Articles | Volume 17, issue 14
https://doi.org/10.5194/amt-17-4369-2024
© 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-17-4369-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jul 2024
Research article |
| 23 Jul 2024
| 23 Jul 2024
First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia
Yeseul Cho
Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea
Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea
Sujung Go
Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Mijin Kim
Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Goddard Earth Sciences Technology and Research (GESTAR) II, Morgan State University, Baltimore, MD 21251, USA
Seoyoung Lee
Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Minseok Kim
Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea
Heesung Chong
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA 02138, USA
Won-Jin Lee
National Institute of Environmental Research, Incheon, Republic of Korea
Dong-Won Lee
National Institute of Environmental Research, Incheon, Republic of Korea
Omar Torres
Climate and Radiation Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Sang Seo Park
Department of Civil, Urban, Earth and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
Viewed
Total article views: 3,767 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Oct 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,817 | 837 | 113 | 3,767 | 267 | 130 | 181 |
- HTML: 2,817
- PDF: 837
- XML: 113
- Total: 3,767
- Supplement: 267
- BibTeX: 130
- EndNote: 181
Total article views: 2,596 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Jul 2024)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,075 | 454 | 67 | 2,596 | 126 | 89 | 132 |
- HTML: 2,075
- PDF: 454
- XML: 67
- Total: 2,596
- Supplement: 126
- BibTeX: 89
- EndNote: 132
Total article views: 1,171 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Oct 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 742 | 383 | 46 | 1,171 | 141 | 41 | 49 |
- HTML: 742
- PDF: 383
- XML: 46
- Total: 1,171
- Supplement: 141
- BibTeX: 41
- EndNote: 49
Viewed (geographical distribution)
Total article views: 3,767 (including HTML, PDF, and XML)
Thereof 3,691 with geography defined
and 76 with unknown origin.
Total article views: 2,596 (including HTML, PDF, and XML)
Thereof 2,525 with geography defined
and 71 with unknown origin.
Total article views: 1,171 (including HTML, PDF, and XML)
Thereof 1,166 with geography defined
and 5 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
18 citations as recorded by crossref.
- Retrieval of pseudo-BRDF-adjusted surface reflectance at 440 nm from the Geostationary Environmental Monitoring Spectrometer (GEMS) S. Sim et al. https://doi.org/10.5194/amt-17-5601-2024
- Estimating hourly ground-level aerosols using Geostationary Environment Monitoring Spectrometer aerosol optical depth: a machine learning approach S. O et al. https://doi.org/10.5194/amt-18-1471-2025
- Hybrid physics–AI aerosol property retrieval algorithm for AMI/GK-2A with a deep learning radiative transfer emulator M. Kim et al. https://doi.org/10.1016/j.jag.2026.105393
- Aerosol optical depth retrieval from Geostationary Environment Monitoring Spectrometer (GEMS): Advancing the first hyperspectral geostationary air quality mission using deep learning H. Choi et al. https://doi.org/10.1016/j.scitotenv.2025.180535
- Deriving 24-h seamless near-surface NO2 in China through integration of GEMS observations, model simulations, and machine learning Z. Zhong et al. https://doi.org/10.1016/j.atmosenv.2026.122204
- A new aerosol type identification algorithm for the Geostationary Environment Monitoring Spectrometer (GEMS) instrument F. Wang et al. https://doi.org/10.1080/20964471.2026.2654984
- 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
- 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
- Machine learning based aerosol classification over South and East Asia using MODIS top of atmosphere reflectance and AERONET-derived clusters: A remote sensing approach M. Awais & L. Wang https://doi.org/10.1016/j.envres.2025.123315
- Aerosol layer height (ALH) retrievals from oxygen absorption bands: intercomparison and validation among different satellite platforms, GEMS, EPIC, and TROPOMI H. Kim et al. https://doi.org/10.5194/amt-18-327-2025
- Coupled Dynamics of Aerosols and Greenhouse Gases at the Socheongcho Ocean Research Station During High-Concentration Episodes S. Ahn et al. https://doi.org/10.3390/rs18050816
- Integration of GEMS and MODIS AOD for enhanced long-term aerosol monitoring over East Asia H. Jeon et al. https://doi.org/10.1080/01431161.2026.2632162
- Pioneering Air Quality Monitoring over East and Southeast Asia with the Geostationary Environment Monitoring Spectrometer (GEMS) K. Lee et al. https://doi.org/10.7780/kjrs.2024.40.5.2.5
- Error characterization and analysis of aerosol products from the geostationary environment monitoring spectrometer (GEMS) G. Du et al. https://doi.org/10.1016/j.atmosres.2025.108682
- Enhancing Radiative Consistency in Retrieval Chains: The Role of NO₂ Absorption in Surface Reflectance Estimation S. Sim et al. https://doi.org/10.1109/LGRS.2026.3711402
- Geostationary Environment Monitoring Spectrometer (GEMS): Long-Term Radiometric Accuracy and Spectral Stability From 4.5 Years of On-Orbit Solar Irradiance Observations J. Bak et al. https://doi.org/10.1109/TGRS.2025.3627802
- Utilisation of WRF-HYSPLIT modelling approach and GEMS to identify PM2.5 sources in Central Kalimantan – study case: 2023 forest fire A. Nurlatifah et al. https://doi.org/10.1071/ES24006
- Impact of Assimilating GEMS Aerosol Optical Depth on Asian Dust Storm Prediction: Comparative Assessment with MODIS Observation E. Lee et al. https://doi.org/10.1007/s13143-025-00407-6
18 citations as recorded by crossref.
