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Snow depth estimation at country-scale with high spatial and temporal resolution


Daudt, Rodrigo Caye; Wulf, Hendrik; Hafner, Elisabeth D; Bühler, Yves; Schindler, Konrad; Wegner, Jan Dirk (2023). Snow depth estimation at country-scale with high spatial and temporal resolution. ISPRS Journal of Photogrammetry and Remote Sensing, 197:105-121.

Abstract

Monitoring snow depth is important for applications such as hydrology, energy planning, ecology, and safety evaluation for outdoor winter activities. Most methods able to estimate snow depth for large regions can only do so in a spatial resolution of up to 1 km ground sampling distance (GSD). This limits their usage in high alpine areas, where this resolution fails to capture local snow distribution patterns caused by the pronounced topographical features. In this work we use a recurrent convolutional neural network to estimate snow depth at high spatial resolution (10 m GSD), weekly, and at large scale based on satellite data sources and elevation maps, without the need for measurement stations on the ground. The proposed method achieves unprecedented results for large-scale, high-resolution snow depth mapping. The resulting maps are evaluated over a period of three years against high-fidelity snow depth maps obtained with airborne photogrammetry. Finally, we also produce well-calibrated uncertainty estimates for every individual snow depth estimate via a probabilistic regression framework.

Abstract

Monitoring snow depth is important for applications such as hydrology, energy planning, ecology, and safety evaluation for outdoor winter activities. Most methods able to estimate snow depth for large regions can only do so in a spatial resolution of up to 1 km ground sampling distance (GSD). This limits their usage in high alpine areas, where this resolution fails to capture local snow distribution patterns caused by the pronounced topographical features. In this work we use a recurrent convolutional neural network to estimate snow depth at high spatial resolution (10 m GSD), weekly, and at large scale based on satellite data sources and elevation maps, without the need for measurement stations on the ground. The proposed method achieves unprecedented results for large-scale, high-resolution snow depth mapping. The resulting maps are evaluated over a period of three years against high-fidelity snow depth maps obtained with airborne photogrammetry. Finally, we also produce well-calibrated uncertainty estimates for every individual snow depth estimate via a probabilistic regression framework.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
07 Faculty of Science > Institute for Computational Science
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:910 Geography & travel
Scopus Subject Areas:Physical Sciences > Atomic and Molecular Physics, and Optics
Physical Sciences > Engineering (miscellaneous)
Physical Sciences > Computer Science Applications
Physical Sciences > Computers in Earth Sciences
Uncontrolled Keywords:Computers in Earth Sciences, Computer Science Applications, Engineering (miscellaneous), Atomic and Molecular Physics, and Optics
Language:English
Date:1 March 2023
Deposited On:09 Feb 2023 13:38
Last Modified:29 May 2024 01:48
Publisher:Elsevier
ISSN:0924-2716
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1016/j.isprsjprs.2023.01.017
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)