The impacts of tree shape, disease distribution and observation geometry on the performances of disease spectral indices of apple trees

Vegetation indices (VIs) are widely employed in remote sensing for quantitative monitoring of plant disease due to their simplicity and robustness. However, factors such as canopy structure, the distribution of diseases in the canopy, and observation geometry may influence the spectral response of diseased canopies, potentially affecting the performance of VIs developed under specific disease conditions (e.g., early-stage). To date, fewer comprehensive analytical strategy has been proposed to quantitatively assess the confounding effects of multiple factors, which has hindered the selection of optimal VIs for practical disease monitoring. This study proposes an integrated analytical strategy that combines a three-dimensional radiative transfer model (3D RTM) with a multi-criteria decision-making method — entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) — to systematically evaluate existing disease-related VIs at the canopy scale, based on simulation outputs and ground measurements. We employed the LargE-Scale remote sensing data and image Simulation framework (LESS) to simulate the bidirectional reflectance factor (BRF) of canopies affected by two representative apple diseases, quantitatively evaluated the confounding effects of tree shape, disease distribution and observation geometry on VIs, and systematically ranked the performance of 40 VIs from two critical perspectives. Results from analyses on two disease types showed that Health Index 2014 (HI2014) and Water Band Index in SWIR (WBISWIR) were the top-performing indices for monitoring apple blotch disease (AMB) and apple mosaic disease (MD), respectively, Notably, WBISWIR emerged as the co-optimal index, exhibiting the highest monitoring efficacy across both diseases. Among all indices, the Normalized PRI (PRIn) demonstrated the greatest robustness against variations in tree shapes and disease distributions. WBISWIR exhibited good performance across diverse observation geometries. When comparing the relative influence of three factors on VI performance, tree shape and disease distribution exerted greater effects than observation geometry. Our findings highlight the complex interactions between VIs and confounding factors, emphasizing the necessity of caution when applying disease-related VIs and advocate for comprehensive consideration of tree shape and stress distribution effects during VI selection, especially for early-stage disease detection. This study offers a robust methodological framework for selecting VIs tailored to specific disease and vegetation characteristics, enhancing the precision of remote sensing-based plant disease assessments.