Soil Moisture Flux Sensor using Machine Intelligence

Our proposal addresses the need expressed in subtopic 26.a to measure vertically resolved soil moisture distributions. While soil moisture content is a key state variable in subsurface hydrobiogeochemical processes, spatially resolved soil moisture flux is an equally important process variable. We propose to develop an intelligent sensor that will use machine learning to infer vertically resolved soil moisture flux from changes in vertical moisture distribution as measured using technology we previously developed. Our technical approach will build on technology we previously developed for measuring vertically resolved soil moisture distributions using time domain reflectometry TDR), which resulted in three U.S. patents and ongoing negotiations to license the technology to established manufacturers. The new flux measurement system will be suitable for monitoring spatially resolved moisture flux within the 1-2 meter length of the probe, including surface infiltration, root zone uptake, and deep drainage. Using well-established modeling codes for unsaturated flow such as HYDRUS) to generate a vast model ensemble of soil water dynamics in a variety of soil types in response to variable surface wetting sequences, we will train a deep learning network to identify flux including source and sink terms. This machine intelligence, validated against field data, will be useable with a variety of soil water content measuring devices to simultaneously infer both soil hydrologic behavioral characteristics and time varying flux from time series observations of vertical moisture content distribution in response to time- varying surface wetting. Phase I will include ensemble model generation, architecture, training and validation of the machine intelligence, and testing against two years of in situ water content data from a DOE field site. Commercial applications and other benefits range from agricultural crop management and water conservation to environmental site management and monitoring earth-atmosphere moisture and energy exchange for climate observation and study.