Articles | Volume 10, issue 4
https://doi.org/10.5194/tc-10-1529-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-10-1529-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
19 Jul 2016
Research article |
| 19 Jul 2016
Retrieval of the thickness of undeformed sea ice from simulated C-band compact polarimetric SAR images
Xi Zhang
CORRESPONDING AUTHOR
The First Institute of Oceanography, State Oceanic Administration, Qingdao, China
Wolfgang Dierking
Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
Jie Zhang
The First Institute of Oceanography, State Oceanic Administration, Qingdao, China
Junmin Meng
The First Institute of Oceanography, State Oceanic Administration, Qingdao, China
Haitao Lang
CORRESPONDING AUTHOR
Beijing University of Chemical Technology, Beijing, China
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Cited
18 citations as recorded by crossref.
- An Arctic Sea Ice Thickness Inversion Method Based on Deep Learning Two-Branch Architecture and Multisource Remote Sensing Data Fusion R. Huang et al.
- Surface roughness signatures of summer arctic snow-covered sea ice in X-band dual-polarimetric SAR H. Han et al.
- X-, C-, and L-band SAR signatures of newly formed sea ice in Arctic leads during winter and spring A. Johansson et al.
- Assessment of RISAT-1 and Radarsat-2 for Sea Ice Observations from a Hybrid-Polarity Perspective M. Espeseth et al.
- Polarimetric SAR Applications of Sea Ice: A Review M. Shokr & M. Dabboor
- Tenuous Correlation between Snow Depth or Sea Ice Thickness and C- or X-Band Backscattering in Nunavik Fjords of the Hudson Strait S. Dufour-Beauséjour et al.
- A Meta-Analysis of Sea Ice Monitoring Using Spaceborne Polarimetric SAR: Advances in the Last Decade H. Lyu et al.
- Enhancing sea ice classification on SAR imagery by integrating texture and polarimetric information with a deep learning model L. Gao
- Interannual Variation of Landfast Ice Using Ascending and Descending Sentinel-1 Images from 2019 to 2021: A Case Study of Cambridge Bay Y. Zhu et al.
- Baltic sea ice thickness estimation based on X-band SAR data and background information J. Karvonen & B. Cheng
- Observations from C-Band SAR Fully Polarimetric Parameters of Mobile Sea Ice Based on Radar Scattering Mechanisms to Support Operational Sea Ice Monitoring M. Shokr et al.
- Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements S. Singha et al.
- Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar S. Cafarella et al.
- A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea M. Mei & T. Maksym
- Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar J. Karvonen et al.
- Polarimetric Autocorrelation Matrix: A New Tool for Joint Characterizing of Target Polarization and Doppler Scattering Mechanism X. Zhang et al.
- Spaceborne infrared imagery for early detection of Weddell Polynya opening C. Heuzé et al.
- Compact Polarimetry Response to Modeled Fast Sea Ice Thickness M. Dabboor & M. Shokr
18 citations as recorded by crossref.
- An Arctic Sea Ice Thickness Inversion Method Based on Deep Learning Two-Branch Architecture and Multisource Remote Sensing Data Fusion R. Huang et al.
- Surface roughness signatures of summer arctic snow-covered sea ice in X-band dual-polarimetric SAR H. Han et al.
- X-, C-, and L-band SAR signatures of newly formed sea ice in Arctic leads during winter and spring A. Johansson et al.
- Assessment of RISAT-1 and Radarsat-2 for Sea Ice Observations from a Hybrid-Polarity Perspective M. Espeseth et al.
- Polarimetric SAR Applications of Sea Ice: A Review M. Shokr & M. Dabboor
- Tenuous Correlation between Snow Depth or Sea Ice Thickness and C- or X-Band Backscattering in Nunavik Fjords of the Hudson Strait S. Dufour-Beauséjour et al.
- A Meta-Analysis of Sea Ice Monitoring Using Spaceborne Polarimetric SAR: Advances in the Last Decade H. Lyu et al.
- Enhancing sea ice classification on SAR imagery by integrating texture and polarimetric information with a deep learning model L. Gao
- Interannual Variation of Landfast Ice Using Ascending and Descending Sentinel-1 Images from 2019 to 2021: A Case Study of Cambridge Bay Y. Zhu et al.
- Baltic sea ice thickness estimation based on X-band SAR data and background information J. Karvonen & B. Cheng
- Observations from C-Band SAR Fully Polarimetric Parameters of Mobile Sea Ice Based on Radar Scattering Mechanisms to Support Operational Sea Ice Monitoring M. Shokr et al.
- Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements S. Singha et al.
- Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar S. Cafarella et al.
- A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea M. Mei & T. Maksym
- Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar J. Karvonen et al.
- Polarimetric Autocorrelation Matrix: A New Tool for Joint Characterizing of Target Polarization and Doppler Scattering Mechanism X. Zhang et al.
- Spaceborne infrared imagery for early detection of Weddell Polynya opening C. Heuzé et al.
- Compact Polarimetry Response to Modeled Fast Sea Ice Thickness M. Dabboor & M. Shokr
Saved (final revised paper)
Latest update: 11 May 2026
Short summary
In this work, we introduced a parameter ("CP ratio") for the retrieval of the thickness of undeformed first-year sea ice that is specifically adapted to compact polarimetric SAR images. Based on a validation using other compact polarimetric SAR images from the Labrador Sea, we found a root mean square error of 8 cm and a maximum correlation coefficient of 0.94 for the retrieval procedure when applying it to level ice between 0.1 m and 0.8 m thick.
In this work, we introduced a parameter ("CP ratio") for the retrieval of the thickness of...
