1676 citations as recorded by crossref.

1. Large contribution of meteorological factors to inter-decadal changes in regional aerosol optical depth H. Che et al. https://doi.org/10.5194/acp-19-10497-2019
2. An observational study of the effects of aerosols on diurnal variation of heavy rainfall and associated clouds over Beijing–Tianjin–Hebei S. Zhou et al. https://doi.org/10.5194/acp-20-5211-2020
3. Optimization of Aerosol Model Selection for TROPOMI/S5P L. Rao et al. https://doi.org/10.3390/rs13132489
4. Observation of PM2.5 using a combination of satellite remote sensing and low-cost sensor network in Siberian urban areas with limited reference monitoring C. Lin et al. https://doi.org/10.1016/j.atmosenv.2020.117410
5. Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives Y. Zhang et al. https://doi.org/10.1016/j.fmre.2021.04.007
6. Future inhibition of ecosystem productivity by increasing wildfire pollution over boreal North America X. Yue et al. https://doi.org/10.5194/acp-17-13699-2017
7. Estimates of African Dust Deposition Along the Trans‐Atlantic Transit Using the Decadelong Record of Aerosol Measurements from CALIOP, MODIS, MISR, and IASI H. Yu et al. https://doi.org/10.1029/2019JD030574
8. Impact of MODIS sensor calibration updates on Greenland Ice Sheet surface reflectance and albedo trends K. Casey et al. https://doi.org/10.5194/tc-11-1781-2017
9. AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, Validation and Merged Products H. Lim et al. https://doi.org/10.3390/rs10050699
10. Aerosol meteorology of the Maritime Continent for the 2012 7SEAS southwest monsoon intensive study – Part 1: regional-scale phenomena J. Reid et al. https://doi.org/10.5194/acp-16-14041-2016
11. An Evaluation of Two Decades of Aerosol Optical Depth Retrievals from MODIS over Australia M. Shaylor et al. https://doi.org/10.3390/rs14112664
12. The influence of dust aerosols on solar radiation and near-surface temperature during a severe duststorm transport episode G. Huang et al. https://doi.org/10.3389/fenvs.2023.1126302
13. National Civil Space Infrastructure Satellite Aerosol Product Validation Network (SIAVNET) measurements: Supporting satellite aerosol product validation for different surface types over China T. Cheng et al. https://doi.org/10.1016/j.atmosres.2022.106508
14. Angular dependence of aerosol information content in CAPI/TanSat observation over land: Effect of polarization and synergy with A-train satellites X. Chen et al. https://doi.org/10.1016/j.rse.2017.05.007
15. Scientific impact of MODIS C5 calibration degradation and C6+ improvements A. Lyapustin et al. https://doi.org/10.5194/amt-7-4353-2014
16. Population exposure across central India to PM2.5 derived using remotely sensed products in a three-stage statistical model P. Maheshwarkar & R. Sunder Raman https://doi.org/10.1038/s41598-020-79229-7
17. Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia N. Kamarul Zaman et al. https://doi.org/10.1016/j.atmosres.2017.04.019
18. Exploring the sensitivity of atmospheric nitrate concentrations to nitric acid uptake rate using the Met Office's Unified Model A. Jones et al. https://doi.org/10.5194/acp-21-15901-2021
19. Clear-Sky Direct Aerosol Radiative Forcing Uncertainty Associated with Aerosol Optical Properties Based on CMIP6 models L. Zhang et al. https://doi.org/10.1175/JCLI-D-21-0479.1
20. An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors J. Gu et al. https://doi.org/10.3390/rs14071617
21. Potential Impacts of Sahara Dust Aerosol on Rainfall Vertical Structure Over the Atlantic Ocean as Identified From EOF Analysis X. Dong et al. https://doi.org/10.1029/2018JD028500
22. Radiative impact of a heavy dust storm over India and surrounding oceanic regions S. Kedia et al. https://doi.org/10.1016/j.atmosenv.2018.05.005
23. MODIS Collection 6.1 3 km resolution aerosol optical depth product: global evaluation and uncertainty analysis J. Wei et al. https://doi.org/10.1016/j.atmosenv.2020.117768
24. Diurnal spatial distributions of aerosol optical and cloud micro-macrophysics properties in Africa based on MODIS observations D. Ntwali & H. Chen https://doi.org/10.1016/j.atmosenv.2018.03.054
25. A fast and accurate radiative transfer model for aerosol remote sensing L. Mei et al. https://doi.org/10.1016/j.jqsrt.2020.107270
26. Satellitendaten zur Erfassung gesundheitsrelevanter Umweltbedingungen: Beispiele und interdisziplinäre Potenziale H. Taubenböck et al. https://doi.org/10.1007/s00103-020-03177-w
27. Post-fire vegetation dynamic patterns and drivers in Greater Hinggan Mountains: Insights from long-term remote sensing data analysis B. Jiang et al. https://doi.org/10.1016/j.ecoinf.2024.102850
28. Visibility, aerosol optical depth, and low-visibility events in Bangkok during the dry season and associated local weather and synoptic patterns N. Aman et al. https://doi.org/10.1007/s10661-022-09880-2
29. Joint features random forest (JFRF) model for mapping hourly surface PM2.5 over China L. Dong et al. https://doi.org/10.1016/j.atmosenv.2022.118969
30. First retrieval of daily 160 m aerosol optical depth over urban areas using Gaofen-1/6 synergistic observations: Algorithm development and validation J. Dong et al. https://doi.org/10.1016/j.isprsjprs.2024.04.020
31. Spatiotemporal characteristics of aerosols and low clouds from MODIS and their implications for marine cloud brightening L. Niu et al. https://doi.org/10.1007/s11430-025-1794-7
32. Investigations of MODIS AOD and cloud properties with CERES sensor based net cloud radiative effect and a NOAA HYSPLIT Model over Bangladesh for the period 2001–2016 M. Ali et al. https://doi.org/10.1016/j.atmosres.2018.09.001
33. Validation and Comparison of MODIS C6.1 and C6 Aerosol Products over Beijing, China X. Tian et al. https://doi.org/10.3390/rs10122021
34. Validation of MODIS and VIIRS derived aerosol optical depth over complex coastal waters M. Bilal et al. https://doi.org/10.1016/j.atmosres.2016.11.009
35. Global Daily Actual and Snow‐Free Blue‐Sky Land Surface Albedo Climatology From 20‐Year MODIS Products A. Jia et al. https://doi.org/10.1029/2021JD035987
36. A Simple and Universal Aerosol Retrieval Algorithm for Landsat Series Images Over Complex Surfaces J. Wei et al. https://doi.org/10.1002/2017JD026922
37. An Analysis of Environmental Effect on VIIRS Nighttime Light Monthly Composite Data at Multiple Scales in China M. Yuan et al. https://doi.org/10.1109/JSTARS.2022.3231960
38. Estimates of the aerosol indirect effect over the Baltic Sea region derived from 12 years of MODIS observations G. Saponaro et al. https://doi.org/10.5194/acp-17-3133-2017
39. Large Air Quality and Public Health Impacts due to Amazonian Deforestation Fires in 2019 E. Butt et al. https://doi.org/10.1029/2021GH000429
40. Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning H. Andersen et al. https://doi.org/10.1080/16000889.2021.1971925
41. Adaptive Aerosol Optical Depth Forecasting Model Using GNSS Observation Q. Zhao et al. https://doi.org/10.1109/TGRS.2021.3129159
42. Extreme dust storm over the eastern Mediterranean in September 2015: satellite, lidar, and surface observations in the Cyprus region R. Mamouri et al. https://doi.org/10.5194/acp-16-13711-2016
43. Bayesian Merging of MISR and MODIS Aerosol Optical Depth Products Using Error Distributions From AERONET M. Singh et al. https://doi.org/10.1109/JSTARS.2017.2734331
44. Aerosol Characteristics during the COVID-19 Lockdown in China: Optical Properties, Vertical Distribution, and Potential Source Y. Jin et al. https://doi.org/10.3390/rs14143336
45. Retrieval of desert dust and carbonaceous aerosol emissions over Africa from POLDER/PARASOL products generated by the GRASP algorithm C. Chen et al. https://doi.org/10.5194/acp-18-12551-2018
46. City-level variations in aerosol optical properties and aerosol type identification derived from long-term MODIS/Aqua observations in the Sichuan Basin, China H. Cai et al. https://doi.org/10.1016/j.uclim.2021.100886
47. Dust Impacts of Rapid Agricultural Expansion on the Great Plains A. Lambert et al. https://doi.org/10.1029/2020GL090347
48. Monitoring aerosols over Europe: an assessment of the potential benefit of assimilating the VIS04 measurements from the future MTG/FCI geostationary imager M. Descheemaecker et al. https://doi.org/10.5194/amt-12-1251-2019
49. Simulation of a Dust‐And‐Rain Event Across the Red Sea Using WRF‐Chem S. Parajuli et al. https://doi.org/10.1029/2022JD038384
50. Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds D. Rosenfeld et al. https://doi.org/10.1126/science.aav0566
51. Spatial and Temporal Distribution of PM2.5 Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets J. Carmona et al. https://doi.org/10.3390/rs12142286
52. Validation, analysis, and comparison of MISR V23 aerosol optical depth products with MODIS and AERONET observations R. Fan et al. https://doi.org/10.1016/j.scitotenv.2022.159117
53. Decadal changes and extreme events of aerosol over China: Spatiotemporal characteristics revealed by multi-satellite observations L. Chen et al. https://doi.org/10.1016/j.atmosres.2025.108643
54. Source profiling of air pollution and its association with acute respiratory infections in the Himalayan-bound region of India M. Wani et al. https://doi.org/10.1007/s11356-021-15413-0
55. Variation of Aerosol Optical Properties over Cluj-Napoca, Romania, Based on 10 Years of AERONET Data and MODIS MAIAC AOD Product H. Ștefănie et al. https://doi.org/10.3390/rs15123072
56. Validation of MODIS 3 km Resolution Aerosol Optical Depth Retrievals Over Asia J. Nichol & M. Bilal https://doi.org/10.3390/rs8040328
57. Characterization of temporal and spatial variability of aerosols from ground-based climatology: towards evaluation of satellite mission requirements C. Chen et al. https://doi.org/10.1016/j.jqsrt.2021.107627
58. Variability of surface solar radiation under clear skies over Qinghai-Tibet Plateau: Role of aerosols and water vapor L. Yu et al. https://doi.org/10.1016/j.atmosenv.2022.119286
59. Lidar Profiling of Aerosol Vertical Distribution in the Urbanized French Alpine Valley of Annecy and Impact of a Saharan Dust Transport Event P. Chazette & J. Totems https://doi.org/10.3390/rs15041070
60. Evaluation of MODIS Deep Blue Aerosol Algorithm in Desert Region of East Asia: Ground Validation and Intercomparison M. Tao et al. https://doi.org/10.1002/2017JD026976
61. Pre-seismic anomalies from optical satellite observations: a review Z. Jiao et al. https://doi.org/10.5194/nhess-18-1013-2018
62. Status update: is smoke on your mind? Using social media to assess smoke exposure B. Ford et al. https://doi.org/10.5194/acp-17-7541-2017
63. Satellite observations of smoke–cloud–radiation interactions over the Amazon rainforest R. Herbert & P. Stier https://doi.org/10.5194/acp-23-4595-2023
64. Aerosol Distributions and Transport over Southern Morocco from Ground-Based and Satellite Observations (2004–2020) T. Millet et al. https://doi.org/10.3390/atmos13060923
65. Historical footprints and future projections of global dust burden from bias-corrected CMIP6 models J. Liu et al. https://doi.org/10.1038/s41612-023-00550-9
