Dr. Vitor Martins
Mapping the World's Crops from Space
Dr. Vitor Martins, an Assistant Professor in the Department of Agricultural and Biological Engineering, uses satellite data to improve agriculture and water resource management. His agriculture-based work helps farmers make informed decisions by providing accessible satellite-derived information on crop growth and climate conditions. Another focus of his is the monitoring of water quality and changes in coastal, lakes and reservoirs. Martins says that together, these efforts advance sustainable management of essential food and water systems.
Martins' inspiration to pursue this field stems from his first experience with Landsat satellite imagery, noting that despite the remarkable era of data availability, a major challenge remains in translating those sources into effective insights.
“My goal as a researcher is to help close that gap, developing methods that improve our understanding of land surface dynamics so we can better track, manage, and sustain our natural resources and agricultural production,” said Martins.
One of the most significant recent achievements from his lab is the development of a fully automated algorithm that can identify every crop field in the world. This system provides detailed information on the geographic location, size, and distribution of fields for each country.
“In the short term, this demonstrates what’s possible with large-scale automation of agricultural monitoring,” said Martins. “In the long term, I hope this work will enable continuous, field-level monitoring of agricultural production worldwide, supporting food production planning and management.”
Martins’ interdisciplinary work is enhanced through close collaboration with colleagues at the Geosystems Research Institute (GRI) and the High-Performance Computing Collaboratory (HPC²). These partnerships are essential for designing and implementing the large-scale data processing systems required for global mapping.
Moving forward, Martins’ research will focus on multi-year agricultural field detection and monitoring using high-performance computing. This is a critical next step toward building a continuous global understanding of agricultural change over time.
