Estimation of crop leaf area index based on Sentinel-2 images and PROSAIL-Transformer coupling model

Abstract

Accurate estimation of leaf area index (LAI) is hindered by challenges in capturing crop-specific spectral variability and integrating complex model-data relationships. To address these issues, this study proposes a novel framework based on Sentinel-2 images, coupling the PROSAIL physical model with a Transformer-based deep learning model. This framework incorporates three key features contributing to its effectiveness. Firstly, Sentinel-2 reflectance was generated using the PROSAIL model and refined through sample matching to ensure optimal alignment with Sentinel-2 imagery specific to each crop type. Secondly, the Maximum Information Coefficient (MIC) and Recursive Feature Elimination (RFE) were employed to identify the most relevant spectral feature combinations for different crop categories. Thirdly, a PROSAIL-Transformer coupling model was constructed based on selected feature combinations to generate accurate Sentinel-2 LAI products. To validate the proposed approach, field crop LAI measurements were collected at five plots within the study area. Quantitative assessments demonstrate a coefficient of determination (R²) of 0.87, root mean square error (RMSE) of 0.48, and mean absolute error (MAE) of 0.36. The proposed framework enables the production of time-series LAI maps at fine resolution, facilitating dynamic crop monitoring and management in areas of high spatial heterogeneity.