Satellite monitoring uncovers progress but large disparities in doubling crop yields
This addresses the problem of monitoring and achieving Sustainable Development Goals (SDGs) for crop productivity in Rwanda, with potential broader applications, but is incremental in applying existing methods to new data.
The study used high-resolution satellite data and machine learning to map crop yields across 15,000 villages in Rwanda, identifying areas that are on or off track to double productivity by 2030, and designed spatially explicit targets to meet national goals inclusively.
High-resolution satellite-based crop yield mapping offers enormous promise for monitoring progress towards the SDGs. Across 15,000 villages in Rwanda we uncover areas that are on and off track to double productivity by 2030. This machine learning enabled analysis is used to design spatially explicit productivity targets that, if met, would simultaneously ensure national goals without leaving anyone behind.