107 citations as recorded by crossref.

1. Comprehensive evaluation of multisource aerosol optical depth gridded products over China D. Jiang et al. 10.1016/j.atmosenv.2022.119088
2. Performance of the NPP-VIIRS and aqua-MODIS Aerosol Optical Depth Products over the Yangtze River Basin L. He et al. 10.3390/rs10010117
3. Spatial Factor Analysis for Aerosol Optical Depth in Metropolises in China with Regard to Spatial Heterogeneity H. Shi et al. 10.3390/atmos9040156
4. High spatiotemporal resolution estimation of AOD from Himawari-8 using an ensemble machine learning gap-filling method A. Chen et al. 10.1016/j.scitotenv.2022.159673
5. Analysis and research of absorbing aerosols in Beijing-Tianjin-Hebei region T. Ju et al. 10.1007/s11869-021-01151-2
6. A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data Y. Si et al. 10.3390/rs15020438
7. 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
8. Himawari-8-Derived Aerosol Optical Depth Using an Improved Time Series Algorithm Over Eastern China D. Li et al. 10.3390/rs12060978
9. Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China W. Wang et al. 10.3390/rs9080858
10. Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models M. Diao et al. 10.1080/10962247.2019.1668498
11. 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
12. Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China J. Pang et al. 10.1016/j.atmosenv.2018.02.011
13. 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
14. A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in China F. Yao et al. 10.1016/j.isprsjprs.2019.03.011
15. Retrieval of Aerosol Optical Depth from the Himawari-8 Advanced Himawari Imager data: Application over Beijing in the summer of 2016 L. Wang et al. 10.1016/j.atmosenv.2020.117788
16. Handling Missing Data in Large-Scale MODIS AOD Products Using a Two-Step Model Y. Chi et al. 10.3390/rs12223786
17. An Ensemble Machine-Learning Model To Predict Historical PM2.5Concentrations in China from Satellite Data Q. Xiao et al. 10.1021/acs.est.8b02917
18. Assessment of GOCI-II satellite remote sensing products in Lake Taihu M. Zhao et al. 10.3389/fmars.2024.1488219
19. Joint Retrieval of PM2.5 Concentration and Aerosol Optical Depth over China Using Multi-Task Learning on FY-4A AGRI B. Li et al. 10.1007/s00376-024-3222-y
20. Impact of the Dust Aerosol Model on the VIIRS Aerosol Optical Depth (AOD) Product across China Y. Wang et al. 10.3390/rs12060991
21. Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations S. Ahn et al. 10.3390/rs13061096
22. Application of multiplatform remote sensing data over East Asia Ocean: aerosol characteristics and aerosol types K. Luan et al. 10.1007/s11356-024-33458-9
23. Assessment of the Representativeness of MODIS Aerosol Optical Depth Products at Different Temporal Scales Using Global AERONET Measurements Y. Tong et al. 10.3390/rs12142330
24. LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion K. Bai et al. 10.5194/essd-14-907-2022
25. Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. 10.1080/10643389.2019.1665944
26. Collocation mismatch uncertainties in satellite aerosol retrieval validation T. Virtanen et al. 10.5194/amt-11-925-2018
27. Satellite-Based Mapping of High-Resolution Ground-Level PM2.5 with VIIRS IP AOD in China through Spatially Neural Network Weighted Regression Y. Chen et al. 10.3390/rs13101979
28. Absorbing Aerosol Optical Properties and Radiative Effects on Near-Surface Photochemistry in East Asia H. Chen et al. 10.3390/rs15112779
29. Improving the quantification of fine particulates (PM2.5) concentrations in Malaysia using simplified and computationally efficient models N. Zaman et al. 10.1016/j.jclepro.2024.141559
30. Top-Down Estimation of Particulate Matter Emissions from Extreme Tropical Peatland Fires Using Geostationary Satellite Fire Radiative Power Observations D. Fisher et al. 10.3390/s20247075
31. Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing–Tianjin–Hebei Region, China L. Li et al. 10.3390/rs10122006
