Articles | Volume 17, issue 7
https://doi.org/10.5194/tc-17-3013-2023
© 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-17-3013-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Arctic sea ice radar freeboard retrieval from the European Remote-Sensing Satellite (ERS-2) using altimetry: toward sea ice thickness observation from 1995 to 2021
Marion Bocquet
CORRESPONDING AUTHOR
LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
Collecte Localisation Satellites (CLS), Toulouse, France
Sara Fleury
LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
Fanny Piras
Collecte Localisation Satellites (CLS), Toulouse, France
Eero Rinne
Marine Research, Finnish Meteorological Institute, Helsinki, Finland
University Centre in Svalbard (UNIS), P.O. Box 156, 9171 Longyearbyen, Norway
Heidi Sallila
Marine Research, Finnish Meteorological Institute, Helsinki, Finland
Florent Garnier
LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
Frédérique Rémy
LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
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Cited
14 citations as recorded by crossref.
- A monthly Arctic sea ice thickness product from 1995 to 2025 using multiple radar altimetry data F. Xiao et al. https://doi.org/10.1080/10095020.2026.2699566
- Estimating Arctic sea ice thickness from satellite-based ice history N. Kimura & H. Hasumi https://doi.org/10.5194/tc-20-2331-2026
- Research Progress of Deep Learning in Sea Ice Prediction J. Ran et al. https://doi.org/10.3390/rs18030419
- Satellite Observation of Sea Ice Concentration Based on Chinese HY-1C/D Data L. Xu et al. https://doi.org/10.1109/JSTARS.2025.3632882
- Enhanced prediction skill of Antarctic sea ice through sea ice thickness assimilation N. Williams et al. https://doi.org/10.5194/tc-20-3795-2026
- Recovery of Time Series of Water Volume in Lake Ranco (South Chile) through Satellite Altimetry and Its Relationship with Climatic Phenomena P. Fuentes-Aguilera et al. https://doi.org/10.3390/w16141997
- A multi-frequency altimetry snow depth product over Arctic sea ice A. Carret et al. https://doi.org/10.1038/s41597-024-04343-4
- Reconstruction of Arctic sea ice thickness (1992–2010) based on a hybrid machine learning and data assimilation approach L. Edel et al. https://doi.org/10.5194/tc-19-731-2025
- Assimilation of radar freeboard and snow altimetry observations in the Arctic and Antarctic with a coupled ocean/sea ice modelling system A. Chenal et al. https://doi.org/10.5194/tc-20-369-2026
- A first approach towards dual-hemisphere sea ice reference measurements from multiple data sources repurposed for evaluation and product intercomparison of satellite altimetry I. Olsen et al. https://doi.org/10.5194/essd-18-2469-2026
- Anticipating CRISTAL: an exploration of multi-frequency satellite altimeter snow depth estimates over Arctic sea ice, 2018–2023 J. Landy et al. https://doi.org/10.5194/tc-20-183-2026
- Extraction of Doppler Shift From Altimetric Radar Signals and Computation for VerticalDeflection of Gravity Field J. Yu et al. https://doi.org/10.1109/TGRS.2025.3609306
- Signal Photon Extraction Method for ICESat-2 Data Using Slope and Elevation Information Provided by Stereo Images L. Gu et al. https://doi.org/10.3390/s23218752
- Snow effects on altimeter waveforms over sea ice in the Weddell Sea - Part II: sea ice and snow spaceborne retrieval L. Zhou et al. https://doi.org/10.1016/j.rse.2026.115360
14 citations as recorded by crossref.
- A monthly Arctic sea ice thickness product from 1995 to 2025 using multiple radar altimetry data F. Xiao et al. https://doi.org/10.1080/10095020.2026.2699566
- Estimating Arctic sea ice thickness from satellite-based ice history N. Kimura & H. Hasumi https://doi.org/10.5194/tc-20-2331-2026
- Research Progress of Deep Learning in Sea Ice Prediction J. Ran et al. https://doi.org/10.3390/rs18030419
- Satellite Observation of Sea Ice Concentration Based on Chinese HY-1C/D Data L. Xu et al. https://doi.org/10.1109/JSTARS.2025.3632882
- Enhanced prediction skill of Antarctic sea ice through sea ice thickness assimilation N. Williams et al. https://doi.org/10.5194/tc-20-3795-2026
- Recovery of Time Series of Water Volume in Lake Ranco (South Chile) through Satellite Altimetry and Its Relationship with Climatic Phenomena P. Fuentes-Aguilera et al. https://doi.org/10.3390/w16141997
- A multi-frequency altimetry snow depth product over Arctic sea ice A. Carret et al. https://doi.org/10.1038/s41597-024-04343-4
- Reconstruction of Arctic sea ice thickness (1992–2010) based on a hybrid machine learning and data assimilation approach L. Edel et al. https://doi.org/10.5194/tc-19-731-2025
- Assimilation of radar freeboard and snow altimetry observations in the Arctic and Antarctic with a coupled ocean/sea ice modelling system A. Chenal et al. https://doi.org/10.5194/tc-20-369-2026
- A first approach towards dual-hemisphere sea ice reference measurements from multiple data sources repurposed for evaluation and product intercomparison of satellite altimetry I. Olsen et al. https://doi.org/10.5194/essd-18-2469-2026
- Anticipating CRISTAL: an exploration of multi-frequency satellite altimeter snow depth estimates over Arctic sea ice, 2018–2023 J. Landy et al. https://doi.org/10.5194/tc-20-183-2026
- Extraction of Doppler Shift From Altimetric Radar Signals and Computation for VerticalDeflection of Gravity Field J. Yu et al. https://doi.org/10.1109/TGRS.2025.3609306
- Signal Photon Extraction Method for ICESat-2 Data Using Slope and Elevation Information Provided by Stereo Images L. Gu et al. https://doi.org/10.3390/s23218752
- Snow effects on altimeter waveforms over sea ice in the Weddell Sea - Part II: sea ice and snow spaceborne retrieval L. Zhou et al. https://doi.org/10.1016/j.rse.2026.115360
Saved (final revised paper)
Latest update: 27 Jul 2026
Short summary
Sea ice has a large interannual variability, and studying its evolution requires long time series of observations. In this paper, we propose the first method to extend Arctic sea ice thickness time series to the ERS-2 altimeter. The developed method is based on a neural network to calibrate past missions on the current one by taking advantage of their differences during the mission-overlap periods. Data are available as monthly maps for each year during the winter period between 1995 and 2021.
Sea ice has a large interannual variability, and studying its evolution requires long time...