93 citations as recorded by crossref.

1. SAR image wave spectra to retrieve the thickness of grease-pancake sea ice using viscous wave propagation models G. De Carolis et al. https://doi.org/10.1038/s41598-021-82228-x
2. An RFI-suppressed SMOS L-band multi-angular brightness temperature dataset spanning over a decade (since 2010) Z. Peng et al. https://doi.org/10.1038/s41597-023-02499-z
3. Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery F. Müller et al. https://doi.org/10.5194/tc-17-809-2023
4. Community review of Southern Ocean satellite data needs A. Pope et al. https://doi.org/10.1017/S0954102016000390
5. Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses F. Cocetta et al. https://doi.org/10.5194/tc-18-4687-2024
6. Sea Ice Production in the 2016 and 2017 Maud Rise Polynyas L. Zhou et al. https://doi.org/10.1029/2022JC019148
7. Map of Arctic and Antarctic Polynyas 2013–2022 Using Sea Ice Concentration K. Yang et al. https://doi.org/10.3390/rs17071213
8. An improved algorithm for retrieving thin sea ice thickness in the Arctic Ocean from SMOS and SMAP L-band radiometer data L. He et al. https://doi.org/10.1007/s13131-023-2280-9
9. PMDRnet: A Progressive Multiscale Deformable Residual Network for Multi-Image Super-Resolution of AMSR2 Arctic Sea Ice Images X. Liu et al. https://doi.org/10.1109/TGRS.2022.3151623
10. Weddell Sea polynya analysis using SMOS–SMAP apparent sea ice thickness retrieval A. Mchedlishvili et al. https://doi.org/10.5194/tc-16-471-2022
11. 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. https://doi.org/10.5194/tc-16-1821-2022
12. SMOS sea ice product: Operational application and validation in the Barents Sea marginal ice zone L. Kaleschke et al. https://doi.org/10.1016/j.rse.2016.03.009
13. On sea ice emission modeling for MOSAiC's L-band radiometric measurements F. Hernández-Macià et al. https://doi.org/10.1017/aog.2024.38
14. Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data L. Istomina et al. https://doi.org/10.5194/tc-19-83-2025
15. The coupled Southern Ocean–Sea ice–Ice shelf Model (SOSIM v1.0): configuration and evaluation C. Liu et al. https://doi.org/10.5194/gmd-19-2985-2026
16. Improving satellite-based monitoring of the polar regions: Identification of research and capacity gaps C. Gabarró et al. https://doi.org/10.3389/frsen.2023.952091
17. AMSR2 Thin Ice Detection Algorithm for the Arctic Winter Conditions M. Makynen & M. Simila https://doi.org/10.1109/TGRS.2022.3142966
18. Retrieval of thin-ice thickness in arctic marginal seas through a combination of thermal and optical observations from polar orbiting satellites Z. Suo et al. https://doi.org/10.1080/15481603.2025.2567117
19. Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval T. Zhang et al. https://doi.org/10.1007/s13131-023-2210-x
20. Sea Ice Thickness Estimation Based on Regression Neural Networks Using L-Band Microwave Radiometry Data from the FSSCat Mission C. Herbert et al. https://doi.org/10.3390/rs13071366
21. Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data M. Dabboor et al. https://doi.org/10.3390/rs9121270
22. Bivariate sea-ice assimilation for global-ocean analysis–reanalysis A. Cipollone et al. https://doi.org/10.5194/os-19-1375-2023
23. Ocean Remote Sensing Using Spaceborne GNSS-Reflectometry: A Review J. Bu et al. https://doi.org/10.1109/JSTARS.2024.3418429
24. Theoretical study of ice cover phenology at large freshwater lakes based on SMOS MIRAS data V. Tikhonov et al. https://doi.org/10.5194/tc-12-2727-2018
25. Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition G. Heinemann et al. https://doi.org/10.3390/atmos12020174
26. Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness P. Sánchez-Gámez et al. https://doi.org/10.3390/rs12040650
27. A Consistent Combination of Brightness Temperatures from SMOS and SMAP over Polar Oceans for Sea Ice Applications A. Schmitt & L. Kaleschke https://doi.org/10.3390/rs10040553
28. Circumpolar Thin Arctic Sea Ice Thickness and Small-Scale Roughness Retrieval Using Soil Moisture and Ocean Salinity and Soil Moisture Active Passive Observations S. Jo et al. https://doi.org/10.3390/rs11232835
29. Early Winter Triggering of the Maud Rise Polynya L. Zhou et al. https://doi.org/10.1029/2021GL096246
30. Sea Ice Thickness Measurement Using Spaceborne GNSS-R: First Results With TechDemoSat-1 Data Q. Yan & W. Huang https://doi.org/10.1109/JSTARS.2020.2966880
31. Spectral Modeling of Ice-Induced Wave Decay Q. Liu et al. https://doi.org/10.1175/JPO-D-19-0187.1
