Articles | Volume 17, issue 2
https://doi.org/10.5194/os-17-455-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-17-455-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
11 Mar 2021
Technical note |
| 11 Mar 2021
| 11 Mar 2021
Technical note: A sensitivity analysis from 1 to 40 GHz for observing the Arctic Ocean with the Copernicus Imaging Microwave Radiometer
Lise Kilic
CORRESPONDING AUTHOR
Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, Paris, France
Catherine Prigent
Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, Paris, France
Estellus, Paris, France
Carlos Jimenez
Estellus, Paris, France
Sorbonne Université, Observatoire de Paris, Université PSL, CNRS, LERMA, Paris, France
Craig Donlon
European Space Agency, Noordwijk, the Netherlands
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Cited
17 citations as recorded by crossref.
- Development of the SURface Fast Emissivity Model for Ocean (SURFEM‐Ocean) Based on the PARMIO Radiative Transfer Model L. Kilic et al. https://doi.org/10.1029/2022EA002785
- Brief communication: Identification of tundra topsoil frozen/thawed state from SMAP and GCOM-W1 radiometer measurements using the spectral gradient method K. Muzalevskiy et al. https://doi.org/10.5194/tc-17-4155-2023
- 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
- Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data J. Rückert et al. https://doi.org/10.1029/2023EA003177
- Microwave satellite remote sensing for a sustainable sea M. Migliaccio et al. https://doi.org/10.1080/22797254.2022.2126798
- High-Sensitivity Seawater Salinity Sensing With Cladding Etched Fiber Bragg Grating Technology J. Guo et al. https://doi.org/10.1109/JSEN.2023.3279324
- Development and Prospect of Satellite Remote Sensing Technology for Ocean Dynamic Environment Q. Zhang et al. https://doi.org/10.2514/1.A35710
- Calibration of Aperture Synthesis Radiometer Based on Deep Learning Y. Li et al. https://doi.org/10.1109/TGRS.2026.3653877
- Estimation of summer pan-Arctic ice draft from satellite passive microwave observations J. Kim et al. https://doi.org/10.1016/j.rse.2023.113662
- Improving the ocean skin temperature for microwave imagers at the European Centre for Medium‐Range Weather Forecasts T. Scanlon et al. https://doi.org/10.1002/qj.70087
- Sea Surface Temperature Retrievals Using K- and Ka-Bands With Weak Brightness Temperature Response Residual Neural Networks P. Mao et al. https://doi.org/10.1109/TGRS.2024.3460875
- Soil Moisture and Sea Surface Salinity Derived from Satellite-Borne Sensors J. Boutin et al. https://doi.org/10.1007/s10712-023-09798-5
- An Adaptive Discriminant Function Algorithm for Monitoring Soil Freeze–Thaw Dynamics Using Chinese FengYun-3 Series Satellites Z. Yang et al. https://doi.org/10.1109/TGRS.2026.3706786
- Comparative analysis of microwave indices for freeze/thaw state monitoring and adaptive thresholding implications Z. Yang et al. https://doi.org/10.1016/j.srs.2025.100239
- Retrieval of Ocean Surface and Atmospheric Parameters From K- and Ka-Band Data Using a Physics-Informed Coupled Neural Network P. Mao et al. https://doi.org/10.1109/TGRS.2026.3686673
- Ocean and Sea Ice Retrievals From an End‐To‐End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4–36.5 GHz Measurements C. Jiménez et al. https://doi.org/10.1029/2021JC017610
- Using 6.9 and 10.65 GHz From the AMSR2 and GMI Microwave Imagers in the ECMWF NWP System (IFS) T. Scanlon et al. https://doi.org/10.1109/TGRS.2025.3627448
17 citations as recorded by crossref.
- Development of the SURface Fast Emissivity Model for Ocean (SURFEM‐Ocean) Based on the PARMIO Radiative Transfer Model L. Kilic et al. https://doi.org/10.1029/2022EA002785
- Brief communication: Identification of tundra topsoil frozen/thawed state from SMAP and GCOM-W1 radiometer measurements using the spectral gradient method K. Muzalevskiy et al. https://doi.org/10.5194/tc-17-4155-2023
- 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
- Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data J. Rückert et al. https://doi.org/10.1029/2023EA003177
- Microwave satellite remote sensing for a sustainable sea M. Migliaccio et al. https://doi.org/10.1080/22797254.2022.2126798
- High-Sensitivity Seawater Salinity Sensing With Cladding Etched Fiber Bragg Grating Technology J. Guo et al. https://doi.org/10.1109/JSEN.2023.3279324
- Development and Prospect of Satellite Remote Sensing Technology for Ocean Dynamic Environment Q. Zhang et al. https://doi.org/10.2514/1.A35710
- Calibration of Aperture Synthesis Radiometer Based on Deep Learning Y. Li et al. https://doi.org/10.1109/TGRS.2026.3653877
- Estimation of summer pan-Arctic ice draft from satellite passive microwave observations J. Kim et al. https://doi.org/10.1016/j.rse.2023.113662
- Improving the ocean skin temperature for microwave imagers at the European Centre for Medium‐Range Weather Forecasts T. Scanlon et al. https://doi.org/10.1002/qj.70087
- Sea Surface Temperature Retrievals Using K- and Ka-Bands With Weak Brightness Temperature Response Residual Neural Networks P. Mao et al. https://doi.org/10.1109/TGRS.2024.3460875
- Soil Moisture and Sea Surface Salinity Derived from Satellite-Borne Sensors J. Boutin et al. https://doi.org/10.1007/s10712-023-09798-5
- An Adaptive Discriminant Function Algorithm for Monitoring Soil Freeze–Thaw Dynamics Using Chinese FengYun-3 Series Satellites Z. Yang et al. https://doi.org/10.1109/TGRS.2026.3706786
- Comparative analysis of microwave indices for freeze/thaw state monitoring and adaptive thresholding implications Z. Yang et al. https://doi.org/10.1016/j.srs.2025.100239
- Retrieval of Ocean Surface and Atmospheric Parameters From K- and Ka-Band Data Using a Physics-Informed Coupled Neural Network P. Mao et al. https://doi.org/10.1109/TGRS.2026.3686673
- Ocean and Sea Ice Retrievals From an End‐To‐End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4–36.5 GHz Measurements C. Jiménez et al. https://doi.org/10.1029/2021JC017610
- Using 6.9 and 10.65 GHz From the AMSR2 and GMI Microwave Imagers in the ECMWF NWP System (IFS) T. Scanlon et al. https://doi.org/10.1109/TGRS.2025.3627448
Saved (final revised paper)
Latest update: 26 Jul 2026
Short summary
The Copernicus Imaging Microwave Radiometer (CIMR) is one of the high-priority satellite missions of the Copernicus program within the European Space Agency. It is designed to respond to the European Union Arctic policy. Its channels, incidence angle, precisions, and spatial resolutions have been selected to observe the Arctic Ocean with the recommendations expressed by the user communities.
In this note, we present the sensitivity analysis that has led to the choice of the CIMR channels.
The Copernicus Imaging Microwave Radiometer (CIMR) is one of the high-priority satellite...
