To accurately monitor the distribution and scale of rice cultivation, address the needs for rapid assessment of rice yields, agricultural water management, and decision support, this study utilizes domestic satellite data to develop a rapid identification strategy for county-level adaptive rice planting areas. It conducts an in-depth analysis of the scattering characteristics and spectral changes of rice echoes. Combining multi-temporal high-resolution images from the Gaofen-2 satellite with normalized vegetation index (NDVI) and other information, the study initially identifies rice planting areas. Based on this, synthetic aperture radar (SAR) images are used to extract the backscattering characteristics of various ground objects under VV polarization mode, generating time-series feature curves. Savitzky-Golay filtering technology is applied to reduce noise interference. Finally, a random forest classification model is used to extract spatial distribution information of rice planting. This study has specifically designed a rice recognition scheme, achieving a completeness accuracy of 92.65% and a quality of 92.82%, surpassing the precision of conventional foreign satellite recognition methods. The research verifies the technical advantages of domestic satellites in fine crop identification, and provides an efficient solution for food security monitoring, farmland management optimization and disaster loss assessment.