proposed method was improved by 4 percentage points compared with the supervised complex Wishart classifier when the six kinds of crops were classified. Furthermore, the number of classes was reduced to 4 and the accuracy was almost improved by 6 percentage points.