125 citations as recorded by crossref.

1. Relationship between Remotely Sensed Ambient PM10 and PM2.5 and Urban Forest in Seoul, South Korea J. Park & P. Lee 10.3390/f11101060
2. Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia Q. Xiao et al. 10.5194/acp-16-1255-2016
3. MODIS 3 km aerosol product: algorithm and global perspective L. Remer et al. 10.5194/amt-6-1829-2013
4. Evaluation of MODIS Collection 6 aerosol retrieval algorithms over Indo-Gangetic Plain: Implications of aerosols types and mass loading A. Mhawish et al. 10.1016/j.rse.2017.09.016
5. High-Resolution PM10 Estimation Using Satellite Data and Model-Agnostic Meta-Learning Y. Yang et al. 10.3390/rs16132498
6. MODIS 3 km and 10 km aerosol optical depth for China: Evaluation and comparison Q. He et al. 10.1016/j.atmosenv.2017.01.023
7. Collocation mismatch uncertainties in satellite aerosol retrieval validation T. Virtanen et al. 10.5194/amt-11-925-2018
8. Estimation of ground-level dry PM2.5 concentrations at 3 km resolution over Beijing using Geostationary Ocean Colour Imager J. Wang & Z. Li 10.1080/2150704X.2020.1795298
9. Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy J. Park et al. 10.5194/amt-16-3039-2023
10. 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
11. Estimating PM2.5 surface concentrations from AOD: A combination of SLSTR and MODIS J. Handschuh et al. 10.1016/j.rsase.2022.100716
12. Spatio-temporal variation and impact factors analysis of satellite-based aerosol optical depth over China from 2002 to 2015 Q. He et al. 10.1016/j.atmosenv.2016.01.002
13. 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
14. Estimating PM2.5 with high-resolution 1-km AOD data and an improved machine learning model over Shenzhen, China W. Chen et al. 10.1016/j.scitotenv.2020.141093
15. Multisensor and Multimodel Monitoring and Investigation of a Wintertime Air Pollution Event Ahead of a Cold Front Over Eastern China X. Hu et al. 10.1029/2020JD033538
16. Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia N. Zaman et al. 10.3390/app11167326
17. 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
18. Future inhibition of ecosystem productivity by increasing wildfire pollution over boreal North America X. Yue et al. 10.5194/acp-17-13699-2017
19. Evaluation of the MODIS Collection 6.1 3 km aerosol optical depth product over China M. Zhang et al. 10.1016/j.atmosenv.2022.118970
20. Observed aerosol-induced radiative effect on plant productivity in the eastern United States S. Strada et al. 10.1016/j.atmosenv.2015.09.051
21. High-Spatial-Resolution Aerosol Optical Properties Retrieval Algorithm Using Chinese High-Resolution Earth Observation Satellite I F. Bao et al. 10.1109/TGRS.2016.2568246
22. Mobile laboratory measurements of air pollutants in Baltimore, MD elucidate issues of environmental justice R. Dickerson et al. 10.1080/10962247.2024.2393178
23. 基于国产碳卫星的在轨光谱性能评估及大气二氧化碳的高精度反演（特邀） 洪. Hong Xinhua et al. 10.3788/AOS241153
24. Intercomparison of CALIOP, MODIS, and AERONET aerosol optical depth over China during the past decade C. Liu et al. 10.1080/01431161.2018.1466070
25. Estimation of high-resolution aerosol optical depth (AOD) from Landsat and Sentinel images using SEMARA model over selected locations in South Asia B. Gayen et al. 10.1016/j.atmosres.2023.107141
26. Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning Techniques Y. Li et al. 10.3390/atmos15030287
27. 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
28. Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East Asia E. Kang et al. 10.1016/j.atmosenv.2023.119951
29. Estimation of PM<sub>10</sub> concentrations over Seoul using multiple empirical models with AERONET and MODIS data collected during the DRAGON-Asia campaign S. Seo et al. 10.5194/acp-15-319-2015
30. GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign M. Choi et al. 10.5194/amt-9-1377-2016
31. 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
32. Interaction between urban heat island and urban pollution island during summer in Berlin H. Li et al. 10.1016/j.scitotenv.2018.04.254
33. Evaluation of MODIS aerosol retrieval algorithms over the Beijing‐Tianjin‐Hebei region during low to very high pollution events M. Bilal & J. Nichol 10.1002/2015JD023082
34. Systematic Evaluation of Four Satellite AOD Datasets for Estimating PM2.5 Using a Random Forest Approach J. Handschuh et al. 10.3390/rs15082064
