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Snow depth variability in the Northern Hemisphere mountains observed from space

Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we sho...

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Autores principales: Lievens, Hans, Demuzere, Matthias, Marshall, Hans-Peter, Reichle, Rolf H., Brucker, Ludovic, Brangers, Isis, de Rosnay, Patricia, Dumont, Marie, Girotto, Manuela, Immerzeel, Walter W., Jonas, Tobias, Kim, Edward J., Koch, Inka, Marty, Christoph, Saloranta, Tuomo, Schöber, Johannes, De Lannoy, Gabrielle J. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6789005/
https://www.ncbi.nlm.nih.gov/pubmed/31604957
http://dx.doi.org/10.1038/s41467-019-12566-y
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author Lievens, Hans
Demuzere, Matthias
Marshall, Hans-Peter
Reichle, Rolf H.
Brucker, Ludovic
Brangers, Isis
de Rosnay, Patricia
Dumont, Marie
Girotto, Manuela
Immerzeel, Walter W.
Jonas, Tobias
Kim, Edward J.
Koch, Inka
Marty, Christoph
Saloranta, Tuomo
Schöber, Johannes
De Lannoy, Gabrielle J. M.
author_facet Lievens, Hans
Demuzere, Matthias
Marshall, Hans-Peter
Reichle, Rolf H.
Brucker, Ludovic
Brangers, Isis
de Rosnay, Patricia
Dumont, Marie
Girotto, Manuela
Immerzeel, Walter W.
Jonas, Tobias
Kim, Edward J.
Koch, Inka
Marty, Christoph
Saloranta, Tuomo
Schöber, Johannes
De Lannoy, Gabrielle J. M.
author_sort Lievens, Hans
collection PubMed
description Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.
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spelling pubmed-67890052019-10-15 Snow depth variability in the Northern Hemisphere mountains observed from space Lievens, Hans Demuzere, Matthias Marshall, Hans-Peter Reichle, Rolf H. Brucker, Ludovic Brangers, Isis de Rosnay, Patricia Dumont, Marie Girotto, Manuela Immerzeel, Walter W. Jonas, Tobias Kim, Edward J. Koch, Inka Marty, Christoph Saloranta, Tuomo Schöber, Johannes De Lannoy, Gabrielle J. M. Nat Commun Article Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change. Nature Publishing Group UK 2019-10-11 /pmc/articles/PMC6789005/ /pubmed/31604957 http://dx.doi.org/10.1038/s41467-019-12566-y Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Lievens, Hans
Demuzere, Matthias
Marshall, Hans-Peter
Reichle, Rolf H.
Brucker, Ludovic
Brangers, Isis
de Rosnay, Patricia
Dumont, Marie
Girotto, Manuela
Immerzeel, Walter W.
Jonas, Tobias
Kim, Edward J.
Koch, Inka
Marty, Christoph
Saloranta, Tuomo
Schöber, Johannes
De Lannoy, Gabrielle J. M.
Snow depth variability in the Northern Hemisphere mountains observed from space
title Snow depth variability in the Northern Hemisphere mountains observed from space
title_full Snow depth variability in the Northern Hemisphere mountains observed from space
title_fullStr Snow depth variability in the Northern Hemisphere mountains observed from space
title_full_unstemmed Snow depth variability in the Northern Hemisphere mountains observed from space
title_short Snow depth variability in the Northern Hemisphere mountains observed from space
title_sort snow depth variability in the northern hemisphere mountains observed from space
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6789005/
https://www.ncbi.nlm.nih.gov/pubmed/31604957
http://dx.doi.org/10.1038/s41467-019-12566-y
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