Summary & Purpose
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.
Date of Publication or Submission
5-28-2024
DOI
https://doi.org/10.18122/cryogars_data.4.boisestate
Funding Citation
BAA-CRREL award W913E520C0017
NASA EPSCoR award 80NSSC20M0222
NASA Idaho Space Grant Consortium summer internship program
SMART (Science, Mathematics, And Research for Transformation) scholarship program
Data Source Credits
Glacier-Wide Mass Balance and Compiled Data Inputs: USGS Benchmark Glaciers
https://alaska.usgs.gov/products/data.php?dataid=79
https://doi.org/10.5066/F7HD7SRF
Landsat 8/9 images courtesy of the U.S. Geological Survey
Sentinel-2 images courtesy of the European Union, ESA, and Copernicus
PlanetScope 4-band images
https://api.planet.com
Single Dataset or Series?
Series
Data Format
*.csv; *.nc; *.txt
File Size
34.1 GB
Data Attributes
Metadata file for the automatically-detected snow cover maps and statistics at the USGS Benchmark Glaciers for 2013-2023. Classified images were constructed using Landsat 8-9, Sentinel-2, and PlanetScope 4-band imagery along with pre-trained supervised machine learning algorithms for each image product. Image classifier training data were constructed by manually classifying thousands of points in cloud-free imagery at the USGS Benchmark Glaciers. Methods, results, and analyses are described in the manuscript by Aberle et al. (2024). All code used to develop and assess the image classifiers, as well as to construct the data products are available through a public GitHub repository (https://github.com/RaineyAbe/glacier-snow-cover-mapping) and through Zenodo (dot:10.5281/zenodo.10616385).
See README.txt for more information
Time Period
May 2013 - November 2023
Privacy and Confidentiality Statement
Boise State is explicitly compliant with federal and state laws surrounding data privacy including the protection of personal financial information through the Gramm-Leach-Bliley Act, personal medical information through HIPAA, HITECH and other regulations. All human subject data (e.g., surveys) has been collected and managed only by personnel with adequate human subject protection certification.
Use Restrictions
Users are free to share, copy, distribute and use the dataset; to create or produce works from the dataset; to adapt, modify, transform and build upon the dataset as long as the user attributes any public use of the dataset, or works produced from the dataset, referencing the author(s) and DOI link. For any use or redistribution of the dataset, or works produced from it, the user must make clear to others the license of the dataset and keep intact any notices on the original dataset.
Disclaimer of Warranty
BOISE STATE UNIVERSITY MAKES NO REPRESENTATIONS ABOUT THE SUITABILITY OF THE INFORMATION CONTAINED IN OR PROVIDED AS PART OF THE SYSTEM FOR ANY PURPOSE. ALL SUCH INFORMATION IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND. BOISE STATE UNIVERSITY HEREBY DISCLAIMS ALL WARRANTIES AND CONDITIONS WITH REGARD TO THIS INFORMATION, INCLUDING ALL WARRANTIES AND CONDITIONS OF MERCHANTABILITY, WHETHER EXPRESS, IMPLIED OR STATUTORY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT.
IN NO EVENT SHALL BOISE STATE UNIVERSITY BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF INFORMATION AVAILABLE FROM THE SYSTEM.
THE INFORMATION PROVIDED BY THE SYSTEM COULD INCLUDE TECHNICAL INACCURACIES OR TYPOGRAPHICAL ERRORS. CHANGES ARE PERIODICALLY ADDED TO THE INFORMATION HEREIN. COMPANY AND/OR ITS RESPECTIVE SUPPLIERS MAY MAKE IMPROVEMENTS AND/OR CHANGES IN THE PRODUCT(S) AND/OR THE PROGRAM(S) DESCRIBED HEREIN AT ANY TIME, WITH OR WITHOUT NOTICE TO YOU.
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Recommended Citation
Aberle, Rainey; Enderlin, Ellyn; O'Neel, Shad; Florentine, Caitlyn; Sass, Louis; Dickson, Adam; Marshall, Hans-Peter; and Flores, Alejandro. (2024). Dataset for Automated Snow Cover Detection on Mountain Glaciers Using Space-Borne Imagery [Data set]. Retrieved from https://doi.org/10.18122/cryogars_data.4.boisestate