Articles | Volume 14, issue 1
https://doi.org/10.5194/essd-14-209-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-209-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
24 Jan 2022
Data description paper |
| 24 Jan 2022
| 24 Jan 2022
An inventory of supraglacial lakes and channels across the West Antarctic Ice Sheet
Diarmuid Corr
CORRESPONDING AUTHOR
Centre of Excellence in Environmental Data Science, Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
Amber Leeson
Centre of Excellence in Environmental Data Science, Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
Data Science Institute, Lancaster University, Lancaster, LA1 4YW, UK
Malcolm McMillan
Centre of Excellence in Environmental Data Science, Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
UK Centre for Polar Observation and Modelling, Lancaster University, Lancaster, LA1 4YW, UK
Centre of Excellence in Environmental Data Science, Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
Data Science Institute, Lancaster University, Lancaster, LA1 4YW, UK
UK Centre for Ecology & Hydrology, Library Avenue, Lancaster, LA1 4AP, UK
Thomas Barnes
Centre of Excellence in Environmental Data Science, Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
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Total article views: 4,097 (including HTML, PDF, and XML)
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Total article views: 3,082 (including HTML, PDF, and XML)
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Total article views: 1,015 (including HTML, PDF, and XML)
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Cited
20 citations as recorded by crossref.
- A machine learning approach to the geomorphometric detection of ribbed moraines in Norway T. Barnes et al. 10.5194/esurf-12-801-2024
- High-resolution imaging of supraglacial hydrological features on the Greenland Ice Sheet with NASA's Airborne Topographic Mapper (ATM) instrument suite M. Studinger et al. 10.5194/tc-16-3649-2022
- Widespread partial-depth hydrofractures in ice sheets driven by supraglacial streams D. Chandler & A. Hubbard 10.1038/s41561-023-01208-0
- Pan-Greenland mapping of supraglacial rivers, lakes, and water-filled crevasses in a cool summer (2018) and a warm summer (2019) W. Zhang et al. 10.1016/j.rse.2023.113781
- Substantial contribution of slush to meltwater area across Antarctic ice shelves R. Dell et al. 10.1038/s41561-024-01466-6
- Detection of Antarctic Surface Meltwater Using Sentinel-2 Remote Sensing Images via U-Net With Attention Blocks: A Case Study Over the Amery Ice Shelf L. Niu et al. 10.1109/TGRS.2023.3275076
- A Conditional Random Fields-Based Identification for Small Lakes Over Multiple Melt Seasons Using Sentinel-2 Imagery in the Larsemann Hills, East Antarctica T. Zhu et al. 10.1109/JSTARS.2024.3391881
- Supraglacial lake evolution on Tracy and Heilprin Glaciers in northwestern Greenland from 2014 to 2021 Y. Wang & S. Sugiyama 10.1016/j.rse.2024.114006
- Characteristics and changes of glacial lakes and outburst floods G. Zhang et al. 10.1038/s43017-024-00554-w
- Detection and mapping of supraglacial lakes on East Antarctic margin using Landsat 8 OLI during 2014–2023 R. Pandey & A. Luis 10.1080/10106049.2024.2356837
- Seasonal evolution of the supraglacial drainage network at Humboldt Glacier, northern Greenland, between 2016 and 2020 L. Rawlins et al. 10.5194/tc-17-4729-2023
- Assessing supraglacial lake depth using ICESat-2, Sentinel-2, TanDEM-X, and in situ sonar measurements over Northeast and Southwest Greenland K. Lutz et al. 10.5194/tc-18-5431-2024
- Polar perspectives: a deep dive into geo-referencing historical Antarctic photos F. Dahle et al. 10.1080/17538947.2024.2406384
- Status and trends in the stability of the three largest ice shelves in Antarctica R. Li et al. 10.1007/s11430-023-1338-8
- Comprehending the surface melt characteristics, calving processes, and seasonal ice velocity of Dålk glacier in Larsemann Hills, East Antarctica P. Mishra et al. 10.1016/j.polar.2024.101081
- Variable temperature thresholds of melt pond formation on Antarctic ice shelves J. van Wessem et al. 10.1038/s41558-022-01577-1
- 南极三大冰架稳定性的现状与变化趋势 荣. 李 et al. 10.1360/SSTe-2023-0160
- An automated algorithm to retrieve the location and depth of supraglacial lakes from ICESat-2 ATL03 data W. Xiao et al. 10.1016/j.rse.2023.113730
- A framework for automated supraglacial lake detection and depth retrieval in ICESat-2 photon data across the Greenland and Antarctic ice sheets P. Arndt & H. Fricker 10.5194/tc-18-5173-2024
- An inventory of supraglacial lakes and channels across the West Antarctic Ice Sheet D. Corr et al. 10.5194/essd-14-209-2022
19 citations as recorded by crossref.
