Optimizing Empirical and Hybrid Modeling for Advanced Canopy Chlorophyll and Nitrogen Retrieval Technique Using EnMAP Data

This study evaluates empirical and hybrid physical models for estimating canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) using hyperspectral imagery from the Environmental Mapping and Analysis Program (EnMAP) over Michigan's Kellogg Biological Station in summer 2023. In the empirical approach, six machine learning regression algorithms (MLRAs) have been evaluated. In the hybrid modeling approach, each MLRA was combined with the PROSAIL radiative transfer model. Results show the empirical model outperforms the hybrid model for CNC retrieval, while both perform similarly for CCC. In the empirical approach, KRR demonstrated the best performance among MLRAs for both CCC (RMSE = 0.10 g/m², NRMSE = 9.76 %, R² = 0.93) and CNC (RMSE = 0.10 g/m², NRMSE = 8.13 %, R² = 0.94). In the hybrid modeling, GPR performed best for CCC (RMSE = 0.10 g/m², NRMSE = 9.43 %, R² = 0.93), while KRR remained the top performer for CNC (RMSE = 0.17 g/m², NRMSE = 13.67 %, R² = 0.83). Furthermore, the findings indicate that the hybrid model exhibits greater sensitivity in heterogeneous areas where field data are limited, while both approaches effectively distinguish between organic and non-organic treatments. The nitrogen conversion factor refined from 4.43 to 3.03 for corn in this study significantly improves the accuracy of the estimated CNC. This enhancement provides further evidence of the efficacy of EnMAP imagery in estimating biochemical parameters and its potential application in Precision Agriculture.