17 May 2024
 | 17 May 2024
Status: this preprint is open for discussion.

Land surface temperature trends derived from Landsat imagery in the Swiss Alps

Deniz Tobias Gök, Dirk Scherler, and Hendrik Wulf

Abstract. The warming of high mountain regions caused by climate change is leading to glacier retreat, decreasing snow cover, and thawing permafrost, which has 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 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 K 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 -0.02 K yr-1 and 0.13 K yr-1, 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 the 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 modelled changes in direct shortwave radiation and propose a simple approach to estimate the LST trend bias. After correcting for the LST trend bias, remaining LST trend values average between 0.07 and 0.10 K yr-1. Further, 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-1. Despite these challenges, we conclude that Landsat LST data offer valuable high-resolution records of spatial and temporal LST variations in mountainous terrain, where monitoring is generally difficult. Our study highlights the significance of understanding and addressing biases in LST trends for reliable monitoring in such challenging terrains.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Deniz Tobias Gök, Dirk Scherler, and Hendrik Wulf

Status: open (until 18 Jul 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Deniz Tobias Gök, Dirk Scherler, and Hendrik Wulf
Deniz Tobias Gök, Dirk Scherler, and Hendrik Wulf


Total article views: 209 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
139 62 8 209 16 5 4
  • HTML: 139
  • PDF: 62
  • XML: 8
  • Total: 209
  • Supplement: 16
  • BibTeX: 5
  • EndNote: 4
Views and downloads (calculated since 17 May 2024)
Cumulative views and downloads (calculated since 17 May 2024)

Viewed (geographical distribution)

Total article views: 210 (including HTML, PDF, and XML) Thereof 210 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 12 Jun 2024
Short summary
We derived Landsat Collection 2 land surface temperature (LST) trends in the Swiss Alps using a harmonic model with 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.