Masemola, Cecilia RBonnet, WCho, Moses A2026-01-212026-01-212025-07979-8-3315-6853-5979-8-3315-6854-22995-0643DOI: 10.1109/Agro-Geoinformatics66479.2025.11136365http://hdl.handle.net/10204/14631Radiative Transfer Models (RTMs) such as PROSAIL, which integrates leaf-level (PROSPECT) and canopy-level (SAIL) reflectance simulations, are increasingly employed to support biophysical trait retrieval in crop monitoring applications. In this study, we assess and compare the performance of three PROSAIL configurations—PROSPECT-5 + SAIL, PROSPECT-D + SAIL, and PROSPECT-PRO + SAIL—for estimating Leaf Area Index (LAI) and Canopy Chlorophyll Content (CCC) in dryland maize systems using synthetic Sentinel-2 reflectance data. Results from synthetic test datasets indicate that the PROSPECT-PRO + SAIL configuration achieved superior performance, with LAI retrieved at an R² of 0.88 and RMSE of 0.35 m²/m², and CCC estimated at an R² of 0.83 and RMSE of 4.1 µg/cm². These outcomes highlight the advantage of using the enhanced biochemical and structural parameterizations in PROSPECT-PRO, especially under semi-arid cropping conditions. Comparative analysis confirms that this configuration consistently yielded the lowest normalized RMSE (nRMSE) for both LAI (9.5%) and CCC (10.7%) across the variants tested. The findings substantiate the added value of improved leaf optical modeling for accurate trait estimation and suggest that PROSPECT-PRO + SAIL provides a robust forward modeling basis for data-driven crop monitoring frameworks.AbstractenPROSAILSentinel-2Random ForestMaizeLAIChlorophyllDryland agricultureMachine learningCrop trait retrievalConference PresentationN/A