This project proposes to apply remote sensing to monitor agricultural productivity and food security at national and sub-national levels. Remote sensing regularly observes millions of crop fields in low-income countries. Burke and Lobell (2017) propose a projection of agricultural productivity based on high-resolution satellite images. Based on satellite images in combination with survey results, machine-learning technologies can project agricultural output and food security. This will also help to monitor inequality across regions within countries.