Winter wheat is a major food source for the inhabitants of North China. However, its yield is affected by drought stress during the growing period. Hence, it is necessary to develop drought-resistant winter wheat varieties. For breeding researchers, yield measurement, a crucial breeding indication, is costly, labor-intensive, and time-consuming. Therefore, in order to breed a drought-resistant variety of winter wheat in a short time, field plot scale crop yield estimation is essential. Unmanned aerial vehicles (UAVs) have developed into a reliable method for gathering crop canopy information in a non-destructive and time-efficient manner in recent years. This study aimed to evaluate strategies for estimating crop yield using multispectral (MS) and hyperspectral (HS) imagery derived from a UAV in single and multiple growth stages of winter wheat. To accomplish our objective, we constructed a simple linear regression model based on the single growth stages of booting, heading, flowering, filling, and maturation and a multiple regression model that combined these five growth stages to estimate winter wheat yield using 36 vegetation indices (VIs) calculated from UAV-based MS and HS imagery, respectively. After comparing these regression models, we came to the following conclusions: (1) the flowering stage of winter wheat showed the highest correlation with crop yield for both MS and HS imagery; (2) the VIs derived from the HS imagery performed better in terms of estimation accuracy than the VIs from the MS imagery; (3) the regression model that combined the information of five growth stages presented better accuracy than the one that considered the growth stages individually. The best estimation regression model for winter wheat yield in this study was the multiple linear regression model constructed by the VI of ‘$\left({\mathrm{b}}_1-{\mathrm{b}}_2\right)/\left({\mathrm{b}}_3-{\mathrm{b}}_4\right)$’ derived from HS imagery, incorporating the five growth stages of booting, heading, flowering, filling, and maturation with r of 0.84 and RMSE of 0.69 t/ha. The corresponding central wavelengths were 782 nm, 874 nm, 762 nm, and 890 nm, respectively. Our study indicates that the multiple temporal VIs derived from UAV-based HS imagery are effective tools for breeding researchers to estimate winter wheat yield on a field plot scale.

The fraction of absorbed photosynthetically active radiation ($f\mathrm{P}\mathrm{A}\mathrm{R}$) is an important parameter reflecting the level of photosynthesis and growth status of vegetation, and is widely used in energy cycling, carbon cycling, and vegetation productivity estimation. In agricultural production, $f\mathrm{P}\mathrm{A}\mathrm{R}$ is often combined with the light use efficiency model to estimate crop yield. Therefore, accurate estimation of PAR is of great importance for improving the accuracy of crop yield estimation and ensuring national food security. Existing studies based on vegetation indices have not considered the effects of genetic variety, light, and water stress on $f\mathrm{P}\mathrm{A}\mathrm{R}$ estimation. This study uses ground-based reflectance data to simulate 21 common Sentinel-2 vegetation indices and compare their estimation ability for winter wheat $f\mathrm{P}\mathrm{A}\mathrm{R}$. The stability of the vegetation index with the highest correlation in inverting $f\mathrm{P}\mathrm{A}\mathrm{R}$ under different cultivars, light, and water stress was tested, and then the model was validated at the satellite scale. Finally, a sensitivity analysis was performed. The results showed that the index model based on modified NDVI (MNDVI) had the highest correlation not only throughout the critical phenological period of winter wheat (${\mathrm{R}}^2$ of 0.6649) but also under different varieties, observation dates, and water stress (${\mathrm{R}}^2$ of 0.918, 0.881, and 0.830, respectively). It even performed the highest ${\mathrm{R}}^2$ of 0.8312 at the satellite scale. Moreover, through comparison, we found that considering water stress and variety differences can improve the estimation accuracy of $f\mathrm{P}\mathrm{A}\mathrm{R}$. The study showed that using MNDVI for $f\mathrm{P}\mathrm{A}\mathrm{R}$ estimation is not only feasible but also has high accuracy and stability, providing a reference for rapid and accurate estimation of $f\mathrm{P}\mathrm{A}\mathrm{R}$ by Sentinel-2 and further exploring the potential of Sentinel-2 data for high-resolution $f\mathrm{P}\mathrm{A}\mathrm{R}$ mapping.