Articles | Volume 17, issue 1
https://doi.org/10.5194/amt-17-145-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-17-145-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Jan 2024
Research article |
| 11 Jan 2024
| 11 Jan 2024
Geostationary Environment Monitoring Spectrometer (GEMS) polarization characteristics and correction algorithm
Haklim Choi
Department of Astronomy and Atmospheric Sciences, Kyungpook National University, Daegu, Republic of Korea
Now at: Kyungpook Institute of Oceanography, Kyungpook National University, Daegu, Republic of Korea
Xiong Liu
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA
Division of Earth Environmental System Science, Major of Spatial Information Engineering, Pukyong National University, Busan, Republic of Korea
Heesung Chong
Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea
Now at: Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA
Jhoon Kim
Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea
Myung Hwan Ahn
Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul, Korea
Dai Ho Ko
Satellite Payload Development Division, Korea Aerospace Research Institute, Daejeon, Republic of Korea
Dong-Won Lee
National Institute of Environmental Research, Environmental Satellite Center, Incheon, Republic of Korea
Kyung-Jung Moon
National Institute of Environmental Research, Environmental Satellite Center, Incheon, Republic of Korea
Kwang-Mog Lee
CORRESPONDING AUTHOR
Department of Astronomy and Atmospheric Sciences, Kyungpook National University, Daegu, Republic of Korea
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Cited
12 citations as recorded by crossref.
- Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS S. Seo et al. https://doi.org/10.5194/amt-17-6163-2024
- First evaluation of the GEMS formaldehyde product against TROPOMI and ground-based column measurements during the in-orbit test period G. Lee et al. https://doi.org/10.5194/acp-24-4733-2024
- Assessing Formaldehyde Exposure in Iron and Steel Industrial Zones Using Tropospheric Measurements and Geospatial Applications H. Akcin et al. https://doi.org/10.1007/s11270-026-09311-9
- Improved mean field estimates from the Geostationary Environment Monitoring Spectrometer (GEMS) Level-3 aerosol optical depth (L3 AOD) product: using spatiotemporal variability S. Kim et al. https://doi.org/10.5194/amt-17-5221-2024
- Satellite-driven prediction of fine particulate matter (PM2.5) concentrations: machine learning and explainable artificial intelligence T. Nguyen & T. Trinh https://doi.org/10.1088/2631-8695/ae7028
- Sentinel Data for Monitoring of Pollutant Emissions by Maritime Transport—A Literature Review T. Batista et al. https://doi.org/10.3390/rs17132202
- Urban PM2.5 concentration monitoring: A review of recent advances in ground-based, satellite, model, and machine learning integration S. Lolli https://doi.org/10.1016/j.uclim.2025.102566
- Total column water vapor retrievals from the Geostationary Environment Monitoring Spectrometer (GEMS) H. Cha et al. https://doi.org/10.1016/j.rse.2026.115401
- Characterizing diurnal variability in power plant carbon emissions in Asia: A top-down estimation approach constrained by geostationary NO2 and OCO-3 CO2 observations T. Xu et al. https://doi.org/10.1016/j.rse.2026.115261
- Stokes physical constraint method for improving polarization imaging-based vision task S. Song & T. Mu https://doi.org/10.1016/j.optlastec.2025.113408
- Comparing rural and urban NO2 column densities from GEMS across East Asia: Diurnal and seasonal differences L. Gagnon et al. https://doi.org/10.1016/j.atmosenv.2026.122176
- Deep Learning Style Transfer for Enhanced Smoke Plume Visibility: A Standardized False Color Composite (SFCC) in GEMS Satellite Imagery Y. Jeong et al. https://doi.org/10.3390/rs18030483
12 citations as recorded by crossref.
- Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS S. Seo et al. https://doi.org/10.5194/amt-17-6163-2024
- First evaluation of the GEMS formaldehyde product against TROPOMI and ground-based column measurements during the in-orbit test period G. Lee et al. https://doi.org/10.5194/acp-24-4733-2024
- Assessing Formaldehyde Exposure in Iron and Steel Industrial Zones Using Tropospheric Measurements and Geospatial Applications H. Akcin et al. https://doi.org/10.1007/s11270-026-09311-9
- Improved mean field estimates from the Geostationary Environment Monitoring Spectrometer (GEMS) Level-3 aerosol optical depth (L3 AOD) product: using spatiotemporal variability S. Kim et al. https://doi.org/10.5194/amt-17-5221-2024
- Satellite-driven prediction of fine particulate matter (PM2.5) concentrations: machine learning and explainable artificial intelligence T. Nguyen & T. Trinh https://doi.org/10.1088/2631-8695/ae7028
- Sentinel Data for Monitoring of Pollutant Emissions by Maritime Transport—A Literature Review T. Batista et al. https://doi.org/10.3390/rs17132202
- Urban PM2.5 concentration monitoring: A review of recent advances in ground-based, satellite, model, and machine learning integration S. Lolli https://doi.org/10.1016/j.uclim.2025.102566
- Total column water vapor retrievals from the Geostationary Environment Monitoring Spectrometer (GEMS) H. Cha et al. https://doi.org/10.1016/j.rse.2026.115401
- Characterizing diurnal variability in power plant carbon emissions in Asia: A top-down estimation approach constrained by geostationary NO2 and OCO-3 CO2 observations T. Xu et al. https://doi.org/10.1016/j.rse.2026.115261
- Stokes physical constraint method for improving polarization imaging-based vision task S. Song & T. Mu https://doi.org/10.1016/j.optlastec.2025.113408
- Comparing rural and urban NO2 column densities from GEMS across East Asia: Diurnal and seasonal differences L. Gagnon et al. https://doi.org/10.1016/j.atmosenv.2026.122176
- Deep Learning Style Transfer for Enhanced Smoke Plume Visibility: A Standardized False Color Composite (SFCC) in GEMS Satellite Imagery Y. Jeong et al. https://doi.org/10.3390/rs18030483
Saved (final revised paper)
Latest update: 03 Jul 2026
Short summary
GEMS is the first geostationary satellite to measure the UV--Vis region, and this paper reports the polarization characteristics of GEMS and an algorithm. We develop a polarization correction algorithm optimized for GEMS based on a look-up-table approach that simultaneously considers the polarization of incoming light and polarization sensitivity characteristics of the instrument. Pre-launch polarization error was adjusted close to zero across the spectral range after polarization correction.
GEMS is the first geostationary satellite to measure the UV--Vis region, and this paper reports...
Special issue