Can 3D model improve the accuracy of leaf chlorophyll content estimation using UAV and sentinel-2 data?

Leaf chlorophyll content (LCC) is a crucial parameter reflecting vegetation’s photosynthetic activity. Many LCC inversion algorithms based on satellite and unmanned aerial vehicle (UAV) data have been developed in recent decades. The one-dimensional radiative transfer model, like PROSAIL (1D model), has been a classic tool for LCC inversion. In recent years, three-dimensional radiative transfer models (3D model) have been developed rapidly. However, studies on 3D models for LCC inversion are limited, and their impact on inversion accuracy across different sensor resolutions remains unclear. This study focuses on winter wheat and integrates the DART, Adel-Wheat, and PROSPECT models to construct the 3D-model-derived look-up table (LUT). The 3D-model-based LUT and 1D-model-based LUT were applied to Sentinel-2 (S2) and UAV data to retrieve LCC. Validation results demonstrate that the 3D-model-based algorithm significantly improves LCC inversion accuracy for both S2 and UAV images. For UAV data, the root mean square error (RMSE) decreases from 9.90 μg/cm² to 7.97 μg/cm², and the coefficient of determination (R²) improves from 0.70 to 0.79. For S2 data, the RMSE decreases from 12.40 μg/cm² to 8.68 μg/cm², while R² increases from 0.66 to 0.85. Additionally, overestimation at low LAI levels and underestimation at high LCC levels are effectively reduced. The high accuracy achieved under varying LAI and LCC conditions allows the 3D model to capture temporal trends throughout the growing season better. The 3D-model-based LCC inversion algorithm can better utilize the high spatial resolution advantages, thereby playing a significant role in vegetation physiological monitoring and crop phenotyping.