Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data

Abstract

Leaf area index (LAI) is a key variable for modeling energy and mass exchange between the land surface and the atmosphere. Inversion of physically based radiative transfer models is the most established technique for estimating LAI from remotely sensed data. This study aims to evaluate the suitability of the PROSAIL model for LAI estimation of three typical row crops (maize, potato, and sunflower) from unmanned aerial vehicle (UAV) hyperspectral data. LAI was estimated using a look-up table (LUT) based on the inversion of the PROSAIL model. The estimated LAI was evaluated against in situ LAI measurements. The results indicated that the LUT-based inversion of the PROSAIL model was suitable for LAI estimation of these three crops, with a root mean square error (RMSE) of approximately 0.62m2m−2, and a relative RMSE (RRMSE) of approximately 15.5%. Dual-angle observations were also used to estimate LAI and proved to be more accurate than single-angle observations, with an RMSE of approximately 0.55m2m−2 and an RRMSE of approximately 13.6%. The results demonstrate that additional directional information improves the performance of LAI estimation.