Developing countries often have poor monitoring of weather and crop health, leading to slow responses to droughts and food shortages. I developed satellite analysis methods and software tools to predict crop yields three months before the harvest. This software measures relative vegetation health based on pixel-level vegetation indices (VIs). VIs are a measure of plant health that are calculated from the light spectrum emitted from the land. Because this method requires no crop mask or subnational yield data, it can be applied to any crop or climate, making it ideal for African countries with small fields and poor ground observations. A validation was first conducted in Illinois where there is reliable county-level crop yield data. The monthly VIs were extremely well correlated with corn, soybean, and rice yields, showing that this model has good forecasting skill for crop yields. Next, the vegetation health was measured in every country in Africa to predict crop yields for the 2018 harvest. The yield predictions were very accurate with a median error of 8.6%. This method is unique because of its simplicity and versatility: it shows that a single user can produce reasonable real-time estimates of crop yields across an entire continent.