32. Climatology and trends of aerosol optical depth with different particle size and shape in northeast China from 2001 to 2018 H. Zhao et al. 10.1016/j.scitotenv.2020.142979
33. Estimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model F. Yao et al. 10.3390/rs11070841
34. Evaluation of the NDVI-Based Pixel Selection Criteria of the MODIS C6 Dark Target and Deep Blue Combined Aerosol Product M. Bilal & J. Nichol 10.1109/JSTARS.2017.2693289
35. Super-Resolution Reconstruction of Remote Sensing Data Based on Multiple Satellite Sources for Forest Fire Smoke Segmentation H. Liang et al. 10.3390/rs15174180
36. Comparison of AOD from CALIPSO, MODIS, and Sun Photometer under Different Conditions over Central China B. Liu et al. 10.1038/s41598-018-28417-7
37. Exploring the spatial-temporal characteristics of the aerosol optical depth (AOD) in Central Asia based on the moderate resolution imaging spectroradiometer (MODIS) D. Wang et al. 10.1007/s10661-020-08299-x
38. Validation of MODIS and VIIRS derived aerosol optical depth over complex coastal waters M. Bilal et al. 10.1016/j.atmosres.2016.11.009
39. Evaluation of the MISR fine resolution aerosol product using MODIS, MISR, and ground observations over China Y. Si et al. 10.1016/j.atmosenv.2019.117229
40. Air pollution scenario over Pakistan: Characterization and ranking of extremely polluted cities using long-term concentrations of aerosols and trace gases M. Bilal et al. 10.1016/j.rse.2021.112617
41. The Spatiotemporal Pattern of the Aerosol Optical Depth (AOD) on the Canopies of Various Forest Types in the Exurban National Park: A Case in Ningbo City, Eastern China Y. Chi et al. 10.1155/2019/4942827
42. 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
43. An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks B. Holben et al. 10.5194/acp-18-655-2018
44. Long-term spatiotemporal variations of aerosol optical depth over Yellow and Bohai Sea X. Shen et al. 10.1007/s11356-019-04203-4
45. Fine resolution air quality dynamics related to socioeconomic and land use factors in the most polluted desert metropolitan in the American Southwest Y. Li & S. Myint 10.1016/j.scitotenv.2021.147713
46. Evaluation and Comparison of Long-Term MODIS C5.1 and C6 Products against AERONET Observations over China A. Fan et al. 10.3390/rs9121269
47. VIIRS Environmental Data Record and Deep Blue aerosol products: validation, comparison, and spatiotemporal variations from 2013 to 2018 in China L. He et al. 10.1016/j.atmosenv.2021.118265
48. FY-4A/AGRI Aerosol Optical Depth Retrieval Capability Test and Validation Based on NNAeroG H. Ding et al. 10.3390/rs14215591
49. Predicting ground-level PM2.5 concentrations in the Beijing-Tianjin-Hebei region: A hybrid remote sensing and machine learning approach X. Li & X. Zhang 10.1016/j.envpol.2019.03.068
50. Evaluation of Himawari-8 version 2.0 aerosol products against AERONET ground-based measurements over central and northern China L. Wang et al. 10.1016/j.atmosenv.2020.117357
51. Improving aerosol optical depth retrievals from Himawari-8 with ensemble learning enhancement: Validation over Asia D. Fu et al. 10.1016/j.atmosres.2023.106624
52. Evaluating temporal and spatial variability and trend of aerosol optical depth (550 nm) over Iran using data from MODIS on board the Terra and Aqua satellites A. Dadashi-Roudbari & M. Ahmadi 10.1007/s12517-020-5232-0
53. Evaluation of VIIRS Land Aerosol Model Selection with AERONET Measurements W. Wang et al. 10.3390/ijerph14091016
54. Exploring the trend, prediction and driving forces of aerosols using satellite and ground data, and implications for climate change mitigation X. Li et al. 10.1016/j.jclepro.2019.03.121
55. Decoupling between PM2.5 concentrations and aerosol optical depth at ground stations in China W. Fu et al. 10.3389/fenvs.2022.979918
