89 citations as recorded by crossref.

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6. Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China D. Li et al. 10.3390/atmos10040215
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26. 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
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29. Meteorology influencing springtime air quality, pollution transport, and visibility in Korea D. Peterson et al. 10.1525/elementa.395
30. New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS) J. Kim et al. 10.1175/BAMS-D-18-0013.1
31. 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
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33. Validating Aerosol Optical Depth Estimation Methods Using the National Institute of Environmental Research Operational Numerical Forecast Model H. Kim et al. 10.3390/app12052556
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35. 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
36. 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
37. 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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39. Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations S. Ahn et al. 10.3390/rs13061096
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41. 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
42. 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
43. Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning C. Lee et al. 10.3390/rs13112121
44. 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
45. 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
46. First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia Y. Cho et al. 10.5194/amt-17-4369-2024
47. 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
48. 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
49. Aerosol Layer Height Retrieval Over Ocean From the Advanced Himawari Imager Using Spectral Reflectance Sensitivity H. Lim et al. 10.1109/LGRS.2023.3236299
50. 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
51. 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
52. 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. 10.5194/amt-15-1075-2022
53. 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
54. 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
55. Space‐Borne Estimation of Volcanic Sulfate Aerosol Lifetime C. Li & R. Cohen 10.1029/2020JD033883
56. 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
57. A high-precision aerosol retrieval algorithm for FY-3D MERSI-II images Q. Wang et al. 10.1016/j.envint.2023.107841
58. The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2 A. Sinyuk et al. 10.5194/amt-13-3375-2020
59. 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
60. Retrieval and Uncertainty Analysis of Land Surface Reflectance Using a Geostationary Ocean Color Imager K. Lee et al. 10.3390/rs14020360
61. 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
62. 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. 10.5194/amt-17-4317-2024
63. 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
64. 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
65. 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
66. 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
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68. Seasonal Dependence of Aerosol Data Assimilation and Forecasting Using Satellite and Ground-Based Observations S. Lee et al. 10.3390/rs14092123
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71. Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data L. Chen et al. 10.3390/rs13122376
72. Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data Y. Lops et al. 10.1029/2021GL093096
73. 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
74. 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
75. 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
76. 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
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79. Modeling Asian Dust Storms Using WRF‐Chem During the DRAGON‐Asia Field Campaign in April 2012 K. Kim et al. 10.1029/2021JD034793
80. Himawari-8-Derived Aerosol Optical Depth Using an Improved Time Series Algorithm Over Eastern China D. Li et al. 10.3390/rs12060978
81. Preliminary Retrieval and Validation of Aerosol Optical Depths from FY-4B Advanced Geostationary Radiation Imager Images D. Zhou et al. 10.3390/rs16020372
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87. A global analysis of diurnal variability in dust and dust mixture using CATS observations Y. Yu et al. 10.5194/acp-21-1427-2021
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