35. Estimating PM 2.5 concentrations in a central region of China using a three-stage model Y. Jing et al. 10.1080/17538947.2023.2175499
36. Satellite data of atmospheric pollution for U.S. air quality applications: Examples of applications, summary of data end-user resources, answers to FAQs, and common mistakes to avoid B. Duncan et al. 10.1016/j.atmosenv.2014.05.061
37. Validation and Accuracy Analysis of Global MODIS Aerosol Products over Land Q. Wang et al. 10.3390/atmos8080155
38. Assessment of urban aerosol pollution over the Moscow megacity by the MAIAC aerosol product E. Zhdanova et al. 10.5194/amt-13-877-2020
39. MODIS Collection 6.1 3 km resolution aerosol optical depth product: global evaluation and uncertainty analysis J. Wei et al. 10.1016/j.atmosenv.2020.117768
40. Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images L. Sun et al. 10.3390/rs8010023
41. Long Temporal Analysis of 3-km MODIS Aerosol Product Over East China Q. Ma et al. 10.1109/JSTARS.2017.2650144
42. Satellite-based PM2.5 estimation using fine-mode aerosol optical thickness over China X. Yan et al. 10.1016/j.atmosenv.2017.09.023
43. Estimating Regional Ground‐Level PM2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks H. Shen et al. 10.1029/2018JD028759
44. Variation of the aerosol optical properties and validation of MODIS AOD products over the eastern edge of the Tibetan Plateau based on ground-based remote sensing in 2017 Y. You et al. 10.1016/j.atmosenv.2019.117257
45. Multi-Spectrum Hierarchical Segmentation Algorithm: A New Aerosol Optical Thickness Retrieval Algorithm for Urban Areas Y. Chen et al. 10.1109/LGRS.2021.3117282
46. A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions A. Chudnovsky et al. 10.5194/acp-13-10907-2013
47. An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness, part 1: Algorithm development X. Yan et al. 10.1016/j.rse.2017.02.005
48. Extreme Aerosol Events Over Eastern North America: 1. Characterizing and Simulating Historical Events Y. Guo et al. 10.1029/2020JD033758
49. The Influence of Underlying Land Cover on the Accuracy of MODIS C6.1 Aerosol Products—A Case Study over the Yangtze River Delta Region of China K. Sun et al. 10.3390/rs14040938
50. Evaluation of MODIS Dark Target AOD Product with 3 and 10 km Resolution in Amazonia R. Palácios et al. 10.3390/atmos13111742
51. Daily estimation of ground-level PM2.5 concentrations at 4 km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations B. Lv et al. 10.1016/j.scitotenv.2016.12.049
52. Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region K. Gonçalves et al. 10.1016/j.atmosenv.2018.03.057
53. Potential Approach for Single-Peak Extinction Fitting of Aerosol Profiles Based on In Situ Measurements for the Improvement of Surface PM2.5 Retrieval from Satellite AOD Product T. Lin et al. 10.3390/rs12132174
54. Performance of MODIS aerosol products at various timescales and in different pollution conditions over eastern Asia Q. Mao et al. 10.1007/s11431-018-9462-5
55. Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment T. Li et al. 10.1016/j.atmosenv.2017.01.004
56. Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia N. Kamarul Zaman et al. 10.1016/j.atmosres.2017.04.019
57. First TanSat CO2 retrieval over land and ocean using both nadir and glint spectroscopy X. Hong et al. 10.1016/j.rse.2024.114053
58. Evaluation of Aerosol Optical Depth (AOD) and PM2.5 associations for air quality assessment Z. Yang et al. 10.1016/j.rsase.2020.100396
59. Comparison of scanning aerosol lidar and in situ measurements of aerosol physical properties and boundary layer heights H. Zhang et al. 10.5194/ar-2-135-2024
60. Monitoring the Impacts of Wildfires on Forest Ecosystems and Public Health in the Exo-Urban Environment Using High-Resolution Satellite Aerosol Products from the Visible Infrared Imaging Radiometer Suite (VIIRS) A. Huff et al. 10.4137/EHI.S19590
61. Validation of MODIS 3 km land aerosol optical depth from NASA's EOS Terra and Aqua missions P. Gupta et al. 10.5194/amt-11-3145-2018
62. GIST-PM-Asia v1: development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia S. Lee et al. 10.5194/gmd-9-17-2016
63. Validation of MODIS C6 Dark Target Aerosol Products at 3 km and 10 km Spatial Resolutions Over the China Seas and the Eastern Indian Ocean X. Shen et al. 10.3390/rs10040573
