Land surface temperature trends derived from Landsat imagery in the Swiss Alps
<p>The warming of high mountain regions caused by climate change is leading to glacier retreat, decreasing snow cover, and thawing permafrost, all of which have far-reaching effects on ecosystems and societies. Landsat Collection 2 provides multi-decadal land surface temperature (LST) data, pr...
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Copernicus Publications
2024-11-01
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| Series: | The Cryosphere |
| Online Access: | https://tc.copernicus.org/articles/18/5259/2024/tc-18-5259-2024.pdf |
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| author | D. T. Gök D. Scherler D. Scherler H. Wulf |
| author_facet | D. T. Gök D. Scherler D. Scherler H. Wulf |
| author_sort | D. T. Gök |
| collection | DOAJ |
| description | <p>The warming of high mountain regions caused by climate change is leading to glacier retreat, decreasing snow cover, and thawing permafrost, all of which have far-reaching effects on ecosystems and societies. Landsat Collection 2 provides multi-decadal land surface temperature (LST) data, principally suited for large-scale monitoring at high spatial resolution. In this study, we assess the potential to extract LST trends using Landsat 5, 7, and 8 time series. We conduct a comprehensive comparison of both LST and LST trends with data from 119 ground stations of the Intercantonal Measurement and Information System (IMIS) network, located at high elevations in the Swiss Alps. The direct comparison of Landsat and IMIS LST yields robust satellite data with a mean accuracy and precision of 0.26 and 4.68 K, respectively. For LST trends derived from a 22.6-year record length, as imposed by the IMIS data, we obtain a mean accuracy and precision of <span class="inline-formula">−0.02</span> and 0.13 K yr<span class="inline-formula"><sup>−1</sup></span>, respectively. However, we find that Landsat LST trends are biased due to unstable diurnal acquisition times, especially for Landsat 5 and 7. Consequently, LST trend maps derived from 38.5-year Landsat data exhibit systematic variations with topographic slope and aspect that we attribute to changes in direct shortwave radiation between different acquisition times. We discuss the origin of the magnitude and spatial variation of the LST trend bias in comparison with modeled changes in direct shortwave radiation and propose a simple approach to estimate the LST trend bias. After correcting for the LST trend bias, the remaining LST trend values average between 0.07 and 0.10 K yr<span class="inline-formula"><sup>−1</sup></span>. Furthermore, the comparison of Landsat- and IMIS-derived LST trends suggests the existence of a clear-sky bias, with an average value of 0.027 K yr<span class="inline-formula"><sup>−1</sup></span>. Despite these challenges, we conclude that Landsat LST data offer valuable high-resolution records of spatial and temporal LST variations in mountainous terrain. In particular, changes in the mountain cryosphere, such as glacier retreat, glacier debris cover evolution, and changes in snow cover, are preserved in the LST trends and potentially contribute to improved prediction of permafrost temperatures with large spatial coverage. Our study highlights the significance of understanding and addressing biases in LST trends for reliable monitoring in such challenging terrains.</p> |
| format | Article |
| id | doaj-art-7b30a0861902427bb1b201316e8af9c3 |
| institution | Kabale University |
| issn | 1994-0416 1994-0424 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The Cryosphere |
| spelling | doaj-art-7b30a0861902427bb1b201316e8af9c32024-11-19T10:18:06ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242024-11-01185259527610.5194/tc-18-5259-2024Land surface temperature trends derived from Landsat imagery in the Swiss AlpsD. T. Gök0D. Scherler1D. Scherler2H. Wulf3GFZ German Research Centre for Geosciences, 14473 Potsdam, GermanyGFZ German Research Centre for Geosciences, 14473 Potsdam, GermanyInstitute of Geographical Sciences, Freie Universität Berlin, 14195 Berlin, GermanyRemote Sensing Laboratories, University of Zurich, 8057, Zurich, Switzerland<p>The warming of high mountain regions caused by climate change is leading to glacier retreat, decreasing snow cover, and thawing permafrost, all of which have far-reaching effects on ecosystems and societies. Landsat Collection 2 provides multi-decadal land surface temperature (LST) data, principally suited for large-scale monitoring at high spatial resolution. In this study, we assess the potential to extract LST trends using Landsat 5, 7, and 8 time series. We conduct a comprehensive comparison of both LST and LST trends with data from 119 ground stations of the Intercantonal Measurement and Information System (IMIS) network, located at high elevations in the Swiss Alps. The direct comparison of Landsat and IMIS LST yields robust satellite data with a mean accuracy and precision of 0.26 and 4.68 K, respectively. For LST trends derived from a 22.6-year record length, as imposed by the IMIS data, we obtain a mean accuracy and precision of <span class="inline-formula">−0.02</span> and 0.13 K yr<span class="inline-formula"><sup>−1</sup></span>, respectively. However, we find that Landsat LST trends are biased due to unstable diurnal acquisition times, especially for Landsat 5 and 7. Consequently, LST trend maps derived from 38.5-year Landsat data exhibit systematic variations with topographic slope and aspect that we attribute to changes in direct shortwave radiation between different acquisition times. We discuss the origin of the magnitude and spatial variation of the LST trend bias in comparison with modeled changes in direct shortwave radiation and propose a simple approach to estimate the LST trend bias. After correcting for the LST trend bias, the remaining LST trend values average between 0.07 and 0.10 K yr<span class="inline-formula"><sup>−1</sup></span>. Furthermore, the comparison of Landsat- and IMIS-derived LST trends suggests the existence of a clear-sky bias, with an average value of 0.027 K yr<span class="inline-formula"><sup>−1</sup></span>. Despite these challenges, we conclude that Landsat LST data offer valuable high-resolution records of spatial and temporal LST variations in mountainous terrain. In particular, changes in the mountain cryosphere, such as glacier retreat, glacier debris cover evolution, and changes in snow cover, are preserved in the LST trends and potentially contribute to improved prediction of permafrost temperatures with large spatial coverage. Our study highlights the significance of understanding and addressing biases in LST trends for reliable monitoring in such challenging terrains.</p>https://tc.copernicus.org/articles/18/5259/2024/tc-18-5259-2024.pdf |
| spellingShingle | D. T. Gök D. Scherler D. Scherler H. Wulf Land surface temperature trends derived from Landsat imagery in the Swiss Alps The Cryosphere |
| title | Land surface temperature trends derived from Landsat imagery in the Swiss Alps |
| title_full | Land surface temperature trends derived from Landsat imagery in the Swiss Alps |
| title_fullStr | Land surface temperature trends derived from Landsat imagery in the Swiss Alps |
| title_full_unstemmed | Land surface temperature trends derived from Landsat imagery in the Swiss Alps |
| title_short | Land surface temperature trends derived from Landsat imagery in the Swiss Alps |
| title_sort | land surface temperature trends derived from landsat imagery in the swiss alps |
| url | https://tc.copernicus.org/articles/18/5259/2024/tc-18-5259-2024.pdf |
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