Articles | Volume 17, issue 9
https://doi.org/10.5194/tc-17-3695-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-3695-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Aug 2023
Research article |
| 31 Aug 2023
| 31 Aug 2023
Observing the evolution of summer melt on multiyear sea ice with ICESat-2 and Sentinel-2
Ellen M. Buckley
CORRESPONDING AUTHOR
Center for Fluid Mechanics, Brown University, Providence, RI, USA
Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, USA
Sinéad L. Farrell
Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, USA
Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Ute C. Herzfeld
Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
Melinda A. Webster
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA
Polar Science Center, University of Washington, Seattle, WA, USA
Thomas Trantow
Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
Oliwia N. Baney
Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Kyle A. Duncan
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Huilin Han
Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
Matthew Lawson
Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
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Cited
13 citations as recorded by crossref.
- Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data—The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-Bifurcate-Seaice) U. Herzfeld et al. https://doi.org/10.1109/TGRS.2023.3268073
- Annotation-free cloud masking for PlanetScope images in the Arctic via cross-platform ability transfer using deep learning and foundation models Z. Li et al. https://doi.org/10.1016/j.rse.2025.115138
- Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges U. Herzfeld et al. https://doi.org/10.3390/rs16111854
- Ice, Cloud, and Land Elevation Satellite-2 Photon-Counting Lidar for 3-Dimensional Ocean Observation: A Comprehensive Review of Algorithms, Applications, and Synergistic Opportunities P. Chen et al. https://doi.org/10.34133/remotesensing.1040
- Comparing elevation and backscatter retrievals from CryoSat-2 and ICESat-2 over Arctic summer sea ice G. Dawson & J. Landy https://doi.org/10.5194/tc-17-4165-2023
- Sea ice melt pond bathymetry reconstructed from aerial photographs using photogrammetry: a new method applied to MOSAiC data N. Fuchs et al. https://doi.org/10.5194/tc-18-2991-2024
- 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 https://doi.org/10.5194/tc-18-5173-2024
- Monitoring Earth’s climate variables with satellite laser altimetry L. Magruder et al. https://doi.org/10.1038/s43017-023-00508-8
- Optimization of automated sea-ice melt-pond-depth determination in ICESat-2 altimeter data with the Density-Dimension Algorithm for bifurcating sea-ice reflectors using airborne campaign data T. Trantow et al. https://doi.org/10.1017/jog.2026.10167
- Sea ice concentration estimates from ICESat-2 linear ice fraction – Part 1: Multi-sensor comparison of sea ice concentration products E. Buckley et al. https://doi.org/10.5194/tc-19-4805-2025
- Joint mapping of melt pond bathymetry and water volume on sea ice using optical remote sensing images and physical reflectance models C. Xiong & X. Li https://doi.org/10.1016/j.rse.2024.114571
- An automated method for estimating the depth of melt ponds using ICESat-2 LiDAR point cloud data: application to surface melt of Arctic sea ice X. Shen et al. https://doi.org/10.1016/j.jag.2025.105033
- Enhanced sea ice classification for ICESat-2 using combined unsupervised and supervised machine learning W. Liu et al. https://doi.org/10.1016/j.rse.2025.114607
13 citations as recorded by crossref.
- Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data—The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-Bifurcate-Seaice) U. Herzfeld et al. https://doi.org/10.1109/TGRS.2023.3268073
- Annotation-free cloud masking for PlanetScope images in the Arctic via cross-platform ability transfer using deep learning and foundation models Z. Li et al. https://doi.org/10.1016/j.rse.2025.115138
- Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges U. Herzfeld et al. https://doi.org/10.3390/rs16111854
- Ice, Cloud, and Land Elevation Satellite-2 Photon-Counting Lidar for 3-Dimensional Ocean Observation: A Comprehensive Review of Algorithms, Applications, and Synergistic Opportunities P. Chen et al. https://doi.org/10.34133/remotesensing.1040
- Comparing elevation and backscatter retrievals from CryoSat-2 and ICESat-2 over Arctic summer sea ice G. Dawson & J. Landy https://doi.org/10.5194/tc-17-4165-2023
- Sea ice melt pond bathymetry reconstructed from aerial photographs using photogrammetry: a new method applied to MOSAiC data N. Fuchs et al. https://doi.org/10.5194/tc-18-2991-2024
- 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 https://doi.org/10.5194/tc-18-5173-2024
- Monitoring Earth’s climate variables with satellite laser altimetry L. Magruder et al. https://doi.org/10.1038/s43017-023-00508-8
- Optimization of automated sea-ice melt-pond-depth determination in ICESat-2 altimeter data with the Density-Dimension Algorithm for bifurcating sea-ice reflectors using airborne campaign data T. Trantow et al. https://doi.org/10.1017/jog.2026.10167
- Sea ice concentration estimates from ICESat-2 linear ice fraction – Part 1: Multi-sensor comparison of sea ice concentration products E. Buckley et al. https://doi.org/10.5194/tc-19-4805-2025
- Joint mapping of melt pond bathymetry and water volume on sea ice using optical remote sensing images and physical reflectance models C. Xiong & X. Li https://doi.org/10.1016/j.rse.2024.114571
- An automated method for estimating the depth of melt ponds using ICESat-2 LiDAR point cloud data: application to surface melt of Arctic sea ice X. Shen et al. https://doi.org/10.1016/j.jag.2025.105033
- Enhanced sea ice classification for ICESat-2 using combined unsupervised and supervised machine learning W. Liu et al. https://doi.org/10.1016/j.rse.2025.114607
Saved (final revised paper)
Latest update: 21 Jul 2026
Short summary
In this study, we use satellite observations to investigate the evolution of melt ponds on the Arctic sea ice surface. We derive melt pond depth from ICESat-2 measurements of the pond surface and bathymetry and melt pond fraction (MPF) from the classification of Sentinel-2 imagery. MPF increases to a peak of 16 % in late June and then decreases, while depth increases steadily. This work demonstrates the ability to track evolving melt conditions in three dimensions throughout the summer.
In this study, we use satellite observations to investigate the evolution of melt ponds on the...