64. Estimating ground-level PM2.5 over a coastal region of China using satellite AOD and a combined model L. Yang et al. 10.1016/j.jclepro.2019.04.231
65. A New MODIS C6 Dark Target and Deep Blue Merged Aerosol Product on a 3 km Spatial Grid M. Bilal et al. 10.3390/rs10030463
66. A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations W. Wang et al. 10.3390/rs14040964
67. Investigation of air quality over the largest city in central China using high resolution satellite derived aerosol optical depth data K. Sun et al. 10.1016/j.apr.2017.12.011
68. Prediction of PM2.5 concentration based on the CEEMDAN-RLMD-BiLSTM-LEC model Q. Guo et al. 10.7717/peerj.15931
69. Simplified and Fast Atmospheric Radiative Transfer model for satellite-based aerosol optical depth retrieval X. Yan et al. 10.1016/j.atmosenv.2020.117362
70. Effect of MODIS Terra radiometric calibration improvements on Collection 6 Deep Blue aerosol products: Validation and Terra/Aqua consistency A. Sayer et al. 10.1002/2015JD023878
71. An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks B. Holben et al. 10.5194/acp-18-655-2018
72. Size-resolved particulate matter concentrations derived from 4.4 km-resolution size-fractionated Multi-angle Imaging SpectroRadiometer (MISR) aerosol optical depth over Southern California M. Franklin et al. 10.1016/j.rse.2017.05.002
73. A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm P. Gupta et al. 10.5194/amt-9-3293-2016
74. Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation A. Sayer et al. 10.5194/amt-8-5277-2015
75. Daily Estimation of Ground-Level PM2.5 Concentrations over Beijing Using 3 km Resolution MODIS AOD Y. Xie et al. 10.1021/acs.est.5b01413
76. A new method of satellite-based haze aerosol monitoring over the North China Plain and a comparison with MODIS Collection 6 aerosol products X. Yan et al. 10.1016/j.atmosres.2015.12.003
77. High Resolution Aerosol Optical Depth Retrieval Using Gaofen-1 WFV Camera Data K. Sun et al. 10.3390/rs9010089
78. High-resolution estimation of PM2.5 concentrations across China using multiple machine learning approaches and model fusion L. Meng et al. 10.1016/j.apr.2024.102110
79. Development of a remote sensing algorithm to retrieve atmospheric aerosol properties using multiwavelength and multipixel information M. Hashimoto & T. Nakajima 10.1002/2016JD025698
80. Aerosol Optical Depth Retrieval From Landsat 8 OLI Images Over Urban Areas Supported by MODIS BRDF/Albedo Data X. Tian et al. 10.1109/LGRS.2018.2827200
81. An Evaluation of Four MODIS Collection 6 Aerosol Products in a Humid Subtropical Region M. Zhang et al. 10.3390/rs9111173
82. A minimum albedo aerosol retrieval method for the new-generation geostationary meteorological satellite Himawari-8 X. Yan et al. 10.1016/j.atmosres.2018.02.021
83. Validation of MODIS Aerosol Optical Depth Retrieval over Mountains in Central China Based on a Sun-Sky Radiometer Site of SONET Y. Ma et al. 10.3390/rs8020111
84. Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions X. Jin et al. 10.7717/peerj.13203
85. Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model Y. Guo et al. 10.1016/j.rse.2017.06.001
86. Evaluating MODIS and MISR aerosol optical depth retrievals over environmentally distinct sites in Pakistan G. Ali et al. 10.1016/j.jastp.2018.12.008
87. Long-term trends in air quality in major cities in the UK and India: a view from space K. Vohra et al. 10.5194/acp-21-6275-2021
88. Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas M. Bilal et al. 10.3390/rs14020373
89. SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm L. She et al. 10.3390/rs9030253
90. Estimating ground-level PM2.5 concentrations in Beijing, China using aerosol optical depth and parameters of the temperature inversion layer Z. Zang et al. 10.1016/j.scitotenv.2016.09.186
91. Validation of MODIS 3 km Resolution Aerosol Optical Depth Retrievals Over Asia J. Nichol & M. Bilal 10.3390/rs8040328
92. Development and assessment of a higher-spatial-resolution (4.4 km) MISR aerosol optical depth product using AERONET-DRAGON data M. Garay et al. 10.5194/acp-17-5095-2017
93. Retrieving High-Resolution Aerosol Optical Depth from GF-4 PMS Imagery in Eastern China Z. Sun et al. 10.3390/rs13183752
94. Applying the Dark Target aerosol algorithm with Advanced Himawari Imager observations during the KORUS-AQ field campaign P. Gupta et al. 10.5194/amt-12-6557-2019
