Preprints
https://doi.org/10.5194/egusphere-2024-548
https://doi.org/10.5194/egusphere-2024-548
29 Feb 2024
 | 29 Feb 2024
Status: this preprint is open for discussion.

Automated snow cover detection on mountain glaciers using space-borne imagery

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

Abstract. Tracking the extent of seasonal snow on glaciers over time is critical for assessing glacier vulnerability and the response of glacierized watersheds to climate change. Existing snow cover products do not reliably distinguish seasonal snow from glacier ice and firn, preventing their use for glacier snow cover detection. Despite previous efforts to classify glacier surface facies on local scales, a unified approach for monitoring glacier snow cover on larger spatial scales remains elusive. We present an automated snow detection workflow for mountain glaciers using supervised machine learning-based image classifiers and Landsat 8/9, Sentinel-2, and PlanetScope satellite imagery. We develop the image classifiers by testing numerous machine learning algorithms with training and validation data from the U.S. Geological Survey Benchmark Glaciers. The workflow produces daily to biweekly time series of several glacier mass balance and snowmelt indicators (snow-covered area, accumulation area ratio, and seasonal snowline) from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snowlines to manual interpretations at each glacier site. The image classifiers exhibit overall accuracies of 92–98 %, Kappa scores of 84–96 %, and F-scores of 93–98 % for all image products. The median difference between automatically and manually delineated median snowline altitudes, along with the interquartile range, averages 27 +/- 79 m across all image products. The Sentinel-2 classifier (Support Vector Machine) produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and firn the most reliably. Yet, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage and frequency of observations. Additionally, the transient accumulation area ratio produces the least noisy time series, providing the most reliable indicator for characterizing seasonal snow trends. The temporally detailed accumulation area ratio time series reveal that the timing of minimum snow cover conditions varies by up to a month between Arctic (63° N) and mid-latitude (48° N) sites, underscoring the potential for bias when estimating glacier minimum snow cover conditions from a single late-summer image. Widespread application of our automated snow detection workflow has the potential to improve regional assessments of glacier mass balance, water resources, and the impacts of climate change on snow cover across broad spatial scales.

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

Status: open (until 29 Apr 2024)

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

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Short summary
Tracking seasonal snow on glaciers is critical for understanding glacier health. However, current snow detection methods struggle to distinguish seasonal snow from glacier ice. To address this, we developed a new automated workflow for tracking seasonal snow on glaciers using satellite imagery and machine learning. Applying this method can help provide insights into glacier health, water resources, and the effects of climate change on snow cover over broad spatial scales.