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.