66. Impact of biomass burning on regional aerosol optical properties: A case study over northern India D. Shaik et al. https://doi.org/10.1016/j.jenvman.2019.04.025
67. Cold-season atmospheric conditions associated with sudden changes in PM10 concentration over Seoul, Korea M. Cho et al. https://doi.org/10.1016/j.apr.2021.03.004
68. Ocean flux of salt, sulfate, and organic components to atmospheric aerosol L. Russell et al. https://doi.org/10.1016/j.earscirev.2023.104364
69. 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 https://doi.org/10.1016/j.rse.2020.111971
70. Evaluation and Comparison of Multi-Satellite Aerosol Optical Depth Products over East Asia Ocean Z. Cao et al. https://doi.org/10.3390/toxics11100813
71. Global assimilation of NRL MODIS aerosol optical thickness and its impact on aerosol direct radiative effect over a full year M. Zhao et al. https://doi.org/10.1016/j.atmosres.2026.108819
72. Generation of typical meteorological years for 33 locations in Greece: adaptation to the needs of various applications H. Kambezidis et al. https://doi.org/10.1007/s00704-020-03264-7
73. Spectral albedo, vegetation greenness, and radiative forcing responses of the Amazon to drought and wet conditions from 2005 to 2016 S. Li et al. https://doi.org/10.1016/j.agrformet.2026.111113
74. A robust and flexible satellite aerosol retrieval algorithm for multi-angle polarimetric measurements with physics-informed deep learning method M. Tao et al. https://doi.org/10.1016/j.rse.2023.113763
75. Vertical Distribution and Transport of Aerosols during a Dust Event in Xinjiang, Northwest China M. Xu et al. https://doi.org/10.1007/s13351-023-2148-0
76. Aerosol Retrieval Algorithm for Sentinel-2 Images Over Complex Urban Areas Y. Yang et al. https://doi.org/10.1109/TGRS.2022.3158061
77. Detectability of the potential climate change effect on transboundary air pollution pathways in the downwind area of China Y. Cai et al. https://doi.org/10.1016/j.scitotenv.2024.173490
78. Evaluating VIIRS EPS Aerosol Optical Depth in China: An intercomparison against ground-based measurements and MODIS C. Li et al. https://doi.org/10.1016/j.jqsrt.2018.12.002
79. Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP) P. Xian et al. https://doi.org/10.1002/qj.3497
80. Impact of atmospheric anthropogenic nitrogen on new production in the northern Indian Ocean: constrained based on satellite aerosol optical depth and particulate nitrogen levels V. Sarma et al. https://doi.org/10.1039/D2EM00234E
81. Temporal coherence in particulate matter in East Asian outflow regions: fingerprints of ENSO and Asian dust M. Kueh et al. https://doi.org/10.1038/s41612-023-00530-z
82. The Lake Urmia environmental disaster in Iran: A look at aerosol pollution A. Hossein Mardi et al. https://doi.org/10.1016/j.scitotenv.2018.03.148
83. Using Satellite‐Derived Fire Arrival Times for Coupled Wildfire‐Air Quality Simulations at Regional Scales of the 2020 California Wildfire Season W. Lassman et al. https://doi.org/10.1029/2022JD037062
84. Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models S. Park et al. https://doi.org/10.1016/j.scitotenv.2020.136516
85. Review of surface particulate monitoring of dust events using geostationary satellite remote sensing M. Sowden et al. https://doi.org/10.1016/j.atmosenv.2018.04.020
86. Modeling atmospheric brown carbon in the GISS ModelE Earth system model M. DeLessio et al. https://doi.org/10.5194/acp-24-6275-2024
87. Investigation of CATS aerosol products and application toward global diurnal variation of aerosols L. Lee et al. https://doi.org/10.5194/acp-19-12687-2019
88. Earth-Observation-Based Estimation and Forecasting of Particulate Matter Impact on Solar Energy in Egypt P. Kosmopoulos et al. https://doi.org/10.3390/rs10121870
89. Himawari-8 Aerosol Optical Depth (AOD) Retrieval Using a Deep Neural Network Trained Using AERONET Observations L. She et al. https://doi.org/10.3390/rs12244125
90. AeroCom phase III multi-model evaluation of the aerosol life cycle and optical properties using ground- and space-based remote sensing as well as surface in situ observations J. Gliß et al. https://doi.org/10.5194/acp-21-87-2021
91. Bias induced by the AOD representation time scale in long-term solar radiation calculations. Part 2: Impact on long-term solar irradiance predictions J. Ruiz-Arias et al. https://doi.org/10.1016/j.solener.2016.06.017
92. 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 https://doi.org/10.1007/s12517-020-5232-0
93. Carbonaceous aerosols remote sensing from geostationary satellite observation, Part I: Algorithm development using critical reflectance F. Bao et al. https://doi.org/10.1016/j.rse.2023.113459
94. A minimum albedo aerosol retrieval method for the new-generation geostationary meteorological satellite Himawari-8 X. Yan et al. https://doi.org/10.1016/j.atmosres.2018.02.021
95. Analysis of spatial and temporal variability of aerosol optical depth over China using MODIS combined Dark Target and Deep Blue product M. Filonchyk et al. https://doi.org/10.1007/s00704-018-2737-5
96. On the signature of the cirrus twilight zone U. Wollner et al. https://doi.org/10.1088/1748-9326/9/9/094010
97. Refined Use of Satellite Aerosol Optical Depth Snapshots to Constrain Biomass Burning Emissions in the GOCART Model M. Petrenko et al. https://doi.org/10.1002/2017JD026693
98. Evaluation of radiometric performance of MODIS visible bands using the Rayleigh scattering method S. Zhu et al. https://doi.org/10.1117/1.JRS.13.018503
99. Using Copernicus Atmosphere Monitoring Service Products to Constrain the Aerosol Type in the Atmospheric Correction Processor MAJA B. Rouquié et al. https://doi.org/10.3390/rs9121230
100. PM2.5 surface concentrations in southern West African urban areas based on sun photometer and satellite observations J. Léon et al. https://doi.org/10.5194/acp-21-1815-2021
101. Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. https://doi.org/10.1080/10643389.2019.1665944
102. Türkiye’de Terra ve Aqua MODIS Uydularından Elde Edilen Aerosol Optik Derinliğinin Zamansal ve Mekansal Değişimi S. KARSLIOĞLU et al. https://doi.org/10.21205/deufmd.2022247027
103. Assessment of the aerosol optical depths measured by satellite-based passive remote sensors in the Alberta oil sands region C. Sioris et al. https://doi.org/10.5194/acp-17-1931-2017
104. Validation of OMI seasonal and spatio-temporal variations in aerosol-cloud interactions over Banizoumbou using AERONET data C. Anoruo https://doi.org/10.1016/j.jastp.2020.105457
105. An end-to-end calibration of the Mini-EUSO detector in space M. Battisti et al. https://doi.org/10.1016/j.astropartphys.2024.103057
106. Satellite record of the transition of air quality over China L. Chen et al. https://doi.org/10.1080/20964471.2018.1514818
107. An adaptive atmospheric correction algorithm for the effective adjacency effect correction of submeter-scale spatial resolution optical satellite images: Application to a WorldView-3 panchromatic image T. Wang et al. https://doi.org/10.1016/j.rse.2021.112412
108. Aerosol profiling using radiometric and polarimetric spectral measurements in the O2 near infrared bands: Estimation of information content and measurement uncertainties M. Choi et al. https://doi.org/10.1016/j.rse.2020.112179
109. Present‐Day Patagonian Dust Emissions: Combining Surface Visibility, Mass Flux, and Reanalysis Data N. Cosentino et al. https://doi.org/10.1029/2020JD034459
110. Exploring the factors responsible for aerosol asymmetric trends over Indo-Gangetic Plain using remote sensing observations K. Shukla et al. https://doi.org/10.1016/j.jastp.2024.106220
111. Simultaneous Use of Ground-Based and Satellite Observation to Evaluate Atmospheric Air Pollution over Amman, Jordan H. Panahifar et al. https://doi.org/10.3390/atmos14020274
112. Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview M. Righi et al. https://doi.org/10.5194/gmd-13-1179-2020
113. Trends in physical, optical and chemical columnar aerosol characteristics and radiative effects over South and East Asia: Satellite and ground-based observations S. Ramachandran & M. Rupakheti https://doi.org/10.1016/j.gr.2021.09.016
114. A new generation aerosol optical depth dataset based on AVHRR data over China from 1981 to 2000 Y. Che et al. https://doi.org/10.1016/j.rse.2025.114899
115. Long-term change in aerosol characteristics over Indo-Gangetic Basin: How significant is the impact of emerging anthropogenic activities? S. Kumar et al. https://doi.org/10.1016/j.uclim.2021.100880
116. Impacts of irregular and strategic lockdown on air quality over Indo-Pak Subcontinent: Pre-to-post COVID-19 analysis F. Saleem et al. https://doi.org/10.1016/j.chaos.2023.114255
117. Hemispheric Contrast in Aerosol-Cloud Interactions: An Attempt for Detection and Attribution A. Henkes et al. https://doi.org/10.16993/tellusb.1886
118. Aerosols heat up the Himalayan climate S. Ramachandran et al. https://doi.org/10.1016/j.scitotenv.2023.164733
119. An updated review of satellite constraints on airborne dust: Current status and future prospects R. Kahn et al. https://doi.org/10.1051/e3sconf/202457501008
120. Evaluating and Improving Light Absorption Retrievals of Black Carbon Using In Situ Polar Nephelometry Q. Xu et al. https://doi.org/10.1021/acs.est.5c05919
121. Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe S. Ibrahim et al. https://doi.org/10.3390/rs13153027
122. Decadal shifts in aerosol hotspots and source attribution over IGP, north-east India and Himalayas: A 25-year (2000–2024) study S. Raul et al. https://doi.org/10.1016/j.atmosenv.2026.121810
123. Aerosol–Cloud–Meteorology Interaction Airborne Field Investigations: Using Lessons Learned from the U.S. West Coast in the Design of ACTIVATE off the U.S. East Coast A. Sorooshian et al. https://doi.org/10.1175/BAMS-D-18-0100.1
124. Retrieval of 500 m Aerosol Optical Depths from MODIS Measurements over Urban Surfaces under Heavy Aerosol Loading Conditions in Winter S. Jin et al. https://doi.org/10.3390/rs11192218
125. Mixing weight determination for retrieving optical properties of polluted dust with MODIS and AERONET data K. Chang et al. https://doi.org/10.1088/1748-9326/11/8/085002
126. Residential energy use emissions dominate health impacts from exposure to ambient particulate matter in India L. Conibear et al. https://doi.org/10.1038/s41467-018-02986-7
127. Response of enhanced vegetation index changes to latent/sensible heat flux and precipitation over Pakistan using remote sensing S. Tariq et al. https://doi.org/10.1007/s11356-022-20391-y
128. Estimating hourly full-coverage PM2.5 over China based on TOA reflectance data from the Fengyun-4A satellite F. Mao et al. https://doi.org/10.1016/j.envpol.2020.116119
129. A Deep-Neural-Network-Based Aerosol Optical Depth (AOD) Retrieval from Landsat-8 Top of Atmosphere Data L. She et al. https://doi.org/10.3390/rs14061411
130. Changes in Earth’s Energy Budget during and after the “Pause” in Global Warming: An Observational Perspective N. Loeb et al. https://doi.org/10.3390/cli6030062
