The sowing date and density are considered to be the main factors affecting crop yield. The determination of the sowing date and sowing density, however, is fraught with uncertainty due to the influence of climatic conditions, topography, variety and other factors. Therefore, it is necessary to find a comprehensive consideration of these factors to guide the production of winter rapeseed. A reliable crop model could be a crucial tool to investigate the response of rapeseed growth to changes in the sowing date and density. At present, few studies related to rapeseed model simulation have been reported, especially in the comprehensive evaluation of the effects of sowing date and density factors on rapeseed development and production. This study aimed to evaluate the performance of the AquaCrop model for winter rapeseed development and yield simulation under various sowing dates and densities, and to optimize the sowing date and density for agricultural high-efficient production in the Jianghuai Plain. Two years of experiments were carried out in the rapeseed growing season in 2020 and 2021. The model parameters were fully calibrated and the simulation performances in different treatments of sowing dates and densities were evaluated. The results indicated that the capability of the AquaCrop model to interpret crop development for different sowing dates was superior to that of sowing densities. For rapeseed canopy development, the RMSE for three sowing dates and densities scenarios were 7–22% and 16–23%, respectively. The simulated biomass and grain yield for different sowing dates treatments (RMSE: 0.8–2.1 t·ha⁻¹, Pe: 0–35.3%) were generally better than those of different densities treatments (RMSE: 0.7–3.9 t·ha⁻¹, Pe: 8.2–90%). Compared with other sowing densities, higher overestimation errors of the biomass and yield were observed for the low-density treatment. Adequate agreement for crop evapotranspiration simulation was achieved, with an R² of 0.79 and RMSE of 26 mm. Combining the simulation results and field data, the optimal sowing scheme for achieving a steadily high yield in the Jianghuai Plain of east China was determined to be sowing in October and a sowing density of 25.0–37.5 plant·m⁻². The study demonstrates the great potential of the AquaCrop model to optimize rapeseed sowing patterns and provides a technical means guidance for the formulation of local winter rapeseed production.

The fraction of absorbed photosynthetically active radiation (FPAR), which represents the capability of vegetation-absorbed solar radiation to accumulate organic matter, is a crucial indicator of photosynthesis and vegetation growth status. Although a simplified semi-empirical FPAR estimation model was easily obtained using vegetation indices (VIs), the sensitivity and robustness of VIs and the optimal inversion method need to be further evaluated and developed for canola FPAR retrieval. The objective of this study was to identify the robust hybrid inversion model for estimating the winter canola FPAR. A field experiment with different sow dates and densities was conducted over two growing seasons to obtain canola FPARs. Moreover, 29 VIs, two machine learning algorithms and the PROSAIL model were incorporated to establish the FPAR inversion model. The results indicate that the OSAVI, WDRVI and mSR had better capability for revealing the variations of the FPAR. Three parameters of leaf area index (LAI), solar zenith angle (SZA) and average leaf inclination angle (ALA) accounted for over 95% of the total variance in the FPARs and OSAVI exhibited a greater resistance to changes in the leaf and canopy parameters of interest. The hybrid inversion model with an artificial neural network (ANN-VIs) performed the best for both datasets. The optimal hybrid inversion model of ANN-OSAVI achieved the highest performance for canola FPAR retrieval, with R² and RMSE values of 0.65 and 0.051, respectively. Finally, the work highlights the usefulness of the radiation transfer model (RTM) in quantifying the crop canopy FPAR and demonstrates the potential of hybrid model methods for retrieving the canola FPAR at each growth stage.