- Retrieval of pseudo-BRDF-adjusted surface reflectance at 440 nm from the Geostationary Environmental Monitoring Spectrometer (GEMS) S. Sim et al. https://doi.org/10.5194/amt-17-5601-2024
- Estimating hourly ground-level aerosols using Geostationary Environment Monitoring Spectrometer aerosol optical depth: a machine learning approach S. O et al. https://doi.org/10.5194/amt-18-1471-2025
- Hybrid physics–AI aerosol property retrieval algorithm for AMI/GK-2A with a deep learning radiative transfer emulator M. Kim et al. https://doi.org/10.1016/j.jag.2026.105393
- Aerosol optical depth retrieval from Geostationary Environment Monitoring Spectrometer (GEMS): Advancing the first hyperspectral geostationary air quality mission using deep learning H. Choi et al. https://doi.org/10.1016/j.scitotenv.2025.180535
- Deriving 24-h seamless near-surface NO2 in China through integration of GEMS observations, model simulations, and machine learning Z. Zhong et al. https://doi.org/10.1016/j.atmosenv.2026.122204
- A new aerosol type identification algorithm for the Geostationary Environment Monitoring Spectrometer (GEMS) instrument F. Wang et al. https://doi.org/10.1080/20964471.2026.2654984
- 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
- 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
- Machine learning based aerosol classification over South and East Asia using MODIS top of atmosphere reflectance and AERONET-derived clusters: A remote sensing approach M. Awais & L. Wang https://doi.org/10.1016/j.envres.2025.123315
- Aerosol layer height (ALH) retrievals from oxygen absorption bands: intercomparison and validation among different satellite platforms, GEMS, EPIC, and TROPOMI H. Kim et al. https://doi.org/10.5194/amt-18-327-2025
- Coupled Dynamics of Aerosols and Greenhouse Gases at the Socheongcho Ocean Research Station During High-Concentration Episodes S. Ahn et al. https://doi.org/10.3390/rs18050816
- Integration of GEMS and MODIS AOD for enhanced long-term aerosol monitoring over East Asia H. Jeon et al. https://doi.org/10.1080/01431161.2026.2632162
- Pioneering Air Quality Monitoring over East and Southeast Asia with the Geostationary Environment Monitoring Spectrometer (GEMS) K. Lee et al. https://doi.org/10.7780/kjrs.2024.40.5.2.5
- Error characterization and analysis of aerosol products from the geostationary environment monitoring spectrometer (GEMS) G. Du et al. https://doi.org/10.1016/j.atmosres.2025.108682
- Enhancing Radiative Consistency in Retrieval Chains: The Role of NO₂ Absorption in Surface Reflectance Estimation S. Sim et al. https://doi.org/10.1109/LGRS.2026.3711402
- Geostationary Environment Monitoring Spectrometer (GEMS): Long-Term Radiometric Accuracy and Spectral Stability From 4.5 Years of On-Orbit Solar Irradiance Observations J. Bak et al. https://doi.org/10.1109/TGRS.2025.3627802
- Utilisation of WRF-HYSPLIT modelling approach and GEMS to identify PM2.5 sources in Central Kalimantan – study case: 2023 forest fire A. Nurlatifah et al. https://doi.org/10.1071/ES24006
- Impact of Assimilating GEMS Aerosol Optical Depth on Asian Dust Storm Prediction: Comparative Assessment with MODIS Observation E. Lee et al. https://doi.org/10.1007/s13143-025-00407-6
Saved (final revised paper)
Latest update: 02 Aug 2026
Short summary
Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world’s first geostationary-Earth-orbit (GEO) satellite instrument designed for atmospheric environmental monitoring. This study describes improvements made to the GEMS aerosol retrieval algorithm (AERAOD) and presents its validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia.
Aerosol optical properties have been provided by the Geostationary Environment Monitoring...
Special issue