131. Validation of Himawari-8 aerosol optical depth retrievals over China Z. Zhang et al. https://doi.org/10.1016/j.atmosenv.2018.11.024
132. Regional effects of atmospheric aerosols on temperature: an evaluation of an ensemble of online coupled models R. Baró et al. https://doi.org/10.5194/acp-17-9677-2017
133. A review of machine learning for modeling air quality: Overlooked but important issues D. Tang et al. https://doi.org/10.1016/j.atmosres.2024.107261
134. Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements W. Li et al. https://doi.org/10.3390/rs11171998
135. Simulation of aerosol deposition flux over the Arabian Peninsula with CHIMERE-2017: Sensitivity to different dry deposition schemes S. Beegum et al. https://doi.org/10.1016/j.atmosres.2020.104949
136. Aerosol Retrieval Method Using Multi-Angle Data from GF-5 02 DPC over the Jing–Jin–Ji Region Z. Wang et al. https://doi.org/10.3390/rs17081415
137. A novel retrieval of global dust optical depth and effective diameter based on MODIS thermal infrared observations J. Zheng et al. https://doi.org/10.1016/j.rse.2025.115083
138. 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. https://doi.org/10.1109/TGRS.2020.3048109
139. 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. https://doi.org/10.3390/rs15010275
140. Retrieval of Horizontal Visibility Using MODIS Data: A Deep Learning Approach B. Hu et al. https://doi.org/10.3390/atmos10120740
141. Global evaluation of NOAA-20 VIIRS dark target aerosol products over land and ocean X. Pei et al. https://doi.org/10.1016/j.atmosenv.2024.120949
142. Long-range PM2.5 pollution and health impacts from the 2023 Canadian wildfires Q. Zhang et al. https://doi.org/10.1038/s41586-025-09482-1
143. Handling Missing Data in Large-Scale MODIS AOD Products Using a Two-Step Model Y. Chi et al. https://doi.org/10.3390/rs12223786
144. Influence of aerosols on clouds, precipitation and freezing level height over the foothills of the Himalayas during the Indian summer monsoon P. Adhikari & J. Mejia https://doi.org/10.1007/s00382-021-05710-2
145. Long-term retrieval and analysis of aerosol components and optical properties in Tianjin, China: Insights from GRASP/component approach and sun-photometer observations (2014–2024) X. Zhang et al. https://doi.org/10.1016/j.atmosres.2025.108695
146. Long-term validation of MODIS C6 and C6.1 Dark Target aerosol products over China using CARSNET and AERONET H. Che et al. https://doi.org/10.1016/j.chemosphere.2019.06.238
147. 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. https://doi.org/10.1016/j.atmosres.2020.105146
148. A novel method of identifying and analysing oil smoke plumes based on MODIS and CALIPSO satellite data A. Mereuţă et al. https://doi.org/10.5194/acp-22-5071-2022
149. Retrieval of Atmospheric Aerosol Optical Depth From AVHRR Over Land With Global Coverage Using Machine Learning Method X. Tian et al. https://doi.org/10.1109/TGRS.2021.3129853
150. Satellite or ground-based measurements for air pollutants (PM2.5, PM10, SO2, NO2, O3) data and their health hazards: which is most accurate and why? Z. Mushtaq et al. https://doi.org/10.1007/s10661-024-12462-z
151. Air pollution and respiratory health risks in Jharkhand: a remote sensing and statistical approach L. Mohammad et al. https://doi.org/10.1007/s10661-025-13976-w
152. Aerosol optical depth retrieval from the EarthCARE Multi-Spectral Imager: the M-AOT product N. Docter et al. https://doi.org/10.5194/amt-16-3437-2023
153. Accounting for the aerosol type and additional satellite-borne aerosol products improves the prediction of PM2.5 concentrations S. Falah et al. https://doi.org/10.1016/j.envpol.2023.121119
154. Synergistic Use of Hyperspectral UV-Visible OMI and Broadband Meteorological Imager MODIS Data for a Merged Aerosol Product S. Go et al. https://doi.org/10.3390/rs12233987
155. Joint Retrieval of Aerosol Optical Depth and Surface Reflectance Over Land Using Geostationary Satellite Data L. She et al. https://doi.org/10.1109/TGRS.2018.2867000
156. High-resolution estimation of PM2.5 concentrations across China using multiple machine learning approaches and model fusion L. Meng et al. https://doi.org/10.1016/j.apr.2024.102110
157. Interdecadal Changes in Aerosol Optical Depth over Pakistan Based on the MERRA-2 Reanalysis Data during 1980–2018 R. Khan et al. https://doi.org/10.3390/rs13040822
158. Influence of smoke aerosols on low-level clouds over the Indian region during winter A. Sarkar et al. https://doi.org/10.1016/j.atmosres.2022.106358
159. Increase in carbon monoxide (CO) and aerosol optical depth (AOD) observed by satellites in the Northern Hemisphere over the summers of 2008–2023, linked to an increase in wildfires A. Ehret et al. https://doi.org/10.5194/acp-25-6365-2025
160. On the cause-and-effect relations between aerosols, water vapor, and clouds over East Asia S. Stathopoulos et al. https://doi.org/10.1007/s00704-021-03563-7
161. Spatial distribution analysis of the OMI aerosol layer height: a pixel-by-pixel comparison to CALIOP observations J. Chimot et al. https://doi.org/10.5194/amt-11-2257-2018
162. Comparisons of aerosol types and optical characters over Shouxian Area China observed from ground- and space-based systems X. Deng et al. https://doi.org/10.1364/OE.524152
163. Assessment of solar PV power potential over Asia Pacific region with remote sensing considering meteorological factors J. Principe & W. Takeuchi https://doi.org/10.1063/1.5059335
164. Comparison of the Anthropogenic Emission Inventory for CMIP6 Models with a Country-Level Inventory over China and the Simulations of the Aerosol Properties T. Fan et al. https://doi.org/10.1007/s00376-021-1119-6
165. SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe N. Ajtai et al. https://doi.org/10.3390/rs13050844
166. Results from the Fourth WMO Filter Radiometer Comparison for aerosol optical depth measurements S. Kazadzis et al. https://doi.org/10.5194/acp-18-3185-2018
167. Spatio-temporal correspondence of aerosol optical depth between CMIP6 simulations and MODIS retrievals over India B. Jaisankar et al. https://doi.org/10.1007/s11356-024-32314-0
168. AERONET Remotely Sensed Measurements and Retrievals of Biomass Burning Aerosol Optical Properties During the 2015 Indonesian Burning Season T. Eck et al. https://doi.org/10.1029/2018JD030182
169. A novel simplified method for surface albedo together with a look-up table to get an 18-year assessment of surface aerosol direct radiative effect in Central and East China Y. Shi et al. https://doi.org/10.1016/j.atmosenv.2020.117858
170. Vertical Structure of Aerosols and Mineral Dust Over the Bay of Bengal From Multisatellite Observations N. Lakshmi et al. https://doi.org/10.1002/2017JD027643
171. Improved Himawari-8 10-minute scale aerosol optical depth product using deep neural network over Japan Y. Tan et al. https://doi.org/10.1016/j.apr.2023.102005
172. High-Resolution Modeling of Particulate Matter: Comparing SEMARA AOD and MAIAC AOD Models M. Bagherinia & S. Boudaghpour https://doi.org/10.1007/s00024-025-03847-3
173. Satellite data to support air quality assessment and management T. Holloway et al. https://doi.org/10.1080/10962247.2025.2484153
174. Reduction of surface radiative forcing observed from remote sensing data during global COVID-19 lockdown U. Mazhar et al. https://doi.org/10.1016/j.atmosres.2021.105729
175. In-flight radiometric and polarimetric calibration of the Directional Polarimetric Camera onboard the GaoFen-5 satellite over the ocean L. Qie et al. https://doi.org/10.1364/AO.422980
176. Constraining the Twomey effect from satellite observations: issues and perspectives J. Quaas et al. https://doi.org/10.5194/acp-20-15079-2020
177. Bayesian Aerosol Retrieval-Based PM2.5 Estimation through Hierarchical Gaussian Process Models J. Zhang et al. https://doi.org/10.3390/math10162878
178. Exposure Assessment of Ambient PM2.5 Levels during a Sequence of Dust Episodes: A Case Study Coupling the WRF-Chem Model with GIS-Based Postprocessing E. Mancinelli et al. https://doi.org/10.3390/ijerph20085598
179. Generation of Hypothetical Radiances for Missing Green and Red Bands in Geostationary Environment Monitoring Spectrometer H. Ryu et al. https://doi.org/10.1109/JSTARS.2023.3280905
180. Diurnal and seasonal variability of PM2.5 and AOD in North China plain: Comparison of MERRA-2 products and ground measurements Z. Song et al. https://doi.org/10.1016/j.atmosenv.2018.08.012
181. Annual and Inter-annual Variability Coupled with Comparison of MODIS-AERONET Retrieved Aerosol Optical Depth over a Rural Site in the Central Indo-Gangetic Basin S. Varpe et al. https://doi.org/10.1007/s41810-022-00135-8
182. Evaluation of Aerosol Optical Depth (AOD) and PM2.5 associations for air quality assessment Z. Yang et al. https://doi.org/10.1016/j.rsase.2020.100396
183. Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018) M. Hammer et al. https://doi.org/10.1021/acs.est.0c01764
184. Aerosol pollution across the Indo-Gangetic plain and the Himalayas: Seasonal patterns and implications for air quality and climate S. Ramachandran & M. Rupakheti https://doi.org/10.1007/s11869-026-01897-7
185. Long-term characteristics of satellite-based PM2.5 over East China Q. He et al. https://doi.org/10.1016/j.scitotenv.2017.09.027
186. Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting W. Yu et al. https://doi.org/10.1038/s41598-017-07478-0
187. Revisiting Pseudo Invariant Calibration Sites (PICS) Over Sand Deserts for Vicarious Calibration of Optical Imagers at 20 km and 100 km Scales C. Bacour et al. https://doi.org/10.3390/rs11101166
188. Changes in the aerosol direct radiative forcing from 2001 to 2015: observational constraints and regional mechanisms F. Paulot et al. https://doi.org/10.5194/acp-18-13265-2018
189. Three Decades of Gross Primary Production (GPP) in China: Variations, Trends, Attributions, and Prediction Inferred from Multiple Datasets and Time Series Modeling Y. Bo et al. https://doi.org/10.3390/rs14112564
190. Accuracy assessment on eight public PM2.5 concentration datasets across China Y. Di et al. https://doi.org/10.1016/j.atmosenv.2024.120799
191. Investigation of aerosol effects on diurnal cycle of precipitation amount, frequency and intensity over Central Africa S. Matho Lontio et al. https://doi.org/10.1007/s00382-024-07303-1
192. Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces Y. Wang et al. https://doi.org/10.1016/j.atmosenv.2018.12.023
193. Satellite-observed relationships between land cover, burned area, and atmospheric composition over the southern Amazon E. Sands et al. https://doi.org/10.5194/acp-24-11081-2024
194. Technical note: Reconstructing missing surface aerosol elemental carbon data in long-term series with ensemble learning Q. Meng et al. https://doi.org/10.5194/acp-25-7485-2025
195. Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach J. Wei et al. https://doi.org/10.1016/j.rse.2019.111221
196. 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. https://doi.org/10.1007/s11356-018-2290-x
