86 citations as recorded by crossref.

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3. Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager M. Kim et al. 10.5194/amt-17-1913-2024
4. Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea S. Park et al. 10.5194/acp-19-1097-2019
5. Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China D. Li et al. 10.3390/atmos10040215
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9. Optical and chemical properties of long-range transported aerosols using satellite and ground-based observations over seoul, South Korea G. Choo et al. 10.1016/j.atmosenv.2020.118024
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20. AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA) M. Bilal et al. 10.3389/fenvs.2022.981522
21. Remote sensing of aerosol properties from multi-wavelength and multi-pixel information over the ocean C. Shi et al. 10.5194/acp-19-2461-2019
22. A Hybrid Approach to Estimate Spatially and Temporally Resolved Pm2.5 Distributions from Multi-Sourced Aod Data Q. Pu & Y. Eun-Hye 10.2139/ssrn.4094009
23. A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing A. Sayer et al. 10.5194/amt-13-373-2020
24. A Geometry-Discrete Minimum Reflectance Aerosol Retrieval Algorithm (GeoMRA) for Geostationary Meteorological Satellite Over Heterogeneous Surfaces T. Zhang et al. 10.1109/TGRS.2022.3200425
25. A gap-filling hybrid approach for hourly PM2.5 prediction at high spatial resolution from multi-sourced AOD data Q. Pu & E. Yoo 10.1016/j.envpol.2022.120419
26. Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM<sub>2.5</sub>): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations S. Zhai et al. 10.5194/acp-21-16775-2021
27. Meteorology influencing springtime air quality, pollution transport, and visibility in Korea D. Peterson et al. 10.1525/elementa.395
28. New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS) J. Kim et al. 10.1175/BAMS-D-18-0013.1
29. Assessment of long-range transboundary aerosols in Seoul, South Korea from Geostationary Ocean Color Imager (GOCI) and ground-based observations S. Lee et al. 10.1016/j.envpol.2020.115924
30. Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns H. Lim et al. 10.5194/amt-14-4575-2021
31. Validating Aerosol Optical Depth Estimation Methods Using the National Institute of Environmental Research Operational Numerical Forecast Model H. Kim et al. 10.3390/app12052556
32. High-resolution mapping of SO2 using airborne observations from the GeoTASO instrument during the KORUS-AQ field study: PCA-based vertical column retrievals H. Chong et al. 10.1016/j.rse.2020.111725
33. Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues K. Lee et al. 10.5194/gmd-13-1055-2020
34. Investigation of the relationship between the fine mode fraction and Ångström exponent: Cases in Korea J. Koo et al. 10.1016/j.atmosres.2020.105217
35. A review of machine learning for modeling air quality: Overlooked but important issues D. Tang et al. 10.1016/j.atmosres.2024.107261
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37. Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations S. Ahn et al. 10.3390/rs13061096
38. Satellite-based estimation of hourly PM2.5 levels during heavy winter pollution episodes in the Yangtze River Delta, China Q. She et al. 10.1016/j.chemosphere.2019.124678
39. An Ultra‐Broadband High Efficiency Polarization Beam Splitter for High Spectral Resolution Polarimetric Imaging in the Near Infrared H. Hsiao et al. 10.1002/advs.202201227
40. Optical Properties of Boundary Layer Aerosols From High Spectral Resolution Lidar Measurements in a Polluted Urban Environment (Seoul, Korea) S. Park et al. 10.1029/2023JD038523
41. Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning C. Lee et al. 10.3390/rs13112121
42. Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models J. Yeom et al. 10.1109/TGRS.2021.3107542
43. Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia S. Park et al. 10.1016/j.envpol.2022.119425
44. The implication of the air quality pattern in South Korea after the COVID-19 outbreak J. Koo et al. 10.1038/s41598-020-80429-4
45. Evaluation and uncertainty analysis of Himawari-8 hourly aerosol product version 3.1 and its influence on surface solar radiation before and during the COVID-19 outbreak C. Tang et al. 10.1016/j.scitotenv.2023.164456
46. Aerosol Layer Height Retrieval Over Ocean From the Advanced Himawari Imager Using Spectral Reflectance Sensitivity H. Lim et al. 10.1109/LGRS.2023.3236299
47. Understanding and improving model representation of aerosol optical properties for a Chinese haze event measured during KORUS-AQ P. Saide et al. 10.5194/acp-20-6455-2020
48. Advancing Exposure Assessment of PM2.5 Using Satellite Remote Sensing: A Review H. Lee 10.5572/ajae.2020.14.4.319
49. Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia Y. Kang et al. 10.1016/j.isprsjprs.2021.11.016
50. Continuous mapping of fine particulate matter (PM<sub>2.5</sub>) air quality in East Asia at daily 6  ×  6 km<sup>2</sup> resolution by application of a random forest algorithm to 2011–2019 GOCI geostationary satellite data D. Pendergrass et al. 10.5194/amt-15-1075-2022
