High-Resolution Canopy Nitrogen Retrieval Using a Novel CCC-Based RTM-MLRA Framework

Accurate and non-destructive estimation of Canopy Nitrogen Content (CNC) is vital for optimizing nitrogen use efficiency and supporting sustainable precision agriculture ecosystems. While hybrid frameworks combining Radiative Transfer Models (RTMs) and Machine Learning Regression Algorithms (MLRAs) are effective for biophysical parameter retrieval, the choice of intermediate physiological proxies significantly influences model robustness. This study proposes a novel approach for CNC estimation using UAV-based MicaSense multispectral imagery, explicitly focusing on a Canopy Chlorophyll Content (CCC) calculation method, and compares its efficacy against an established Leaf Area Index (LAI) and protein-based baseline. Spectral data was processed through an RTM-MLRA framework utilizing Gaussian Process Regression (GPR) for the inversion of key biophysical traits. Field validations established a strong linear relationship between measured CCC and CNC (Rsqure = 0.8218). When deployed for spatial retrieval, the proposed CCC-based hybrid model outperformed the standard LAI-based approach, demonstrating higher predictive accuracy (Rsqure = 0.676 vs. 0.658) and reduced error margins ($RMSE = 7.950 g/sq meter vs. 8.693 g/sq meter; NRMSE = 16.232% vs. 17.750%). Furthermore, the high-resolution spatial maps generated via the CCC-driven method effectively captured fine-scale within-field variability, providing reliable delineations of nitrogen status. These findings demonstrate that integrating accessible multispectral UAV data with a CCC-focused RTM-MLRA approach provides a highly scalable, superior alternative for operational nitrogen monitoring and decision support in digital farming environments.