197. An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data J. Liu et al. https://doi.org/10.1080/17538947.2015.1130087
198. The greening of Northwest Indian subcontinent and reduction of dust abundance resulting from Indian summer monsoon revival Q. Jin & C. Wang https://doi.org/10.1038/s41598-018-23055-5
199. Exploring analog-based schemes for aerosol optical depth forecasting with WRF-Chem A. Raman et al. https://doi.org/10.1016/j.atmosenv.2020.118134
200. An algorithm for retrieving aerosol optical depth from Landsat 8 Operational Land Imager in Vietnam V. Tran Tuan et al. https://doi.org/10.1080/10106049.2023.2228748
201. Radiative characteristics of aerosol during extreme fire event over Siberia in summer 2012 T. Zhuravleva et al. https://doi.org/10.5194/amt-10-179-2017
202. Regional transport of anthropogenic pollution and dust aerosols in spring to Tianjin — A coastal megacity in China X. Su et al. https://doi.org/10.1016/j.scitotenv.2017.01.016
203. Long-term characterisation and discrimination of aerosol types across Africa using AERONET observations (1998–2024) Y. Situma et al. https://doi.org/10.1016/j.jastp.2026.106877
204. Characteristics of the Spatio-Temporal Dynamics of Aerosols in Central Asia and Their Influencing Factors Y. Zhou et al. https://doi.org/10.3390/rs14112684
205. An improved dark target method for aerosol optical depth retrieval over China from Himawari-8 L. Gao et al. https://doi.org/10.1016/j.atmosres.2020.105399
206. A deep learning-based imputation method for missing gaps in satellite aerosol products by fusing numerical model data N. Liu et al. https://doi.org/10.1016/j.atmosenv.2024.120440
207. Validation, Analysis, and Comparison of Misr V23 Aerosol Optical Depth Products with Modis and Aeronet Observations R. Fan et al. https://doi.org/10.2139/ssrn.4143337
208. Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land A. Lipponen et al. https://doi.org/10.5194/amt-11-1529-2018
209. Multi-timescale variations and drivers of atmospheric dust activity in the Yellow River Jiziwan region over the past 70 years R. Wang et al. https://doi.org/10.1016/j.atmosres.2026.109138
210. A Multiphysics Method for Increasing Model Background Error in the Aerosol Data Assimilation with an Ensemble Kalman Filter S. Lee et al. https://doi.org/10.1016/j.apr.2025.102878
211. Spatiotemporal coherence of mean and extreme aerosol particle events over eastern North America as observed from satellite R. Sullivan et al. https://doi.org/10.1016/j.atmosenv.2015.04.026
212. A Machine Learning Approach to Retrieving Aerosol Optical Depth Using Solar Radiation Measurements S. Logothetis et al. https://doi.org/10.3390/rs16071132
213. Winter haze amplification by aerosol hygroscopic growth over eastern Indo- Gangetic Plain T. Kallihosur et al. https://doi.org/10.1038/s43247-024-01792-y
214. Satellite assessment of sea spray aerosol productivity: Southern Ocean case study M. Witek et al. https://doi.org/10.1002/2015JD023726
215. Validation of VIIRS AOD through a Comparison with a Sun Photometer and MODIS AODs over Wuhan W. Wang et al. https://doi.org/10.3390/rs9050403
216. A review of extreme air pollution measurement and modeling techniques with applications K. Rautela et al. https://doi.org/10.1016/j.rser.2026.117178
217. Aerosol Optical Thickness: Organic Composition, Associated Particle Water, and Aloft Extinction A. Christiansen et al. https://doi.org/10.1021/acsearthspacechem.8b00163
218. Comparison and evaluation of the MODIS Collection 6 aerosol data in China M. Tao et al. https://doi.org/10.1002/2015JD023360
219. Subregional inversion of North African dust sources J. Escribano et al. https://doi.org/10.1002/2016JD025020
220. Measurement-based climatology of aerosol direct radiative effect, its sensitivities, and uncertainties from a background southeast US site J. Sherman & A. McComiskey https://doi.org/10.5194/acp-18-4131-2018
221. Retrieval of aerosol properties using relative radiance measurements from an all-sky camera R. Román et al. https://doi.org/10.5194/amt-15-407-2022
222. Preliminary Investigation of a New AHI Aerosol Optical Depth (AOD) Retrieval Algorithm and Evaluation with Multiple Source AOD Measurements in China F. Yang et al. https://doi.org/10.3390/rs10050748
223. What temporal resolution is required for remote sensing of regional aerosol concentrations using the Himawari-8 geostationary satellite M. Sowden et al. https://doi.org/10.1016/j.atmosenv.2019.116914
224. Intercomparison of aerosol optical depths from four reanalyses and their multi-reanalysis consensus P. Xian et al. https://doi.org/10.5194/acp-24-6385-2024
225. The short-term variability of aerosols and their impact on cloud properties and radiative effect over the Indo-Gangetic Plain S. Pandey et al. https://doi.org/10.1016/j.apr.2019.12.017
226. Parameterization of size of organic and secondary inorganic aerosol for efficient representation of global aerosol optical properties H. Zhu et al. https://doi.org/10.5194/acp-23-5023-2023
227. Seasonal aerosol variations over a coastal city, Zhoushan, China from CALIPSO observations K. Reddy et al. https://doi.org/10.1016/j.atmosres.2018.11.011
228. Retrieval of High-Resolution Aerosol Optical Depth for Urban Air Pollution Monitoring R. Bai et al. https://doi.org/10.3390/atmos13050756
229. Defining pathways to healthy sustainable urban development C. Tonne et al. https://doi.org/10.1016/j.envint.2020.106236
230. Sunlight-absorbing aerosol amplifies the seasonal cycle in low-cloud fraction over the southeast Atlantic J. Zhang & P. Zuidema https://doi.org/10.5194/acp-21-11179-2021
231. Cellular Statistical Models of Broken Cloud Fields. Part IV: Effects of Pixel Size on Idealized Satellite Observations M. Alexandrov & A. Marshak https://doi.org/10.1175/JAS-D-18-0345.1
232. A high-resolution, multi-decadal outgoing longwave radiation product from MODIS: algorithm and product D. Li et al. https://doi.org/10.1080/20964471.2026.2679327
233. Impacts of elevated-aerosol-layer and aerosol type on the correlation of AOD and particulate matter with ground-based and satellite measurements in Nanjing, southeast China Y. Han et al. https://doi.org/10.1016/j.scitotenv.2015.05.136
234. Integrating Fixed Monitoring Systems with Low-Cost Sensors to Create High-Resolution Air Quality Maps for the Northern China Plain Region C. Chao et al. https://doi.org/10.1021/acsearthspacechem.1c00174
235. Regional and global temperature response to anthropogenic SO2 emissions from China in three climate models M. Kasoar et al. https://doi.org/10.5194/acp-16-9785-2016
236. Spatiotemporal Heterogeneity of Aerosol and Cloud Properties Over the Southeast Atlantic: An Observational Analysis I. Chang et al. https://doi.org/10.1029/2020GL091469
237. Insights into recent aerosol trends over Asia from observations and CMIP6 simulations S. Ramachandran et al. https://doi.org/10.1016/j.scitotenv.2021.150756
238. 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. https://doi.org/10.5194/acp-14-2015-2014
239. Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data N. Luo et al. https://doi.org/10.1016/j.envpol.2024.123838
240. Performance comparison of Fengyun-4A and Himawari-8 in PM2.5 estimation in China Y. Hu et al. https://doi.org/10.1016/j.atmosenv.2021.118898
241. A Review of Satellite Constraints on Airborne Dust: What We Can Say, and What We Can't R. Kahn et al. https://doi.org/10.1051/e3sconf/20199901008
242. Validation of MODIS-Aqua Aerosol Products C051 and C006 over the Beijing-Tianjin-Hebei Region K. Wang et al. https://doi.org/10.3390/atmos8090172
243. Understanding the long-term trend of organic aerosol and the influences from anthropogenic emission and regional climate change in China W. Zhang et al. https://doi.org/10.5194/acp-25-3857-2025
244. MODIS 3 km and 10 km aerosol optical depth for China: Evaluation and comparison Q. He et al. https://doi.org/10.1016/j.atmosenv.2017.01.023
245. Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014–2017 Period L. Cheng et al. https://doi.org/10.3390/ijerph16193522
246. Multi-decadal aerosol variations from 1980 to 2009: a perspective from observations and a global model M. Chin et al. https://doi.org/10.5194/acp-14-3657-2014
247. Multicore Processors and Graphics Processing Unit Accelerators for Parallel Retrieval of Aerosol Optical Depth From Satellite Data: Implementation, Performance, and Energy Efficiency J. Liu et al. https://doi.org/10.1109/JSTARS.2015.2438893
248. Aerosol in global oceanic regions: Four-decade trends, spatial patterns, and policy implications L. Li et al. https://doi.org/10.1016/j.scitotenv.2024.174176
249. A Novel Physics-Informed Transformer Algorithm (PITA) for Near-Real-Time Synergistic Multiparameter Aerosol Retrieval From FY-4B/AGRI Geostationary Satellite Y. Jiao et al. https://doi.org/10.1109/TGRS.2026.3700045
250. Estimation of surface-level PM concentration from satellite observation taking into account the aerosol vertical profiles and hygroscopicity K. Kim et al. https://doi.org/10.1016/j.chemosphere.2015.09.040
251. Seasonal impact of regional outdoor biomass burning on air pollution in three Indian cities: Delhi, Bengaluru, and Pune T. Liu et al. https://doi.org/10.1016/j.atmosenv.2017.10.024
252. Fusing Retrievals of High Resolution Aerosol Optical Depth from Landsat-8 and Sentinel-2 Observations over Urban Areas H. Lin et al. https://doi.org/10.3390/rs13204140
253. The extreme forest fires in California/Oregon in 2020: Aerosol optical and physical properties and comparisons of aged versus fresh smoke T. Eck et al. https://doi.org/10.1016/j.atmosenv.2023.119798
254. Monthly analysis of PM ratio characteristics and its relation to AOD M. Sorek-Hamer et al. https://doi.org/10.1080/10962247.2016.1208121
255. Three-dimensional reconstruction of aerosols based on spectral radiance matching P. Li et al. https://doi.org/10.1016/j.atmosenv.2024.120485
256. Assessment of long-term trends in chlorophyll-a and sea surface temperature in the Arabian Sea and their association with aerosols using remote sensing Y. Sun et al. https://doi.org/10.1016/j.ocecoaman.2023.106716
257. Impact of Aerosol Vertical Distribution on Aerosol Optical Depth Retrieval from Passive Satellite Sensors C. Li et al. https://doi.org/10.3390/rs12091524
258. Aerosol Optical Properties and Long-Term Variations over the Northeastern Tibetan Plateau: Insights from Ground and Space Observations and MERRA-2 Data P. Tang et al. https://doi.org/10.3390/rs18091283
259. Role of atmospheric nutrient pollution in stimulating phytoplankton growth in small area and shallow depth water bodies: Arabian Gulf and the sea of Oman A. Farahat & A. Abuelgasim https://doi.org/10.1016/j.atmosenv.2019.117045
260. 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. https://doi.org/10.1016/j.rse.2021.112617
261. Evaluation of VIIRS Land Aerosol Model Selection with AERONET Measurements W. Wang et al. https://doi.org/10.3390/ijerph14091016
262. Remote Sensing of Aerosols at Night with the CoSQM Sky Brightness Data C. Marseille et al. https://doi.org/10.3390/rs13224623
