Articles | Volume 17, issue 14
https://doi.org/10.5194/amt-17-4317-2024
© 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-17-4317-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jul 2024
Research article |
| 19 Jul 2024
| 19 Jul 2024
Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods
Minseok Kim
Department of Atmospheric Sciences, Yonsei University, Seoul, 03722, South Korea
Department of Atmospheric Sciences, Yonsei University, Seoul, 03722, South Korea
Hyunkwang Lim
National Institute for Environmental Studies (NIES), Ibaraki, 305-8506, Japan
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 (GSFC), Greenbelt, MD 20771, USA
Yeseul Cho
Department of Atmospheric Sciences, Yonsei University, Seoul, 03722, South Korea
Yun-Gon Lee
Department of Atmospheric Sciences, Chungnam National University, Daejeon, 34134, South 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 (GSFC), Greenbelt, MD 20771, USA
Kyunghwa Lee
National Institute of Environmental Research (NIER), Incheon, 22689, South Korea
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Cited
16 citations as recorded by crossref.
- 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
- Aerosol and cloud retrieval algorithm of TANSO-3/GOSAT-GW: theoretical basis and validations during the 2024 ASIA-AQ campaign using the TROPOMI observations as a testbed H. Lim et al. https://doi.org/10.1186/s40645-026-00820-z
- Integrating Traditional and Artificial Intelligence Methods in Dust Aerosol Research: A Comprehensive Review T. Sha et al. https://doi.org/10.1007/s40726-026-00402-z
- 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
- 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
- Improved Land AOD Retrieval of GK-2A/AMI via Background Surface Reflectance Based on sRTLS-BRDF Inversion D. Jung et al. https://doi.org/10.3390/rs18071018
- Daytime-like nighttime aerosol optical depth detection for geostationary environment monitoring spectrometer Y. Kim et al. https://doi.org/10.1016/j.atmosres.2025.108290
- Synergistic Fusion of Aerosol Optical Depth over India from multi-sensor satellite retrievals with ground-based measurements S. Gouda et al. https://doi.org/10.5194/amt-19-3687-2026
- 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
- Near-Time Measurement of Aerosol Optical Depth and Black Carbon Concentration at Socheongcho Ocean Research Station: Aerosol Episode Case Analysis S. Ahn et al. https://doi.org/10.3390/rs17030382
- Geometric Accuracy Analysis of Geostationary Environment Monitoring Spectrometer Images by Spatiotemporal and Spectral Collocation S. Choi et al. https://doi.org/10.1109/JSTARS.2026.3673604
- 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
- Quantitative reconstruction of long-term spatiotemporal patterns of high-resolution ground-level NO2 concentrations in mainland China using fusion techniques and a machine learning framework Z. Li et al. https://doi.org/10.1016/j.envint.2025.109672
- A decadal, hourly high-resolution satellite dataset of aerosol optical properties over East Asia J. Lee et al. https://doi.org/10.5194/essd-17-5761-2025
- Long-Term Satellite-Derived Chlorophyll-<i>a</i> Trends and Their Linkage to Ocean Warming Around the Ieodo Ocean Research Station N. Cha et al. https://doi.org/10.5467/JKESS.2025.46.5.482
- Distribution Characteristics of VOCs over the Metropolitan Area Based on Aircraft Observations in March 2020 J. Kim et al. https://doi.org/10.5572/KOSAE.2026.42.1.015
16 citations as recorded by crossref.
- 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
- Aerosol and cloud retrieval algorithm of TANSO-3/GOSAT-GW: theoretical basis and validations during the 2024 ASIA-AQ campaign using the TROPOMI observations as a testbed H. Lim et al. https://doi.org/10.1186/s40645-026-00820-z
- Integrating Traditional and Artificial Intelligence Methods in Dust Aerosol Research: A Comprehensive Review T. Sha et al. https://doi.org/10.1007/s40726-026-00402-z
- 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
- 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
- Improved Land AOD Retrieval of GK-2A/AMI via Background Surface Reflectance Based on sRTLS-BRDF Inversion D. Jung et al. https://doi.org/10.3390/rs18071018
- Daytime-like nighttime aerosol optical depth detection for geostationary environment monitoring spectrometer Y. Kim et al. https://doi.org/10.1016/j.atmosres.2025.108290
- Synergistic Fusion of Aerosol Optical Depth over India from multi-sensor satellite retrievals with ground-based measurements S. Gouda et al. https://doi.org/10.5194/amt-19-3687-2026
- 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
- Near-Time Measurement of Aerosol Optical Depth and Black Carbon Concentration at Socheongcho Ocean Research Station: Aerosol Episode Case Analysis S. Ahn et al. https://doi.org/10.3390/rs17030382
- Geometric Accuracy Analysis of Geostationary Environment Monitoring Spectrometer Images by Spatiotemporal and Spectral Collocation S. Choi et al. https://doi.org/10.1109/JSTARS.2026.3673604
- 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
- Quantitative reconstruction of long-term spatiotemporal patterns of high-resolution ground-level NO2 concentrations in mainland China using fusion techniques and a machine learning framework Z. Li et al. https://doi.org/10.1016/j.envint.2025.109672
- A decadal, hourly high-resolution satellite dataset of aerosol optical properties over East Asia J. Lee et al. https://doi.org/10.5194/essd-17-5761-2025
- Long-Term Satellite-Derived Chlorophyll-<i>a</i> Trends and Their Linkage to Ocean Warming Around the Ieodo Ocean Research Station N. Cha et al. https://doi.org/10.5467/JKESS.2025.46.5.482
- Distribution Characteristics of VOCs over the Metropolitan Area Based on Aircraft Observations in March 2020 J. Kim et al. https://doi.org/10.5572/KOSAE.2026.42.1.015
Saved (final revised paper)
Latest update: 17 Jul 2026
Short summary
Information about aerosol loading in the atmosphere can be collected from various satellite instruments. Aerosol products from various satellite instruments have their own error characteristics. This study statistically merged aerosol optical depth datasets from multiple instruments aboard geostationary satellites considering uncertainties. Also, a deep neural network technique is adopted for aerosol data merging.
Information about aerosol loading in the atmosphere can be collected from various satellite...
Special issue