摘要Precision management in high-density orchards requires individual-tree,nondestructive monitoring of canopy nitrogen concentration(CNC),but hyperspectral applications are limited by two factors:unmodeled vertical stratification of CNC within 3D canopies and mixed-pixel effects near canopy boundaries.We develop a cross-modal framework that co-registers RGB-derived 3D point clouds with hyperspectral orthomosaics,enabling individual-tree localization in dense orchards.With this framework,we quantified layer-specific nitrogen-spectral relationships and assessed mixed-pixel effects across canopy positions.Stratified sampling,continuous wavelet transform(CWT),and partial least squares regression(PLSR)with variable importance in projection(VIP)-based band selection were used for spectral optimization,and K-means was applied to isolate represen-tative canopy pixels.Field experiments over two consecutive years(2023-2024)revealed consistent CNC gra-dients,with the lower canopy exceeding the upper by 0.5-9.5％across fertilization treatments.CWT-2 delivered the most accurate and robust performance across years.VIP-PLSR indicated layer-dependent CNC-informative wavelengths spanning the visible,red-edge,and near-infrared regions,with scale-dependent cross-layer overlap after CWT.Pixel clustering revealed distinct spatial structure:canopy-interior pixels exhibited characteristic vegetation spectra and achieved R2val of 0.69-0.76,substantially outperforming boundary-affected pixels with R2val of 0.48-0.57.These results demonstrate that coupling spectral feature optimization with layer-specific modeling and clustering-based pixel screening improves the accuracy of tree-level CNC estimation in complex canopies.The proposed framework provides a mechanistic and operational basis for robust biochemical retrieval in structurally complex orchard systems.