Abstract BACKGROUND Pine wood nematode (PWN) is a major invasive species causing severe degradation of pine forest ecosystems. During PWN infection, pine trees undergo quantifiable physicochemical changes, including variations in pigment content and water status, which can effectively characterize tree health. Retrieving these indicators using remote sensing techniques therefore provides a promising approach for early PWN monitoring. In this study, nine UAV flight campaigns were conducted from June to September 2024, acquiring multispectral imagery at four flight altitudes (50, 100, 150 and 200 m). Field‐measured chlorophyll a (Chla), chlorophyll b (Chlb), carotenoids (Car) and water content (WC) data were integrated with UAV data to develop retrieval models based on 58 vegetation indices for estimating pigment content and water status of PWN‐infected Pinus massoniana . RESULTS (1) Chla, Chlb and Car initially increased during the early infection stage (within the first 14 days) and subsequently declined, whereas WC exhibited a continuous decreasing trend; (2) significant spectral differences between PWN‐infected and control trees emerged in the red‐edge and near‐infrared bands at Day 14, earlier than ground‐based symptom observation; (3) WC retrieval achieved substantially higher accuracy than pigment‐related parameters, indicating that inversion‐derived WC could be more sensitive and effective in characterizing the health status of PWN‐infected trees; and (4) MR‐DSWI (Multiple Ratio Disease–Water Stress Index), Li5 and Guo4 consistently exhibited stable responses to both pigment and water status indicators across all flight altitudes. CONCLUSION Overall, the robust performance of these indices highlights their strong general applicability for retrieving canopy pigment content and water status, providing reliable indicators for UAV‐based monitoring of PWN‐induced canopy degradation. © 2026 Society of Chemical Industry.
You et al. (Sun,) studied this question.