Estimating Fraction of Absorbed Photosynthetically Active Radiation of Winter Wheat Based on Simulated Sentinel-2 Data under Different Varieties and Water Stress

Abstract

The fraction of absorbed photosynthetically active radiation ((Formula presented.)) 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, (Formula presented.) 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 (Formula presented.) estimation. This study uses ground-based reflectance data to simulate 21 common Sentinel-2 vegetation indices and compare their estimation ability for winter wheat (Formula presented.). The stability of the vegetation index with the highest correlation in inverting (Formula presented.) 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 ((Formula presented.) of 0.6649) but also under different varieties, observation dates, and water stress ((Formula presented.) of 0.918, 0.881, and 0.830, respectively). It even performed the highest (Formula presented.) 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 (Formula presented.). The study showed that using MNDVI for (Formula presented.) estimation is not only feasible but also has high accuracy and stability, providing a reference for rapid and accurate estimation of (Formula presented.) by Sentinel-2 and further exploring the potential of Sentinel-2 data for high-resolution (Formula presented.) mapping.