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