1 km sea-ice concentration from Sentinel-2 reflectances in the Arctic marginal seas between February and May 2019 ...

This dataset comprises quality-checked sea-ice concentration at 1 km spatial resolution from Sentinel-2 imagery. 83 Sentinel-2 scenes distributed over the East Siberian, Laptev, Kara, Barents and Beaufort Seas as well as the Fram Strait between February and May 2019 are analysed. For each scene, we...

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Bibliographic Details
Main Authors: Ludwig, Valentin, Spreen, Gunnar, Pedersen, Leif Toudal
Format: Dataset
Language:English
Published: PANGAEA 2021
Subjects:
Online Access:https://dx.doi.org/10.1594/pangaea.933913
https://doi.pangaea.de/10.1594/PANGAEA.933913
id ftdatacite:10.1594/pangaea.933913
record_format openpolar
spelling ftdatacite:10.1594/pangaea.933913 2024-02-27T08:37:33+00:00 1 km sea-ice concentration from Sentinel-2 reflectances in the Arctic marginal seas between February and May 2019 ... Ludwig, Valentin Spreen, Gunnar Pedersen, Leif Toudal 2021 application/zip https://dx.doi.org/10.1594/pangaea.933913 https://doi.pangaea.de/10.1594/PANGAEA.933913 en eng PANGAEA https://dx.doi.org/10.3390/rs12193183 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Sea-ice concentration Sentinel-2 optical Arctic satellite reflectance Processes and impacts of climate change in the North Atlantic Ocean and the Canadian Arctic ArcTrain dataset Dataset 2021 ftdatacite https://doi.org/10.1594/pangaea.93391310.3390/rs12193183 2024-02-01T14:42:11Z This dataset comprises quality-checked sea-ice concentration at 1 km spatial resolution from Sentinel-2 imagery. 83 Sentinel-2 scenes distributed over the East Siberian, Laptev, Kara, Barents and Beaufort Seas as well as the Fram Strait between February and May 2019 are analysed. For each scene, we investigated histograms of the Level 1C (L1C) reflectance of band 4 (665 nm) at 10 m resolution and manually identified two thresholds for each scene to separate it into water, thin ice and thick ice. We use the classified water/thin-ice/thick-ice images to create two sea-ice concentration datasets at 1 km spatial resolution: One which contains the thin ice (sic_thin in the netCDF files) and one which contains the thick ice (sic_thick in the netCDF files). To this end, we average the images over 100x100 pixels and interpret the ratio of sea-ice pixels within these 100x100 pixel windows as sea-ice concentration. The total sea-ice concentration can be obtained as the sum of sic_thin and sic_thick. The ice thickness ... Dataset Arctic Climate change Fram Strait laptev North Atlantic Sea ice DataCite Metadata Store (German National Library of Science and Technology) Arctic
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language English
topic Sea-ice concentration Sentinel-2 optical Arctic satellite reflectance
Processes and impacts of climate change in the North Atlantic Ocean and the Canadian Arctic ArcTrain
spellingShingle Sea-ice concentration Sentinel-2 optical Arctic satellite reflectance
Processes and impacts of climate change in the North Atlantic Ocean and the Canadian Arctic ArcTrain
Ludwig, Valentin
Spreen, Gunnar
Pedersen, Leif Toudal
1 km sea-ice concentration from Sentinel-2 reflectances in the Arctic marginal seas between February and May 2019 ...
topic_facet Sea-ice concentration Sentinel-2 optical Arctic satellite reflectance
Processes and impacts of climate change in the North Atlantic Ocean and the Canadian Arctic ArcTrain
description This dataset comprises quality-checked sea-ice concentration at 1 km spatial resolution from Sentinel-2 imagery. 83 Sentinel-2 scenes distributed over the East Siberian, Laptev, Kara, Barents and Beaufort Seas as well as the Fram Strait between February and May 2019 are analysed. For each scene, we investigated histograms of the Level 1C (L1C) reflectance of band 4 (665 nm) at 10 m resolution and manually identified two thresholds for each scene to separate it into water, thin ice and thick ice. We use the classified water/thin-ice/thick-ice images to create two sea-ice concentration datasets at 1 km spatial resolution: One which contains the thin ice (sic_thin in the netCDF files) and one which contains the thick ice (sic_thick in the netCDF files). To this end, we average the images over 100x100 pixels and interpret the ratio of sea-ice pixels within these 100x100 pixel windows as sea-ice concentration. The total sea-ice concentration can be obtained as the sum of sic_thin and sic_thick. The ice thickness ...
format Dataset
author Ludwig, Valentin
Spreen, Gunnar
Pedersen, Leif Toudal
author_facet Ludwig, Valentin
Spreen, Gunnar
Pedersen, Leif Toudal
author_sort Ludwig, Valentin
title 1 km sea-ice concentration from Sentinel-2 reflectances in the Arctic marginal seas between February and May 2019 ...
title_short 1 km sea-ice concentration from Sentinel-2 reflectances in the Arctic marginal seas between February and May 2019 ...
title_full 1 km sea-ice concentration from Sentinel-2 reflectances in the Arctic marginal seas between February and May 2019 ...
title_fullStr 1 km sea-ice concentration from Sentinel-2 reflectances in the Arctic marginal seas between February and May 2019 ...
title_full_unstemmed 1 km sea-ice concentration from Sentinel-2 reflectances in the Arctic marginal seas between February and May 2019 ...
title_sort 1 km sea-ice concentration from sentinel-2 reflectances in the arctic marginal seas between february and may 2019 ...
publisher PANGAEA
publishDate 2021
url https://dx.doi.org/10.1594/pangaea.933913
https://doi.pangaea.de/10.1594/PANGAEA.933913
geographic Arctic
geographic_facet Arctic
genre Arctic
Climate change
Fram Strait
laptev
North Atlantic
Sea ice
genre_facet Arctic
Climate change
Fram Strait
laptev
North Atlantic
Sea ice
op_relation https://dx.doi.org/10.3390/rs12193183
op_rights Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cc-by-4.0
op_doi https://doi.org/10.1594/pangaea.93391310.3390/rs12193183
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