Preprints
https://doi.org/10.5194/tc-2022-239
https://doi.org/10.5194/tc-2022-239
20 Dec 2022
 | 20 Dec 2022
Status: a revised version of this preprint was accepted for the journal TC and is expected to appear here in due course.

Measuring the spatiotemporal variability of snow depth in subarctic environments using unmanned aircraft systems (UAS) – Part 1: Measurements, processing, and accuracy assessment

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

Abstract. Snow conditions in the northern hemisphere are rapidly changing, and information on snow depth is critical for decision-making and other societal needs. Unmanned aircraft systems (UASs) can offer data resolutions of a few centimeters at a catchment-scale, and thus provide a low-cost solution to bridge the gap between sparse manual probing and low-resolution satellite data. In this study, we present a series of snow depth measurements using different UAS platforms throughout the winter in the Finnish subarctic site Pallas, which has a heterogeneous landscape. We discuss the different platforms, the methods utilized, difficulties working in the harsh northern environment, and the results and their accuracy compared to in situ measurements. Generally, all UASs produced spatially representative estimates of snow depth in open areas after reliable georeferencing by using the Structure from Motion (SfM) photogrammetry technique. However, significant differences were observed in the accuracies produced by the different UASs compared to manual snow depth measurements, with overall RMSEs varying between 13.0 to 25.2 cm, depending on the UAS. Additionally, a reduction in accuracy was observed when moving from an open mire area to forest covered areas. We demonstrate the potential of low-cost UASs to efficiently map snow surface conditions, and we give some recommendations on UAS platform selection and operation in a harsh subarctic environment with variable canopy cover.

Anssi Rauhala et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-239', Anonymous Referee #1, 13 Feb 2023
    • AC1: 'Reply on RC1', Anssi Rauhala, 18 Apr 2023
  • RC2: 'Comment on tc-2022-239', Anonymous Referee #2, 01 Mar 2023
    • AC2: 'Reply on RC2', Anssi Rauhala, 18 Apr 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-239', Anonymous Referee #1, 13 Feb 2023
    • AC1: 'Reply on RC1', Anssi Rauhala, 18 Apr 2023
  • RC2: 'Comment on tc-2022-239', Anonymous Referee #2, 01 Mar 2023
    • AC2: 'Reply on RC2', Anssi Rauhala, 18 Apr 2023

Anssi Rauhala et al.

Data sets

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

Anssi Rauhala et al.

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Short summary
Snow conditions in the northern hemisphere are rapidly changing and information on snow depth is important for decision-making. We present snow depth measurements using different drones throughout the winter in a subarctic site. Generally, all drones produced good estimates of snow depth in open areas. However, differences were observed in the accuracies produced by the different drones and a reduction in accuracy was observed when moving from an open mire area to forest covered areas.