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.

Date (Creation): 2025-06-11T00:00:00

Citation identifier: https://geoservice.dlr.de/catalogue/srv/metadata/99749b06-345e-4aef-ae4e-d18995f46a75

Presentation form: Digital map

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

Hydrography
Land cover

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.

Access constraints: Other restrictions

Other constraints: There are no limitations on public access to spatial data sets and services.

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

Language: English

Character set: UTF8

Topic category

Environment

Begin date: 2014-11-01

End date: 2024-03-31

Unique resource identifier: EPSG:3031

Distribution format

GeoTiff ()

OnLine resource: EOC Download Service ( WWW:LINK-1.0-http--link )

OnLine resource: EOC Geoservice Dataset

Hierarchy level: Series

Domain consistency

Measure identification: INSPIRE / Conformity_001

Conformance result

Date (Publication): 2010-12-08

Explanation: See the referenced specification

Pass: Yes

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%.