在大区域尺度实现快速、精确的作物产量估测对我国粮食安全、作物种植结构调整、进出口贸易等具有重要意义。遥感技术的发展为农业估产领域带来了新的技术和手段。以湖北省油菜为研究对象，针对如何利用有限的地面观测数据进行大区域范围油菜产量估测的问题，结合遥感数据和气象数据，通过WOFOST模型进行数据同化，模拟油菜生长过程中的叶面积指数（LAI）变化，提取油菜关键生长期的LAI，以弥补大区域尺度数据的不足。之后，利用LAI作为中间量构建基于GF-1 WFV数据的大区域尺度油菜估产算法。研究发现，油菜蕾苔期和花期的综合LAI能够实现提前、准确的油菜产量预估，在蕾苔期SR植被指数与LAI相关性最好，在花期则是可见光大气阻抗（VARI_green）植被指数与LAI相关性最好。为了验证估产算法的有效性和鲁棒性，在阳新县进行了测试。结果表明，与统计年鉴的产量数据相比估产误差低于6%，说明所提算法在大区域尺度油菜估产领域具有很强的潜力。

Achieving rapid and accurate crop yield estimation on a large regional scale is significant for China's food security, crop planting structure adjustment, and import and export trade. Oilseed rape is one such commodity in high demand for both national and global consumptions. The development of remote sensing technology has brought new innovations to agricultural yield estimation. Research on oilseed rape in Hubei province sought effective, practical use of limited ground observation data to estimate its yield in a large area. By combining remote sensing data and meteorological data, changes in leaf area index (LAI) during growth and key growth periods are simulated through WOFOST model. The results were used to build a large regional rape yield estimation algorithm based on GF-1 WFV data. The study found that the comprehensive LAI of rape bud moss stage and flowering stage can achieve early, accurate prediction of rape yield. In the bud moss stage, the SR vegetation index showed the best correlation with LAI whereas in the flowering stage, the visible light atmospheric impedance (VARI_green) vegetation index has the best correlation with LAI. The yield estimation algorithm was then tested in Yangxin county to verify its effectiveness and robustness. Results show the yield estimation error is <6% in contrast to the yield data in the statistical yearbook, indicating that the proposed algorithm has potential usability in large regional scale rape yield estimation.