Corn is an important food crop worldwide, and its yield is directly related to Chinese food security. Accurate remote sensing extraction of corn can realize the rational application of land resources, which is of great significance to the sustainable development of modern agriculture. In the field of large-scale crop remote sensing classification, single-period optical remote sensing images often cannot achieve high-precision classification. To improve classification accuracy, multiple time series image combinations have gradually been adopted. However, due to the influence of cloudy and rainy weather, it is often difficult to obtain complete time series optical images. Synthetic aperture radar (SAR) data are imaged by microwaves, which have strong penetrating power and are not affected by clouds. A critical way to solve this problem is to use SAR images to compensate for the lack of optical images and obtain a complete time series image in the corn-growing season. However, SAR images have limited wavelengths and cannot provide important wavelengths, such as visible light bands and near-infrared information. To solve this problem, this study took Zhaodong City, a vital corn-planting base in China, as the research area; took GF-6/GF-3 and Sentinel-1/Sentinel-2 as remote sensing data sources; designed12 classification scenarios; analyzed the best classification period and the best time series combination of corn classification; studied the influence of SAR images on the classification results of time series images; and compared the classification differences between GF-6/GF-3 and Sentinel-1/Sentinel-2. The results show that the classification accuracy of time series combinations is much higher than that of single-period images. The polarization characteristics of SAR images can improve the classification accuracy with time series images. The classification accuracy of GF series images from China is obviously higher than that of Sentinel series images. The research performed in this paper can provide a reference for agricultural classification by using remote sensing data.