Accurate cropland maps at the global and local scales are crucial for scientists, government and nongovernment agencies, farmers and other stakeholders, particularly in food-insecure regions, such as Sub-Saharan Africa. In this study, we aim to qualify the crop classes of the MODIS Land Cover Product (LCP) in Sub-Saharan Africa using FAO (Food and Agricultural Organisation) and AGRHYMET (AGRiculture, Hydrology and METeorology) statistical data of agriculture and a sample of 55 very-high-resolution images. In terms of cropland acreage and dynamics, we found that the correlation between the statistical data and MODIS LCP decreases when we localize the spatial scale (from R2
= 0.86 ***
at the national scale to R2
= 0.26 ***
at two levels below the national scale). In terms of the cropland spatial distribution, our findings indicate a strong relationship between the user accuracy and the fragmentation of the agricultural landscape, as measured by the MODIS LCP; the accuracy decreases as the crop fraction increases. In addition, thanks to the Pareto boundary method, we were able to isolate and quantify the part of the MODIS classification error that could be directly linked to the performance of the adopted classification algorithm. Finally, based on these results, (i) a regional map of the MODIS LCP user accuracy estimates for cropland classes was produced for the entire Sub-Saharan region; this map presents a better accuracy in the western part of the region (43%–70%) compared to the eastern part (17%–43%); (ii) Theoretical user and producer accuracies for a given set of spatial resolutions were provided; the simulated future Sentinel-2 system would provide theoretical 99% user and producer accuracies given the landscape pattern of the region.