To better promote the research and development of regional crop yield simulations, we review related research on regional crop yield simulations over the past ten years, summarize the research progress on regional crop yield simulations at home and abroad from the three aspects of crop growth models, remote sensing technology, and data assimilation technology, and propose three future development directions for regional crop yield simulations: (1) Based on the agronomic mechanism of crop yield estimation, it is necessary to fully consider crop growth characteristics and yield formation mechanisms. (2) With respect to the remote sensing mechanism of crop yield estimation, we can consider combining radar stereo lateral view measurement technology to determine crop characteristics and remote sensing information. (3) From the perspective of combining agronomy and remote sensing, the crop yield spatiotemporal simulation assimilation algorithm should be optimized, and the yield simulation unit should be extended. It is expected that these considerations can provide new ideas for regional crop yield simulations with high accuracy, a large scale, and full coverage.

Timely and accurate monitoring of key agricultural information on crops is the premise and foundation for ensuring national food security and guiding agricultural production in a scientific and precise manner [1]. In recent years, domestic and foreign governments and research departments have included agricultural information monitoring within key research programs.

Real-time regional rapeseed yield estimation is essential for agricultural management, market stability, and public safety. However, traditional assimilation systems that use the leaf area index (LAI) as a state variable underestimate rapeseed yield because they ignore the photosynthetic contribution of siliques. To address this limitation, this paper introduces the total photosynthetic area index (TPAI), which integrates both leaf and silique contributions, and incorporates it into an improved assimilation framework. A phenology-driven, layered microwave scattering model was developed by combining rapeseed canopy architecture, regional characteristics, and microwave scattering theory. This model accurately captures the temporal dynamics of the TPAI and achieves strong inversion performance, with ${R^{2}}$values of 0.743 (training) and 0.819 (validation). In the crop growth model, a localization approach that relies on the TPAI for parameter adjustment was introduced, improving the agreement between the observed and simulated total weight of storage organs (TWSO) from ${R^{2}}$ = 0.500 (LAI-based) to 0.982. In the data assimilation component, key parameters were selected using the recursive feature elimination algorithm, and the yield was estimated using the ensemble Kalman filter with the TPAI as the state variable. Compared with LAI-based assimilation, the TPAI-based approach substantially improved TWSO estimation accuracy at the reginal scale, with ${R^{2}}$= 0.791, RMSE = 1050.835 kg/ha, outperforming the LAI-based results (${R^{2}}$= 0.052, RMSE = 2330.970 kg/ha). Overall, by accounting for plant structure and regional characteristics and by assimilating a novel SAR-derived canopy parameter, this TPAI-based framework effectively mitigates the yield underestimation inherent in LAI-driven methods.