Developing a Surrogate Model for SWAT with Remotely Sensed Soil Moisture Using Python

This research developed a neural network representation for the the USDA Soil & Water Assessment Tool (SWAT) using remotely sensed soil moisture to provide near-real-time soil-moisture predictions. A recurrent neural network (RNN) with long short-term memory (LSTM) architecture was used to create a mapping, targeting pattern recognition in time series data. This project uses several Python packages. SMAP data acquisition was performed using the pytesmo package, which has tools for importing and comparing soil moisture data from several remote sensing sources on-the-fly. Popular neural network frameworks such as Keras and PyTorch were used to build a neural network with RNN-LSTM architecture.

 Speaker: Katherine Breen, Baylor University, Department of Geosciences
 Speaker: Scott James, Baylor University, Departments of Geosciences and Mechanical Engineering
 Speaker: Joseph White, Baylor University, Department of Biology