Assessing the Sensitivity of Sentinel-1 and Sentinel-2 Time Series to Wheat Yellow Rust Using Feature Evaluation, Separability, and Random Forest Classification

Yellow rust (YR) is one of the most damaging diseases of wheat, causing substantial economic losses and threatening food security worldwide. The potential of Synthetic Aperture Radar (SAR) for YR monitoring remains largely unexplored compared with optical observations. This study investigates the ability of Sentinel-1 SAR time series to discriminate YR-infected and healthy wheat fields and compares their temporal responses with Sentinel-2 observations. Sentinel-1 intensity-based and polarimetric features and Sentinel-2 vegetation indices were computed and assessed using correlation analysis, the Mann–Whitney U test, Youden’s J statistic, and Random Forest classification. Sentinel-2 indices showed the strongest discrimination in May, with Mann–Whitney effect sizes of up to r = 0.60 and Youden’s J = 0.70 for TVI. The best-performing Random Forest models were based on NDMI and DSWI-1 in May and achieved a macro F1-score of 0.84. In contrast, Sentinel-1 features generally showed limited sensitivity, but some features revealed significant differences as early as March, several months before the fields were identified as severely YR-infected. SEI reached r = 0.60 and J = 0.60, while the best Sentinel-1 Random Forest model, based on LPR, achieved a macro F1-score of 0.70 in March. These findings indicate that Sentinel-2 vegetation indices provide stronger discrimination of YR-infected and healthy fields during the period of greatest disease spread while suggesting that Sentinel-1 may capture early differences associated with moisture conditions that favor subsequent YR development.