263. Water quality assessment in a wetland complex using Sentinel 2 satellite images F. Torres-Bejarano et al. https://doi.org/10.1007/s13762-020-02988-3
264. Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate M. Christensen et al. https://doi.org/10.5194/acp-17-13151-2017
265. Multi-Decadal Trends in Aerosol Optical Depth of the Main Aerosol Species Based on MERRA-2 Reanalysis: A Case Study in the Baltic Sea Basin E. Mancinelli et al. https://doi.org/10.3390/rs16132421
266. Estimating PM1 concentrations from MODIS over Yangtze River Delta of China during 2014–2017 K. Qin et al. https://doi.org/10.1016/j.atmosenv.2018.09.054
267. Validation of SOAR VIIRS Over‐Water Aerosol Retrievals and Context Within the Global Satellite Aerosol Data Record A. Sayer et al. https://doi.org/10.1029/2018JD029465
268. VIIRS Environmental Data Record and Deep Blue aerosol products: validation, comparison, and spatiotemporal variations from 2013 to 2018 in China L. He et al. https://doi.org/10.1016/j.atmosenv.2021.118265
269. Simultaneous determination of aerosol optical depth and exponent of the Junge power law from MODIS shortwave infrared bands over Qinghai Lake L. Zhu et al. https://doi.org/10.1364/AO.57.006497
270. New Regression Method to Merge Different MODIS Aerosol Products Based on NDVI Datasets W. Xu et al. https://doi.org/10.3390/atmos10060303
271. Influence of Hygroscopic Aerosols for Understanding the Cloud Microphysical Properties in the Atmosphere Over Mangaluru, Southwest Coast of India A. Ahmed et al. https://doi.org/10.1007/s12524-025-02138-7
272. An observationally constrained estimate of global dust aerosol optical depth D. Ridley et al. https://doi.org/10.5194/acp-16-15097-2016
273. High Spatial Resolution PM2.5 Retrieval Using MODIS and Ground Observation Station Data Based on Ensemble Random Forest X. Chen et al. https://doi.org/10.1109/ACCESS.2019.2908975
274. Development of a Practical Atmospheric Correction Algorithm for Inland and Nearshore Coastal Waters D. Zhao et al. https://doi.org/10.1109/TGRS.2021.3092839
275. Aerosol optical depth over Northeastern Brazil: A multi-platform intercomparison study G. Münchow et al. https://doi.org/10.1016/j.atmosres.2024.107864
276. Weakened dust activity over China and Mongolia from 2001 to 2020 associated with climate change and land-use management S. Wang et al. https://doi.org/10.1088/1748-9326/ac3b79
277. Detection of aerosol pollution sources during sandstorms in Northwestern China using remote sensed and model simulated data M. Filonchyk et al. https://doi.org/10.1016/j.asr.2017.11.037
278. Assessment of aerosol optical properties using remote sensing over highly urbanised twin cities of Pakistan M. Awais et al. https://doi.org/10.1016/j.jastp.2018.04.008
279. Effect of MODIS Terra radiometric calibration improvements on Collection 6 Deep Blue aerosol products: Validation and Terra/Aqua consistency A. Sayer et al. https://doi.org/10.1002/2015JD023878
280. Credibility and statistical characteristics of CAMSRA and MERRA-2 AOD reanalysis products over the Sichuan Basin during 2003–2018 H. Liu et al. https://doi.org/10.1016/j.atmosenv.2020.117980
281. Evaluation of the representativeness of ground-based visibility for analysing the spatial and temporal variability of aerosol optical thickness in China Z. Zhang et al. https://doi.org/10.1016/j.atmosenv.2016.09.060
282. Assessment of the relative influences of long-range transport, fossil fuel and biomass burning from aerosol pollution under restricted anthropogenic emissions: A national scenario in India M. Dutta & A. Chatterjee https://doi.org/10.1016/j.atmosenv.2021.118423
283. How is the spatiotemporal representativeness of ground- and satellite-based aerosol optical depth (AOD) measurements over Asia? L. Chen et al. https://doi.org/10.1016/j.atmosres.2024.107857
284. Estimating PM2.5 Concentrations Based on MODIS AOD and NAQPMS Data over Beijing–Tianjin–Hebei Q. Wang et al. https://doi.org/10.3390/s19051207
285. Satellite observations of aerosols and clouds over southern China from 2006 to 2015: analysis of changes and possible interaction mechanisms N. Benas et al. https://doi.org/10.5194/acp-20-457-2020
286. Characterization of satellite-based proxies for estimating nucleation mode particles over South Africa A. Sundström et al. https://doi.org/10.5194/acp-15-4983-2015
287. A global-scale analysis of the MISR Level-3 aerosol optical depth (AOD) product: Comparison with multi-platform AOD data sources K. Gui et al. https://doi.org/10.1016/j.apr.2021.101238
288. Improved MODIS Dark Target aerosol optical depth algorithm over land: angular effect correction Y. Wu et al. https://doi.org/10.5194/amt-9-5575-2016
289. How Long should the MISR Record Be when Evaluating Aerosol Optical Depth Climatology in Climate Models? H. Lee et al. https://doi.org/10.3390/rs10091326
290. Comparison and evaluation of multiple satellite aerosol products over China in different scenarios under a unified criterion: Preparation for consistent and high-quality dataset construction H. Zhu et al. https://doi.org/10.1016/j.atmosres.2022.106374
291. Do aerosols induce the differences in low cloud frequency between eastern China and eastern United States? H. Xu et al. https://doi.org/10.1016/j.atmosres.2025.107994
292. Spatial Particulate Fields during High Winds in the Imperial Valley, California F. Freedman et al. https://doi.org/10.3390/atmos11010088
293. High-Resolution Satellite-Derived PM2.5 from Optimal Estimation and Geographically Weighted Regression over North America A. van Donkelaar et al. https://doi.org/10.1021/acs.est.5b02076
294. Characterization of aerosol optical depth (AOD) anomalies in September and October 2022 over Skukuza in South Africa M. Ranaivombola et al. https://doi.org/10.5194/acp-25-3519-2025
295. From column to surface: connecting the performance in simulating aerosol optical properties and PM2.5 concentrations in the NASA GEOSCCM C. Mogno et al. https://doi.org/10.5194/acp-26-3025-2026
296. Aerosol effects on clouds are concealed by natural cloud heterogeneity and satellite retrieval errors A. Arola et al. https://doi.org/10.1038/s41467-022-34948-5
297. The Variations and Trends of MODIS C5 & C6 Products’ Errors in the Recent Decade over the Background and Urban Areas of North China Q. Zhang et al. https://doi.org/10.3390/rs8090754
298. Confidently Uncertain: Validating Satellite ECV Measurement Uncertainty Estimates T. Verhoelst et al. https://doi.org/10.1007/s10712-026-09939-6
299. Multi-Sensor Observation of a Saharan Dust Outbreak over Transylvania, Romania in April 2019 N. Ajtai et al. https://doi.org/10.3390/atmos11040364
300. Evaluation of aerosol optical depths and clear-sky radiative fluxes of the CERES Edition 4.1 SYN1deg data product D. Fillmore et al. https://doi.org/10.5194/acp-22-10115-2022
301. Determination of optimal solar-viewing geometry for in-flight polarization calibration using sun glint over ocean M. Zhu et al. https://doi.org/10.1364/AO.536792
302. Inter-comparison of multi-satellites and Aeronet AOD over Indian Region R. Mangla et al. https://doi.org/10.1016/j.atmosres.2020.104950
303. Improvement of the Aerosol Forecast and Analysis Over East Asia With Joint Assimilation of Two Geostationary Satellite Observations Y. Cheng et al. https://doi.org/10.1029/2022GL099908
304. Combined use of satellite and surface observations to study aerosol optical depth in different regions of China M. Filonchyk et al. https://doi.org/10.1038/s41598-019-42466-6
305. Aerosol Optical Depth Retrieval Over South Asia Using FY-4A/AGRI Data Y. Xie et al. https://doi.org/10.1109/TGRS.2021.3124421
306. Introducing the MISR level 2 near real-time aerosol product M. Witek et al. https://doi.org/10.5194/amt-14-5577-2021
307. Spatial shifting of COVID-19 clusters and disease association with environmental parameters in India: A time series analysis A. Jana et al. https://doi.org/10.1016/j.envres.2023.115288
308. Using the Gaofen-4 geostationary satellite to retrieve aerosols with high spatiotemporal resolution X. Chen et al. https://doi.org/10.1117/1.JRS.12.042606
309. Revisiting the Role of SMAP Soil Moisture Retrievals in WRF-Chem Dust Emission Simulations over the Western U.S. P. Jiménez y Muñoz et al. https://doi.org/10.3390/rs17081345
310. A comparative study of aerosol microphysical properties retrieved from ground-based remote sensing and aircraft in situ measurements during a Saharan dust event M. Granados-Muñoz et al. https://doi.org/10.5194/amt-9-1113-2016
311. A Geostatistics-Based Method to Determine the Pixel Distance in a Structure Function Model for Aerosol Optical Depth Inversion G. Zhou et al. https://doi.org/10.3390/atmos8010006
312. Global dust optical depth climatology derived from CALIOP and MODIS aerosol retrievals on decadal timescales: regional and interannual variability Q. Song et al. https://doi.org/10.5194/acp-21-13369-2021
313. Climate-specific and global validation of MODIS Aqua and Terra aerosol optical depth at 452 AERONET stations J. Bright & C. Gueymard https://doi.org/10.1016/j.solener.2019.03.043
314. Non-Monotonic Aerosol Effect on Precipitation in Convective Clouds over Tropical Oceans H. Liu et al. https://doi.org/10.1038/s41598-019-44284-2
315. Aerosol optical depth in a western Mediterranean site: An assessment of different methods A. Sanchez-Romero et al. https://doi.org/10.1016/j.atmosres.2016.02.002
316. Exploring biases in the GISS ModelE Earth system model brown carbon representation with in-situ flight observations M. DeLessio et al. https://doi.org/10.5194/acp-26-7325-2026
317. Improving the image fusion procedure for high-spatiotemporal aerosol optical depth retrieval: a case study of urban area in Taiwan C. Huang et al. https://doi.org/10.1117/1.JRS.12.042605
318. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China J. Wei et al. https://doi.org/10.1016/j.envint.2020.106290
319. Global Sources of Fine Particulate Matter: Interpretation of PM2.5 Chemical Composition Observed by SPARTAN using a Global Chemical Transport Model C. Weagle et al. https://doi.org/10.1021/acs.est.8b01658
320. Enhanced Atmospheric Pollutants Strengthened Winter Convective Mixing and Phytoplankton Blooms in the Northern Arabian Sea B. Sridevi & V. Sarma https://doi.org/10.1029/2021JG006527
321. Near‐Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects C. Yang et al. https://doi.org/10.1029/2022GL098274
322. Meteorological and anthropogenic contributions to changes in the Aerosol Optical Depth (AOD) over China during the last decade G. de Leeuw et al. https://doi.org/10.1016/j.atmosenv.2023.119676
323. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought R. Field et al. https://doi.org/10.1073/pnas.1524888113
324. Trends in MODIS and AERONET derived aerosol optical thickness over Northern Europe P. Glantz et al. https://doi.org/10.1080/16000889.2018.1554414
325. Aerosol Absorption Over Land Derived From the Ultra-Violet Aerosol Index by Deep Learning J. Sun et al. https://doi.org/10.1109/JSTARS.2021.3108669
326. A Laboratory Experiment for the Statistical Evaluation of Aerosol Retrieval (STEAR) Algorithms G. Schuster et al. https://doi.org/10.3390/rs11050498
