70 citations as recorded by crossref.

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3. Improvement of snow/haze confusion data gaps in MODIS Dark Target aerosol retrievals in East China X. Zhang et al. 10.1016/j.atmosres.2020.105063
4. Tracking ambient PM2.5 build-up in Delhi national capital region during the dry season over 15 years using a high-resolution (1 km) satellite aerosol dataset S. Chowdhury et al. 10.1016/j.atmosenv.2019.02.029
5. 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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12. Mapping daily PM2.5 at 500 m resolution over Beijing with improved hazy day performance Y. Xie et al. 10.1016/j.scitotenv.2018.12.365
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27. Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals T. Zhang et al. 10.1016/j.rse.2018.06.030
28. Effect of COVID-19 Response Policy on Air Quality: A Study in South China Context X. Jin et al. 10.3390/atmos13050842
29. An example of aerosol pattern variability over bright surface using high resolution MODIS MAIAC: The eastern and western areas of the Dead Sea and environs L. Sever et al. 10.1016/j.atmosenv.2017.06.047
30. An Analysis of Factors Influencing the Relationship between Satellite-Derived AOD and Ground-Level PM10 R. Stirnberg et al. 10.3390/rs10091353
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32. Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria T. Etchie et al. 10.1016/j.scitotenv.2021.145187
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34. The development of Air Quality Indices using AOD-Retrieved Images during haze events in Peninsular Malaysia K. Ali Ahmed Ben Youssef et al. 10.1088/1755-1315/373/1/012027
35. Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. 10.1029/2019JD031380
36. 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
37. Characterization of temporal and spatial variability of aerosols from ground-based climatology: towards evaluation of satellite mission requirements C. Chen et al. 10.1016/j.jqsrt.2021.107627
38. PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China N. Chen et al. 10.3390/ijgi10010031
39. Seasonal monitoring and estimation of regional aerosol distribution over Po valley, northern Italy, using a high-resolution MAIAC product B. Arvani et al. 10.1016/j.atmosenv.2016.06.037
40. Spatiotemporal variations in aerosol optical depth and associated risks for populations in the arid region of Central Asia X. Ma et al. 10.1016/j.scitotenv.2021.151558
41. Spatial and temporal variations of air quality and six air pollutants in China during 2015–2017 H. Guo et al. 10.1038/s41598-019-50655-6
42. A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges A. Ranjan et al. 10.1007/s13143-020-00215-0
43. A novel calibration approach using satellite and visibility observations to estimate fine particulate matter exposures in Southwest Asia and Afghanistan S. Masri et al. 10.1080/10962247.2016.1230079
44. Multitechnique Observations on the Impacts of Declining Air Pollution on the Atmospheric Convective Processes During COVID-19 Pandemic at a Tropical Metropolis G. Rakshit et al. 10.1109/LGRS.2021.3049887
45. Estimation of daily ground-level PM2.5 concentrations over the Pearl River Delta using 1 km resolution MODIS AOD based on multi-feature BiLSTM L. Miao et al. 10.1016/j.atmosenv.2022.119362
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47. Estimating daily PM 2.5 and PM 10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data I. Kloog et al. 10.1016/j.atmosenv.2015.10.004
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49. Application of low-cost fine particulate mass monitors to convert satellite aerosol optical depth to surface concentrations in North America and Africa C. Malings et al. 10.5194/amt-13-3873-2020
50. Developing particle emission inventories using remote sensing (PEIRS) C. Tang et al. 10.1080/10962247.2016.1214630
51. Evaluating the use of satellite observations to supplement ground-level air quality data in selected cities in low- and middle-income countries M. Alvarado et al. 10.1016/j.atmosenv.2019.117016
52. Mitigating MODIS AOD non-random sampling error on surface PM2.5 estimates by a combined use of Bayesian Maximum Entropy method and linear mixed-effects model D. Fu et al. 10.1016/j.apr.2019.11.020
53. Aerosol pattern changes over the dead sea from west to east - Using high-resolution satellite data S. Lee et al. 10.1016/j.atmosenv.2020.117737
54. Spatial and Temporal Variation of Aerosol Optical Depth in Huaihai Economic Zone from 1982 to 2021 S. Wu et al. 10.3390/atmos14050822
55. Spatial and temporal variability in desert dust and anthropogenic pollution in Iraq, 1997–2010 A. Chudnovsky et al. 10.1080/10962247.2016.1153528
56. MODIS Collection 6 MAIAC algorithm A. Lyapustin et al. 10.5194/amt-11-5741-2018
57. Satellite-Based Estimation of Daily Ground-Level PM2.5 Concentrations over Urban Agglomeration of Chengdu Plain W. Han & L. Tong 10.3390/atmos10050245
58. MODIS AOD sampling rate and its effect on PM2.5 estimation in North China Z. Song et al. 10.1016/j.atmosenv.2019.04.020
59. Estimating Air Particulate Matter Using MODIS Data and Analyzing Its Spatial and Temporal Pattern over the Yangtze Delta Region J. Xu et al. 10.3390/su8090932
60. Monthly analysis of PM ratio characteristics and its relation to AOD M. Sorek-Hamer et al. 10.1080/10962247.2016.1208121
61. Spatiotemporal Distribution of Satellite-Retrieved Ground-Level PM2.5 and Near Real-Time Daily Retrieval Algorithm Development in Sichuan Basin, China C. Gao et al. 10.3390/atmos9020078
62. Satellite Aerosol Retrieval Using Scene Simulation and Deep Belief Network C. Jia et al. 10.1109/TGRS.2021.3116154
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67. Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model X. Hu et al. 10.1016/j.rse.2013.08.032
68. Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data S. Alexeeff et al. 10.1038/jes.2014.40
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