56. Advancing application of satellite remote sensing technologies for linking atmospheric and built environment to health Y. Tian et al. 10.3389/fpubh.2023.1270033
57. A full-coverage estimation of PM2.5 concentrations using a hybrid XGBoost-WD model and WRF-simulated meteorological fields in the Yangtze River Delta Urban Agglomeration, China J. Wang et al. 10.1016/j.envres.2021.111799
58. A Characteristic Analysis of Various Air Pollutants and Their Correlation with O3 in the Jiangsu, Shandong, Henan, and Anhui Provinces of China T. Ju et al. 10.3390/su142113737
59. An Evaluation of Four MODIS Collection 6 Aerosol Products in a Humid Subtropical Region M. Zhang et al. 10.3390/rs9111173
60. Diurnal time representation of MODIS, VIIRS, MISR, and AHI over Asia and Oceania Z. Yang et al. 10.1016/j.rse.2023.113878
61. Atmospheric Aerosol Over Ukraine Region: Current Status of Knowledge and Research Efforts G. Milinevsky & V. Danylevsky 10.3389/fenvs.2018.00059
62. Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China K. Zhang et al. 10.3390/rs11222679
63. Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm X. Su et al. 10.1016/j.envint.2022.107343
64. An exploratory study on the aerosol height retrieval from OMI measurements of the 477  nm O<sub>2</sub> − O<sub>2</sub> spectral band using a neural network approach J. Chimot et al. 10.5194/amt-10-783-2017
65. Performance evaluation of MODIS and VIIRS satellite AOD products over the Indian subcontinent S. Payra et al. 10.3389/fenvs.2023.1158641
66. Absorbable aerosols based on OMI data: a case study in three provinces of Northeast China J. Duan et al. 10.1007/s10661-021-09249-x
67. High-Resolution Planetscope Imagery and Machine Learning for Estimating Suspended Particulate Matter in the Ebinur Lake, Xinjiang, China P. Duan et al. 10.1109/JSTARS.2022.3233113
68. Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments Z. Chen et al. 10.1016/j.scitotenv.2024.170593
69. Overview of atmospheric aerosol studies in Malaysia: Known and unknown K. Kanniah et al. 10.1016/j.atmosres.2016.08.002
70. Associations between birth outcomes and maternal PM2.5 exposure in Shanghai: A comparison of three exposure assessment approaches Q. Xiao et al. 10.1016/j.envint.2018.04.050
71. Analysis of Remote Sensing Monitoring of Atmospheric Ozone in Japan from 2010 to 2021 S. Lei et al. 10.1007/s11270-023-06586-0
72. Evaluation and uncertainty analysis of Himawari-8 hourly aerosol product version 3.1 and its influence on surface solar radiation before and during the COVID-19 outbreak C. Tang et al. 10.1016/j.scitotenv.2023.164456
73. Evaluation of MODIS DT, DB, and MAIAC Aerosol Products over Different Land Cover Types in the Yangtze River Delta of China J. Jiang et al. 10.3390/rs15010275
74. Evaluation and Comparison of Multi-Satellite Aerosol Optical Depth Products over East Asia Ocean Z. Cao et al. 10.3390/toxics11100813
75. A comparative time series analysis and modeling of aerosols in the contiguous United States and China X. Li et al. 10.1016/j.scitotenv.2019.07.072
76. Evaluation of LJ1-01 Nighttime Light Imagery for Estimating Monthly PM2.5 Concentration: A Comparison With NPP-VIIRS Nighttime Light Data G. Zhang et al. 10.1109/JSTARS.2020.3002671
77. Atmospheric aerosol pollution across China: a spatiotemporal analysis of satellite-based aerosol optical depth during 2000–2016 Y. Feng et al. 10.1080/17538947.2018.1486892
78. An Adaptive Dark-Target Algorithm for Retrieving Land AOD Applied to FY-4B/AGRI Data Y. Si et al. 10.1109/JSTARS.2024.3408251
79. Long-term (2006–2015) variations and relations of multiple atmospheric pollutants based on multi-remote sensing data over the North China Plain Y. Si et al. 10.1016/j.envpol.2019.113323
80. Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns G. de Leeuw et al. 10.5194/acp-18-1573-2018
81. Synergistic data fusion of multimodal AOD and air quality data for near real-time full coverage air pollution assessment K. Li et al. 10.1016/j.jenvman.2021.114121
