Articles | Volume 18, issue 11
https://doi.org/10.5194/tc-18-5259-2024
© Author(s) 2024. 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-18-5259-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Land surface temperature trends derived from Landsat imagery in the Swiss Alps
Deniz Tobias Gök
CORRESPONDING AUTHOR
GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Dirk Scherler
GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Institute of Geographical Sciences, Freie Universität Berlin, 14195 Berlin, Germany
Hendrik Wulf
Remote Sensing Laboratories, University of Zurich, 8057, Zurich, Switzerland
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Deniz Tobias Gök, Dirk Scherler, and Leif Stefan Anderson
The Cryosphere, 17, 1165–1184, https://doi.org/10.5194/tc-17-1165-2023, https://doi.org/10.5194/tc-17-1165-2023, 2023
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We performed high-resolution debris-thickness mapping using land surface temperature (LST) measured from an unpiloted aerial vehicle (UAV) at various times of the day. LSTs from UAVs require calibration that varies in time. We test two approaches to quantify supraglacial debris cover, and we find that the non-linearity of the relationship between LST and debris thickness increases with LST. Choosing the best model to predict debris thickness depends on the time of the day and the terrain aspect.
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The Cryosphere, 17, 1567–1583, https://doi.org/10.5194/tc-17-1567-2023, https://doi.org/10.5194/tc-17-1567-2023, 2023
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Deniz Tobias Gök, Dirk Scherler, and Leif Stefan Anderson
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We performed high-resolution debris-thickness mapping using land surface temperature (LST) measured from an unpiloted aerial vehicle (UAV) at various times of the day. LSTs from UAVs require calibration that varies in time. We test two approaches to quantify supraglacial debris cover, and we find that the non-linearity of the relationship between LST and debris thickness increases with LST. Choosing the best model to predict debris thickness depends on the time of the day and the terrain aspect.
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Dirk Scherler and Wolfgang Schwanghart
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Wolfgang Schwanghart and Dirk Scherler
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Xingdong Li, Di Long, Yanhong Cui, Tingxi Liu, Jing Lu, Mohamed A. Hamouda, and Mohamed M. Mohamed
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Short summary
We derived Landsat Collection 2 land surface temperature (LST) trends in the Swiss Alps using a harmonic model with a linear trend. Validation with LST data from 119 high-altitude weather stations yielded robust results, but Landsat LST trends are biased due to unstable acquisition times. The bias varies with topographic slope and aspect. We discuss its origin and propose a simple correction method in relation to modeled changes in shortwave radiation.
We derived Landsat Collection 2 land surface temperature (LST) trends in the Swiss Alps using a...