Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions

Meriö, Leo-Juhani; Rauhala, Anssi; Ala-aho, Pertti; Kuzmin, Anton; Korpelainen, Pasi; Kumpula, Timo; Kløve, Bjørn; Marttila, Hannu

doi:https://doi.org/10.5194/tc-17-4363-2023

Articles | Volume 17, issue 10

https://doi.org/10.5194/tc-17-4363-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-17-4363-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 10

Research article

|

17 Oct 2023

Research article |

| 17 Oct 2023

Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions

Leo-Juhani Meriö, Anssi Rauhala, Pertti Ala-aho, Anton Kuzmin, Pasi Korpelainen, Timo Kumpula, Bjørn Kløve, and Hannu Marttila

Supplement

https://doi.org/10.5194/tc-17-4363-2023-supplement

Data sets

Unmanned aircraft system (UAS) snow depth mapping at the Pallas Atmosphere-Ecosystem Supersite Anssi Rauhala, Leo-Juhani Meriö, Pasi Korpelainen, and Anton Kuzmin https://doi.org/10.23729/43d37797-e8cf-4190-80f1-ff567ec62836

Short summary

Information on seasonal snow cover is essential in understanding snow processes and operational forecasting. We study the spatiotemporal variability in snow depth and snow processes in a subarctic, boreal landscape using drones. We identified multiple theoretically known snow processes and interactions between snow and vegetation. The results highlight the applicability of the drones to be used for a detailed study of snow depth in multiple land cover types and snow–vegetation interactions.