Sensitivity of
Sentinel‐1
and
Sentinel‐2
features for detecting pine wilt disease under complex interference
Zhihe Qian, Geng Wang, Chen Zhang, Heya Sa, Xin Li, Xiaoli Zhang Abstract
BACKGROUND
Pine wilt disease (PWD) has caused severe ecological and economic losses worldwide, creating an urgent need for cost‐effective and large‐scale monitoring tools to support pest management. Satellite remote sensing complements UAV observations by enabling wide‐area, repeated monitoring at low cost; however, its effectiveness is limited by background interference from red‐yellow soil and seasonal broadleaf discoloration. This study evaluated the ability of multisource satellite time‐series features to improve PWD detection under complex environmental conditions.
RESULTS
Multitemporal features derived from Sentinel‐1 (S1) and Sentinel‐2 (S2) data using Complementary Ensemble Empirical Mode Decomposition and Hilbert–Huang Transform showed strong discrimination ability. The vegetation index VIgreen and its IMF 3 temporal component were the most sensitive indicators. Red‐edge, near‐infrared and shortwave infrared features effectively reduced background interference. Although S1 features alone showed limited performance, incorporating temporal information improved their sensitivity. The integration of single‐time, sparse‐temporal and time‐series features achieved an overall accuracy of 0.76.
CONCLUSION
Satellite time‐series features significantly improve PWD detection under complex interference conditions by capturing disease‐related temporal dynamics. This approach provides a reliable and scalable tool for operational forest pest monitoring and supports improved surveillance and management of PWD. © 2026 Society of Chemical Industry.