32. Estimates of spectral wave attenuation in Antarctic sea ice, using model/data inversion W. Rogers et al. https://doi.org/10.1016/j.coldregions.2020.103198
33. Estimation of Thin-Ice Thickness and Discrimination of Ice Type From AMSR-E Passive Microwave Data K. Nakata et al. https://doi.org/10.1109/TGRS.2018.2853590
34. Distribution of modified Circumpolar Deep Water and its threat in Vincennes Bay, East Antarctica W. Ye et al. https://doi.org/10.1007/s00343-024-3164-3
35. Expected Performances of the Copernicus Imaging Microwave Radiometer (CIMR) for an All‐Weather and High Spatial Resolution Estimation of Ocean and Sea Ice Parameters L. Kilic et al. https://doi.org/10.1029/2018JC014408
36. On the Ocean Wave Attenuation Rate in Grease‐Pancake Ice, a Comparison of Viscous Layer Propagation Models With Field Data F. De Santi et al. https://doi.org/10.1029/2018JC013865
37. Estimating thin ice thickness around Svalbard using MODIS satellite imagery Ø. Rudjord et al. https://doi.org/10.1080/04353676.2022.2070158
38. Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment A. Shah et al. https://doi.org/10.1080/16000870.2019.1697166
39. Operational Service for Mapping the Baltic Sea Landfast Ice Properties M. Mäkynen et al. https://doi.org/10.3390/rs12244032
40. Spaceborne GNSS Reflectometry K. Yu et al. https://doi.org/10.3390/rs14071605
41. Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets S. Sandven et al. https://doi.org/10.1007/s10712-023-09781-0
42. Open-Ocean Polynyas in the Cooperation Sea, Antarctica Q. Qin et al. https://doi.org/10.1175/JPO-D-21-0197.1
43. The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset V. Ludwig et al. https://doi.org/10.5194/tc-13-2051-2019
44. Snow thickness retrieval from L-band brightness temperatures: a model comparison N. Maass et al. https://doi.org/10.3189/2015AoG69A886
45. Applications of Deep Learning-Based Super-Resolution Networks for AMSR2 Arctic Sea Ice Images T. Feng et al. https://doi.org/10.3390/rs15225401
46. Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations N. Ivanova et al. https://doi.org/10.5194/tc-9-1797-2015
47. Phytoplankton blooms in the new Southern Ocean sea-ice regime T. Schlosser & P. Strutton https://doi.org/10.1525/elementa.2024.00055
48. SMOS-derived Antarctic thin sea ice thickness: data description and validation in the Weddell Sea L. Kaleschke et al. https://doi.org/10.5194/essd-16-3149-2024
49. 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. https://doi.org/10.1029/2019MS001938
50. Estimation of ICESat-Equivalent Arctic winter sea ice thickness From AMSR-E brightness temperature data based on machine learning approach L. Liu et al. https://doi.org/10.1016/j.rsase.2026.101911
51. Arctic Sea Ice Thickness Retrieval From SMOS Using Unsupervised-Classification-Based Selection of Representative Tie Points J. Li et al. https://doi.org/10.1109/JSTARS.2026.3687866
52. The spatiotemporal patterns of sea ice in the Bohai Sea during the winter seasons of 2000–2016 L. Ouyang et al. https://doi.org/10.1080/17538947.2017.1365957
53. Observing Waves in Sea Ice With ICESat‐2 C. Horvat et al. https://doi.org/10.1029/2020GL087629
54. Towards reliable Arctic sea ice prediction using multivariate data assimilation J. Liu et al. https://doi.org/10.1016/j.scib.2018.11.018
55. 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. https://doi.org/10.3390/rs12071214
56. New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator C. Gabarro et al. https://doi.org/10.5194/tc-11-1987-2017
57. Intensity of Level Ice Simulated with the CICE Model for Oil-Gas Exploitation in the Southern Kara Sea, Arctic C. Duan et al. https://doi.org/10.1007/s11802-022-4914-5
58. Retrieval of sea ice thickness from the reflected signal of a satellite navigation system W. Wang & Q. Gao https://doi.org/10.1080/01431161.2020.1751898
59. A new method for parameterization of wave dissipation by sea ice J. Yu et al. https://doi.org/10.1016/j.coldregions.2022.103582
60. Sea‐Ice Impacts Inter‐Annual Variability of Phytoplankton Bloom Characteristics and Carbon Export in the Weddell Sea I. Giddy et al. https://doi.org/10.1029/2023GL103695
61. Spectral attenuation of ocean waves in pack ice and its application in calibrating viscoelastic wave-in-ice models S. Cheng et al. https://doi.org/10.5194/tc-14-2053-2020
62. Assimilation of SMOS sea ice thickness in the regional ice prediction system M. Gupta et al. https://doi.org/10.1080/01431161.2021.1897183
63. Ice-thickness based scaling of wave attenuation in sea ice: Application and assessment of wave spectra W. Rogers et al. https://doi.org/10.1016/j.ocemod.2026.102809
