Eric Gagliano
Postdoctoral Scholar · Remote Sensing · Cryosphere · SAR
Terrain Analysis and Cryosphere Observation Lab · University of Washington
About
I use satellite synthetic aperture radar (SAR) to understand when and where mountain snowpacks melt — a critical signal for water resources, flood forecasting, and climate science. My dissertation built a global, 80-meter resolution dataset of snowmelt runoff onset timing from a decade of Sentinel-1 imagery (~3.9 million scenes, 2015–2024), covering ~150 mountain ranges worldwide.
I recently finished my PhD in Civil & Environmental Engineering (Data Science option) at UW and am now a postdoctoral scholar in the TACO Lab, where I'm integrating NASA NISAR L-band data into our Sentinel-1 workflows and building cloud-native geospatial processing pipelines. I also regularly teach Geospatial Data Analysis in Python (CEE 467/CEWA 567) at UW.
Featured Work
Global Snowmelt Runoff Onset Dataset
A decade (2015–2024) of annual snowmelt timing across mountain ranges worldwide, derived from ~3.9 million Sentinel-1 SAR scenes at 80 m resolution. Validated against 900+ weather stations.
easysnowdata
A Python package for easily accessing snow-related geospatial datasets — SWE, snowmelt timing, SNOTEL/CCSS stations, and more. Published on Zenodo and PyPI.
Capturing Mountain Snowmelt Runoff Onset with SAR
Geophysical Research Letters (2023). Demonstrated SAR-based detection of snowmelt timing across Western U.S. mountain ranges using change-point analysis of backscatter time series.
Geospatial Data Analysis in Python — Course Materials
Open-access JupyterBook for the UW graduate course I teach (CEE 467/CEWA 567), covering vector/raster processing, remote sensing, and cloud-based workflows.