82. Validation of VIIRS AOD through a Comparison with a Sun Photometer and MODIS AODs over Wuhan W. Wang et al. 10.3390/rs9050403
83. An Estimation of Daily PM2.5 Concentration in Thailand Using Satellite Data at 1-Kilometer Resolution S. Buya et al. 10.3390/su151310024
84. 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
85. An Improved Aerosol Retrieval Algorithm Based on Nonlinear Surface Model From FY-3D/MERSI-II Remote Sensing Data Y. Si et al. 10.1109/TGRS.2024.3367883
86. Generating Hourly Fine Seamless Aerosol Optical Depth Products by Fusing Multiple Satellite and Numerical Model Data B. Zou et al. 10.1109/TGRS.2024.3385397
87. Impacts of snow and cloud covers on satellite-derived PM2.5 levels J. Bi et al. 10.1016/j.rse.2018.12.002
88. Satellite-based estimation of hourly PM2.5 levels during heavy winter pollution episodes in the Yangtze River Delta, China Q. She et al. 10.1016/j.chemosphere.2019.124678
89. Advances in the estimation of high Spatio-temporal resolution pan-African top-down biomass burning emissions made using geostationary fire radiative power (FRP) and MAIAC aerosol optical depth (AOD) data H. Nguyen & M. Wooster 10.1016/j.rse.2020.111971
90. A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China F. Yao et al. 10.1016/j.scitotenv.2017.08.209
91. Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period S. Ha et al. 10.5194/acp-20-6015-2020
92. Research progress, challenges, and prospects of PM2.5 concentration estimation using satellite data S. Zhu et al. 10.1139/er-2022-0125
93. Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models Q. Xiao et al. 10.1016/j.atmosenv.2020.117921
94. The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future L. Remer et al. 10.3390/rs12182900
95. A comparative study of EOF and NMF analysis on downward trend of AOD over China from 2011 to 2019 Q. Ma et al. 10.1016/j.envpol.2021.117713
96. Controlling factors analysis for the Himawari-8 aerosol optical depth accuracy from the standpoint of size distribution, solar zenith angles and scattering angles M. Zhang et al. 10.1016/j.atmosenv.2020.117501
97. Estimation of health benefits from air quality improvement using the MODIS AOD dataset in Seoul, Korea D. Kim et al. 10.1016/j.envres.2019.03.042
98. Verification, improvement and application of aerosol optical depths in China Part 1: Inter-comparison of NPP-VIIRS and Aqua-MODIS J. Wei et al. 10.1016/j.atmosenv.2017.11.048
99. GOCI-II geostationary satellite hourly aerosol optical depth obtained by data-driven methods: Validation and comparison Y. Fan et al. 10.1016/j.atmosenv.2023.119965
100. Impact of environmental pollution on the retrieval of AOD products from Visible Infrared Imaging Radiometer Suite (VIIRS) over wuhan Y. Ma et al. 10.1016/j.apr.2019.09.014
101. Evaluation of the Aqua-MODIS C6 and C6.1 Aerosol Optical Depth Products in the Yellow River Basin, China M. Zhang et al. 10.3390/atmos10080426
102. Evaluating VIIRS EPS Aerosol Optical Depth in China: An intercomparison against ground-based measurements and MODIS C. Li et al. 10.1016/j.jqsrt.2018.12.002
103. Long-term variation of aerosol optical properties associated with aerosol types over East Asia using AERONET and satellite (VIIRS, OMI) data (2012–2019) S. Eom et al. 10.1016/j.atmosres.2022.106457
104. Carbonaceous aerosols remote sensing from geostationary satellite observation, Part I: Algorithm development using critical reflectance F. Bao et al. 10.1016/j.rse.2023.113459
105. Discerning the pre-monsoon urban atmosphere aerosol characteristic and its potential source type remotely sensed by AERONET over the Bengal Gangetic plain B. Priyadharshini et al. 10.1007/s11356-018-2290-x
106. Evaluation and Comparison of Himawari-8 L2 V1.0, V2.1 and MODIS C6.1 aerosol products over Asia and the oceania regions X. Yang et al. 10.1016/j.atmosenv.2019.117068
107. Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia N. Zaman et al. 10.3390/app11167326