Abstract
Glaciers in Alaska contribute greatly to sea-level rise and are losing mass at a faster rate than any other region. Yet, our understanding of ongoing changes and ability to model them are hindered by a lack of observations, particularly at high spatiotemporal resolution. Here, we leverage Sentinel-1 synthetic aperture radar (SAR) data to produce temporally-varying glacier melt extents and snowlines from mid-2016 to 2024 for 99% of glaciers in Alaska greater than 2 km2. The melt extents are strongly correlated with temperatures, revealing that each 1°C increase in summer temperature causes up to 3 additional weeks of glacier melt. The high spatiotemporal resolution also captures subseasonal changes such as the 2019 heat wave, which caused subregional snowlines to retreat up to 105 m higher and exposed up to 28% more of the underlying glacier compared to typical years. Our snowlines agree well with optical datasets (r2 up to 0.94), thus providing unprecedented reliable data unencumbered by clouds or lighting conditions. Moving forward, our automated, open-source workflow can easily be applied to other regions. These data also present unique opportunities to calibrate and validate large-scale glacier evolution models, a critical step for improving projections of glacier changes and their impacts.
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Data availability
All data produced in this work are available with open access, and the source data are publicly accessible. Sentinel-1 SAR data can be downloaded through the Alaska Satellite Facility Vertex tool (https://search.asf.alaska.edu/#/). Glacier outlines are available online through the Global Land Ice Measurements from Space (GLIMS) initiative (https://www.glims.org/RGI/). Climate data are available online at the Copernicus Climate Change Service (C3S) Climate Data Store (cds.climate.copernicus.eu/). All glacier transient snowlines, melt extents, and binned backscatter products are available from Zenodo (https://zenodo.org/records/17108203) and can be easily accessed, visualized, and downloaded for any glacier through an online tool (https://alaskasnowlines.streamlit.app/).
Code availability
Python was used to generate all results and analysis in this study. Information regarding Python installation and system requirements is available online (https://www.python.org/). Code used to produce these results and generate figures is available on GitHub (https://github.com/albinwwells/SAR-Alaska-Processing) with a sample dataset and accompanying tutorial available on Zenodo (https://zenodo.org/records/17108203).
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Acknowledgements
A.W. and D.R.R. were supported by the National Science Foundation (NSF) Glacier Extremes project, the National Aeronautics and Space Administration (NASA) Sea Level Change Team (SLCT) 2, and the NASA SLCT 3. We acknowledge Mira Khadka for early insights and work on this project. We thank Joseph Kennedy and Louis Sass for useful input and feedback regarding the data processing and results. We acknowledge the ASF DAAC HyP3 2025 using the hyp3_gamma plugin version 9.0.6 running GAMMA release 20240627 for RTC Sentinel-1 SAR data, which is modified Copernicus Sentinel data 2021, processed by ESA.
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All authors (A.W., D.R.R., and M.F.) contributed to the concept and design of the study. All authors contributed to the data processing and programming. A.W. led the formal analysis, writing, and visualization, and all authors contributed to it.
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Wells, A., Rounce, D.R. & Fahnestock, M. Seasonal progression of melt and snowlines in Alaska from SAR reveals impacts of warming. npj Clim Atmos Sci (2026). https://doi.org/10.1038/s41612-026-01321-y
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DOI: https://doi.org/10.1038/s41612-026-01321-y
