Hyperspectral Mapping of Pasture Nitrogen Content and Metabolizable Energy in New Zealand Hill Country Grasslands

Hyperspectral airborne data combined with machine learning has proven effective for characterizing plant nutritional quality. However, terrain, viewing geometry, and illumination can distort spectral signatures, leading to biased models with limited generalizability for large-scale mapping across farms with a heterogeneous landscape. In this study, we developed a framework for mapping pasture quality using airborne hyperspectral imaging while explicitly accounting for in-field acquisition and environmental effects. Nitrogen content (N%) and metabolizable energy (ME) were used as reference indicators across four hill country farms in New Zealand with contrasting environmental and management conditions. Ground truth was obtained using standard laboratory wet chemistry methods and paired with AisaFENIX airborne hyperspectral data, resulting in 1610 spectral samples derived from 161 spatially independent ground plots. Gaussian Process Regression (GPR) and a one-dimensional convolutional neural network (1D-CNN) were trained and evaluated on an independent test dataset. Both models achieved strong predictive performance (R2 > 0.8); however, GPR provided more reliable estimates through predictive uncertainty. Using a 95% confidence interval threshold to mask uncertain predictions increased overall performance (R2 > 0.9) and consequently improved the reliability of the mapped outputs. This approach enables spatially explicit pasture nutrient assessment to support precision land management for carbon and nitrogen.