Estimating Groundwater Dynamics from Artificial Intelligence and InSAR

Groundwater represents about a third of global water withdrawals, and approximately half of global irrigation water. In many arid and semi-arid regions, groundwater is rapidly being depleted, which is affecting agricultural productivity over the long term. The over-exploitation of groundwater due to the current drought episode in South-Western U.S. has already led farmers to fallow hundreds of thousands of acres of farmland.We leverage recent developments in artificial intelligence in order to improve deformation detection thresholds in Interferometric Synthetic Aperture Radar (InSAR). Our technology allows to deconvolve signal from noise in InSAR data, and lowers the detection threshold of surface deformation in spatially continuous InSAR time series of ground deformation by about an order of magnitude compared to the state-of-the-art. Phase I focused on separating ground deformation signals from atmospheric noise, and improving the time resolution of the associated InSAR time series. In this Phase II proposal, we focus on further improving detection capabilities by taking directly into account a second major source of noise in InSAR data, due to soil moisture. nbsp;We specifically train models on time series of seasonal depletion and recharge in order to study variations in groundwater levels in InSAR data, with the goal of allowing governments and organizations to take informed assessments and policies related to groundwater supplies.