Using Interferometric SAR to Quantify Snow Water Equivalent and Environmental Change

Seasonal snowpacks are a critical component of the global water cycle, governing river discharge, groundwater recharge, and freshwater availability for billions of people worldwide. Yet current satellite-based methods for estimating snow water equivalent (SWE) suffer from fundamental limitations, including coarse spatial resolution, limited coverage, and reliance on uncertain a priori assumptions.

In this talk, I will present recent advances in using spaceborne Synthetic Aperture Radar Interferometry (InSAR) to retrieve SWE at high spatial resolution and continental scales. I will focus on our 2024 work demonstrating, for the first time, that Sentinel-1 repeat-pass interferometric time series can be used to reliably estimate SWE across regions with complex topography and variable snow conditions. This approach leverages a physically grounded relationship between interferometric phase delay and SWE change, careful mitigation of atmospheric effects, and time-series integration to recover total SWE. Validation against SNOTEL and airborne LIDAR data shows strong agreement across multiple western U.S. basins.

I will then discuss how this work has motivated ongoing NASA-funded efforts to extend SWE retrieval to L-band NISAR data, enabling global coverage and improved performance in forested and mountainous regions. Finally, I will outline future directions that integrate physics-based radar models with machine learning to link SWE dynamics with streamflow, groundwater storage, wildfire recovery, and agricultural resilience. Together, these efforts aim to establish a new framework for high-resolution environmental monitoring and water-resource management under a changing climate.

Speaker: Shadi Oveisgharan, NASA Jet Propusion Laboratory