95. PM2.5 over North China based on MODIS AOD and effect of meteorological elements during 2003–2015 Y. Chen et al. 10.1007/s11783-019-1202-8
96. Evaluation of satellite-based aerosol datasets and the CAMS reanalysis over the ocean utilizing shipborne reference observations J. Witthuhn et al. 10.5194/amt-13-1387-2020
97. Interpretation of satellite retrievals of PM2.5 over the southern African Interior M. Kneen et al. 10.1016/j.atmosenv.2015.12.016
98. Validation of MODIS and VIIRS derived aerosol optical depth over complex coastal waters M. Bilal et al. 10.1016/j.atmosres.2016.11.009
99. National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD W. You et al. 10.3390/rs8030184
100. Evaluating the potential for application of MODIS satellite data for urban air quality monitoring in Vietnam B. Hieu et al. 10.1088/1757-899X/869/2/022035
101. Overview of atmospheric aerosol studies in Malaysia: Known and unknown K. Kanniah et al. 10.1016/j.atmosres.2016.08.002
102. Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model T. Amnuaylojaroen & I. Levy 10.1155/2022/3190484
103. Estimating spatial variability of ground-level PM2.5 based on a satellite-derived aerosol optical depth product: Fuzhou, China L. Yang et al. 10.1016/j.apr.2018.05.007
104. 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
105. Short period PM2.5 prediction based on multivariate linear regression model R. Zhao et al. 10.1371/journal.pone.0201011
106. Aerosol Retrieval Algorithm for Sentinel-2 Images Over Complex Urban Areas Y. Yang et al. 10.1109/TGRS.2022.3158061
107. The Collection 6 MODIS aerosol products over land and ocean R. Levy et al. 10.5194/amt-6-2989-2013
108. Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals A. Chudnovsky et al. 10.1016/j.atmosenv.2014.02.019
109. Observations of rapid aerosol optical depth enhancements in the vicinity of polluted cumulus clouds T. Eck et al. 10.5194/acp-14-11633-2014
110. Effects of Boundary Layer Height on the Model of Ground-Level PM2.5 Concentrations from AOD: Comparison of Stable and Convective Boundary Layer Heights from Different Methods Z. Zang et al. 10.3390/atmos8060104
111. 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
112. Validation and accuracy assessment of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low and high aerosol loadings and dust storms M. Bilal et al. 10.1016/j.rse.2014.07.015
113. Quantifying urban air quality through multispectral satellite imagery and Google earth Engine F. Aghdam et al. 10.1016/j.jastp.2024.106301
114. Evaluation of Aqua MODIS Collection 6 AOD Parameters for Air Quality Research over the Continental United States J. Belle & Y. Liu 10.3390/rs8100815
115. Comparison of MODIS 3 km and 10 km resolution aerosol optical depth retrievals over land with airborne sunphotometer measurements during ARCTAS summer 2008 J. Livingston et al. 10.5194/acp-14-2015-2014
116. GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia M. Choi et al. 10.5194/amt-11-385-2018
117. Exploring Aerosols Near Clouds With High‐Spatial‐Resolution Aircraft Remote Sensing During SEAC4RS R. Spencer et al. 10.1029/2018JD028989
118. The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future L. Remer et al. 10.3390/rs12182900
119. Development of the TOA-Related Models for PM2.5 Prediction Pre- and Post-COVID-19 Outbreak over Yangtze River Delta Region of China L. Yang et al. 10.1155/2022/2994885
120. 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
121. A Spatial-Temporal Interpretable Deep Learning Model for improving interpretability and predictive accuracy of satellite-based PM2.5 X. Yan et al. 10.1016/j.envpol.2021.116459
122. Validation of MODIS C6 AOD products retrieved by the Dark Target method in the Beijing–Tianjin–Hebei urban agglomeration, China J. Zhang et al. 10.1007/s00376-016-6217-5
123. Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts P. Saide et al. 10.5194/acp-13-10425-2013
124. Validation and Accuracy Analysis of the Collection 6.1 MODIS Aerosol Optical Depth Over the Westernmost City in China Based on the Sun‐Sky Radiometer Observations From SONET G. Huang et al. 10.1029/2019EA001041
125. Evaluation of a MISR-Based High-Resolution Aerosol Retrieval Method Using AERONET DRAGON Campaign Data T. Moon et al. 10.1109/TGRS.2015.2395722