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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume X-G-2025
https://doi.org/10.5194/isprs-annals-X-G-2025-333-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-annals-X-G-2025-333-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
10 Jul 2025
| 10 Jul 2025
Towards representation learning of radar altimeter waveforms for sea ice surface classification
Lena Happ
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
University of Bremen, Center for Industrial Mathematics, Bibliothekstraße 5, Bremen, Germany
Sonali Patil
German Aerospace Center (DLR), Institute of Software Technology, Braunschweig, Germany
Stefan Hendricks
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Riccardo Fellegara
German Aerospace Center (DLR), Institute of Software Technology, Braunschweig, Germany
Lars Kaleschke
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
Andreas Gerndt
German Aerospace Center (DLR), Institute of Software Technology, Braunschweig, Germany
University of Bremen, Center for Industrial Mathematics, Bibliothekstraße 5, Bremen, Germany
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Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Arttu Jutila, Stefan Hendricks, Robert Ricker, Luisa von Albedyll, Thomas Krumpen, and Christian Haas
The Cryosphere, 16, 259–275, https://doi.org/10.5194/tc-16-259-2022, https://doi.org/10.5194/tc-16-259-2022, 2022
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Sea-ice thickness retrieval from satellite altimeters relies on assumed sea-ice density values because density cannot be measured from space. We derived bulk densities for different ice types using airborne laser, radar, and electromagnetic induction sounding measurements. Compared to previous studies, we found high bulk density values due to ice deformation and younger ice cover. Using sea-ice freeboard, we derived a sea-ice bulk density parameterisation that can be applied to satellite data.
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