327. 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. https://doi.org/10.1016/j.atmosres.2015.12.003
328. Evaluation of the WRF-Chem performance for the air pollutants over the United Arab Emirates Y. Yarragunta et al. https://doi.org/10.5194/acp-25-1685-2025
329. Abrupt cloud clearing of marine stratocumulus in the subtropical southeast Atlantic S. Yuter et al. https://doi.org/10.1126/science.aar5836
330. Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products J. Wei et al. https://doi.org/10.5194/acp-19-7183-2019
331. Aerosol characteristics from earth observation systems: A comprehensive investigation over South Asia (2000–2019) A. Mhawish et al. https://doi.org/10.1016/j.rse.2021.112410
332. Visibility-derived aerosol optical depth over global land from 1959 to 2021 H. Hao et al. https://doi.org/10.5194/essd-16-3233-2024
333. A Generalized Aerosol Algorithm for Multi‐Spectral Satellite Measurement With Physics‐Informed Deep Learning Method J. Jiang et al. https://doi.org/10.1029/2023GL106806
334. A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land G. Zhong et al. https://doi.org/10.3390/rs9060524
335. Review of Atmospheric Environmental Change from Earth Observing Satellites K. Lee et al. https://doi.org/10.5572/ajae.2021.147
336. The Canadian Optimized Statistical Smoke Exposure Model (CanOSSEM): A machine learning approach to estimate national daily fine particulate matter (PM2.5) exposure N. Paul et al. https://doi.org/10.1016/j.scitotenv.2022.157956
337. Long-term validation and error analysis of DB and MAIAC aerosol products over bright surface of China W. Ji et al. https://doi.org/10.1016/j.atmosres.2023.107106
338. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements D. Giles et al. https://doi.org/10.5194/amt-12-169-2019
339. Spatiotemporal variation of aerosol and potential long-range transport impact over the Tibetan Plateau, China J. Zhu et al. https://doi.org/10.5194/acp-19-14637-2019
340. Evaluating the Effect of Emission Schemes on Dust Simulation in East Asia During Spring 2023 S. Wang et al. https://doi.org/10.3390/atmos17020154
341. CALIPSO inferred most probable heights of global dust and smoke layers J. Huang et al. https://doi.org/10.1002/2014JD022898
342. Estimating PM2.5 surface concentrations from AOD: A combination of SLSTR and MODIS J. Handschuh et al. https://doi.org/10.1016/j.rsase.2022.100716
343. Case Study of the Aerosol Optical Properties in the Atmosphere over Wuhan, China Q. Mao et al. https://doi.org/10.3390/atmos14071108
344. Changing patterns in aerosol vertical distribution over South and East Asia M. Ratnam et al. https://doi.org/10.1038/s41598-020-79361-4
345. Aerosol Detection from the Cloud–Aerosol Transport System on the International Space Station: Algorithm Overview and Implications for Diurnal Sampling E. Nowottnick et al. https://doi.org/10.3390/atmos13091439
346. An Improved Global Land Anthropogenic Aerosol Product Based on Satellite Retrievals From 2008 to 2016 C. Liang et al. https://doi.org/10.1109/LGRS.2020.2991730
347. Contrast in column-integrated aerosol optical properties during heating and non-heating seasons at Urumqi — Its causes and implications X. Li et al. https://doi.org/10.1016/j.atmosres.2017.03.008
348. A Global Survey of Apparent Aerosol‐Cloud Interaction Signals L. Oreopoulos et al. https://doi.org/10.1029/2019JD031287
349. Apparent surface-to-sky radiance ratio of natural waters including polarization and aerosol effects: implications for above-water radiometry T. Harmel https://doi.org/10.3389/frsen.2023.1307976
350. Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach X. Hu et al. https://doi.org/10.1021/acs.est.7b01210
351. Spatially coordinated airborne data and complementary products for aerosol, gas, cloud, and meteorological studies: the NASA ACTIVATE dataset A. Sorooshian et al. https://doi.org/10.5194/essd-15-3419-2023
352. Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations J. Zheng et al. https://doi.org/10.5194/acp-23-8271-2023
353. 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. https://doi.org/10.1029/2022JD037298
354. A Novel Framework Integrating Spectrum Analysis and AI for Near-Ground-Surface PM2.5 Concentration Estimation H. Qin et al. https://doi.org/10.3390/rs17223780
355. Contrasting nature of aerosols over South Asian cities and its surrounding environment A. Pandey et al. https://doi.org/10.1016/j.envpol.2024.124622
356. On the influence of the diurnal variations of aerosol content to estimate direct aerosol radiative forcing using MODIS data H. Xu et al. https://doi.org/10.1016/j.atmosenv.2016.06.067
357. Contrasting seasonal pattern between ground-based PM2.5 and MODIS satellite-based aerosol optical depth (AOD) at an urban site in Hanoi, Vietnam N. Luong et al. https://doi.org/10.1007/s11356-021-16464-z
358. Estimation of PM2.5 Using Multi-Angle Polarized TOA Reflectance Data from the GF-5B Satellite R. Zhang et al. https://doi.org/10.3390/rs16213944
359. CERES MODIS Cloud Product Retrievals for Edition 4—Part I: Algorithm Changes P. Minnis et al. https://doi.org/10.1109/TGRS.2020.3008866
360. Dust Aerosol Retrieval Over the Oceans With the MODIS/VIIRS Dark Target Algorithm: 2. Nonspherical Dust Model Y. Zhou et al. https://doi.org/10.1029/2020EA001222
361. 基于正-反演模拟的气溶胶卫星遥感偏振和多角度指标依赖 李. Li Yanfeng et al. https://doi.org/10.3788/AOS241885
362. Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty A. van Donkelaar et al. https://doi.org/10.1021/acs.est.1c05309
363. Mega Asian dust event over China on 27–31 March 2021 observed with space-borne instruments and ground-based polarization lidar Y. He et al. https://doi.org/10.1016/j.atmosenv.2022.119238
364. Ground-based validation of aerosol optical depth from CAMS reanalysis project: An uncertainty input on direct normal irradiance under cloud-free conditions V. Salamalikis et al. https://doi.org/10.1016/j.renene.2021.02.025
365. Investigating the long-term trends in aerosol optical depth and its association with meteorological parameters and enhanced vegetation index over Turkey Ö. Zeydan et al. https://doi.org/10.1007/s11356-022-23553-0
366. Remote sensing of two exceptional winter aerosol pollution events and representativeness of ground-based measurements A. Baron et al. https://doi.org/10.5194/acp-20-6749-2020
367. Saharan Dust Modeling Over the Mediterranean Basin and Central Europe: Does the Resolution Matter? L. Palacios-Peña et al. https://doi.org/10.3389/feart.2019.00290
368. An Aerosol Retrieval Algorithm Over Land From Multispectral Single-Viewing Measurements of Intensity and Polarization Z. Ji et al. https://doi.org/10.1109/TGRS.2025.3562783
369. Biomass burning smoke transport and radiative impact over the city of São Paulo: an extreme event case study J. Rosas Santana et al. https://doi.org/10.5194/acp-25-15935-2025
370. 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. https://doi.org/10.1016/j.atmosenv.2017.06.047
371. Estimation of ground-level particulate matter concentrations based on synergistic use of MODIS, MERRA-2 and AERONET AODs over a coastal site in the Eastern Mediterranean G. Tuna Tuygun et al. https://doi.org/10.1016/j.atmosenv.2021.118562
372. Atmospheric rivers fueling the intensification of fog and haze over Indo-Gangetic Plains S. Verma et al. https://doi.org/10.1038/s41598-022-09206-9
373. First Mapping of Monthly and Diurnal Climatology of Saharan Dust Layer Height Over the Atlantic Ocean From EPIC/DSCOVR in Deep Space Z. Lu et al. https://doi.org/10.1029/2022GL102552
374. The synoptic skill of aerosol optical depth and angstrom exponent levels over the Mediterranean Basin S. Bodenheimer et al. https://doi.org/10.1002/joc.6931
375. How Does COVID-19 Lockdown Impact Air Quality in India? Z. Hu et al. https://doi.org/10.3390/rs14081869
376. Minimum aerosol layer detection sensitivities and their subsequent impacts on aerosol optical thickness retrievals in CALIPSO level 2 data products T. Toth et al. https://doi.org/10.5194/amt-11-499-2018
377. Aerosol model evaluation using two geostationary satellites over East Asia in May 2016 D. Goto et al. https://doi.org/10.1016/j.atmosres.2018.10.016
378. Spatio-temporal distribution of aerosol optical depth and cloud properties over Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia B. Getachew et al. https://doi.org/10.1016/j.rsase.2020.100401
379. A Capsule Network Model for Aerosol Retrieval from DPC/Gaofen-5(02) Satellite Multi-Angle Polarimetric Observation H. Gu et al. https://doi.org/10.34133/remotesensing.1008
380. Constraining Uncertainty in Aerosol Direct Forcing D. Watson‐Parris et al. https://doi.org/10.1029/2020GL087141
381. Characterizing the long‐term dynamics of aerosol optical depth in the Yangtze River Middle‐Reach urban agglomeration, China M. Zhang et al. https://doi.org/10.1002/joc.6945
382. Synergetic retrieval from multi-mission spaceborne measurements for enhanced aerosol and surface characterization P. Litvinov et al. https://doi.org/10.5194/amt-18-7679-2025
383. Spatial variability of the aerosol optical thickness over Southern Ocean and coastal Antarctica: Comparison with MODIS and MERRA-2 aerosol products K. Hareef baba shaeb et al. https://doi.org/10.1016/j.dsr2.2020.104776
384. PM2.5 over North China based on MODIS AOD and effect of meteorological elements during 2003–2015 Y. Chen et al. https://doi.org/10.1007/s11783-019-1202-8
385. Hybrid physics–AI aerosol property retrieval algorithm for AMI/GK-2A with a deep learning radiative transfer emulator M. Kim et al. https://doi.org/10.1016/j.jag.2026.105393
386. Comparing multiple model-derived aerosol optical properties to spatially collocated ground-based and satellite measurements I. Ocko & P. Ginoux https://doi.org/10.5194/acp-17-4451-2017
387. Evaluation of MERRA-2 and CAMS reanalysis for black carbon aerosol in China W. Li et al. https://doi.org/10.1016/j.envpol.2023.123182
388. Aerosol optical depth in East Asia from VIIRS, MODIS, and MISR: Evaluation, variability, and meteorological associations J. Gong et al. https://doi.org/10.1016/j.atmosenv.2025.121760
389. Assessment of MODIS thermal emissive bands calibration performance using deep convective clouds T. Chang et al. https://doi.org/10.1117/1.JRS.13.044526
390. Monitoring multiple satellite aerosol optical depth (AOD) products within the Copernicus Atmosphere Monitoring Service (CAMS) data assimilation system S. Garrigues et al. https://doi.org/10.5194/acp-22-14657-2022
391. Investigation of artificial neural network performance in the aerosol properties retrieval N. Srivastava et al. https://doi.org/10.2166/wcc.2021.336
392. Mortality and economic burden of PM2.5 and NO2 in Thailand using satellite remote sensing and Random Forest algorithms T. Khempunjakul et al. https://doi.org/10.1016/j.envc.2025.101366
393. 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 https://doi.org/10.1016/j.apr.2024.102226
394. A complex aerosol transport event over Europe during the 2017 Storm Ophelia in CAMS forecast systems: analysis and evaluation D. Akritidis et al. https://doi.org/10.5194/acp-20-13557-2020
