Artificial intelligence for the monitoring of Antarctic supraglacial lake dynamics in 2015-2021 using Sentinel-1 SAR and optical Sentinel-2 data

Earth Observation (EO) provides a wealth of data for the monitoring of the Antarctic continent. In this context, data of the Sentinel-1 Synthetic Aperture Radar (SAR) and optical Sentinel-2 satellite missions of the European Copernicus programme deliver valuable information on key ice sheet paramete...

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Main Authors: Dirscherl, Mariel, Dietz, Andreas, Künzer, Claudia
Format: Conference Object
Language:unknown
Published: 2022
Subjects:
Online Access:https://elib.dlr.de/186855/
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author Dirscherl, Mariel
Dietz, Andreas
Künzer, Claudia
author_facet Dirscherl, Mariel
Dietz, Andreas
Künzer, Claudia
author_sort Dirscherl, Mariel
collection Unknown
description Earth Observation (EO) provides a wealth of data for the monitoring of the Antarctic continent. In this context, data of the Sentinel-1 Synthetic Aperture Radar (SAR) and optical Sentinel-2 satellite missions of the European Copernicus programme deliver valuable information on key ice sheet parameters including the location of the calving front and grounding line, the ice velocity and elevation as well as the Antarctic surface hydrological network. The monitoring of the latter is crucial for an improved understanding of processes such as hydrofracture triggering ice shelf collapse and ultimately ice flow accelerations and increased ice discharge. To establish a monitoring service for supraglacial lake extent delineation in Sentinel-1 SAR and optical Sentinel-2 imagery, a fully automated processing chain based on machine learning and deep learning was developed and integrated within the internal processing infrastructure of the German Aerospace Center (DLR). Here, we present first results of the implemented machine learning processing pipeline over six major Antarctic ice shelves. In particular, the full archive of Sentinel-1 and Sentinel-2 was exploited to provide bi-weekly supraglacial lake extent mappings during 2015-2021 at unprecedented 10 m spatial resolution. The results over Antarctic Peninsula ice shelves reveal comparatively low lake coverage in 2015-2018 and high lake coverage during summers 2019-2020 and 2020-2021. Over East Antarctic ice shelves, supraglacial lake extents fluctuated more substantially with comparatively high lake coverage during most of 2016-2019 and low lake coverage throughout melting season 2020-2021. Further, the data reveal a coupling between supraglacial lake formation and the near-surface climate, the local glaciological setting and large-scale atmospheric modes. The final data products on Antarctic supraglacial lake extent dynamics during 2015-2021 are available via the GeoService of the Earth Observation Center (EOC) at DLR. To establish a near-real-time monitoring service ...
format Conference Object
genre Antarc*
Antarctic
Antarctic Peninsula
Ice Sheet
Ice Shelf
Ice Shelves
genre_facet Antarc*
Antarctic
Antarctic Peninsula
Ice Sheet
Ice Shelf
Ice Shelves
geographic Antarctic
The Antarctic
Antarctic Peninsula
The Sentinel
Low Lake
High Lake
geographic_facet Antarctic
The Antarctic
Antarctic Peninsula
The Sentinel
Low Lake
High Lake
id ftdlr:oai:elib.dlr.de:186855
institution Open Polar
language unknown
long_lat ENVELOPE(73.317,73.317,-52.983,-52.983)
ENVELOPE(142.677,142.677,-66.993,-66.993)
ENVELOPE(142.675,142.675,-66.995,-66.995)
op_collection_id ftdlr
op_doi https://doi.org/10.5194/egusphere-egu22-4526
op_relation Dirscherl, Mariel und Dietz, Andreas und Künzer, Claudia (2022) Artificial intelligence for the monitoring of Antarctic supraglacial lake dynamics in 2015-2021 using Sentinel-1 SAR and optical Sentinel-2 data. EGU General Assembly 2022, 2022-05-23 - 2022-05-27, Wien, Österreich. doi:10.5194/egusphere-egu22-4526 <https://doi.org/10.5194/egusphere-egu22-4526>.
