Improving crop biophysical parameter estimation using high-resolution multispectral UAV imagery and PROSAIL model

Timely, field-scale retrieval of crop biophysical variables is widely regarded as central to data-driven agronomy. In this study, a practical workflow was evaluated in which high-resolution unmanned aerial system (UAS) multispectral imagery was coupled with PROSAIL inversion to map rice canopy traits across three phenological stages. Multispectral and RGB acquisitions were processed, and indices sensitive to chlorophyll, water, and pigment dynamics (e.g., Normalized Difference Red-Edge Index (NDRE), Leaf Chlorophyll Index (LCI), Modified Chlorophyll Absorption Ratio Index (MCARI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Structure-Insensitive Pigment Index 2 (SIPI2), Triangular Greenness Index (TGI), and Visible Atmospherically Resistant Index (VARI)) were derived. Leaf and canopy parameters, leaf chlorophyll content (Cab), carotenoids (Car), leaf water content (Cw), dry matter (Cm), mesophyll structure (N), and leaf area index (LAI)—were retrieved via lookup-table (LUT) inversion of PROSAIL. Independent ground measurements were used for validation, and a same-date Sentinel-2 benchmark was performed (subject to cloud constraints). Consistent phenological trajectories were observed: NDRE/LCI and Cab/LAI were found to peak at maximum greenness, while SIPI2 was observed to rise during senescence alongside declining Cab and Cw. Stage-dependent errors were identified in PROSAIL RMSE maps, with the lowest and most homogeneous errors detected at peak canopy. Strong agreement with field data was obtained (R² > 0.98 for most variables at the first date). For Cab, R²/RMSE values of 0.996/1.555, 0.978/2.104, and 0.972/0.2 were recorded across the three dates, respectively. Lower accuracy was produced by Sentinel-2 at field scale (e.g., LAI R²/RMSE ≈ 0.81/0.7; Cab ≈ 0.78/6.5), although useful cross-sensor complementarity was indicated. An operational pathway to within-field mapping of rice biophysics is thereby offered by the “UAS multispectral + PROSAIL” pipeline. The results demonstrate high accuracy at field scale, with phenology-dependent retrievals outperforming Sentinel-2-based estimates, highlighting the potential of UAV-based approaches for precise crop monitoring. Enhanced robustness to phenological change and cloud-related gaps is achieved when red-edge and pigment-ratio indices are fused with physical inversion, and straightforward extensibility to other cereals and management contexts is suggested.