The Cryosphere
The Cryosphere
TC
Articles | Volume 19, issue 10
Articles | Volume 19, issue 10
https://doi.org/10.5194/tc-19-4585-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-4585-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 10
Research article
 | 
16 Oct 2025
Research article |  | 16 Oct 2025

UAV LiDAR surveys and machine learning improve snow depth and water equivalent estimates in boreal landscapes

Maiju Ylönen, Hannu Marttila, Joschka Geissler, Anton Kuzmin, Pasi Korpelainen, Timo Kumpula, and Pertti Ala-Aho

Viewed

Total article views: 1,430 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,112 290 28 1,430 53 35 44
  • HTML: 1,112
  • PDF: 290
  • XML: 28
  • Total: 1,430
  • Supplement: 53
  • BibTeX: 35
  • EndNote: 44
Views and downloads (calculated since 05 May 2025)
Cumulative views and downloads (calculated since 05 May 2025)

Viewed (geographical distribution)

Total article views: 1,430 (including HTML, PDF, and XML) Thereof 1,423 with geography defined and 7 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Nov 2025
Download
Short summary
We collected snow depth maps four times during the winter from two different sites and used them as input for a model to predict daily snow depth and snow water equivalent (SWE). Our results show similar snow depth patterns at different sites, with snow depths being the highest in forests and forest gaps and the lowest in open areas. The results can extend operational snow course measurements and their temporal and spatial coverage, helping hydrological forecasting and water resource management.
Read more
Share