69 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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24. Validation of satellite and model aerosol optical depth and precipitable water vapour observations with AERONET data over Pune, India K. Vijayakumar et al. 10.1080/01431161.2018.1476789
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26. Application of Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) and Weather Research Forecasting (WRF) model meteorological data for assessment of fine particulate matter (PM2.5) over India Y. Sathe et al. 10.1016/j.apr.2018.08.016
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
31. Assessment of PM2.5 concentrations and exposure throughout China using ground observations H. Guo et al. 10.1016/j.scitotenv.2017.05.263
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
33. Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA A. Just et al. 10.3390/rs10050803
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
46. The observation‐based relationships between PM2.5 and AOD over China J. Xin et al. 10.1002/2015JD024655
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
48. Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion G. Geng et al. 10.1021/acs.est.1c01863
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
63. Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China J. Wu et al. 10.3390/rs11192223
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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
69. A study of the impact of spatial resolution on the estimation of particle matter concentration from the aerosol optical depth retrieved from satellite observations L. Mei et al. 10.1080/01431161.2019.1601279