Enhancing maize streak virus detection: a comparative analysis of Sentinel-2 MSI and Landsat 9 OLI data across vegetative and reproductive growth stages

Abstract

Maize streak virus (MSV) threatens food production in Africa and globally. This study evaluates the effectiveness of Sentinel-2 MSI and Landsat 9 in detecting MSV-infected maize at different growth stages in Ofcolaco farms, Tzaneen, South Africa, using the Random Forest algorithm in Google Earth Engine. Sentinel-2 achieved higher classification accuracies (76.07% vegetative, 79.64% reproductive) than Landsat 9 (62.5% and 75.4%), with corresponding Kappa coefficients of 0.728 and 0.768 for Sentinel-2, and 0.571 and 0.719 for Landsat 9. Sentinel-2 Red Edge 1–3 and Near-Infrared (NIR) bands effectively identified infected maize, while Landsat 9 struggled, particularly in the reproductive stage. Sentinel-2’s superior spectral resolution and revisit frequency enhanced MSV detection, supporting early intervention strategies to mitigate crop losses and improve maize production. These findings highlight the potential of multispectral remote sensing for precision agriculture and disease monitoring.