64. Calibrating a Viscoelastic Sea Ice Model for Wave Propagation in the Arctic Fall Marginal Ice Zone S. Cheng et al. https://doi.org/10.1002/2017JC013275
65. Estimation of Daily Arctic Winter Sea Ice Thickness from Thermodynamic Parameters Using a Self-Attention Convolutional Neural Network Z. Liang et al. https://doi.org/10.3390/rs15071887
66. Finely resolved along-track wave attenuation estimates in the Antarctic marginal ice zone from ICESat-2 J. Voermans et al. https://doi.org/10.5194/tc-19-3381-2025
67. Concept-driven extraction of the Antarctic marginal sea ice zone from remote sensing image time series X. Zhao et al. https://doi.org/10.1016/j.spasta.2021.100578
68. Measurements of 540–1740 MHz Brightness Temperatures of Sea Ice During the Winter of the MOSAiC Campaign O. Demir et al. https://doi.org/10.1109/TGRS.2021.3105360
69. Thin Ice Detection in the Barents and Kara Seas Using AMSR2 High-Frequency Radiometer Data M. Makynen & M. Simila https://doi.org/10.1109/TGRS.2019.2913283
70. Weekly gridded Aquarius L-band radiometer/scatterometer observations and salinity retrievals over the polar regions – Part 2: Initial product analysis L. Brucker et al. https://doi.org/10.5194/tc-8-915-2014
71. Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution C. Wang et al. https://doi.org/10.5194/tc-13-1661-2019
72. Southern Ocean polynyas in CMIP6 models M. Mohrmann et al. https://doi.org/10.5194/tc-15-4281-2021
73. Linking timescale-dependent Antarctic sea ice kinematic observations to ice thickness T. Tian et al. https://doi.org/10.1016/j.rse.2023.113813
74. Essential gaps and uncertainties in the understanding of the roles and functions of Arctic sea ice S. Gerland et al. https://doi.org/10.1088/1748-9326/ab09b3
75. STDNet: Spatio-Temporal Decompose Network for Predicting Arctic Sea Ice Concentration X. Zhu et al. https://doi.org/10.3390/rs16234534
76. Spatial and Temporal Variability of Minimum Brightness Temperature at the 6.925 GHz Band of AMSR2 for the Arctic and Antarctic Oceans Y. Kwon et al. https://doi.org/10.3390/rs13112122
77. Retrieval of the thickness of undeformed sea ice from simulated C-band compact polarimetric SAR images X. Zhang et al. https://doi.org/10.5194/tc-10-1529-2016
78. Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system J. Xie et al. https://doi.org/10.5194/tc-10-2745-2016
79. Step-by-Step Validation of Antarctic ASI AMSR-E Sea-Ice Concentrations by MODIS and an Aerial Image Q. Shi et al. https://doi.org/10.1109/TGRS.2020.2989037
80. The sustained expansion (2011−2022) and abrupt contraction (2023) of the Ross Sea polynyas X. Ou et al. https://doi.org/10.1016/j.jmarsys.2026.104255
81. Arctic sea ice in transformation: A review of recent observed changes and impacts on biology and human activity W. Meier et al. https://doi.org/10.1002/2013RG000431
82. Combined SMAP–SMOS thin sea ice thickness retrieval C. Paţilea et al. https://doi.org/10.5194/tc-13-675-2019
83. Southern Ocean sea ice concentration budgets of five ocean-sea ice reanalyses Y. Nie et al. https://doi.org/10.1007/s00382-022-06260-x
84. On the retrieval of sea-ice thickness using SMOS polarization differences M. GUPTA et al. https://doi.org/10.1017/jog.2019.26
85. Assessment of Machine Learning-Driven Retrievals of Arctic Sea Ice Thickness from L-Band Radiometry Remote Sensing F. Hernández-Macià et al. https://doi.org/10.3390/computers14080305
86. Effects of decimetre-scale surface roughness on L-band brightness temperature of sea ice M. Miernecki et al. https://doi.org/10.5194/tc-14-461-2020
87. Validation of SMOS sea ice thickness retrieval in the northern Baltic Sea N. Maaß et al. https://doi.org/10.3402/tellusa.v67.24617
88. Comparison of Arctic Sea Ice Thickness from Satellites, Aircraft, and PIOMAS Data X. Wang et al. https://doi.org/10.3390/rs8090713
89. Winter sea ice export from the Laptev Sea preconditions the local summer sea ice cover and fast ice decay P. Itkin & T. Krumpen https://doi.org/10.5194/tc-11-2383-2017
90. Arctic Thin Ice Detection Using AMSR2 and FY-3C MWRI Radiometer Data M. Mäkynen & M. Similä https://doi.org/10.3390/rs16091600
91. An Arctic Sea Ice Thickness Inversion Method Based on Deep Learning Two-Branch Architecture and Multisource Remote Sensing Data Fusion R. Huang et al. https://doi.org/10.1109/JSTARS.2025.3636999
92. 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. https://doi.org/10.1029/2019JC015749
93. First results of the ARIEL L-band radiometer on the MOSAiC Arctic Expedition during the late summer and autumn period C. Gabarró et al. https://doi.org/10.1525/elementa.2022.00031