Articles | Volume 12, issue 3
https://doi.org/10.5194/tc-12-993-2018
© Author(s) 2018. 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-12-993-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
22 Mar 2018
Research article | Highlight paper |
| 22 Mar 2018
| 22 Mar 2018
On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
Jiping Liu
Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY, USA
Bin Wang
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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Cited
17 citations as recorded by crossref.
- Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model Y. Ren & X. Li 10.1109/TGRS.2023.3279089
- Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network J. Liu et al. 10.3390/rs11232864
- A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval J. Anheuser et al. 10.5194/tc-16-4403-2022
- Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice S. Xu et al. 10.5194/tc-14-751-2020
- A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data Z. Dong et al. 10.3390/rs14041041
- On the retrieval of sea-ice thickness using SMOS polarization differences M. GUPTA et al. 10.1017/jog.2019.26
- Characterization of site‐specific vegetation activity in Alaskan wet and dry tundra as related to climate and soil state M. Brown et al. 10.1002/ecs2.3939
- Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements H. Shi et al. 10.5194/tc-14-3761-2020
- Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements F. Garnier et al. 10.5194/tc-15-5483-2021
- Arctic Sea Ice Freeboard Estimation and Variations From Operation IceBridge S. Zhang et al. 10.1109/TGRS.2022.3185230
- Estimating Arctic Sea Ice Thickness with CryoSat-2 Altimetry Data Using the Least Squares Adjustment Method F. Xiao et al. 10.3390/s20247011
- Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network A. Braakmann-Folgmann & C. Donlon 10.5194/tc-13-2421-2019
- Estimation of sea ice parameters from sea ice model with assimilated ice concentration and SST S. Prasad et al. 10.5194/tc-12-3949-2018
- Estimation of Arctic sea ice thickness from CryoSat-2 altimetry data J. Jiang et al. 10.1080/01431161.2023.2195575
- Theoretical study of ice cover phenology at large freshwater lakes based on SMOS MIRAS data V. Tikhonov et al. 10.5194/tc-12-2727-2018
- Thickness simulation of landfast ice along Mawson Coast, East Antarctica based on a snow/ice high-resolution thermodynamic model X. Li et al. 10.1016/j.accre.2022.02.005
- Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI L. Li et al. 10.3390/rs13081457
17 citations as recorded by crossref.
- Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model Y. Ren & X. Li 10.1109/TGRS.2023.3279089
- Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network J. Liu et al. 10.3390/rs11232864
- A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval J. Anheuser et al. 10.5194/tc-16-4403-2022
- Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice S. Xu et al. 10.5194/tc-14-751-2020
- A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data Z. Dong et al. 10.3390/rs14041041
- On the retrieval of sea-ice thickness using SMOS polarization differences M. GUPTA et al. 10.1017/jog.2019.26
- Characterization of site‐specific vegetation activity in Alaskan wet and dry tundra as related to climate and soil state M. Brown et al. 10.1002/ecs2.3939
- Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements H. Shi et al. 10.5194/tc-14-3761-2020
- Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements F. Garnier et al. 10.5194/tc-15-5483-2021
- Arctic Sea Ice Freeboard Estimation and Variations From Operation IceBridge S. Zhang et al. 10.1109/TGRS.2022.3185230
- Estimating Arctic Sea Ice Thickness with CryoSat-2 Altimetry Data Using the Least Squares Adjustment Method F. Xiao et al. 10.3390/s20247011
- Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network A. Braakmann-Folgmann & C. Donlon 10.5194/tc-13-2421-2019
- Estimation of sea ice parameters from sea ice model with assimilated ice concentration and SST S. Prasad et al. 10.5194/tc-12-3949-2018
- Estimation of Arctic sea ice thickness from CryoSat-2 altimetry data J. Jiang et al. 10.1080/01431161.2023.2195575
- Theoretical study of ice cover phenology at large freshwater lakes based on SMOS MIRAS data V. Tikhonov et al. 10.5194/tc-12-2727-2018
- Thickness simulation of landfast ice along Mawson Coast, East Antarctica based on a snow/ice high-resolution thermodynamic model X. Li et al. 10.1016/j.accre.2022.02.005
- Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI L. Li et al. 10.3390/rs13081457
Latest update: 29 Jun 2024
Short summary
This work proposes a new data synergy method for the retrieval of sea ice thickness and snow depth by using colocating L-band passive remote sensing and active laser altimetry. Physical models are adopted for the retrieval, including L-band radiation model and buoyancy relationship. Covariability of snow depth and total freeboard is further utilized to mitigate resolution differences and improve retrievability. The method can be applied to future campaigns including ICESat-2 and WCOM.
This work proposes a new data synergy method for the retrieval of sea ice thickness and snow...
