Articles | Volume 13, issue 2
https://doi.org/10.5194/tc-13-675-2019
© Author(s) 2019. 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-13-675-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Feb 2019
Research article |
| 28 Feb 2019
Combined SMAP–SMOS thin sea ice thickness retrieval
Cătălin Paţilea
CORRESPONDING AUTHOR
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Georg Heygster
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Marcus Huntemann
Alfred Wegener Institute, Bremerhaven, Germany
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Gunnar Spreen
Institute of Environmental Physics, University of Bremen, Bremen, Germany
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Cited
16 citations as recorded by crossref.
- 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
- 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
- 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
- Weddell Sea polynya analysis using SMOS–SMAP apparent sea ice thickness retrieval A. Mchedlishvili et al. 10.5194/tc-16-471-2022
- The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset V. Ludwig et al. 10.5194/tc-13-2051-2019
- Study of changes of vessel’s speed in ice conditions on the Northern Sea Route E. Ol’khovik 10.1088/1755-1315/378/1/012096
- 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
- Estimates of spectral wave attenuation in Antarctic sea ice, using model/data inversion W. Rogers et al. 10.1016/j.coldregions.2020.103198
- 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. 10.3390/rs11232835
- Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index Z. Wang et al. 10.1016/j.scitotenv.2021.145807
- Remote Sensing of Sea Ice Thickness and Salinity With 0.5–2 GHz Microwave Radiometry K. Jezek et al. 10.1109/TGRS.2019.2922163
- Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. 10.1029/2019MS001937
- Overview of the MOSAiC expedition: Snow and sea ice M. Nicolaus et al. 10.1525/elementa.2021.000046
- Polar Ocean Observations: A Critical Gap in the Observing System and Its Effect on Environmental Predictions From Hours to a Season G. Smith et al. 10.3389/fmars.2019.00429
- Gaps Analysis and Requirements Specification for the Evolution of Copernicus System for Polar Regions Monitoring: Addressing the Challenges in the Horizon 2020–2030 E. Lancheros et al. 10.3390/rs10071098
- Selection of the Key Earth Observation Sensors and Platforms Focusing on Applications for Polar Regions in the Scope of Copernicus System 2020–2030 E. Lancheros et al. 10.3390/rs11020175
14 citations as recorded by crossref.
- 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
- 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
- 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
- Weddell Sea polynya analysis using SMOS–SMAP apparent sea ice thickness retrieval A. Mchedlishvili et al. 10.5194/tc-16-471-2022
- The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset V. Ludwig et al. 10.5194/tc-13-2051-2019
- Study of changes of vessel’s speed in ice conditions on the Northern Sea Route E. Ol’khovik 10.1088/1755-1315/378/1/012096
- 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
- Estimates of spectral wave attenuation in Antarctic sea ice, using model/data inversion W. Rogers et al. 10.1016/j.coldregions.2020.103198
- 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. 10.3390/rs11232835
- Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index Z. Wang et al. 10.1016/j.scitotenv.2021.145807
- Remote Sensing of Sea Ice Thickness and Salinity With 0.5–2 GHz Microwave Radiometry K. Jezek et al. 10.1109/TGRS.2019.2922163
- Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model L. Mu et al. 10.1029/2019MS001937
- Overview of the MOSAiC expedition: Snow and sea ice M. Nicolaus et al. 10.1525/elementa.2021.000046
- Polar Ocean Observations: A Critical Gap in the Observing System and Its Effect on Environmental Predictions From Hours to a Season G. Smith et al. 10.3389/fmars.2019.00429
2 citations as recorded by crossref.
- Gaps Analysis and Requirements Specification for the Evolution of Copernicus System for Polar Regions Monitoring: Addressing the Challenges in the Horizon 2020–2030 E. Lancheros et al. 10.3390/rs10071098
- Selection of the Key Earth Observation Sensors and Platforms Focusing on Applications for Polar Regions in the Scope of Copernicus System 2020–2030 E. Lancheros et al. 10.3390/rs11020175
Latest update: 31 Mar 2023
Short summary
Sea ice thickness is important for representing atmosphere–ocean interactions in climate models. A validated satellite sea ice thickness measurement algorithm is transferred to a new sensor. The results offer a better temporal and spatial coverage of satellite measurements in the polar regions. Here we describe the calibration procedure between the two sensors, taking into account their technical differences. In addition a new filter for interference from artificial radio sources is implemented.
Sea ice thickness is important for representing atmosphere–ocean interactions in climate models....
