465 citations as recorded by crossref.

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3. Climatological Characteristics and Aerosol Loading Trends from 2001 to 2020 Based on MODIS MAIAC Data for Tianjin, North China Plain Z. Wu et al. 10.3390/su14031072
4. High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China L. Li 10.3390/rs13122324
5. Effects of COVID-19 lockdowns on fine particulate matter concentrations M. Hammer et al. 10.1126/sciadv.abg7670
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8. Remote sensing estimation of surface PM2.5 concentrations using a deep learning model improved by data augmentation and a particle size constraint S. Yin et al. 10.1016/j.atmosenv.2022.119282
9. From lowland plains to the Altiplano: The impacts of regional transport of wildfire smoke on the air quality of Bolivian cities E. Mollinedo et al. 10.1016/j.atmosenv.2023.120137
10. Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020 Q. He et al. 10.1016/j.atmosres.2022.106481
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13. Improving the Estimation of PM2.5 Concentration in the North China Area by Introducing an Attention Mechanism into Random Forest L. Zhang et al. 10.3390/atmos15030384
14. Comparison of different methods of determining land surface reflectance for AOD retrieval Q. Wang et al. 10.1016/j.apr.2021.101143
15. Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment H. Bai et al. 10.3390/rs14122933
16. 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
17. Short-term effects of exposure to ambient PM1, PM2.5, and PM10 on ischemic and hemorrhagic stroke incidence in Shandong Province, China H. Wu et al. 10.1016/j.envres.2022.113350
18. Improved 1 km resolution PM<sub>2.5</sub> estimates across China using enhanced space–time extremely randomized trees J. Wei et al. 10.5194/acp-20-3273-2020
19. On the retrieval of aerosol optical depth over cryosphere using passive remote sensing L. Mei et al. 10.1016/j.rse.2020.111731
20. Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling L. Li et al. 10.1016/j.rse.2019.111584
21. Improving MODIS Aerosol Estimates Over Land With the Surface BRDF Reflectances Using the 3-D Discrete Cosine Transform and RossThick-LiSparse Models X. Tian et al. 10.1109/TGRS.2020.3048109
22. Shapley values reveal the drivers of soil organic carbon stock prediction A. Wadoux et al. 10.5194/soil-9-21-2023
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24. Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model T. Jiang et al. 10.1016/j.atmosres.2020.105146
25. Satellite-Derived 1-km-Resolution PM1 Concentrations from 2014 to 2018 across China J. Wei et al. 10.1021/acs.est.9b03258
26. Deterioration of air quality associated with the 2020 US wildfires M. Filonchyk et al. 10.1016/j.scitotenv.2022.154103
27. Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm T. Zhang et al. 10.1080/15481603.2022.2060596
28. Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States D. Goldberg et al. 10.1016/j.atmosenv.2018.11.049
29. Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions L. Liang et al. 10.1016/j.envpol.2023.121832
30. Ensemble averaging based assessment of spatiotemporal variations in ambient PM2.5 concentrations over Delhi, India, during 2010–2016 S. Mandal et al. 10.1016/j.atmosenv.2020.117309
31. The Relationship Between MAIAC Smoke Plume Heights and Surface PM M. Cheeseman et al. 10.1029/2020GL088949
32. Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach J. Wei et al. 10.1016/j.rse.2019.111221
33. Inferring iron-oxide species content in atmospheric mineral dust from DSCOVR EPIC observations S. Go et al. 10.5194/acp-22-1395-2022
34. 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
35. Accuracy assessment and climatology of MODIS aerosol optical properties over North Africa A. Merdji et al. 10.1007/s11356-022-22997-8
36. Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire X. Ye et al. 10.5194/acp-21-14427-2021
37. A Deep-Neural-Network-Based Aerosol Optical Depth (AOD) Retrieval from Landsat-8 Top of Atmosphere Data L. She et al. 10.3390/rs14061411
38. Robust Analysis of PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina W. Hernandez et al. 10.3390/s19214648
39. Impact of COVID-19 induced lockdown on land surface temperature, aerosol, and urban heat in Europe and North America B. Parida et al. 10.1016/j.scs.2021.103336
40. An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation N. Schutgens et al. 10.5194/acp-20-12431-2020
41. A deep learning-based imputation method for missing gaps in satellite aerosol products by fusing numerical model data N. Liu et al. 10.1016/j.atmosenv.2024.120440
42. First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI S. Li et al. 10.3390/rs11242990
43. Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire” P. Makkaroon et al. 10.1029/2022JD037298
44. Accuracy assessment of MODIS land aerosol optical thickness algorithms using AERONET measurements over North America H. Jethva et al. 10.5194/amt-12-4291-2019
45. Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8 T. Su et al. 10.1016/j.rse.2020.112093
46. Review: Strategies for using satellite-based products in modeling PM2.5 and short-term pollution episodes M. Sorek-Hamer et al. 10.1016/j.envint.2020.106057
47. Surface and satellite observations of air pollution in India during COVID-19 lockdown: Implication to air quality Y. Sathe et al. 10.1016/j.scs.2020.102688
48. Effects of Air Pollutants on Summer Precipitation in Different Regions of Beijing Y. Yang et al. 10.3390/atmos13010141
