Tree-level inversion of leaf chlorophyll content from UAV imagery using a 3D model-based visual factor correction method for adjacency scattering effect

• New ray tracing visual factor quantifies and correct tree-level adjacency effects. • Crown and sky VFs strongly correlate with adjacency effects; ground VF negligible. • All spectral bands are affected by the adjacency effect, especially the NIR band. • Corrected NIR ratio VIs boost individual-tree LCC inversion; multi-VI fusion better. For tree-level inversion of leaf chlorophyll content (LCC) from UAV imagery, the adjacency scattering effect of individual trees can affect the reflectance of the specific tree crown, thereby increasing the uncertainty and difficulty of LCC inversion. In this study, a novel visual factor (VF) correction framework for such adjacency scattering effect is proposed, which can efficiently quantify the adjacency effects by geometric probabilities instead of computationally intensive 3D radiative transfer modeling (3D-RTM). This framework is realized by extracting the light intersection probability between the target tree crown and the adjacency scenes (Crown, Ground, and Sky) via ray tracing on LiDAR-derived 3D scenes. We constructed a VF-based adjacency effect correction equation to optimize the extracted vegetation indices (VIs) of tree crowns which were utilized for tree-level LCC inversion. The results indicate that most ratio-based vegetation indices that include near-infrared bands, such as NDVI, RVI, MCARI, CIrededge, and GNDVI, increased the accuracy of LCC after calibration (the mean R 2 of univariate prediction was improved to 0.46), while DVI and EVI were insensitive to the adjacency effect and TVI showed a decrease in accuracy. Notably, VIs after adjacency effect correction demonstrated a remarkable improvement under the multivariate regression algorithm, with the R 2 increasing by 0.4 compared to that before correction, achieving an accuracy of R 2 = 0.67. The method overcame the uncertainty of tree-level signals in UAV imagery by eliminating the influence of the adjacency scattering and provided an efficient solution for the accurate inversion of parameters at the individual-tree scale, which is of great application value for the assessment of precise forestry management.