From Spectral Characteristics to Index Bands: Utilizing UAV Hyperspectral Index Optimization on Algorithms for Estimating Canopy Nitrogen Concentration in Carya Cathayensis Sarg

Employing drones and hyperspectral imagers for large-scale, precise evaluation of nitrogen (N) concentration in Carya cathayensis Sarg canopies is crucial for accurately managing nitrogen fertilization in C. cathayensis Sarg cultivation. This study gathered five sets of hyperspectral imagery data from C. cathayensis Sarg plantations across four distinct locations with varying environmental stresses using drones. The research assessed the canopy nitrogen concentration of C. cathayensis Sarg trees both during singular growth periods and throughout their entire growth cycles. The objective was to explore the influence of band combinations and spectral index formula configurations on the predictive capability of the hyperspectral indices (HIs) for canopy N concentration (CNC), optimize the performance between HIs and machine learning approaches, and validate the efficacy of optimized HI algorithms. The findings revealed the following: (i) Optimized HIs demonstrated optimal predictive performance during both singular growth periods and the full growth cycles of C. cathayensis Sarg. The most effective HI model for singular growth periods was the optimized–modified–normalized difference vegetation index (opt-mNDVI), achieving an adjusted coefficient of determination (R²) of 0.96 and a root mean square error (RMSE) of 0.71. For the entire growth cycle, the HI model, also opt-mNDVI, attained an R² of 0.75 and an RMSE of 2.11; (ii) optimized band combinations substantially enhanced HIs’ predictive performance by 16% to 71%, while the choice between three-band and two-band combinations influenced the predictive capacity of optimized HIs by 4% to 46%. Hence, utilizing optimized HIs combined with Unmanned Aerial Vehicle (UAV) hyperspectral imaging to evaluate nitrogen concentration in C. cathayensis Sarg trees under complex field conditions offers significant practical value.