51. Geostationary Satellite-Derived Ground-Level PM ₁₀ and PM _2.5 Concentrations Using Real-Time Machine Learning in Northeast Asia S. Park et al. 10.2139/ssrn.3983971
52. A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data M. Bilal et al. 10.3390/rs11111344
53. Space‐Borne Estimation of Volcanic Sulfate Aerosol Lifetime C. Li & R. Cohen 10.1029/2020JD033883
54. Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models S. Park et al. 10.1016/j.scitotenv.2020.136516
55. A high-precision aerosol retrieval algorithm for FY-3D MERSI-II images Q. Wang et al. 10.1016/j.envint.2023.107841
56. The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2 A. Sinyuk et al. 10.5194/amt-13-3375-2020
57. Potential role of urban forest in removing PM2.5: A case study in Seoul by deep learning with satellite data A. Lee et al. 10.1016/j.uclim.2021.100795
58. Retrieval and Uncertainty Analysis of Land Surface Reflectance Using a Geostationary Ocean Color Imager K. Lee et al. 10.3390/rs14020360
59. Temporal variation of surface reflectance and cloud fraction used to identify background aerosol retrieval information over East Asia S. Park et al. 10.1016/j.atmosenv.2023.119916
60. Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign M. Choi et al. 10.5194/amt-12-4619-2019
61. An Efficient and Accurate Model Coupled With Spatiotemporal Kalman Filter and Linear Mixed Effect for Hourly PM2.5 Mapping N. Liu et al. 10.1109/TGRS.2023.3324393
62. Ground-based retrievals of aerosol column absorption in the UV spectral region and their implications for GEMS measurements S. Go et al. 10.1016/j.rse.2020.111759
63. Synergistic Use of Hyperspectral UV-Visible OMI and Broadband Meteorological Imager MODIS Data for a Merged Aerosol Product S. Go et al. 10.3390/rs12233987
64. Airborne observations during KORUS-AQ show that aerosol optical depths are more spatially self-consistent than aerosol intensive properties S. LeBlanc et al. 10.5194/acp-22-11275-2022
65. Seasonal Dependence of Aerosol Data Assimilation and Forecasting Using Satellite and Ground-Based Observations S. Lee et al. 10.3390/rs14092123
66. AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, Validation and Merged Products H. Lim et al. 10.3390/rs10050699
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68. Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data L. Chen et al. 10.3390/rs13122376
69. Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data Y. Lops et al. 10.1029/2021GL093096
70. Aerosol profiling using radiometric and polarimetric spectral measurements in the O2 near infrared bands: Estimation of information content and measurement uncertainties M. Choi et al. 10.1016/j.rse.2020.112179
71. Aerosol data assimilation and forecast using Geostationary Ocean Color Imager aerosol optical depth and in-situ observations during the KORUS-AQ observing period G. Kim et al. 10.1080/15481603.2021.1972714
72. 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. 10.5194/amt-16-3039-2023
73. Comparison of Aerosol Properties in the Korean Peninsula Between AERONET Version 2 and 3 Data Set J. Lee et al. 10.1007/s13143-020-00221-2
74. Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning With Satellite AOD S. Lee et al. 10.1029/2021GL096066
75. The Impact of the Direct Effect of Aerosols on Meteorology and Air Quality Using Aerosol Optical Depth Assimilation During the KORUS‐AQ Campaign J. Jung et al. 10.1029/2019JD030641
76. Modeling Asian Dust Storms Using WRF‐Chem During the DRAGON‐Asia Field Campaign in April 2012 K. Kim et al. 10.1029/2021JD034793
77. Himawari-8-Derived Aerosol Optical Depth Using an Improved Time Series Algorithm Over Eastern China D. Li et al. 10.3390/rs12060978
78. Preliminary Retrieval and Validation of Aerosol Optical Depths from FY-4B Advanced Geostationary Radiation Imager Images D. Zhou et al. 10.3390/rs16020372
79. A High-Precision Aerosol Retrieval Algorithm (HiPARA) for Advanced Himawari Imager (AHI) data: Development and verification X. Su et al. 10.1016/j.rse.2020.112221
80. Generating Hourly Fine Seamless Aerosol Optical Depth Products by Fusing Multiple Satellite and Numerical Model Data B. Zou et al. 10.1109/TGRS.2024.3385397
81. Retrieval of aerosol optical properties from GOCI-II observations: Continuation of long-term geostationary aerosol monitoring over East Asia S. Lee et al. 10.1016/j.scitotenv.2023.166504
82. Aerosol model evaluation using two geostationary satellites over East Asia in May 2016 D. Goto et al. 10.1016/j.atmosres.2018.10.016
83. Employing relaxed smoothness constraints on imaginary part of refractive index in AERONET aerosol retrieval algorithm A. Sinyuk et al. 10.5194/amt-15-4135-2022
84. A global analysis of diurnal variability in dust and dust mixture using CATS observations Y. Yu et al. 10.5194/acp-21-1427-2021
85. Role of emissions and meteorology in the recent PM2.5 changes in China and South Korea from 2015 to 2018 M. Bae et al. 10.1016/j.envpol.2020.116233
86. Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements over Asia M. Kim et al. 10.3390/rs10020162