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Crop diseases monitoring is critical in understanding the effects of diseases on crop production and associated implications on food security. The aim of this study was to assess the utility of the 10?m resolution Sentinel 2 data set, in detecting and mapping Maize Streak Virus (MSV) disease in Ofcolaco farms in Tzaneen, South Africa. Specifically, the study sought to spectrally discriminate and map maize infected with MSV from other land-cover classes. To achieve this objective two analysis approaches were used: spectral analysis (Test I: spectral bands; Test II: spectral bands?+?spectral vegetation indices) using random forest algorithm in a supervised classification approach. The indices combined with spectral bands were EVI, SAVI, NDVI, GNDVI, GLI and MSAVI. Results indicated that infected maize was highly separable from health maize and other land cover classes (TDSI > 1.8). The mapping accuracy was high using spectral data (Overall accuracy = 85.29% and Kappa = 0.79) and even higher when spectral bands were combined with derived vegetation indices (Overall accuracy = 89.43% and Kappa = 0.84). The results of the study show that the 10?m resolution multispectral Sentinel 2 data set can be used to detect and map maize infected by MSV. The findings are important in showing the value of combining 10?m spectral data with derived indices from Sentinel 2 in improving monitoring of maize steak virus in resource-constrained nations.