57 citations as recorded by crossref.

1. 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
2. Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China D. Li et al. 10.3390/atmos10040215
3. Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period S. Ha et al. 10.5194/acp-20-6015-2020
4. Diurnal variation of aerosol optical depth and PM<sub>2.5</sub> in South Korea: a synthesis from AERONET, satellite (GOCI), KORUS-AQ observation, and the WRF-Chem model E. Lennartson et al. 10.5194/acp-18-15125-2018
5. 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
6. The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future L. Remer et al. 10.3390/rs12182900
7. Analysis of long-range transboundary transport (LRTT) effect on Korean aerosol pollution during the KORUS-AQ campaign S. Lee et al. 10.1016/j.atmosenv.2019.02.020
8. Comparison of GOCI and Himawari-8 aerosol optical depth for deriving full-coverage hourly PM2.5 across the Yangtze River Delta D. Tang et al. 10.1016/j.atmosenv.2019.116973
9. Retrieval of Aerosol Optical Depth from the Himawari-8 Advanced Himawari Imager data: Application over Beijing in the summer of 2016 L. Wang et al. 10.1016/j.atmosenv.2020.117788
10. Quantifying the Impact of Synoptic Weather Systems on High PM 2.5 Episodes in the Seoul Metropolitan Area, Korea L. Chang et al. 10.1029/2020JD034085
11. Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia Y. Kang et al. 10.1016/j.envpol.2021.117711
12. 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
13. 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
14. 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
15. Meteorology influencing springtime air quality, pollution transport, and visibility in Korea D. Peterson et al. 10.1525/elementa.395
16. New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS) J. Kim et al. 10.1175/BAMS-D-18-0013.1
17. 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
18. 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
19. 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
20. 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
21. 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
22. Intercomparing the Aerosol Optical Depth Using the Geostationary Satellite Sensors (AHI, GOCI and MI) from Yonsei AErosol Retrieval (YAER) Algorithm H. Lim et al. 10.5467/JKESS.2018.39.2.119
23. Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations S. Ahn et al. 10.3390/rs13061096
24. 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
25. Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning C. Lee et al. 10.3390/rs13112121
26. 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
27. 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
28. 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
29. Advancing Exposure Assessment of PM2.5 Using Satellite Remote Sensing: A Review H. Lee 10.5572/ajae.2020.14.4.319
30. 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
31. 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
32. 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
33. Space‐Borne Estimation of Volcanic Sulfate Aerosol Lifetime C. Li & R. Cohen 10.1029/2020JD033883
34. 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
35. The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2 A. Sinyuk et al. 10.5194/amt-13-3375-2020
36. 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
37. Retrieval and Uncertainty Analysis of Land Surface Reflectance Using a Geostationary Ocean Color Imager K. Lee et al. 10.3390/rs14020360
38. 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
39. Impact of Aerosols From Urban and Shipping Emission Sources on Terrestrial Carbon Uptake and Evapotranspiration: A Case Study in East Asia M. Huang et al. 10.1029/2019JD030818
40. 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
41. 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
42. AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, Validation and Merged Products H. Lim et al. 10.3390/rs10050699
43. An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data H. Kim et al. 10.3390/rs14020389
44. Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data L. Chen et al. 10.3390/rs13122376
45. Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data Y. Lops et al. 10.1029/2021GL093096
46. 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
47. 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
48. 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
49. Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning With Satellite AOD S. Lee et al. 10.1029/2021GL096066
50. 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
51. Modeling Asian Dust Storms Using WRF‐Chem During the DRAGON‐Asia Field Campaign in April 2012 K. Kim et al. 10.1029/2021JD034793
52. Himawari-8-Derived Aerosol Optical Depth Using an Improved Time Series Algorithm Over Eastern China D. Li et al. 10.3390/rs12060978
53. 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
54. Aerosol model evaluation using two geostationary satellites over East Asia in May 2016 D. Goto et al. 10.1016/j.atmosres.2018.10.016
55. A global analysis of diurnal variability in dust and dust mixture using CATS observations Y. Yu et al. 10.5194/acp-21-1427-2021
56. 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
57. Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements over Asia M. Kim et al. 10.3390/rs10020162