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50. Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. X. Ye et al. 10.3390/rs14236113
51. Comparative evaluation of backpropagation neural network and genetic algorithm-backpropagation neural network models for PM2.5 concentration prediction based on aerosol optical depth, meteorological factors, and air pollutants J. Gu et al. 10.1117/1.JRS.18.012006
52. Evaluation and comparison of VIIRS dark target and deep blue aerosol products over land Q. Wang et al. 10.1016/j.scitotenv.2023.161667
53. Using deep ensemble forest for high-resolution mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran H. Bagheri 10.1007/s10661-023-10951-1
54. Spatial downscaling of surface ozone concentration calculation from remotely sensed data based on mutual information X. Wang et al. 10.3389/fenvs.2022.925979
55. 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
56. Benefits of High Resolution PM2.5 Prediction using Satellite MAIAC AOD and Land Use Regression for Exposure Assessment: California Examples H. Lee 10.1021/acs.est.9b03799
57. Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018) M. Hammer et al. 10.1021/acs.est.0c01764
58. A physical knowledge-based machine learning method for near-real-time dust aerosol properties retrieval from the Himawari-8 satellite data J. Li et al. 10.1016/j.atmosenv.2022.119098
59. 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
60. Hourly Mapping of the Layer Height of Thick Smoke Plumes Over the Western U.S. in 2020 Severe Fire Season Z. Lu et al. 10.3389/frsen.2021.766628
61. Radiative interaction of atmosphere and surface: Write-up with elements of code S. Korkin & A. Lyapustin 10.1016/j.jqsrt.2023.108663
62. Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK K. Dimakopoulou et al. 10.3390/ijerph19095401
63. Mineral dust optical properties for remote sensing and global modeling: A review P. Castellanos et al. 10.1016/j.rse.2023.113982
64. AERONET Remotely Sensed Measurements and Retrievals of Biomass Burning Aerosol Optical Properties During the 2015 Indonesian Burning Season T. Eck et al. 10.1029/2018JD030182
65. A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5 L. Li 10.3390/rs12020264
66. Long short-term memory network model to estimate PM2.5 concentrations with missing-filled satellite data in Beijing S. Jia et al. 10.1007/s00477-022-02253-8
67. Instantaneous aerosol and surface retrieval using satellites in geostationary orbit (iAERUS-GEO) – estimation of 15 min aerosol optical depth from MSG/SEVIRI and evaluation with reference data X. Ceamanos et al. 10.5194/amt-16-2575-2023
68. Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products M. Román et al. 10.1016/j.rse.2023.113963
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70. Himawari-8/AHI Aerosol Optical Depth Detection Based on Machine Learning Algorithm Y. Chen et al. 10.3390/rs14132967
71. Spatio-temporal variations of aerosol optical depth over Ukraine under the Russia-Ukraine war D. Jiadan et al. 10.1016/j.atmosenv.2023.120114
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75. The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2 A. Sinyuk et al. 10.5194/amt-13-3375-2020
76. Merging regional and global aerosol optical depth records from major available satellite products L. Sogacheva et al. 10.5194/acp-20-2031-2020
77. A Erosol S  Characteristics, Sources, and Drive Factors Analysis  In Typical Megacities, Nw China z. zhang 10.2139/ssrn.4111394
78. Meteorological controls on daily variations of nighttime surface urban heat islands J. Lai et al. 10.1016/j.rse.2020.112198
79. Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth X. Jin et al. 10.5194/acp-19-295-2019
80. Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China X. Chen et al. 10.5194/essd-14-5233-2022
81. UV Reflectance of the Ocean from DSCOVR/EPIC: Comparisons with a Theoretical Model and Aura/OMI Observations A. Vasilkov et al. 10.1175/JTECH-D-18-0150.1
82. Recurring South Asian smog episodes: Call for regional cooperation and improved monitoring M. Khokhar et al. 10.1016/j.atmosenv.2022.119534
83. Multi-Criteria Assessment for City-Wide Rooftop Solar PV Deployment: A Case Study of Bandung, Indonesia A. Sakti et al. 10.3390/rs14122796
84. Himawari-8-Derived Aerosol Optical Depth Using an Improved Time Series Algorithm Over Eastern China D. Li et al. 10.3390/rs12060978
85. Evaluation and comparison of MODIS and VIIRS aerosol optical depth (AOD) products over regions in the Eastern Mediterranean and the Black Sea P. Ettehadi Osgouei et al. 10.1016/j.atmosenv.2021.118784
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87. City-Scale Aerosol Loading Changes in the Sichuan Basin from 2001 to 2020 as Revealed by MODIS 1 km Aerosol Product R. Wang & H. Cai 10.3390/atmos14121715
88. Limitations of cloud cover for optical remote sensing of agricultural areas across South America V. Prudente et al. 10.1016/j.rsase.2020.100414
89. 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
90. Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020 Q. He et al. 10.1016/j.jenvman.2023.118145
91. Estimation of PM2.5 Concentration across China Based on Multi-Source Remote Sensing Data and Machine Learning Methods Y. Yang et al. 10.3390/rs16030467
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153. Profiling of Dust and Urban Haze Mass Concentrations during the 2019 National Day Parade in Beijing by Polarization Raman Lidar Z. Wang et al. 10.3390/rs13163326
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