Adebowale Daniel Adebayo

Adebowale Daniel Adebayo is a Ph.D. Candidate in the Xylem Lab research group within the Department of Geographical Sciences at the University of Maryland, College Park, where he works under the supervision of Dr. Catherine Nakalembe. His research sits at the intersection of remote sensing, machine learning, and smallholder agriculture, with a focus on early warning systems for food insecurity across Sub-Saharan Africa.

Drawn to geographical sciences by Tobler's first law that "everything is related to everything else," and by the vast applications of remote sensing to humanity, Mr. Adebayo brings that curiosity directly into his work. He develops open-source, scalable workflows for high-resolution cropland mapping and crop area estimation, harnessing satellite image time series and advanced machine learning techniques to support data-driven decision-making in agriculture and food security.

At the heart of this work is his dissertation, which aims to advance agricultural drought monitoring in Sub-Saharan Africa by integrating satellite remote sensing, in-situ soil moisture observations, and deep learning. The work addresses three connected questions: how well machine learning can predict the impact of drought on crop productivity, how accurate satellite soil moisture products are for smallholder farming systems, and what other environmental variables drive drought at a local, field-relevant scale. A key output of this research, available as a preprint (here), demonstrated that it is possible to detect crop failure in Eastern and Southern Africa up to 32 days in advance, providing the kind of early warning that gives farmers and decision-makers meaningful time to respond.

The depth of this research reflects a career built close to the problems it seeks to solve. Long before his doctoral work, Mr. Adebayo was already working at the intersection of remote sensing and real-world development challenges: as a GIS Consultant with Propcom Mai-karfi at Palladium International in Abuja, a Remote Sensing GIS Intern with the FAO in Rome, and a Geospatial Developer on the ESA-CCI Demonstrator project. He holds an M.S. in the Erasmus Mundus Copernicus Master in Digital Earth from the University of Salzburg, Austria & Université de Bretagne Sud, France, and a B.Tech. in Remote Sensing and GIS from the Federal University of Technology, Akure, Nigeria. Currently, he serves as an Earth Observation Data Scientist with the NASA Harvest Consortium. Together, these experiences reflect a consistent commitment to using Earth observation and AI to address the food and water security challenges facing some of the world's most vulnerable communities in Africa.