Multi-source remote sensing data fusion can provide more effective features for crop recognition, but too many feature parameters will cause data redundancy. This work takes the Honghe Hani area of Yunnan Province as the research area, relies on the Google Earth Engine cloud platform, and collaborates with Sentinel-1/2 and Shuttle Radar Topography Mission digital elevation model multi-source remote sensing data to propose a strategy for extracting paddy rice planting areas based on feature optimization and phenological information. First, four kinds of features, including spectrum, texture, polarization, and terrain, are constructed for multi-source remote sensing data, and three critical phenological periods are determined by combining normalized difference vegetation index and VH backscattering coefficient time series curves. Then, recursive feature elimination combined with random forest (RF) is used to optimize the characteristics and determine the optimal phenological period. Finally, paddy rice planting areas are extracted using an RF classifier. The results show that the optimal phenological period for paddy rice extraction is the flooding and transplanting period, the number of features decreases from 48 to 13, the overall accuracy is 98.41%, and the Kappa coefficient is 0.97. The optimized scheme not only reduces the data redundancy but also has higher extraction accuracy than the other 14 feature combination and comparison schemes.