Articles | Volume 14, issue 6
https://doi.org/10.5194/amt-14-4575-2021
© 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/amt-14-4575-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jun 2021
Research article |
| 21 Jun 2021
| 21 Jun 2021
Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns
Hyunkwang Lim
Department of Atmospheric Sciences, Yonsei University, Seoul 03722,
Republic of Korea
Sujung Go
Department of Atmospheric Sciences, Yonsei University, Seoul 03722,
Republic of Korea
current address: Joint Center for Earth Systems Technology, University
of Maryland, Baltimore County, Baltimore, MD 21250, USA
current address: NASA Goddard
Space Flight Center, Greenbelt, MD 20771, USA
Department of Atmospheric Sciences, Yonsei University, Seoul 03722,
Republic of Korea
Particulate Matter Research Institute, Samsung Advanced Institute of
Technology, Suwon 16678, Republic of Korea
Myungje Choi
Department of Atmospheric Sciences, Yonsei University, Seoul 03722,
Republic of Korea
current address: Joint Center for Earth Systems Technology, University
of Maryland, Baltimore County, Baltimore, MD 21250, USA
current address: NASA Goddard
Space Flight Center, Greenbelt, MD 20771, USA
Seoyoung Lee
Department of Atmospheric Sciences, Yonsei University, Seoul 03722,
Republic of Korea
Chang-Keun Song
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology, Ulsan 44919, Republic of Korea
Yasuko Kasai
National Institute of Information and Communications Technology, Tokyo
184-8759, Japan
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Cited
16 citations as recorded by crossref.
- Spatial–Temporal Fusion of 10-Min Aerosol Optical Depth Products with the GEO–LEO Satellite Joint Observations X. Xia et al. https://doi.org/10.3390/rs15082038
- 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 M. Kim et al. https://doi.org/10.5194/amt-17-4317-2024
- AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA) M. Bilal et al. https://doi.org/10.3389/fenvs.2022.981522
- Quasi‐Global Maps of Daily Aerosol Optical Depth From a Ring of Five Geostationary Meteorological Satellites Using AERUS‐GEO X. Ceamanos et al. https://doi.org/10.1029/2021JD034906
- Himawari-8 High Temporal Resolution AOD Products Recovery: Nested Bayesian Maximum Entropy Fusion Blending GEO With SSO Satellite Observations T. Zhang et al. https://doi.org/10.1109/TGRS.2023.3262785
- 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
- Aerosol Layer Height Retrieval Over Ocean From the Advanced Himawari Imager Using Spectral Reflectance Sensitivity H. Lim et al. https://doi.org/10.1109/LGRS.2023.3236299
- Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy J. Park et al. https://doi.org/10.5194/amt-16-3039-2023
- Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM2.5): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations S. Zhai et al. https://doi.org/10.5194/acp-21-16775-2021
- Fine particulate concentrations over East Asia derived from aerosols measured by the advanced Himawari Imager using machine learning Y. Cho et al. https://doi.org/10.1016/j.atmosres.2023.106787
- Continuous mapping of fine particulate matter (PM2.5) air quality in East Asia at daily 6 × 6 km2 resolution by application of a random forest algorithm to 2011–2019 GOCI geostationary satellite data D. Pendergrass et al. https://doi.org/10.5194/amt-15-1075-2022
- Comparison and evaluation of multiple satellite aerosol products over China in different scenarios under a unified criterion: Preparation for consistent and high-quality dataset construction H. Zhu et al. https://doi.org/10.1016/j.atmosres.2022.106374
- 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
- Coupling Hourly Land Surface Models With Spatiotemporal Variability Characteristics: A Study on FY-4B/AGRI Land Aerosol Retrieval Algorithm Y. Si et al. https://doi.org/10.1109/TGRS.2025.3591646
- Improved hourly estimate of aerosol optical thickness over Asian land by fusing geostationary satellites Fengyun-4B and Himawari-9 Y. Cheng et al. https://doi.org/10.1016/j.scitotenv.2024.171541
- 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
16 citations as recorded by crossref.
- Spatial–Temporal Fusion of 10-Min Aerosol Optical Depth Products with the GEO–LEO Satellite Joint Observations X. Xia et al. https://doi.org/10.3390/rs15082038
- 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 M. Kim et al. https://doi.org/10.5194/amt-17-4317-2024
- AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA) M. Bilal et al. https://doi.org/10.3389/fenvs.2022.981522
- Quasi‐Global Maps of Daily Aerosol Optical Depth From a Ring of Five Geostationary Meteorological Satellites Using AERUS‐GEO X. Ceamanos et al. https://doi.org/10.1029/2021JD034906
- Himawari-8 High Temporal Resolution AOD Products Recovery: Nested Bayesian Maximum Entropy Fusion Blending GEO With SSO Satellite Observations T. Zhang et al. https://doi.org/10.1109/TGRS.2023.3262785
- 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
- Aerosol Layer Height Retrieval Over Ocean From the Advanced Himawari Imager Using Spectral Reflectance Sensitivity H. Lim et al. https://doi.org/10.1109/LGRS.2023.3236299
- Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy J. Park et al. https://doi.org/10.5194/amt-16-3039-2023
- Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM2.5): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations S. Zhai et al. https://doi.org/10.5194/acp-21-16775-2021
- Fine particulate concentrations over East Asia derived from aerosols measured by the advanced Himawari Imager using machine learning Y. Cho et al. https://doi.org/10.1016/j.atmosres.2023.106787
- Continuous mapping of fine particulate matter (PM2.5) air quality in East Asia at daily 6 × 6 km2 resolution by application of a random forest algorithm to 2011–2019 GOCI geostationary satellite data D. Pendergrass et al. https://doi.org/10.5194/amt-15-1075-2022
- Comparison and evaluation of multiple satellite aerosol products over China in different scenarios under a unified criterion: Preparation for consistent and high-quality dataset construction H. Zhu et al. https://doi.org/10.1016/j.atmosres.2022.106374
- 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
- Coupling Hourly Land Surface Models With Spatiotemporal Variability Characteristics: A Study on FY-4B/AGRI Land Aerosol Retrieval Algorithm Y. Si et al. https://doi.org/10.1109/TGRS.2025.3591646
- Improved hourly estimate of aerosol optical thickness over Asian land by fusing geostationary satellites Fengyun-4B and Himawari-9 Y. Cheng et al. https://doi.org/10.1016/j.scitotenv.2024.171541
- 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
Saved (final revised paper)
Latest update: 04 Nov 2026
Short summary
Aerosol property observations by satellites from geostationary Earth orbit (GEO) in particular have advantages of frequent sampling better than 1 h in addition to broader spatial coverage. This study provides data fusion products of aerosol optical properties from four different algorithms for two different GEO satellites: GOCI and AHI. The fused aerosol products adopted ensemble-mean and maximum-likelihood estimation methods. The data fusion provides improved results with better accuracy.
Aerosol property observations by satellites from geostationary Earth orbit (GEO) in particular...
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