Articles | Volume 17, issue 8
https://doi.org/10.5194/tc-17-3383-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-3383-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Aug 2023
Research article |
| 22 Aug 2023
| 22 Aug 2023
Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas
Leon J. Bührle
WSL Institute for Snow and Avalanche Research SLF, Davos,
7260, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions
Research Center CERC, 7260 Davos, Switzerland
Mauro Marty
Swiss Federal Institute for Forest, Snow and Landscape Research
WSL, Birmensdorf, 8903, Switzerland
Lucie A. Eberhard
WSL Institute for Snow and Avalanche Research SLF, Davos,
7260, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions
Research Center CERC, 7260 Davos, Switzerland
Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, 8092,
Switzerland
Andreas Stoffel
WSL Institute for Snow and Avalanche Research SLF, Davos,
7260, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions
Research Center CERC, 7260 Davos, Switzerland
Elisabeth D. Hafner
WSL Institute for Snow and Avalanche Research SLF, Davos,
7260, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions
Research Center CERC, 7260 Davos, Switzerland
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, Zurich,
8092, Switzerland
WSL Institute for Snow and Avalanche Research SLF, Davos,
7260, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions
Research Center CERC, 7260 Davos, Switzerland
Viewed
Total article views: 4,589 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Apr 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,137 | 1,320 | 132 | 4,589 | 158 | 219 |
- HTML: 3,137
- PDF: 1,320
- XML: 132
- Total: 4,589
- BibTeX: 158
- EndNote: 219
Total article views: 3,065 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Aug 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,202 | 761 | 102 | 3,065 | 122 | 191 |
- HTML: 2,202
- PDF: 761
- XML: 102
- Total: 3,065
- BibTeX: 122
- EndNote: 191
Total article views: 1,524 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Apr 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 935 | 559 | 30 | 1,524 | 36 | 28 |
- HTML: 935
- PDF: 559
- XML: 30
- Total: 1,524
- BibTeX: 36
- EndNote: 28
Viewed (geographical distribution)
Total article views: 4,589 (including HTML, PDF, and XML)
Thereof 4,463 with geography defined
and 126 with unknown origin.
Total article views: 3,065 (including HTML, PDF, and XML)
Thereof 2,970 with geography defined
and 95 with unknown origin.
Total article views: 1,524 (including HTML, PDF, and XML)
Thereof 1,493 with geography defined
and 31 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
13 citations as recorded by crossref.
- MAPunet: High-resolution snow depth mapping through U-Net pixel-wise regression A. Betato et al. https://doi.org/10.1016/j.rsase.2025.101477
- A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery D. Dunmire et al. https://doi.org/10.1016/j.rse.2024.114369
- Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al. https://doi.org/10.1007/s40641-025-00207-0
- Creating probability maps for avalanche hazardous areas reflecting snowpack uncertainty updates T. Tanabe et al. https://doi.org/10.1016/j.coldregions.2025.104716
- Autonomous and efficient large-scale snow avalanche monitoring with an Unmanned Aerial System (UAS) J. Lim et al. https://doi.org/10.5194/nhess-26-411-2026
- High-resolution hydrometeorological and snow data for the Dischma catchment in Switzerland J. Magnusson et al. https://doi.org/10.5194/essd-17-703-2025
- Fluidization and snow cover effects in rock-ice-snow avalanches: Lessons from Piz Cengalo, Fluchthorn, and Piz Scerscen events Y. Zhuang et al. https://doi.org/10.1016/j.compgeo.2025.107456
- Towards slope-scale assessment of avalanche formation: Exploring UAV-borne GPR for unveiling spatial snowpack variability A. Siebenbrunner et al. https://doi.org/10.1016/j.coldregions.2025.104741
- High-resolution mapping of snow depth and snow thermal insulation using low-cost UAVs in complex Arctic Urban Terrains M. Mueller et al. https://doi.org/10.1139/as-2025-0062
- Evaluating precipitation corrections to enhance high-alpine hydrological modeling T. Pulka et al. https://doi.org/10.1016/j.jhydrol.2024.132202
- Monitoring snow depth variations in an avalanche release area using low-cost lidar and optical sensors P. Ruttner et al. https://doi.org/10.5194/nhess-25-1315-2025
- A seasonal snowpack model forced with dynamically downscaled forcing data resolves hydrologically relevant accumulation patterns J. Berg et al. https://doi.org/10.3389/feart.2024.1393260
- Machine learning for snow depth estimation over the European Alps, using Sentinel-1 observations, meteorological forcing data and process-based model simulations L. Boeykens et al. https://doi.org/10.5194/tc-20-3187-2026
13 citations as recorded by crossref.
- MAPunet: High-resolution snow depth mapping through U-Net pixel-wise regression A. Betato et al. https://doi.org/10.1016/j.rsase.2025.101477
- A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery D. Dunmire et al. https://doi.org/10.1016/j.rse.2024.114369
- Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al. https://doi.org/10.1007/s40641-025-00207-0
- Creating probability maps for avalanche hazardous areas reflecting snowpack uncertainty updates T. Tanabe et al. https://doi.org/10.1016/j.coldregions.2025.104716
- Autonomous and efficient large-scale snow avalanche monitoring with an Unmanned Aerial System (UAS) J. Lim et al. https://doi.org/10.5194/nhess-26-411-2026
- High-resolution hydrometeorological and snow data for the Dischma catchment in Switzerland J. Magnusson et al. https://doi.org/10.5194/essd-17-703-2025
- Fluidization and snow cover effects in rock-ice-snow avalanches: Lessons from Piz Cengalo, Fluchthorn, and Piz Scerscen events Y. Zhuang et al. https://doi.org/10.1016/j.compgeo.2025.107456
- Towards slope-scale assessment of avalanche formation: Exploring UAV-borne GPR for unveiling spatial snowpack variability A. Siebenbrunner et al. https://doi.org/10.1016/j.coldregions.2025.104741
- High-resolution mapping of snow depth and snow thermal insulation using low-cost UAVs in complex Arctic Urban Terrains M. Mueller et al. https://doi.org/10.1139/as-2025-0062
- Evaluating precipitation corrections to enhance high-alpine hydrological modeling T. Pulka et al. https://doi.org/10.1016/j.jhydrol.2024.132202
- Monitoring snow depth variations in an avalanche release area using low-cost lidar and optical sensors P. Ruttner et al. https://doi.org/10.5194/nhess-25-1315-2025
- A seasonal snowpack model forced with dynamically downscaled forcing data resolves hydrologically relevant accumulation patterns J. Berg et al. https://doi.org/10.3389/feart.2024.1393260
- Machine learning for snow depth estimation over the European Alps, using Sentinel-1 observations, meteorological forcing data and process-based model simulations L. Boeykens et al. https://doi.org/10.5194/tc-20-3187-2026
Saved (final revised paper)
Latest update: 13 Jun 2026
Short summary
Information on the snow depth distribution is crucial for numerous applications in high-mountain regions. However, only specific measurements can accurately map the present variability of snow depths within complex terrain. In this study, we show the reliable processing of images from aeroplane to large (> 100 km2) detailed and accurate snow depth maps around Davos (CH). We use these maps to describe the existing snow depth distribution, other special features and potential applications.
Information on the snow depth distribution is crucial for numerous applications in high-mountain...
