【摘要】 为精确监测水稻的分布与规模，满足水稻产量快速评估、农业水管理及决策支持需求，本研究基于国产卫星数据源构建了一套适用于县级区域的水稻种植面积快速识别策略。通过融合多时相高分二号多光谱遥感图像与合成孔径雷达（SAR）数据，结合归一化植被指数NDVI分析，进一步提取在VV极化模式下各种地物的后向散射特性，生成时序特征曲线。应用Savitzky-Golay滤波降噪技术优化数据质量，并采用随机森林分类模型提取水稻种植空间分布信息，系统揭示了水稻回波散射特性及其光谱动态变化规律。实验表明，本方法针对性地设计的水稻识别方案，提取水稻种植区域完整率精度达到92.65%，质量92.82%，优于常规国外卫星识别方法的精度。研究验证了国产卫星在农作物精细识别中的技术优势，为粮食安全监测、农田管理优化及灾害损失评估提供了高效解决方案。更多还原

【Abstract】 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 multitemporal 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.更多还原