For winter rapeseed, timely monitoring of the leaf area index (LAI) near the winter solstice is critical for estimating its overwintering survival rate and yield. While unmanned aerial vehicle (UAV) remote sensing techniques have emerged as a promising means for crop LAI monitoring, many existing crop LAI models have only been evaluated using a single sensor system, a single cultivar, a single development stage, and/or a single site. As a result, the generalization capability of these LAI models is limited. In this study, four empirical statistical models (ESMs) and a new PROSAIL-based lookup table (LUT) method were developed to estimate the LAI of 24 winter rapeseed cultivars mainly grown in the Yangtze River Basin. The raw LUT was optimized using a vege-tation index (VI) strategy after screening out inefficient parameter combinations using in-situ UAV reflectance and measured LAIs. The model transferability was evaluated across three years, six regional sites around Yangtze River Basin, different development stages, and two data sources from different sensors. An artificial neural network model in the ESMs performed best for data acquired in 2019 and 2020, but could not be used to estimate the LAI in 2018 due to the use of different sensors for data acquisition. In contrast, the optimized VI-LUT method was robust and outperformed the raw LUT for LAI retrieval with a root mean squared error of <0.7 for all data sets. These findings suggest that the optimized VI-LUT method has potential for estimating LAI at different spatial and temporal scales, especially for the situation where sensor types are difficult to be unified in multiple locations. Moreover, the genotype in these cultivars did not appear to dominate LAI generation before overwintering, but the climatic zone did. These findings have implications for the study of genotype-environment interactions in rapeseed cultivation.