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

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

doi:10.5194/tc-20-3187-2026

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

Data sets

Data for the figures and analyses performed in "Machine learning for snow depth estimation over the European Alps, using Sentinel-1 observations, meteorological forcing data and process-based model simulations" Lucas Boeykens https://doi.org/10.5281/zenodo.19697176

IMIS measuring network Intercantonal Measurement and Information System IMIS https://doi.org/10.16904/envidat.406

Manual measuring network WSL Institute for Snow and Avalanche Research SLF https://doi.org/10.16904/envidat.408

Snow depth mapping by airplane photogrammetry (2017-ongoing) L. Bührle et al. https://doi.org/10.16904/envidat.418

Snow Depth Mapping M. Marty et al. https://doi.org/10.16904/envidat.62

SAR wet snow 2016-present (raster 60 m) Copernicus Land Monitoring Service https://doi.org/10.2909/cd23c4bb-b3cb-4331-bb89-93321b46f8ed

Copernicus DEM - Global and European Digital Elevation Model CDSE https://doi.org/10.5270/ESA-c5d3d65

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.