the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 May 2024
Land surface temperature trends derived from Landsat imagery in the Swiss Alps
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.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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(24017 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1228', Anonymous Referee #1, 13 Jun 2024
Surface warming trend detection based on land surface temperature estimations is a hot topic in recent years. This study provides a good attempt with the use of Landsat LST products, associated with good discussions. In general, the manuscript is well structured and the analysis is plentiful. Some additional revisions should be added before final acceptance.
- L104: Section 0？ There are serveral places with this number. Please
- Figure 2: I think the acquisition time here should be local solar time but not UTC time. Please check the details. Meanwhile, the symbols in the figure are not consistent.
- Some basic introduction about the equipment for surface temperature monitoring should be added in this section.
- L165: I think the threshold should be set for one direction about the extreme low values, which should be affected by cloud cover.
- L224: Some more explanations about the high uncertainties appear at around 0-degree region should be added. Meanwhile, there should be no so many snow cover at this temperature range. The authors should confirm the impact factor.
- Table 1: The metrics can be shown in Figure 4 and the table can be deleted.
- L246: From the simple comparison, it is hard to fully present the advantage of the LST data from LE07 because of the spatial discrepancy between satellite observation and field measurement. Meanwhile, there are gaps in LE07 data which may worsen the reliability of the trend assessment. I think it will be better to select the stations with good spatial representations to demonstrate the reliability of different products. Meanwhile, I think the trend analysis can be derived from all available LST observations but not from single one.
- Table 2: Similar as table 1, the metrics in table 2 can be moved to table 1.
- L303: how to get this bias value? Please explain it in the text.
- How to consider the topographic influence on LST trends? It seems that here should be the variations of LST trends at different topographic conditions.
- About the impact from the trend detection based on clear-sky observations, recent study has revealed this issue based on the comparison between the annual mean temperature from clear observations and all-weather observations from reconstruction works. Please refer to: https://doi.org/10.1109/TGRS.2024.3377670
- For the maps of the LST trend and other components, I think it will be better to show the Swiss Alps only with other countries removed out.
- About the uncertainty of the detected LST trends, although there are intercomparison with in situ observations, some additional comparison can be conducted with the observations from MODIS products. It will be much more helpful to identify the consistency or discrepancy.
- For the LST changes detected in this study, the discussion of the driving factors is not yet provided in current version. However, some necessary discussions should be added for this issue. Please refer to following articles: https://doi.org/10.1016/j.xinn.2024.100588, https://doi.org/10.1038/s41467-023-35799-4, https://doi.org/10.1016/j.rse.2018.06.010
Citation: https://doi.org/10.5194/egusphere-2024-1228-RC1 -
AC1: 'Reply on RC1', Deniz Gök, 22 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1228/egusphere-2024-1228-AC1-supplement.pdf
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RC2: 'Comment on egusphere-2024-1228', Anonymous Referee #2, 29 Jul 2024
This study used Landsat-derived LST to analyze long-term LST trends in the Swiss Alps, referenced against ground observations from the IMIS network. Overall, this work is nicely done and offers valuable insights into identifying the origin of potential biases in Landsat-derived LST trends in mountainous terrain. I also find this paper is generally well written and structured. Below are some comments, primarily regarding the clarification of methodologies, which I hope the authors can address:
L55-L60: Please describe the spatial resolution of the thermal bands of AVHRR and Landsat.
L103: Change "artefacts" to "artifacts".
L104: Clarify what "section 0" refers to, similarly for L116.
L134: Ensure temperature units are consistent throughout the text.
L156: Please briefly explain the filters used for masking clouds and duplicates.
L165: Explain how the specific threshold is determined. It is unclear if applying an upper threshold of +30 K makes sense when trying to find cold extremes caused by undetected clouds.
L228-L229: The values of metrics do not match those shown in Figure 4 and Table 1. Please verify and correct them.
Figure 5: You mention a total of 119 stations providing surface temperature observations, but only 115 are included in Figure 5d. Does this mean the remaining four stations had short time series and were excluded from the trend analysis? However, Figure 1 suggests all stations used should have consistent records for at least five years, please clarify. Additionally, the overall trends across stations derived from Landsat and IMIS LST should be given and compared.
L244-L245: While suggesting that LE07 is the most robust, it would be useful to see its distribution when restricting the record length to be comparable to LT05/LC08. This will help understand the impact of temporal overlap on the residuals.
L298-303: Does the ∆LST here represent the trend fitted by IMIS LST at 9:29 minus the trend at 10:16? If so, I assume you are explaining the LST trend bias caused by different acquisition times. Please ensure clarity. Also, explain how the average trend bias is calculated from the ∆LST.
L318-320: This statement is unclear; please elaborate on its significance.
L349-355: Although mentioned in the conclusion, it is helpful to emphasize that the Landsat trends can be less reliable when the data period being examined is relatively short (e.g., less than 10-15 years).
