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Since remote sensing technology based on optical images is usually influenced by cloud and rain, it’s difficult to acquire continuous crop growth curves in some areas. Radar, as an active remote sensing technique, can overcome the disadvantage successfully. Taking the farm located in the city of Weinan of Shaanxi Province as study area, two methods of maximum likelihood (ML) and support vector machine (SVM) were adopted to combine multi-sensor remote sensing data of Sentinel-1 and Sentinel-2, and thus improve crop classification accuracy. The results showed that classification results with fusion data were better than those of optical data. The classification result of fusion data composed of Sentinel-1 and Sentinel-2’s red, green, blue and near-infrared bands with no cloud were improved evidently with SVM method. The overall accuracy and Kappa coefficient were raised by 2 percentage points and 5 percentage points, respectively. In the case of a few cloud cover in the study site, the overall accuracy and Kappa coefficient with ML method were increased by 2 percentage points and 4 percentage points, respectively. With SVM method, the overall accuracy and Kappa coefficient were raised by almost 6 percentage points and 8 percentage points, respectively.