Crop-type classification is one of the most significant applications in polarimetric synthetic aperture radar (PolSAR) imagery. As a remote sensing technique, PolSAR has been proved to have the ability to provide high-resolution information of illustrated objects. However, single-temporal PolSAR data are restricted to provide sufficient information for crop identification due to the complicated condition of varying morphology within various growing stages. With an increasing number of spaceborne PolSAR systems launched, a large amount of real PolSAR data are being generated and used to provide great opportunities for multitemporal analysis. The main contribution of this paper is to improve crop classification accuracy with various features of classical <inline-formula> <tex-math notation="LaTeX">H </tex-math></inline-formula>/<inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> parameters. First, in order to deal with dual-PolSAR data, <inline-formula> <tex-math notation="LaTeX">H </tex-math></inline-formula>/<inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> decomposition algorithm for quad-PolSAR is modified to suit to the case of dual polarization. Second, according to the differential scattering characteristics of main crops, a new parameter is innovatively defined to measure the differential characteristics in the <inline-formula> <tex-math notation="LaTeX">H </tex-math></inline-formula>/<inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> classification plane. Third, crop types are discriminated by applying a supervised classification method with the newly defined parameter. Furthermore, the correctness of the parameter is verified with simulated and real Sentinel-1 data as well as AirSAR data. Finally, the performances of the classification method are investigated by the comparison with complex Wishart, Freeman-Wishart, and support vector machine (SVM) classifiers. Hence, the experimental results show that the proposed method and SVM classifier with the newly defined parameter have the ability to improve crop classification accuracy.