New Project: Leveraging machine learning to provide high resolution soil moisture and evapotranspiration data to support farm-scale decision making

May 3, 2021

New Project: Leveraging machine learning to provide high resolution soil moisture and evapotranspiration data to support farm-scale decision making

Image
Steven Quiring: Leveraging Machine Learning
Description

Dr. Steven Quiring, Professor of Geography and Byrd Center Principal Investigator, was recently awarded $500K by the National Institute of Food and Agriculture to improve the accuracy and utility of national soil moisture and evapotranspiration products by integrating new data sources and downscaling them to individual farms across the continental United States.

Advanced
Text

What are you setting out to do?

Dr. Quiring: This research addresses the critical need to enhance the accuracy and utility of national soil moisture (SM) and evapotranspiration (ET) products by integrating new data sources and downscaling them to farm-scale. 

The goal is to provide soil moisture and evapotranspiration data at farm-scale (< 400 m) across the continental United States. We will be integrating data from in situ measurements, satellites and land surface models. The downscaling and blending of these datasets will use a variety of machine learning approaches.

This project builds on my NOAA funded work that developed: nationalsoilmoisture.com. I will be leading the soil moisture work at Ohio State and the Co-PI, Vahid Rahmani, will be leading the evapotranspiration work at Kansas State University.


Why is this project important?

Dr. Quiring: This project specifically addresses the USDA FACT priorities by integrating disparate datasets and by building a scalable data infrastructure system for collecting, processing and distributing SM and ET data to agricultural producers, agribusinesses, natural resource managers and scientists. These data are important for supporting on-farm decision making for applications such as precision agriculture and irrigation scheduling. They also are important for modeling crop yield, as well as insect and disease outbreaks. The results of this project will create substantial value for the U.S. agricultural enterprise. We will disseminate the new soil moisture and ET data in near-real-time.

This project specifically addresses the USDA FACT priorities by integrating disparate datasets and by building a scalable data infrastructure system for collecting, processing and distributing soil moisture and evapotranspiration data to agricultural producers, agribusinesses, natural resource managers and scientists.

These data are important for supporting on-farm decision making for applications such as precision agriculture and irrigation scheduling. They also are important for modeling crop yield, as well as insect and disease outbreaks. The results of this project will create substantial value for the U.S. agricultural enterprise.

We will disseminate the new soil moisture and evapotranspiration data in near-real-time.