The Cryosphere
The Cryosphere
TC
Articles | Volume 20, issue 5
Articles | Volume 20, issue 5
https://doi.org/10.5194/tc-20-3187-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-3187-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 5
Research article
 | 
29 May 2026
Research article |  | 29 May 2026

Machine learning for snow depth estimation over the European Alps, using Sentinel-1 observations, meteorological forcing data and process-based model simulations

Lucas Boeykens, Devon Dunmire, Jonas-Frederik Jans, Willem Waegeman, Gabriëlle De Lannoy, Ezra Beernaert, Niko E. C. Verhoest, and Hans Lievens

Viewed

Total article views: 15,331 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
12,172 2,814 345 15,331 542 522
  • HTML: 12,172
  • PDF: 2,814
  • XML: 345
  • Total: 15,331
  • BibTeX: 542
  • EndNote: 522
Views and downloads (calculated since 30 Jul 2025)
Cumulative views and downloads (calculated since 30 Jul 2025)

Viewed (geographical distribution)

Total article views: 15,331 (including HTML, PDF, and XML) Thereof 15,148 with geography defined and 183 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 13 Nov 2026
Download
Short summary
We used AI to better estimate the height of the snowpack present on the ground across the European Alps, by using novel satellite data, complemented by weather information or snow depth estimates from a computer model. We found that both combinations improve the accuracy of our AI-based snow depth estimates, performing almost equally well. This helps us better monitor how much water is stored as snow, which is vital for drinking water, farming, and clean energy production in Europe.
Read more
Share
Altmetrics
Final-revised paper
Preprint