Automated snow cover detection on mountain glaciers using spaceborne imagery and machine learning

Aberle, Rainey; Enderlin, Ellyn; O'Neel, Shad; Florentine, Caitlyn; Sass, Louis; Dickson, Adam; Marshall, Hans-Peter; Flores, Alejandro

doi:https://doi.org/10.5194/tc-19-1675-2025

Articles | Volume 19, issue 4

https://doi.org/10.5194/tc-19-1675-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-19-1675-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 4

Research article

|

24 Apr 2025

Research article |

| 24 Apr 2025

Automated snow cover detection on mountain glaciers using spaceborne imagery and machine learning

Rainey Aberle, Ellyn Enderlin, Shad O'Neel, Caitlyn Florentine, Louis Sass, Adam Dickson, Hans-Peter Marshall, and Alejandro Flores

Supplement

https://doi.org/10.5194/tc-19-1675-2025-supplement

Data sets

Dataset for Automated Snow Cover Detection on Mountain Glaciers Using Space-Borne Imagery Rainey Aberle et al. https://doi.org/10.18122/cryogars_data.4.boisestate

Model code and software

RaineyAbe/glacier-snow-cover-mapping: Second release (v0.2) Rainey Aberle et al. https://doi.org/10.5281/zenodo.10616385

Interactive computing environment

RaineyAbe/glacier-snow-cover-mapping Rainey Aberle et al. https://doi.org/10.5281/zenodo.10616385

Short summary

Tracking seasonal snow on glaciers is critical for understanding glacier health. Yet previous work has not directly compared machine learning algorithms for snow classification across satellite image products. To address this, we developed a new automated workflow for tracking seasonal snow on glaciers using several image products and machine learning models. Applying this method can help provide insights into glacier health, water resources, and the effects of climate change on snow cover.