528 citations as recorded by crossref.

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3. A Sequence-to-Sequence Transformer Model for Satellite Retrieval of Aerosol Optical and Microphysical Parameters from Space L. Zhang et al. 10.3390/rs16244659
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5. High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China L. Li 10.3390/rs13122324
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9. Impact of Model Spatial Resolution on Global Geophysical Satellite-Derived Fine Particulate Matter D. Zhang et al. 10.1021/acsestair.4c00084
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12. 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
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14. 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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20. Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment H. Bai et al. 10.3390/rs14122933
21. 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
22. 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
23. Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees J. Wei et al. 10.5194/acp-20-3273-2020
24. On the retrieval of aerosol optical depth over cryosphere using passive remote sensing L. Mei et al. 10.1016/j.rse.2020.111731
25. Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling L. Li et al. 10.1016/j.rse.2019.111584
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27. 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
28. 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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30. 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
31. Satellite-Derived 1-km-Resolution PM1 Concentrations from 2014 to 2018 across China J. Wei et al. 10.1021/acs.est.9b03258
32. Deterioration of air quality associated with the 2020 US wildfires M. Filonchyk et al. 10.1016/j.scitotenv.2022.154103
33. Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm T. Zhang et al. 10.1080/15481603.2022.2060596
34. 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
35. Harmonized Landsat and Sentinel-2 Data with Google Earth Engine E. Berra et al. 10.3390/rs16152695
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37. 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
38. The Relationship Between MAIAC Smoke Plume Heights and Surface PM M. Cheeseman et al. 10.1029/2020GL088949
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40. Inferring iron-oxide species content in atmospheric mineral dust from DSCOVR EPIC observations S. Go et al. 10.5194/acp-22-1395-2022
41. 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
42. Identification of Aerosol Pollution Hotspots in Jiangsu Province of China Y. Wang et al. 10.3390/rs13142842
43. Accuracy assessment and climatology of MODIS aerosol optical properties over North Africa A. Merdji et al. 10.1007/s11356-022-22997-8
44. 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
45. A Deep-Neural-Network-Based Aerosol Optical Depth (AOD) Retrieval from Landsat-8 Top of Atmosphere Data L. She et al. 10.3390/rs14061411
46. Robust Analysis of PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina W. Hernandez et al. 10.3390/s19214648
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48. An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation N. Schutgens et al. 10.5194/acp-20-12431-2020
49. 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
50. First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI S. Li et al. 10.3390/rs11242990
51. 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
52. 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
53. 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
54. 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
55. 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
56. Effects of Air Pollutants on Summer Precipitation in Different Regions of Beijing Y. Yang et al. 10.3390/atmos13010141
57. Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI F. Yin et al. 10.5194/gmd-15-7933-2022
58. Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. X. Ye et al. 10.3390/rs14236113
59. 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
60. Evaluation and comparison of VIIRS dark target and deep blue aerosol products over land Q. Wang et al. 10.1016/j.scitotenv.2023.161667
61. 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
62. Spatial downscaling of surface ozone concentration calculation from remotely sensed data based on mutual information X. Wang et al. 10.3389/fenvs.2022.925979
63. 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
64. 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
65. Accuracy assessment on eight public PM2.5 concentration datasets across China Y. Di et al. 10.1016/j.atmosenv.2024.120799
66. Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018) M. Hammer et al. 10.1021/acs.est.0c01764
67. 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
68. 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
69. Big data analyses for determining the spatio-temporal trends of air pollution due to wildfires in California using Google Earth Engine A. Saim & M. Aly 10.1016/j.apr.2024.102226
70. 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
71. Radiative interaction of atmosphere and surface: Write-up with elements of code S. Korkin & A. Lyapustin 10.1016/j.jqsrt.2023.108663
72. 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
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74. 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
75. A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5 L. Li 10.3390/rs12020264
76. 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
77. Global aerosol retrieval over land from Landsat imagery integrating Transformer and Google Earth Engine J. Wei et al. 10.1016/j.rse.2024.114404
78. 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
79. Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products M. Román et al. 10.1016/j.rse.2023.113963
80. Matrix exponential in C/C++ version of vector radiative transfer code IPOL S. Korkin & A. Lyapustin 10.1016/j.jqsrt.2019.02.009
81. Himawari-8/AHI Aerosol Optical Depth Detection Based on Machine Learning Algorithm Y. Chen et al. 10.3390/rs14132967
82. Spatio-temporal variations of aerosol optical depth over Ukraine under the Russia-Ukraine war D. Jiadan et al. 10.1016/j.atmosenv.2023.120114
83. The Impact of the Control Measures during the COVID-19 Outbreak on Air Pollution in China C. Fan et al. 10.3390/rs12101613
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86. The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2 A. Sinyuk et al. 10.5194/amt-13-3375-2020
87. Merging regional and global aerosol optical depth records from major available satellite products L. Sogacheva et al. 10.5194/acp-20-2031-2020
88. A Erosol S  Characteristics, Sources, and Drive Factors Analysis  In Typical Megacities, Nw China z. zhang 10.2139/ssrn.4111394
89. Meteorological controls on daily variations of nighttime surface urban heat islands J. Lai et al. 10.1016/j.rse.2020.112198
90. 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
91. 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
92. 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
93. Recurring South Asian smog episodes: Call for regional cooperation and improved monitoring M. Khokhar et al. 10.1016/j.atmosenv.2022.119534
94. Multi-Criteria Assessment for City-Wide Rooftop Solar PV Deployment: A Case Study of Bandung, Indonesia A. Sakti et al. 10.3390/rs14122796
95. Himawari-8-Derived Aerosol Optical Depth Using an Improved Time Series Algorithm Over Eastern China D. Li et al. 10.3390/rs12060978
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98. Impact of satellite AOD data on top-down estimation of biomass burning particulate matter emission X. Ye et al. 10.1016/j.scitotenv.2022.161055
99. 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
100. A new constant scattering angle solar geometry definition for normalization of GOES-R ABI reflectance times series to support land surface phenology studies S. Gao et al. 10.1016/j.rse.2024.114407
101. Limitations of cloud cover for optical remote sensing of agricultural areas across South America V. Prudente et al. 10.1016/j.rsase.2020.100414
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103. 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
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119. Estimating PM2.5 in Southern California using satellite data: factors that affect model performance J. Stowell et al. 10.1088/1748-9326/ab9334
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128. Environmental hazards posed by mine dust, and monitoring method of mine dust pollution using remote sensing technologies: An overview H. Yu & I. Zahidi 10.1016/j.scitotenv.2022.161135
129. Columnar and surface urban aerosol in the Moscow megacity according to measurements and simulations with the COSMO-ART model N. Chubarova et al. 10.5194/acp-22-10443-2022
130. Accuracy and error cause analysis, and recommendations for usage of Himawari-8 aerosol products over Asia and Oceania L. Feng et al. 10.1016/j.scitotenv.2021.148958
131. Assessment of the impact of waste fires on air quality and atmospheric aerosol optical depth: A case study in Poland R. Oleniacz et al. 10.1016/j.egyr.2023.03.087
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135. A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations W. Wang et al. 10.3390/rs14040964
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137. Heat flux assumptions contribute to overestimation of wildfire smoke injection into the free troposphere L. Thapa et al. 10.1038/s43247-022-00563-x
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