Synergetic inversion of leaf chlorophyll content and leaf area index from Sentinel-2 data using artificial neural networks trained with a radiative transfer model

Leaf area index (LAI) and leaf chlorophyll content (LCC) are two key vegetation traits affecting vegetation growth and function. They have often been retrieved individually from multi-spectral remote sensing data by ignoring their mutual influence on spectral signals. In this study, we developed a machine learning algorithm for synergetically retrieving both traits at the same time through training the algorithm using simulations of a radiative transfer model PROSAIL. The algorithm determines LAI based on visible, red-edge, near-infrared and shortwave infrared bands, while it infers LCC mostly from visible and red-edge bands. In this way, the mutual influences of LAI and LCC on visible and red-edge bands are considered in the algorithm. The algorithm is applied to a rice field over multiple growing seasons (2017-2018, 2024) using Sentinel-2 data. It is found that synergetically retrieved LAI and LCC are more accurate (R2=0.82 and 0.85, RMSE=0.83 and 5.00 μg/cm², respectively) than individually retrieved LAI (R2=0.54, RMSE=1.49) and LCC (R2=0.85, RMSE=9.35 μg/cm²). Our results suggest that this synergetic retrieval principle and methodology may be utilized to improve regional and global LAI and LCC products.