395. Global Aerosol Retrieval Over Land Using the Chinese Satellite Polarimeter DPC-2/GF-5(02) Z. Zhang et al. https://doi.org/10.1109/TGRS.2025.3633391
396. Exploring systematic offsets between aerosol products from the two MODIS sensors R. Levy et al. https://doi.org/10.5194/amt-11-4073-2018
397. Metal Concentrations and Source Apportionment of PM2.5 in Chiang Rai and Bangkok, Thailand during a Biomass Burning Season J. Kayee et al. https://doi.org/10.1021/acsearthspacechem.0c00140
398. Estimation of PM2.5 concentrations using satellite-derived aerosol metrics over an Indian summer monsoon region A. Singh et al. https://doi.org/10.1016/j.pce.2025.104264
399. Pre-monsoon aerosol intensification and enhanced atmospheric warming over the South-Eastern Arabian Sea E. Reethu et al. https://doi.org/10.1007/s11356-026-37651-w
400. Assessing the Aerosols, Clouds and Their Relationship Over the Northern Bay of Bengal Using a Global Climate Model P. Sharma et al. https://doi.org/10.1029/2022EA002706
401. Surface albedo and reflectance: Review of definitions, angular and spectral effects, and intercomparison of major data sources in support of advanced solar irradiance modeling over the Americas C. Gueymard et al. https://doi.org/10.1016/j.solener.2019.02.040
402. Validation of Aqua-MODIS C051 and C006 Operational Aerosol Products Using AERONET Measurements Over Pakistan M. Bilal et al. https://doi.org/10.1109/JSTARS.2015.2481460
403. 基于神经网络的星载高精度偏振扫描仪数据气溶胶光学厚度反演研究 杨. Yang Chenyu et al. https://doi.org/10.3788/AOS250438
404. A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model C. Jung et al. https://doi.org/10.3390/rs13183657
405. Modeling and Analysis of Long-Range Dust Transport from the Sahara and Arabian Deserts To West Asia Using WRF-Chem and Remote Sensing: Insights from the Spring 2015 Dust Event Y. Aydin et al. https://doi.org/10.1007/s41748-026-01043-4
406. New interpretable deep learning model to monitor real-time PM2.5 concentrations from satellite data X. Yan et al. https://doi.org/10.1016/j.envint.2020.106060
407. Improving AOD Algorithm Evaluation: A Spatial Matching Method for Minimizing Quality Control Bias B. Du et al. https://doi.org/10.3390/rs17071235
408. Simultaneous retrievals of biomass burning aerosols and trace gases from the ultraviolet to near-infrared over northern Thailand during the 2019 pre-monsoon season U. Jeong et al. https://doi.org/10.5194/acp-22-11957-2022
409. Spatio-temporal air quality assessment in Tehran, Iran, during the COVID-19 lockdown periods M. Bagherinia et al. https://doi.org/10.1080/10106049.2023.2169374
410. Frontiers in Satellite‐Based Estimates of Cloud‐Mediated Aerosol Forcing D. Rosenfeld et al. https://doi.org/10.1029/2022RG000799
411. Avoided mortality by particulate air pollution control measures in China A. Georgoulias et al. https://doi.org/10.1016/j.scitotenv.2025.180566
412. Synergy of Satellite‐ and Ground‐Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach J. Li et al. https://doi.org/10.1029/2019JD031884
413. Analysis of contrasting aerosol indirect effects in liquid water clouds over the northern part of Arabian Sea K. Anwar et al. https://doi.org/10.1016/j.atmosenv.2025.121123
414. Seasonal variation in particle contribution and aerosol types in Shanghai based on satellite data from MODIS and CALIOP Q. Liu et al. https://doi.org/10.1016/j.partic.2019.10.001
415. Effects of aerosol decline on surface solar radiation during the implementation period of air pollution prevention and control policies over China K. Zhao et al. https://doi.org/10.1016/j.jhazmat.2026.141966
416. Global assessment of aerosol radiative effects: New insights from observations, reanalysis, and climate models K. Ansari et al. https://doi.org/10.1016/j.scitotenv.2025.180871
417. 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. https://doi.org/10.1016/j.atmosenv.2020.117501
418. Improved Clear Sky Model from In Situ Observations and Spatial Distribution of Aerosol Optical Depth for Satellite-Derived Solar Irradiance over the Korean Peninsula C. Kim et al. https://doi.org/10.3390/rs14092167
419. Long-term spatio-temporal trends in atmospheric aerosols and trace gases over Pakistan using remote sensing M. Javed et al. https://doi.org/10.1007/s11600-023-01143-z
420. Assessing the effect of long-range pollutant transportation on air quality in Seoul using the conditional potential source contribution function method U. Jeong et al. https://doi.org/10.1016/j.atmosenv.2016.11.017
421. Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations J. Lee et al. https://doi.org/10.1002/2015JD023567
422. Advancing Sustainable Development Goals through Earth Observation Satellite Data: Current Insights and Future Directions Q. Zhao & L. Yu https://doi.org/10.34133/remotesensing.0403
423. Accuracy assessment of CAMS and MERRA-2 reanalysis PM2.5 and PM10 concentrations over China M. Ali et al. https://doi.org/10.1016/j.atmosenv.2022.119297
424. Performance of MODIS C6 Aerosol Product during Frequent Haze-Fog Events: A Case Study of Beijing W. Chen et al. https://doi.org/10.3390/rs9050496
425. An Algorithm for Aerosol Optical Properties Retrieval Over the Ocean Accelerated by a Neural Network From Single-View Multispectral Measurements of Intensity and Polarization Z. Ji et al. https://doi.org/10.1109/TGRS.2025.3630102
426. 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. https://doi.org/10.1016/j.apr.2019.11.020
427. Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance R. Levy et al. https://doi.org/10.5194/amt-8-4083-2015
428. Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool A. Lauer et al. https://doi.org/10.1016/j.rse.2017.01.007
429. Critical Corrections of MODIS Over-Land Aerosol Optical Depth Data From 2016 to 2017 for Near Real Time Applications X. Shen et al. https://doi.org/10.1109/JSTARS.2026.3678522
430. Assessment of satellite-estimated near-surface sulfate and nitrate concentrations and their precursor emissions over China from 2006 to 2014 Y. Si et al. https://doi.org/10.1016/j.scitotenv.2019.02.180
431. AQUAVis: Landsat-sentinel virtual constellation of remote sensing reflectance (Rrs) product for coastal and inland waters T. Lima et al. https://doi.org/10.1016/j.srs.2025.100294
432. Single Scattering Albedo of High Loading Aerosol Estimated Across East Asia From S-NPP VIIRS F. Bao et al. https://doi.org/10.1109/TGRS.2021.3076796
433. Development of a Multispectral Albedometer and Deployment on an Unmanned Aircraft for Evaluating Satellite Retrieved Surface Reflectance over Nevada’s Black Rock Desert J. Boehmler et al. https://doi.org/10.3390/s18103504
434. Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe Y. Xue et al. https://doi.org/10.1016/j.rse.2017.06.036
435. A Priori Land Surface Reflectance Synergized With Multiscale Features Convolution Neural Network for MODIS Imagery Cloud Detection N. Ma et al. https://doi.org/10.1109/JSTARS.2023.3261326
436. Optimal choice of proxy for cloud condensation nuclei reduces uncertainty in aerosol-cloud-climate forcing H. Jia et al. https://doi.org/10.1126/sciadv.aea4828
437. A bibliometric and scientometric: analysis towards global pattern and trends related to aerosol and precipitation studies from 2002 to 2022 R. Kumar et al. https://doi.org/10.1007/s11869-022-01290-0
438. Interannual variability and trends of combustion aerosol and dust in major continental outflows revealed by MODIS retrievals and CAM5 simulations during 2003–2017 H. Yu et al. https://doi.org/10.5194/acp-20-139-2020
439. SCIATRAN software package (V4.6): update and further development of aerosol, clouds, surface reflectance databases and models L. Mei et al. https://doi.org/10.5194/gmd-16-1511-2023
440. Opinion: Aerosol remote sensing over the next 20 years L. Remer et al. https://doi.org/10.5194/acp-24-2113-2024
441. Weekday AOD smaller than weekend AOD in eastern China on the basis of the MODIS AOD product J. Song et al. https://doi.org/10.1007/s00704-017-2142-5
442. Retrievals of smoke particle properties using AirMSPI measurements acquired during FIREX-AQ field campaign B. Chen et al. https://doi.org/10.1016/j.atmosenv.2026.121960
443. An aerosol odyssey: Navigating nutrient flux changes to marine ecosystems D. Hamilton et al. https://doi.org/10.1525/elementa.2023.00037
444. Neural network for aerosol retrieval from hyperspectral imagery S. Mauceri et al. https://doi.org/10.5194/amt-12-6017-2019
445. 中国东部地区气溶胶光学特性和类型特性分析 韩. Han Yafang et al. https://doi.org/10.3788/LOP221323
446. Aerosol Optical Depth Retrieval From Gaofen-5B POSP Using Attention-Enhanced Kolmogorov–Arnold Networks Z. Liu et al. https://doi.org/10.1109/TGRS.2026.3706811
447. Spatial characteristics and temporal evolution of the relationship between PM2.5 and aerosol optical depth over the eastern USA during 2003–2017 Q. Jin et al. https://doi.org/10.1016/j.atmosenv.2020.117718
448. First assessment of Aeolus Standard Correct Algorithm particle backscatter coefficient retrievals in the eastern Mediterranean A. Gkikas et al. https://doi.org/10.5194/amt-16-1017-2023
449. Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia Q. Xiao et al. https://doi.org/10.5194/acp-16-1255-2016
450. Sources, variability, long-term trends, and radiative forcing of aerosols in the Arctic: implications for Arctic amplification J. Kuttippurath et al. https://doi.org/10.1007/s11356-023-31245-6
451. Impact of dust transport events on PM10 concentrations on the western coasts of Turkey: a case of April 2022 Ö. Zeydan & Y. Yıldırım https://doi.org/10.1007/s11869-023-01481-3
452. Climatology and trends of aerosol optical depth over the Mediterranean basin during the last 12 years (2002–2014) based on Collection 006 MODIS-Aqua data A. Floutsi et al. https://doi.org/10.1016/j.scitotenv.2016.01.192
453. Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales X. Su et al. https://doi.org/10.1016/j.scitotenv.2023.163794
454. Aerosol-heavy precipitation relationship within monsoonal regimes in the Western Himalayas S. Battula et al. https://doi.org/10.1016/j.atmosres.2023.106728
455. Simultaneous Characterization of Wildfire Smoke and Surface Properties With Imaging Spectroscopy During the FIREX‐AQ Field Campaign P. Brodrick et al. https://doi.org/10.1029/2021JD034905
456. Vertical distribution of smoke aerosols over upper Indo-Gangetic Plain K. Vinjamuri et al. https://doi.org/10.1016/j.envpol.2019.113377
457. On the Abundance and Common Properties of Continental, Organized Shallow (Green) Clouds T. Dror et al. https://doi.org/10.1109/TGRS.2020.3023085
458. Intercomparison of CALIOP, MODIS, and AERONET aerosol optical depth over China during the past decade C. Liu et al. https://doi.org/10.1080/01431161.2018.1466070
459. The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 2: Evaluation of aerosol optical thickness P. Bhattacharjee et al. https://doi.org/10.5194/gmd-11-2333-2018
460. Investigating horizontal and vertical pollution gradients in the atmosphere associated with an urban location in complex terrain, Reno, Nevada, USA A. Pierce et al. https://doi.org/10.1016/j.atmosenv.2018.09.063
