Observing Snow Depth at Sub-Kilometer Resolution Over the European Alps from Sentinel-1

Hans Lievens; Isis Brangers; Hans-Peter Marshall; Tobias Jones; Marc Olefs; Gabriëlle De Lannoy

Observing Snow Depth at Sub-Kilometer Resolution Over the European Alps from Sentinel-1

Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jones, Marc Olefs, Gabriëlle De Lannoy

Geosciences Department

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Seasonal snow is an essential source of water, especially in mountain regions. However, accurate satellite observations of the amount of snow stored in mountains are still lacking. We provide estimates of snow depth at sub-kilometer resolution over the European Alps for 2017–2019 from Sentinel-1 observations. The retrievals are based on a change detection algorithm that includes the masking of wet snow. For dry snow conditions, 300-m Sentinel-1 retrievals have a spatiotemporal correlation of 0.82 and mean absolute error of 0.19m compared with in situ measurements from 743 sites across the Alps. The results show the potential of Sentinel-1 to provide unprecedented snow estimates in regions with complex topography, where satellite observations of snow mass are currently lacking.

Original language	American English
Title of host publication	2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
State	Published - 1 Jan 2021

Keywords

European Alps
Sentinel-1
snow depth

EGS Disciplines

Earth Sciences
Geophysics and Seismology

Access to Document

https://doi.org/10.1109/IGARSS47720.2021.9553420

Cite this

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title = "Observing Snow Depth at Sub-Kilometer Resolution Over the European Alps from Sentinel-1",

abstract = " Seasonal snow is an essential source of water, especially in mountain regions. However, accurate satellite observations of the amount of snow stored in mountains are still lacking. We provide estimates of snow depth at sub-kilometer resolution over the European Alps for 2017–2019 from Sentinel-1 observations. The retrievals are based on a change detection algorithm that includes the masking of wet snow. For dry snow conditions, 300-m Sentinel-1 retrievals have a spatiotemporal correlation of 0.82 and mean absolute error of 0.19m compared with in situ measurements from 743 sites across the Alps. The results show the potential of Sentinel-1 to provide unprecedented snow estimates in regions with complex topography, where satellite observations of snow mass are currently lacking.",

keywords = "European Alps, Sentinel-1, snow depth",

author = "Hans Lievens and Isis Brangers and Hans-Peter Marshall and Tobias Jones and Marc Olefs and {De Lannoy}, Gabri{\"e}lle",

note = "Lievens, Hans; Brangers, Isis; Marshall, Hans-Peter; Jonas, Tobias; Olefs, Marc; and De Lannoy, Gabrielle. (2021). {"}Observing Snow Depth at Sub-Kilometer Resolution over the European Alps from Sentinel-1{"}. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 618-621). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553420",

year = "2021",

month = jan,

day = "1",

language = "American English",

booktitle = "2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS",

}

TY - CHAP

T1 - Observing Snow Depth at Sub-Kilometer Resolution Over the European Alps from Sentinel-1

AU - Lievens, Hans

AU - Brangers, Isis

AU - Marshall, Hans-Peter

AU - Jones, Tobias

AU - Olefs, Marc

AU - De Lannoy, Gabriëlle

N1 - Lievens, Hans; Brangers, Isis; Marshall, Hans-Peter; Jonas, Tobias; Olefs, Marc; and De Lannoy, Gabrielle. (2021). "Observing Snow Depth at Sub-Kilometer Resolution over the European Alps from Sentinel-1". In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 618-621). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553420

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Seasonal snow is an essential source of water, especially in mountain regions. However, accurate satellite observations of the amount of snow stored in mountains are still lacking. We provide estimates of snow depth at sub-kilometer resolution over the European Alps for 2017–2019 from Sentinel-1 observations. The retrievals are based on a change detection algorithm that includes the masking of wet snow. For dry snow conditions, 300-m Sentinel-1 retrievals have a spatiotemporal correlation of 0.82 and mean absolute error of 0.19m compared with in situ measurements from 743 sites across the Alps. The results show the potential of Sentinel-1 to provide unprecedented snow estimates in regions with complex topography, where satellite observations of snow mass are currently lacking.

AB - Seasonal snow is an essential source of water, especially in mountain regions. However, accurate satellite observations of the amount of snow stored in mountains are still lacking. We provide estimates of snow depth at sub-kilometer resolution over the European Alps for 2017–2019 from Sentinel-1 observations. The retrievals are based on a change detection algorithm that includes the masking of wet snow. For dry snow conditions, 300-m Sentinel-1 retrievals have a spatiotemporal correlation of 0.82 and mean absolute error of 0.19m compared with in situ measurements from 743 sites across the Alps. The results show the potential of Sentinel-1 to provide unprecedented snow estimates in regions with complex topography, where satellite observations of snow mass are currently lacking.

KW - European Alps

KW - Sentinel-1

KW - snow depth

UR - https://scholarworks.boisestate.edu/geo_facpubs/755

UR - https://doi.org/10.1109/IGARSS47720.2021.9553420

M3 - Chapter

BT - 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

ER -

Observing Snow Depth at Sub-Kilometer Resolution Over the European Alps from Sentinel-1

Abstract

Keywords

EGS Disciplines

Access to Document

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