Rajat Bindlish, a research associate in Earth science remote sensing at NASA’s
Goddard Space Flight Center in Greenbelt, Maryland, the tool provides more thorough spatial coverage and consistency than other soil moisture measurement methods.
“Soil moisture is a very important piece of information for agricultural yield and productivity,” said Bindlish. “This will provide a means of using NASA remote sensing data to guide predictions of moisture conditions and water availability. Information on the field conditions is important for agricultural operations.”
Some of Crop-CASMA’s primary users will be NASS researchers and statisticians who release weekly
Crop Progress Reports that currently classify states into moisture categories (very short, short, adequate, surplus) to aid farmers and farm managers. The reports also track crops’ health and growing progress.
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Ziheng Sun, Research Assistant Professor, Center for Spatial Information Science and Systems (CSISS), and Daniel Tong, Associate Professor, Atmospheric, Oceanic and Earth Sciences, are set to begin a project to make artificial intelligence (AI)-based Earth scientific workflows more shareable, replicable, reusable, and scalable. They will do this by further developing the existing open-source workflow management system (WfMS), GeoWeaver, into a stable operational platform for NASA s Earth Observing System Data and Information System (EOSDIS). Upon the success of the project, machine learning practitioners in NASA Earth data sciences will save numerous hours and efforts on preparing training datasets from [the] NASA EOSDIS archive, tuning their machine learning models, and accelerate the generation of in-time actionable information for stakeholders to take actions, Sun said.