Crop Yield and Condition Forecasting from Field to Global Scales
What We Do
We monitor and forecast agricultural yields and conditions globally, ranging from single fields to entire countries, by applying machine learning algorithms to earth observation data.
Location
United States of America, Vietnam, Japan, Philippines, Brazil, Mexico, Australia, South Africa, India, Canada, Pakistan, Indonesia, Paraguay, Turkey, Argentina, China, Italy, Romania, Kazakhstan, France, Germany, U.K., Ukraine, Russia, Spain, Hungary
How Satellites Make This Work
Access to timely Earth Observation (EO) data can ensure effective monitoring of agricultural conditions globally. We use EO datasets (NDVI, Temperature, Precipitation, Evaporative Stress Index, Soil Moisture, Growing Degree Days), GEOGLAM crop calendars, GEOGLAM crop masks to create dashboards of crop conditions across a range of crops and countries. These dashboards are updated weekly and provide a useful source of information in producing the GEOGLAM crop monitor reports. These EO datasets are used to create machine learning models of crop yields that are used for in-season yield and condition forecasting.
Lead
Inbal Becker-Reshef, University of Maryland
Ritvik Sahajpal, University of Maryland
Brian Barker, University of Maryland
Team Members
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