Improving leaf chlorophyll content estimation through constrained PROSAIL model from airborne hyperspectral and LiDAR data

Leaf chlorophyll plays an important role in forest management and ecosystem balance. Hyperspectral images have been widely applied in leaf chlorophyll content (LCC) estimation. However, the complexity of canopy structures, such as leaf area index (LAI), weakens the performance of LCC estimation from hyperspectral images. The effect of LAI must be considered. Light detection and ranging (LiDAR) has advantages in LAI extraction. Therefore, this study theoretically and experimentally explored the potential of integrating the hyperspectral image and LiDAR data to improve LCC estimation. The PROSAIL model constrained by LiDAR-derived LAI was developed when the hyperspectral image was applied to LCC estimation. Theoretical evaluation and experimental validation were conducted to determine the performance of constrained PROSAIL models with four minimization algorithms. Four minimization algorithms included the fminsearchbnd, simulated annealing, genetic algorithm and Pareto sets. Three datasets were used: the synthetic dataset followed a uniform distribution, the synthetic dataset followed a normal distribution, and the airborne sensor dataset (airborne hyperspectral and LiDAR data). The non-constrained PROSAIL model was as a comparison. Results showed the effectiveness of the constrained PROSAIL model through integrating hyperspectral and LiDAR data for the improvement of LCC estimation. The constrained PROSAIL model always had better performance than non-constrained PROSAIL model among all datasets and minimization algorithms. It demonstrated that constrained PROSAIL model with LAI constraint could improve the accuracy of LCC estimation, where the R2 could be increased by 10% to 29%, and 9% to 62% for the synthetic datasets and airborne sensor dataset, respectively. The constrained PROSAIL model with Pareto sets had the best accuracy with an R2 of 0.81 from airborne sensor dataset. This study provides a new strategy for the improvement of LCC estimation and has great potential to serve precision forestry