Articles | Volume 8, issue 2
The Cryosphere, 8, 439–451, 2014
https://doi.org/10.5194/tc-8-439-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
The Cryosphere, 8, 439–451, 2014
https://doi.org/10.5194/tc-8-439-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
18 Mar 2014
Research article
| 18 Mar 2014
Empirical sea ice thickness retrieval during the freeze-up period from SMOS high incident angle observations
M. Huntemann et al.
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56 citations as recorded by crossref.
- SAR image wave spectra to retrieve the thickness of grease-pancake sea ice using viscous wave propagation models G. De Carolis et al. 10.1038/s41598-021-82228-x
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- Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations C. Yang et al. 10.1029/2019MS001938
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- Observing Waves in Sea Ice With ICESat‐2 C. Horvat et al. 10.1029/2020GL087629
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- Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. 10.1016/j.scib.2018.11.018
- Evaluation of AMSR-E Thin Ice Thickness Algorithm from a Mooring-Based Observation: How Can the Satellite Observe a Sea Ice Field with Nonuniform Thickness Distribution? H. Kashiwase et al. 10.1175/JTECH-D-18-0218.1
- Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme M. Mäkynen et al. 10.3390/rs12071214
- New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator C. Gabarro et al. 10.5194/tc-11-1987-2017
- SMOS sea ice product: Operational application and validation in the Barents Sea marginal ice zone L. Kaleschke et al. 10.1016/j.rse.2016.03.009
- Retrieval of sea ice thickness from the reflected signal of a satellite navigation system W. Wang & Q. Gao 10.1080/01431161.2020.1751898
- AMSR2 Thin Ice Detection Algorithm for the Arctic Winter Conditions M. Makynen & M. Simila 10.1109/TGRS.2022.3142966
- Sea Ice Thickness Estimation Based on Regression Neural Networks Using L-Band Microwave Radiometry Data from the FSSCat Mission C. Herbert et al. 10.3390/rs13071366
- Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data M. Dabboor et al. 10.3390/rs9121270
- Spectral attenuation of ocean waves in pack ice and its application in calibrating viscoelastic wave-in-ice models S. Cheng et al. 10.5194/tc-14-2053-2020
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- Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition G. Heinemann et al. 10.3390/atmos12020174
- Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness P. Sánchez-Gámez et al. 10.3390/rs12040650
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- Concept-driven extraction of the Antarctic marginal sea ice zone from remote sensing image time series X. Zhao et al. 10.1016/j.spasta.2021.100578
- Measurements of 540–1740 MHz Brightness Temperatures of Sea Ice During the Winter of the MOSAiC Campaign O. Demir et al. 10.1109/TGRS.2021.3105360
- Thin Ice Detection in the Barents and Kara Seas Using AMSR2 High-Frequency Radiometer Data M. Makynen & M. Simila 10.1109/TGRS.2019.2913283
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- Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter Q. Yang et al. 10.1002/2014JC009963
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- Comparison of Arctic Sea Ice Thickness from Satellites, Aircraft, and PIOMAS Data X. Wang et al. 10.3390/rs8090713
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- Bayesian Unsupervised Machine Learning Approach to Segment Arctic Sea Ice Using SMOS Data C. Herbert et al. 10.1029/2020GL091285
- Sea Ice and Atmospheric Parameter Retrieval From Satellite Microwave Radiometers: Synergy of AMSR2 and SMOS Compared With the CIMR Candidate Mission R. Scarlat et al. 10.1029/2019JC015749
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