Although Rwanda has accomplished significant improvements in food production in recent years, one fifth of its population remains food insecure. Agricultural information is currently collected through seasonal agricultural surveys, but more frequent and timely data collection is needed to adequately inform public and private decision-makers about the status of crops during the growing season. Sentinel-1 and -2 data are freely available with new images provided every 4-5 days. While analysis of these multispectral images has been used for agricultural applications, there are few applications to smallholder agriculture. Major challenges for satellite image analysis in the context of Rwanda include heavily clouded scenes and small plot sizes that are often intercropped. Sentinel-2 scenes corresponding to mid-season were analyzed, and spectral signatures of maize could be distinguished from those of other crops. Seasonal mean filtering was applied to Sentinel-1 scenes, and there was significant overlap in the spectral signatures across different types of vegetation. Random Forest models for classification of Sentinel scenes were developed using a training dataset that was constructed from high-resolution multispectral images acquired by unmanned aerial vehicles (UAVs) in several different locations in Rwanda and labeled as to the crop type by trained observers. The models were applied to satellite images of the whole country of Rwanda and validated using a test dataset from the UAV images. The Sentinel-2 model had the user’s accuracy for maize classification of 75%, while the Sentinel-1 model overestimated the maize area resulting in a user’s accuracy of <50%.