The selection of methods for extracting crop planting areas is of great significance for agricultural remote sensing monitoring. To explore the differences between optimum phase scheme, time series optical data scheme and optical-SAR (synthetic aperture radar) fusion phase scheme in remote sensing recognition of summer maize planting areas, Shanghe County of Shandong Province is taken as the study area. Based on the Sentinel-1/2 data from the GEE (Google Earth Engine) cloud platform, three datasets are constructed. Combined with ground survey samples, random forest method is used to extract the summer maize planting areas in the study area using three schemes, and the accuracy of each scheme is analyzed. The result shows that all the three schemes can achieve high accuracy in distinguishing summer maize planting areas from other crops. Compared with the optimum phase scheme, the time series optical data scheme improves the overall classification accuracy of summer maize from 83.01% to 89.44%, and the Kappa coefficient increases from 0.77 to 0.86. Compared with the optimum phase scheme and time series optical data scheme, the overall classification accuracy of the optical-SAR fusion phase scheme is the highest, reaching 92.51%, and the Kappa coefficient reaches 0.89. The classification results show that the optical-SAR fusion phase scheme can effectively recognize summer maize planting areas with high accuracy, providing reference for agricultural investigation and management during the growing season.