Crop mapping—identifying what farmers are growing on their fields—is essential to agricultural and land use planning and management. It is used to generate yield estimates and crop acreage statistics, predict food prices and assess damages from disasters and also to evaluate ecosystem health—all functions essential for agricultural export nations such as Kenya.
In a recent study, we utilized time-series Sentinel-2 satellite imagery and advanced machine learning techniques to map key crop types and ecosystems in Nandi County in western Kenya. This effort aims to generate actionable insights for enhancing sustainable agricultural practices and supporting climate-resilient land management. The work was part of Living Labs for People (LL4P), which employs collaborative approaches drawing on local knowledge to develop sustainable agricultural innovations, under the CGIAR Research Initiative on Low-Emission Food Systems (Mitigate+).