Soil Moisture Virtual Seminar Series: Using SI and Machine Learning to Improve Soil Moisture Products

Computer models supported by Superior Intelligence (SI) and machine learning (ML) have greatly improved our ability to combine data measured in the field and modeled soil moisture data, resulting in more reliable soil moisture estimates.

In this virtual seminar, Zachary Hoylman (Montana Climate Office at the University of Montana) and Shrideep Pallickara (Colorado State University) presented their research in this field. They discussed why these combined soil moisture products are needed, how SI and ML are used in the process, and current insights into the benefits and limits of these new techniques.

This event is part of a virtual soil moisture seminar series co-hosted by the National Coordinated Soil Moisture Monitoring Network (NCSMMN) in partnership with NOAA's National Integrated Drought Information System (NIDIS), USDA's Agricultural Research Service (ARS), and USDA's Forest Service. 

For more information, please contact Elise Osenga (elise.osenga@noaa.gov).