The capability of very high-resolution satellite imagery for plot-level early wheat stem rust disease detection, monitoring, and phenotyping in Ethiopia

Abstract Very high‑resolution satellites (VHRS) have potential for early crop disease detection and enhanced food security. The capability of multispectral SkySat and Pleiades‑Neo imagery for early-stage detection and monitoring was assessed for wheat stem rust (SR) at the plot level. In randomized trials with contrasting fungicide and irrigation treatments, six bread wheat varieties with differing SR susceptibility were monitored using VHRS. 113 multispectral features were evaluated for their association with SR progression and associated yield loss. Several features demonstrated moderate to strong correlations with SR disease levels. Across early stages (healthy, mildly diseased, and moderately diseased), spectral sensitivity was dominated by Blue (B)‑based features, with Red-Blue (R-B) and Green-Blue (G-B) two-band features at mildly and moderately diseased levels, respectively. During late stages (severely diseased and fully diseased), spectral sensitivity was driven by R-based features (e.g., R-B, R-G on both sensors) and by Deep Blue (DB), Red-Edge-DB (RE-DB), and R-DB combinations on Pleiades‑Neo. Early-stage SR detection under moderate disease pressure was possible using ratio (RSI) and normalized difference (NDSI) spectral indices with B-G combinations. SkySat features such as RSI(NIR,B), RSI(R,B), and RSI(G,B) were consistently associated with SR across multiple disease scenarios, trials, and disease metrics. This work delivers the first VHRS-based SR detection at the plot scale and provides a foundation for future investigations into SR monitoring across larger spatial scales.