GF-5A Hyperspectral LAI Retrieval over Complex Vegetation Canopies Using Physically Informed PROSAIL Lookup Table Inversion

In physical model inversion of leaf area index (LAI) over complex vegetation canopies, variations in canopy structure, background moisture, and background brightness can cause spectrally similar lookup table (LUT) candidates to represent different combinations of canopy and background parameters, reducing retrieval stability. This study proposes a PROSAIL lookup table inversion method for GaoFen-5A (GF-5A) hyperspectral imagery based on physically informed parameter range refinement. A small set of key GF-5A feature bands was selected to preserve information sensitive to canopy structure and background conditions while reducing spectral redundancy. Vegetation type information was used to determine the LIDFa range, while the normalized difference infrared index (NDII) and brightness index (BI) were used to guide the refinement of psoil and rsoil ranges according to moisture related and brightness related spectral responses, respectively. Combining compact spectral inputs with refined PROSAIL parameter ranges improved LUT candidate selection under variable canopy and background conditions. Validation using 62 field LAI values aggregated at the GF-5A pixel scale yielded a coefficient of determination (R²) of 0.6000, a root mean square error (RMSE) of 1.1461, a mean absolute error (MAE) of 0.8712, and a Bias of −0.0092, outperforming the comparison schemes. These results indicate improved physical consistency and stability in LAI inversion under complex canopy and background conditions.