Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps
Seasonal snow is an essential water resource in many mountain regions. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) at regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval alg...
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Copernicus Publications
2022
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00059905 2024-09-15T18:39:00+00:00 Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps Lievens, Hans Brangers, Isis Marshall, Hans-Peter Jonas, Tobias Olefs, Marc De Lannoy, Gabriëlle 2022-01 electronic https://doi.org/10.5194/tc-16-159-2022 https://noa.gwlb.de/receive/cop_mods_00059905 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00059554/tc-16-159-2022.pdf https://tc.copernicus.org/articles/16/159/2022/tc-16-159-2022.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-16-159-2022 https://noa.gwlb.de/receive/cop_mods_00059905 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00059554/tc-16-159-2022.pdf https://tc.copernicus.org/articles/16/159/2022/tc-16-159-2022.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2022 ftnonlinearchiv https://doi.org/10.5194/tc-16-159-2022 2024-06-26T04:34:57Z Seasonal snow is an essential water resource in many mountain regions. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) at regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We investigate the ability of the Sentinel-1 mission to monitor snow depth at sub-kilometer (100 m, 500 m, and 1 km) resolutions over the European Alps for 2017–2019. The Sentinel-1 backscatter observations, especially in cross-polarization, show a high correlation with regional model simulations of snow depth over Austria and Switzerland. The observed changes in radar backscatter with the accumulation or ablation of snow are used in an empirical change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. Compared to in situ measurements at 743 sites in the European Alps, dry snow depth retrievals at 500 m and 1 km resolution have a spatio-temporal correlation of 0.89. The mean absolute error equals 20 %–30 % of the measured values for snow depths between 1.5 and 3 m. The performance slightly degrades for retrievals at the finer 100 m spatial resolution as well as for retrievals of shallower and deeper snow. The results demonstrate the ability of Sentinel-1 to provide snow estimates in mountainous regions where satellite-based estimates of snow mass are currently lacking. The retrievals can improve our knowledge of seasonal snow mass in areas with complex topography and benefit a number of applications, such as water resource management, flood forecasting, and numerical weather prediction. However, future research is recommended to further investigate the physical basis of the sensitivity of Sentinel-1 backscatter observations to snow accumulation. Article in Journal/Newspaper The Cryosphere Niedersächsisches Online-Archiv NOA The Cryosphere 16 1 159 177 |
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English |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Lievens, Hans Brangers, Isis Marshall, Hans-Peter Jonas, Tobias Olefs, Marc De Lannoy, Gabriëlle Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps |
topic_facet |
article Verlagsveröffentlichung |
description |
Seasonal snow is an essential water resource in many mountain regions. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) at regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We investigate the ability of the Sentinel-1 mission to monitor snow depth at sub-kilometer (100 m, 500 m, and 1 km) resolutions over the European Alps for 2017–2019. The Sentinel-1 backscatter observations, especially in cross-polarization, show a high correlation with regional model simulations of snow depth over Austria and Switzerland. The observed changes in radar backscatter with the accumulation or ablation of snow are used in an empirical change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. Compared to in situ measurements at 743 sites in the European Alps, dry snow depth retrievals at 500 m and 1 km resolution have a spatio-temporal correlation of 0.89. The mean absolute error equals 20 %–30 % of the measured values for snow depths between 1.5 and 3 m. The performance slightly degrades for retrievals at the finer 100 m spatial resolution as well as for retrievals of shallower and deeper snow. The results demonstrate the ability of Sentinel-1 to provide snow estimates in mountainous regions where satellite-based estimates of snow mass are currently lacking. The retrievals can improve our knowledge of seasonal snow mass in areas with complex topography and benefit a number of applications, such as water resource management, flood forecasting, and numerical weather prediction. However, future research is recommended to further investigate the physical basis of the sensitivity of Sentinel-1 backscatter observations to snow accumulation. |
format |
Article in Journal/Newspaper |
author |
Lievens, Hans Brangers, Isis Marshall, Hans-Peter Jonas, Tobias Olefs, Marc De Lannoy, Gabriëlle |
author_facet |
Lievens, Hans Brangers, Isis Marshall, Hans-Peter Jonas, Tobias Olefs, Marc De Lannoy, Gabriëlle |
author_sort |
Lievens, Hans |
title |
Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps |
title_short |
Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps |
title_full |
Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps |
title_fullStr |
Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps |
title_full_unstemmed |
Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps |
title_sort |
sentinel-1 snow depth retrieval at sub-kilometer resolution over the european alps |
publisher |
Copernicus Publications |
publishDate |
2022 |
url |
https://doi.org/10.5194/tc-16-159-2022 https://noa.gwlb.de/receive/cop_mods_00059905 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00059554/tc-16-159-2022.pdf https://tc.copernicus.org/articles/16/159/2022/tc-16-159-2022.pdf |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_relation |
The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-16-159-2022 https://noa.gwlb.de/receive/cop_mods_00059905 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00059554/tc-16-159-2022.pdf https://tc.copernicus.org/articles/16/159/2022/tc-16-159-2022.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/tc-16-159-2022 |
container_title |
The Cryosphere |
container_volume |
16 |
container_issue |
1 |
container_start_page |
159 |
op_container_end_page |
177 |
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1810483395216539648 |