UAV Multispectral Data Combined with the PROSAIL Model Using the Adjusted Average Leaf Angle for the Prediction of Canopy Chlorophyll Content in Citrus Fruit Trees

Canopy chlorophyll content (CCC) is an important index for monitoring the growth and estimating the productivity of citrus fruit trees. This study optimized the PROSAIL model by adjusting the average leaf angle (ALA) parameter. A hybrid inversion model was then developed by combining the simulated data with UAV multispectral measurements using machine learning to determine the optimal data fusion ratio for improved citrus CCC prediction. The results show that (1) the most pragmatic accommodation for the hybrid inversion model in this study is the 1:4 ratio of measured data to simulated data; (2) the adjusted ALA (ALAadj) value of citrus fruit trees is 42°, and the spectral response region of the adjusted PROSAIL parameters is more conducive to leaf chlorophyll content (LCC) and the leaf area index (LAI) for CCC modeling; and (3) the ALAadj hybrid inversion model showed significantly better performance than the ALA-unadjusted model under all four machine learning methods, with the peak prediction accuracy, measured by R2, rising from 0.723 to 0.823—a 13.8% increase. The proposed method effectively improves the prediction accuracy of citrus CCCs, demonstrating the strong potential of the ALAadj-based PROSAIL model for UAV-scale CCC monitoring.