Soil moisture is easy to see when your favorite Little Leaguer slides into second base the day after a big summer storm.
Soil moisture is easy to see when your favorite Little Leaguer slides into second base the day after a big summer storm. The mud splattered on that little hustler’s uniform tells the story.
Trying to gauge soil moisture across large areas — regions, nations, continents — is a whole ‘nother challenge, and a critical one. Knowledge of this dimension of our ecosystem is extremely important for farmers, planners, scientists, insurance companies and anyone concerned about preparing for global environmental change.
“Understanding these patterns is critical to national and international security,” said Rodrigo Vargas, associate professor of ecosystem ecology and environmental change in the Department of Plant and Soil Sciences at the University of Delaware. “We cannot measure everything everywhere all the time…. So we are using alternative approaches, such as machine learning that helps us get insight from complex sets of data.”
Now Vargas and doctoral student Mario Guevara have developed a new approach that sharpens our ability to predict soil moisture, even in large areas where no data have been available. Compared to standard estimates produced by satellite-based sensors, the new approach increases the accuracy of these estimates by more than 20 percent. It also makes it possible to predict soil moisture conditions in much smaller areas and in greater detail than standard models have been able to show. They described their work in a recent issue of PLOS ONE, a peer-reviewed journal published by the Public Library of Science.
Read more at University of Delaware
Image: Rodrigo Vargas (left), associate professor of ecosystem ecology and environmental change at the University of Delaware, and doctoral student Mario Guevara have developed a new, more accurate way to map predicted soil moisture, even in areas where no data have been available. (Credit: University of Delaware/ Kathy F. Atkinson)