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spelling ftdlr:oai:elib.dlr.de:186855 2025-06-15T14:11:58+00:00 Artificial intelligence for the monitoring of Antarctic supraglacial lake dynamics in 2015-2021 using Sentinel-1 SAR and optical Sentinel-2 data Dirscherl, Mariel Dietz, Andreas Künzer, Claudia 2022-05-27 https://elib.dlr.de/186855/ unknown Dirscherl, Mariel und Dietz, Andreas und Künzer, Claudia (2022) Artificial intelligence for the monitoring of Antarctic supraglacial lake dynamics in 2015-2021 using Sentinel-1 SAR and optical Sentinel-2 data. EGU General Assembly 2022, 2022-05-23 - 2022-05-27, Wien, Österreich. doi:10.5194/egusphere-egu22-4526 <https://doi.org/10.5194/egusphere-egu22-4526>. Dynamik der Landoberfläche Konferenzbeitrag NonPeerReviewed 2022 ftdlr https://doi.org/10.5194/egusphere-egu22-4526 2025-06-04T04:58:08Z Earth Observation (EO) provides a wealth of data for the monitoring of the Antarctic continent. In this context, data of the Sentinel-1 Synthetic Aperture Radar (SAR) and optical Sentinel-2 satellite missions of the European Copernicus programme deliver valuable information on key ice sheet parameters including the location of the calving front and grounding line, the ice velocity and elevation as well as the Antarctic surface hydrological network. The monitoring of the latter is crucial for an improved understanding of processes such as hydrofracture triggering ice shelf collapse and ultimately ice flow accelerations and increased ice discharge. To establish a monitoring service for supraglacial lake extent delineation in Sentinel-1 SAR and optical Sentinel-2 imagery, a fully automated processing chain based on machine learning and deep learning was developed and integrated within the internal processing infrastructure of the German Aerospace Center (DLR). Here, we present first results of the implemented machine learning processing pipeline over six major Antarctic ice shelves. In particular, the full archive of Sentinel-1 and Sentinel-2 was exploited to provide bi-weekly supraglacial lake extent mappings during 2015-2021 at unprecedented 10 m spatial resolution. The results over Antarctic Peninsula ice shelves reveal comparatively low lake coverage in 2015-2018 and high lake coverage during summers 2019-2020 and 2020-2021. Over East Antarctic ice shelves, supraglacial lake extents fluctuated more substantially with comparatively high lake coverage during most of 2016-2019 and low lake coverage throughout melting season 2020-2021. Further, the data reveal a coupling between supraglacial lake formation and the near-surface climate, the local glaciological setting and large-scale atmospheric modes. The final data products on Antarctic supraglacial lake extent dynamics during 2015-2021 are available via the GeoService of the Earth Observation Center (EOC) at DLR. To establish a near-real-time monitoring service ... Conference Object Antarc* Antarctic Antarctic Peninsula Ice Sheet Ice Shelf Ice Shelves Unknown Antarctic The Antarctic Antarctic Peninsula The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) Low Lake ENVELOPE(142.677,142.677,-66.993,-66.993) High Lake ENVELOPE(142.675,142.675,-66.995,-66.995)
spellingShingle Dynamik der Landoberfläche
Dirscherl, Mariel
Dietz, Andreas
Künzer, Claudia
Artificial intelligence for the monitoring of Antarctic supraglacial lake dynamics in 2015-2021 using Sentinel-1 SAR and optical Sentinel-2 data
title Artificial intelligence for the monitoring of Antarctic supraglacial lake dynamics in 2015-2021 using Sentinel-1 SAR and optical Sentinel-2 data
title_full Artificial intelligence for the monitoring of Antarctic supraglacial lake dynamics in 2015-2021 using Sentinel-1 SAR and optical Sentinel-2 data
title_fullStr Artificial intelligence for the monitoring of Antarctic supraglacial lake dynamics in 2015-2021 using Sentinel-1 SAR and optical Sentinel-2 data
title_full_unstemmed Artificial intelligence for the monitoring of Antarctic supraglacial lake dynamics in 2015-2021 using Sentinel-1 SAR and optical Sentinel-2 data
title_short Artificial intelligence for the monitoring of Antarctic supraglacial lake dynamics in 2015-2021 using Sentinel-1 SAR and optical Sentinel-2 data
title_sort artificial intelligence for the monitoring of antarctic supraglacial lake dynamics in 2015-2021 using sentinel-1 sar and optical sentinel-2 data
topic Dynamik der Landoberfläche
topic_facet Dynamik der Landoberfläche
url https://elib.dlr.de/186855/