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  PolarLakes - Sentinel-1/2 - Antarctica, Bi-weekly/Annually

The PolarLakes dataset provides bi-weekly observations of supraglacial lakes on Antarctic ice shelves, utilizing imagery from Sentinel-2 and Sentinel-1 to address time series gaps caused by frequent cloud cover. These observations detect the extents of supraglacial lakes with a U-Net model for every two weeks from November to March, with each sensor operating independently before the data is merged. The resulting bi-weekly product reflects the maximum lake extents for the first and second halves of each month. When combined for an entire season, the dataset consolidates all bi-weekly records over these five months, allowing for analysis of the maximum lake extent per season and the frequency of lake formation, which can occur up to ten times (5 months á two weeks). The year indicated in the dataset corresponds to January of the melt season, as this month typically experiences the highest melt rates (e.g., 2023 refers to the season from November 2022 to March 2023). The aggregation of all annual datasets creates a recurrence layer that illustrates the frequency of lake presence throughout the entire observation period, which spans from 2014 to 2024, depending on satellite data availability for each ice shelf. The PolarLakes dataset provides valuable insights into the dynamics of supraglacial lakes and serves as a crucial resource for hydrological and climate modeling.
 
Citation proposal
Celia Baumhoer (German Aerospace Center (DLR)). PolarLakes - Sentinel-1/2 - Antarctica, Bi-weekly/Annually. https://gdk.gdi-de.org/geonetwork/srv/api/records/99749b06-345e-4aef-ae4e-d18995f46a75
 
  • Identification
  • Distribution
  • Quality
  • Spatial rep.
  • Ref. system
  • Content
  • Portrayal
  • Metadata
  • Md. constraints
  • Md. maintenance
  • Schema info

Identification

Data identification

Citation

Date ( Creation )
2025-06-11T00:00:00
Identifier
https://geoservice.dlr.de/catalogue/srv/metadata/99749b06-345e-4aef-ae4e-d18995f46a75
Presentation form
Digital map
Other citation details
Purpose
The PolarLakes Dataset provides bi-weekly maximum supraglacial lake extents. This dataset helps to understand bi-weekly changes in surface hydrology on Antarctic ice shelves during austral summer (November to March).
Status
Under development

  Author

German Aerospace Center (DLR) - Celia Baumhoer  

Maintenance and update frequency
As needed
Keywords
  • DLR
  • EOC
  • supraglacial lakes
  • Antarctica
  • hydrology
  • ice sheet
  • water
  • deep learning
  • Sentinel-1
  • Sentinel-2
  • opendata
GEMET - INSPIRE themes, version 1.0 ( Theme )
  • Hydrography
  • Land cover
Keywords ( Place )
  • regional
Use limitation
Nutzungseinschränkungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Use Limitations: DLR not liable for damage resulting from use.

Legal constraints

Access constraints
Other restrictions

Legal constraints

Use constraints
Other restrictions
Other constraints
Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by/4.0 / terms of use: https://creativecommons.org/licenses/by/4.0/
Other constraints

{"id": "cc-by/4.0",

"name": "Creative Commons Namensnennung – 4.0 International (CC BY 4.0)",

"url": "http://dcat-ap.de/def/licenses/cc-by/4.0",

"quelle": "Copyright DLR (year of production)"}

Spatial representation type
Grid
Denominator
20000
Metadata language
English
Character set
UTF8
Topic category
  • Environment

Extent

N
S
E
W


 

Distribution

Distribution

Distribution format
  • GeoTiff ()

Digital transfer options

OnLine resource
EOC Download Service  

EOC Download Service

OnLine resource
EOC Geoservice Dataset  

EOC Geoservice Dataset

 

Quality

Data quality

Hierarchy level
Series

Domain consistency

Measure identification
INSPIRE / Conformity_001

Conformance result

Citation

Date ( Publication )
2010-12-08
Explanation
See the referenced specification
Pass
true

Lineage

Statement
PolarLakes maximum lake extents are derived from Sentinel-1 SAR and Sentinel-2 optical imagery between November and March to cover the melt season in Antarctica.
Description

A deep neural network detects lake extensions in Sentinel-1 and Sentinel-2 data seperately. For each first (day 1-15) and second (day 16-30/31) half of the month, the detected lake areas are combined to a bi-weekly maximum lake extent.

We assessed the accuracy of lake detection with a comprehensive validation. F1-score detection accuracies in Sentinel-1 data is 93% and in Sentinel-2 data 91%.

 

Metadata

Metadata

File identifier
99749b06-345e-4aef-ae4e-d18995f46a75   XML
Metadata language
English
Character set
UTF8
Hierarchy level
Series
Hierarchy level name
Dataseries
Date stamp
2025-06-12T11:30:21
Metadata standard name
ISO 19115-1:2014/19139

  Point of contact

German Aerospace Center (DLR)  

OnLine resource

 
 

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99749b06-345e-4aef-ae4e-d18995f46a75   Access to the portal Read here the full details and access to the data.

  Associated resources

Not available


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