A Two-Step Method for Winter Wheat Leaf Chlorophyll Estimation from UAV Hyperspectral Imagery

Leaf chlorophyll content (LCC) is a critical indicator for precision agriculture. Accurately estimating winter wheat LCC based on remote sensing at high spatial resolution is of great significance for agricultural management and decision. In this study, a two-step method was used to retrieve wheat LCC from UAV hyperspectral imagery. The first step is converting canopy reflectance to leaf reflectance using Look-up tables (LUTs) generated from the unified model of bidirectional reflectance distribution function (BRDF). The second step is retrieving wheat LCC from derived leaf reflectance using the PROSPECT-PRO model. Retrieved wheat LAI, leaf reflectance, and LCC were validated against field measurements. The results indicate a good agreement between retrieved and measured LAI with RMSE of 0.092 and R
²
of 0.605. Leaf reflectance retrieved from UAV canopy reflectance exhibit good consistency with measured leaf reflectance with RMSE of 0.017 and R
²
of 0.962. Retrieved wheat LCC is of good quality compared to measured LCC, with R
²
of 0.7675 and RMSE of 4.33 μg/cm
²
. In conclusion, this study holds potential in estimating wheat LCC from UAV hyperspectral imagery.