1961年以来云南省红河州冰雹频发，对当地农业生产造成了重大损失。当前基于气象台站数据采用统计分析方法获得的县市、站点尺度雹灾分布数据无法满足农业防雹需求，少数雹灾监测遥感方法受限于遥感数据源单一及针对全局分析特点，模型在山区适用性不足。为掌握红河州冰雹发生的时空分布特征与规律，本文采用2009~2022年防雹点冰雹记录，研究基于Ross-Li与STARFM的多源遥感卫星影像时空融合方法，提出多级格网雹灾遥感监测模型与冰雹识别指数RNDVI_M进行雹灾区遥感监测，最大相对误差为9.08%，平均误差为5.62%，标准偏差为1.66%。采用空间叠加分析与空间相关分析，在耕地地块级别定量分析了不同地貌类型、地形起伏度、坡度、地形类型等冰雹频次，构建冰雹孕灾风险评估模型计算气候、气象、地形、地貌等自然条件造成冰雹风险空间分布特征。本研究优势是综合利用多源遥感数据优势，多层次格网模型参数自适应的方式，提高了模型适应性，将雹灾监测精度与风险等级评估精度提高到耕地地块尺度。研究结果有助于合理调整农作物种植结构，规划布局人工防雹作业点，减少冰雹灾害损失具有重要作用。

Hail has occurred frequently and caused significant losses to local agricultural production in Honghe Prefecture, Yunnan Province, Since 1961. The hail disasters distribution data at the county scale or weather station scale, which obtained by using statistical analysis method, cannot meet the needs of agricultural hail prevention. Some hail disaster remote sensing monitoring methods, which limited by single remote sensing data sources and the characteristics of designing for global scale, lacks applicability in mountainous areas. In order to grasp the spatial and temporal distribution characteristics of hail, the paper used hail record data from hail suppression operation stations since 2009 to 2022, and research on multi-source data fusion approach based on Ross Li and STARFM, propose a multi-level grid normalized vegetation index standardization model and hail remote sensing monitoring recognition index RNDVI_M to extract hail disasters area, and accuracy is better than 91.82%. Use spatial overlay analysis and spatial correlation analysis method to quantitative analysis hail frequency in different disaster-prone environments such as landform types, terrain undulations, slopes, and terrain types at the level of cultivated land plots. Propose hail disaster risk assessment model to calculate the spatial distribution characteristics of hail risk caused by natural conditions such as climate, meteorology, terrain, and topography. The advantages are comprehensive utilization of multi-source remote sensing data, using parameter adaptation for multi-level grid models to improve model adaptability, and improving the accuracy of hail monitoring and risk assessment from county scale to cultivated land plots scale. The research results contribute to the rational adjustment of crop planting structure, the planning and layout of artificial hail control operation points, and the reduction of hail disaster losses.