- A machine learning approach to the geomorphometric detection of ribbed moraines in Norway T. Barnes et al. 10.5194/esurf-12-801-2024
- High-resolution imaging of supraglacial hydrological features on the Greenland Ice Sheet with NASA's Airborne Topographic Mapper (ATM) instrument suite M. Studinger et al. 10.5194/tc-16-3649-2022
- Widespread partial-depth hydrofractures in ice sheets driven by supraglacial streams D. Chandler & A. Hubbard 10.1038/s41561-023-01208-0
- Pan-Greenland mapping of supraglacial rivers, lakes, and water-filled crevasses in a cool summer (2018) and a warm summer (2019) W. Zhang et al. 10.1016/j.rse.2023.113781
- Substantial contribution of slush to meltwater area across Antarctic ice shelves R. Dell et al. 10.1038/s41561-024-01466-6
- Detection of Antarctic Surface Meltwater Using Sentinel-2 Remote Sensing Images via U-Net With Attention Blocks: A Case Study Over the Amery Ice Shelf L. Niu et al. 10.1109/TGRS.2023.3275076
- A Conditional Random Fields-Based Identification for Small Lakes Over Multiple Melt Seasons Using Sentinel-2 Imagery in the Larsemann Hills, East Antarctica T. Zhu et al. 10.1109/JSTARS.2024.3391881
- Supraglacial lake evolution on Tracy and Heilprin Glaciers in northwestern Greenland from 2014 to 2021 Y. Wang & S. Sugiyama 10.1016/j.rse.2024.114006
- Characteristics and changes of glacial lakes and outburst floods G. Zhang et al. 10.1038/s43017-024-00554-w
- Detection and mapping of supraglacial lakes on East Antarctic margin using Landsat 8 OLI during 2014–2023 R. Pandey & A. Luis 10.1080/10106049.2024.2356837
- Seasonal evolution of the supraglacial drainage network at Humboldt Glacier, northern Greenland, between 2016 and 2020 L. Rawlins et al. 10.5194/tc-17-4729-2023
- Assessing supraglacial lake depth using ICESat-2, Sentinel-2, TanDEM-X, and in situ sonar measurements over Northeast and Southwest Greenland K. Lutz et al. 10.5194/tc-18-5431-2024
- Polar perspectives: a deep dive into geo-referencing historical Antarctic photos F. Dahle et al. 10.1080/17538947.2024.2406384
- Status and trends in the stability of the three largest ice shelves in Antarctica R. Li et al. 10.1007/s11430-023-1338-8
- Comprehending the surface melt characteristics, calving processes, and seasonal ice velocity of Dålk glacier in Larsemann Hills, East Antarctica P. Mishra et al. 10.1016/j.polar.2024.101081
- Variable temperature thresholds of melt pond formation on Antarctic ice shelves J. van Wessem et al. 10.1038/s41558-022-01577-1
- 南极三大冰架稳定性的现状与变化趋势 荣. 李 et al. 10.1360/SSTe-2023-0160
- An automated algorithm to retrieve the location and depth of supraglacial lakes from ICESat-2 ATL03 data W. Xiao et al. 10.1016/j.rse.2023.113730
- A framework for automated supraglacial lake detection and depth retrieval in ICESat-2 photon data across the Greenland and Antarctic ice sheets P. Arndt & H. Fricker 10.5194/tc-18-5173-2024
1 citations as recorded by crossref.
Latest update: 13 Dec 2024
Short summary
We identify 119 km2 of meltwater area over West Antarctica in January 2017. In combination with Stokes et al., 2019, this forms the first continent-wide assessment helping to quantify the mass balance of Antarctica and its contribution to global sea level rise. We apply thresholds for meltwater classification to satellite images, mapping the extent and manually post-processing to remove false positives. Our study provides a high-fidelity dataset to train and validate machine learning methods.
We identify 119 km2 of meltwater area over West Antarctica in January 2017. In combination with...
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