L413: It appears the LST trend peaks for an aspect of ~255° (Figure 9c?). Please verify it.Citation: https://doi.org/10.5194/egusphere-2024-1228-RC2 -
AC2: 'Reply on RC2', Deniz Gök, 22 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1228/egusphere-2024-1228-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Deniz Gök, 22 Aug 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1228', Anonymous Referee #1, 13 Jun 2024
Surface warming trend detection based on land surface temperature estimations is a hot topic in recent years. This study provides a good attempt with the use of Landsat LST products, associated with good discussions. In general, the manuscript is well structured and the analysis is plentiful. Some additional revisions should be added before final acceptance.
- L104: Section 0？ There are serveral places with this number. Please
- Figure 2: I think the acquisition time here should be local solar time but not UTC time. Please check the details. Meanwhile, the symbols in the figure are not consistent.
- Some basic introduction about the equipment for surface temperature monitoring should be added in this section.
- L165: I think the threshold should be set for one direction about the extreme low values, which should be affected by cloud cover.
- L224: Some more explanations about the high uncertainties appear at around 0-degree region should be added. Meanwhile, there should be no so many snow cover at this temperature range. The authors should confirm the impact factor.
- Table 1: The metrics can be shown in Figure 4 and the table can be deleted.
- L246: From the simple comparison, it is hard to fully present the advantage of the LST data from LE07 because of the spatial discrepancy between satellite observation and field measurement. Meanwhile, there are gaps in LE07 data which may worsen the reliability of the trend assessment. I think it will be better to select the stations with good spatial representations to demonstrate the reliability of different products. Meanwhile, I think the trend analysis can be derived from all available LST observations but not from single one.
- Table 2: Similar as table 1, the metrics in table 2 can be moved to table 1.
- L303: how to get this bias value? Please explain it in the text.
- How to consider the topographic influence on LST trends? It seems that here should be the variations of LST trends at different topographic conditions.
- About the impact from the trend detection based on clear-sky observations, recent study has revealed this issue based on the comparison between the annual mean temperature from clear observations and all-weather observations from reconstruction works. Please refer to: https://doi.org/10.1109/TGRS.2024.3377670
- For the maps of the LST trend and other components, I think it will be better to show the Swiss Alps only with other countries removed out.
- About the uncertainty of the detected LST trends, although there are intercomparison with in situ observations, some additional comparison can be conducted with the observations from MODIS products. It will be much more helpful to identify the consistency or discrepancy.
- For the LST changes detected in this study, the discussion of the driving factors is not yet provided in current version. However, some necessary discussions should be added for this issue. Please refer to following articles: https://doi.org/10.1016/j.xinn.2024.100588, https://doi.org/10.1038/s41467-023-35799-4, https://doi.org/10.1016/j.rse.2018.06.010
Citation: https://doi.org/10.5194/egusphere-2024-1228-RC1 -
AC1: 'Reply on RC1', Deniz Gök, 22 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1228/egusphere-2024-1228-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2024-1228', Anonymous Referee #2, 29 Jul 2024
This study used Landsat-derived LST to analyze long-term LST trends in the Swiss Alps, referenced against ground observations from the IMIS network. Overall, this work is nicely done and offers valuable insights into identifying the origin of potential biases in Landsat-derived LST trends in mountainous terrain. I also find this paper is generally well written and structured. Below are some comments, primarily regarding the clarification of methodologies, which I hope the authors can address:
L55-L60: Please describe the spatial resolution of the thermal bands of AVHRR and Landsat.
L103: Change "artefacts" to "artifacts".
L104: Clarify what "section 0" refers to, similarly for L116.
L134: Ensure temperature units are consistent throughout the text.
L156: Please briefly explain the filters used for masking clouds and duplicates.
L165: Explain how the specific threshold is determined. It is unclear if applying an upper threshold of +30 K makes sense when trying to find cold extremes caused by undetected clouds.
L228-L229: The values of metrics do not match those shown in Figure 4 and Table 1. Please verify and correct them.
Figure 5: You mention a total of 119 stations providing surface temperature observations, but only 115 are included in Figure 5d. Does this mean the remaining four stations had short time series and were excluded from the trend analysis? However, Figure 1 suggests all stations used should have consistent records for at least five years, please clarify. Additionally, the overall trends across stations derived from Landsat and IMIS LST should be given and compared.
L244-L245: While suggesting that LE07 is the most robust, it would be useful to see its distribution when restricting the record length to be comparable to LT05/LC08. This will help understand the impact of temporal overlap on the residuals.
L298-303: Does the ∆LST here represent the trend fitted by IMIS LST at 9:29 minus the trend at 10:16? If so, I assume you are explaining the LST trend bias caused by different acquisition times. Please ensure clarity. Also, explain how the average trend bias is calculated from the ∆LST.
L318-320: This statement is unclear; please elaborate on its significance.
L349-355: Although mentioned in the conclusion, it is helpful to emphasize that the Landsat trends can be less reliable when the data period being examined is relatively short (e.g., less than 10-15 years).
L413: It appears the LST trend peaks for an aspect of ~255° (Figure 9c?). Please verify it.Citation: https://doi.org/10.5194/egusphere-2024-1228-RC2 -
AC2: 'Reply on RC2', Deniz Gök, 22 Aug 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1228/egusphere-2024-1228-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Deniz Gök, 22 Aug 2024
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Deniz Tobias Gök
Dirk Scherler
Hendrik Wulf
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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(24017 KB) - BibTeX
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