461. Retrieving the Optical Properties of Aerosols Over Land With Directional Polarimetric Camera Observations and an Adaptive Algorithm H. Wang et al. https://doi.org/10.1109/LGRS.2021.3049806
462. Winter AOD trend changes over the Eastern Mediterranean and Middle East region A. Shaheen et al. https://doi.org/10.1002/joc.7139
463. Observationally constrained analysis of sea salt aerosol in the marine atmosphere H. Bian et al. https://doi.org/10.5194/acp-19-10773-2019
464. Study on Accuracy Evaluation of MODIS AOD Products and Spatio-Temporal Distribution Characteristics of AOD in Hangzhou X. Yuan et al. https://doi.org/10.3390/su151310171
465. Improving Aerosol Retrieval From MISR With a Physics-Informed Deep Learning Method W. Man et al. https://doi.org/10.1109/TGRS.2024.3376598
466. Two decades observing smoke above clouds in the south-eastern Atlantic Ocean: Deep Blue algorithm updates and validation with ORACLES field campaign data A. Sayer et al. https://doi.org/10.5194/amt-12-3595-2019
467. Observation-based quantification of aerosol transport using optical flow: A satellite perspective to characterize interregional transport of atmospheric pollution T. Zhang et al. https://doi.org/10.1016/j.rse.2024.114457
468. Incorporation of advanced aerosol activation treatments into CESM/CAM5: model evaluation and impacts on aerosol indirect effects B. Gantt et al. https://doi.org/10.5194/acp-14-7485-2014
469. Analysis of Aerosol Properties in Beijing Based on Ground-Based Sun Photometer and Air Quality Monitoring Observations from 2005 to 2014 W. Chen et al. https://doi.org/10.3390/rs8020110
470. Exploration of the potential for using Spark-I observations to derive atmospheric parameters X. Chen et al. https://doi.org/10.1080/01431161.2017.1416694
471. Effect of Wind Speed on Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth over the North Pacific L. Merkulova et al. https://doi.org/10.3390/atmos9020060
472. Comprehensive Validation of MODIS-MAIAC Aerosol Products and Long-Term Aerosol Detection over an Urban–Rural Area Around Rome in Central Italy V. Terenzi et al. https://doi.org/10.3390/rs17122051
473. Spatio-Temporal Variation in AOD and Correlation Analysis with PAR and NPP in China from 2001 to 2017 X. Li et al. https://doi.org/10.3390/rs12060976
474. Multi-satellite retrieval of single scattering albedo using the OMI–MODIS algorithm K. Eswaran et al. https://doi.org/10.5194/acp-19-3307-2019
475. Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations S. Ahn et al. https://doi.org/10.3390/rs13061096
476. Type dependent role of aerosols in reversing the first indirect effect over the north Indian Ocean S. Jose https://doi.org/10.1080/2150704X.2022.2115861
477. Observational evidence of changing cloud macro-physical properties under warming climate over the Indian summer monsoon region S. Sharma et al. https://doi.org/10.1016/j.scitotenv.2024.174454
478. Evaluation of the MODIS Collection 6.1 3 km aerosol optical depth product over China M. Zhang et al. https://doi.org/10.1016/j.atmosenv.2022.118970
479. Validation of MODIS C6 Aerosol Optical Depth in China T. Zhang et al. https://doi.org/10.1088/1757-899X/780/4/042045
480. Volcanic Drivers of Stratospheric Sulfur in GFDL ESM4 C. Gao et al. https://doi.org/10.1029/2022MS003532
481. Sensitivities of simulated global aerosol optical depth and aerosol-radiation interactions to different horizontal resolutions in CAS-FGOALS-f3 M. Zhao et al. https://doi.org/10.1016/j.atmosenv.2021.118920
482. Subgrid-scale variability in clear-sky relative humidity and forcing by aerosol–radiation interactions in an atmosphere model P. Petersik et al. https://doi.org/10.5194/acp-18-8589-2018
483. AIRS and MODIS Satellite-Based Assessment of Air Pollution in Southwestern China: Impact of Stratospheric Intrusions and Cross-Border Transport of Biomass Burning P. Lian et al. https://doi.org/10.3390/rs16132409
484. Possible Relationship of Weakened Aleutian Low with Air Quality Improvement in Seoul, South Korea H. Oh et al. https://doi.org/10.1175/JAMC-D-17-0308.1
485. Assessment of aerosol burden over Ghana K. Fosu-Amankwah et al. https://doi.org/10.1016/j.sciaf.2021.e00971
486. Seasonal Dependence of Aerosol Data Assimilation and Forecasting Using Satellite and Ground-Based Observations S. Lee et al. https://doi.org/10.3390/rs14092123
487. Spatially gap free analysis of aerosol type grids in China: First retrieval via satellite remote sensing and big data analytics K. Li et al. https://doi.org/10.1016/j.isprsjprs.2022.09.001
488. Spatiotemporal evolution and anomaly assessment of wildfire-induced air pollution across Canada using satellite AOD analysis Y. Su et al. https://doi.org/10.1016/j.envpol.2026.127667
489. 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. https://doi.org/10.1029/2019EA001041
490. Estimating the Columnar Concentrations of Black Carbon Aerosols in China Using MODIS Products F. Bao et al. https://doi.org/10.1021/acs.est.0c00816
491. Long-term AOD trend assessment over the Eastern Mediterranean region: A comparative study including a new merged aerosol product A. Shaheen et al. https://doi.org/10.1016/j.atmosenv.2020.117736
492. Improving MISR AOD Retrievals With Low-Light-Level Corrections for Veiling Light M. Witek et al. https://doi.org/10.1109/TGRS.2017.2727342
493. Novel aerosol extinction coefficients and lidar ratios over the ocean from CALIPSO–CloudSat: evaluation and global statistics D. Painemal et al. https://doi.org/10.5194/amt-12-2201-2019
494. Long-term spatiotemporal variations of aerosol optical depth over Yellow and Bohai Sea X. Shen et al. https://doi.org/10.1007/s11356-019-04203-4
495. Using Rayleigh scattering over the ocean for satellite in-flight polarization calibration M. Zhu et al. https://doi.org/10.1364/OE.562308
496. Constraining chemical transport PM2.5 modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley M. Friberg et al. https://doi.org/10.5194/acp-18-12891-2018
497. Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing, China Q. Pu & E. Yoo https://doi.org/10.1080/13658816.2019.1664742
498. A physically constrained and interpretable deep learning framework for PM2.5 inversion under sparse monitoring conditions in arid regions X. Zhang et al. https://doi.org/10.1016/j.envpol.2026.128233
499. Spatiotemporal Investigations of Aerosol Optical Properties Over Bangladesh for the Period 2002–2016 M. Islam et al. https://doi.org/10.1007/s41748-019-00120-1
500. Characterization of Subgrid-Scale Variability in Particulate Matter with Respect to Satellite Aerosol Observations M. Franklin et al. https://doi.org/10.3390/rs10040623
501. Adaption of the MODIS aerosol retrieval algorithm using airborne spectral surface reflectance measurements over urban areas: a case study E. Jäkel et al. https://doi.org/10.5194/amt-8-5237-2015
502. The impact of Sahara dust aerosols on the three-dimensional structure of precipitation systems of different sizes in spring J. Xi et al. https://doi.org/10.5194/acp-26-15-2026
503. AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA) M. Bilal et al. https://doi.org/10.3389/fenvs.2022.981522
504. Assessment of Adjacency Correction over Inland Waters Using Sentinel-2 MSI Images R. Paulino et al. https://doi.org/10.3390/rs14081829
505. New seasonal pattern of pollution emerges from changing North American wildfires R. Buchholz et al. https://doi.org/10.1038/s41467-022-29623-8
506. Analysis and Variation of the Maiac Aerosol Optical Depth in Underexplored Urbanized Area of National Capital Region, India V. Sharma et al. https://doi.org/10.2478/jlecol-2022-0019
507. Contemporary sources dominate carbonaceous aerosol on the North Slope of Alaska C. Moffett et al. https://doi.org/10.1016/j.scitotenv.2022.154641
508. A Simple Band Ratio Library (BRL) Algorithm for Retrieval of Hourly Aerosol Optical Depth Using FY-4A AGRI Geostationary Satellite Data X. Jiang et al. https://doi.org/10.3390/rs14194861
509. Aerosol optical depth trends and variability over the Middle East from MODIS, MISR, OMI, and AERONET observations N. Rezaei-Nokandeh et al. https://doi.org/10.1038/s41598-025-25895-4
510. Evaluation of LaSRC aerosol optical depth from Landsat-8 and Sentinel-2 in Guangdong-Hong Kong-Macao greater bay area, China Y. Jin et al. https://doi.org/10.1016/j.atmosenv.2022.119128
511. 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. https://doi.org/10.3390/rs8020111
512. Characterizing the 2015 Indonesia fire event using modified MODIS aerosol retrievals Y. Shi et al. https://doi.org/10.5194/acp-19-259-2019
513. Accounting for 3D radiative effects in MODIS aerosol retrievals near clouds using CALIPSO observations G. Wen et al. https://doi.org/10.3389/frsen.2023.1333814
514. An Adaptive Dark-Target Algorithm for Retrieving Land AOD Applied to FY-4B/AGRI Data Y. Si et al. https://doi.org/10.1109/JSTARS.2024.3408251
515. The Role of Midlatitude Cyclones in the Emission, Transport, Production, and Removal of Aerosols in the Northern Hemisphere J. Robinson et al. https://doi.org/10.1029/2022JD038131
516. Differences between daytime and nighttime in the impacts of anthropogenic aerosol-radiative-cloud interaction on the summer precipitation over the mid-lower Yangtze River Basin Z. Wang et al. https://doi.org/10.1016/j.atmosenv.2025.121687
517. Retrieval of aerosol single scattering albedo using joint satellite and surface visibility measurements Y. Dong et al. https://doi.org/10.1016/j.rse.2023.113654
518. Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis J. Li et al. https://doi.org/10.5194/amt-7-2531-2014
519. Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia P. Crippa et al. https://doi.org/10.1038/srep37074
520. Toward More Representative Gridded Satellite Products A. Povey & R. Grainger https://doi.org/10.1109/LGRS.2018.2881762
521. Relative contributions of component-segregated aerosols to trends in aerosol optical depth over land (2007–2019): Insights from CAMS aerosol reanalysis H. Zhao et al. https://doi.org/10.1016/j.atmosenv.2024.120676
522. Sensitivity studies of nighttime top-of-atmosphere radiances from artificial light sources using a 3-D radiative transfer model for nighttime aerosol retrievals J. Zhang et al. https://doi.org/10.5194/amt-16-2531-2023
523. New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water M. Witek et al. https://doi.org/10.5194/amt-11-429-2018
524. Understanding global changes in fine-mode aerosols during 2008–2017 using statistical methods and deep learning approach X. Yan et al. https://doi.org/10.1016/j.envint.2021.106392
525. How much of the global aerosol optical depth is found in the boundary layer and free troposphere? Q. Bourgeois et al. https://doi.org/10.5194/acp-18-7709-2018
526. Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018–2020 Q. Zeng et al. https://doi.org/10.3390/rs14030623
527. Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager M. Kim et al. https://doi.org/10.5194/amt-17-1913-2024
528. Monitoring of aerosol optical-microphysical properties from DPC/GF-5(02): A case study of dust event in north China plain Y. Cao et al. https://doi.org/10.1016/j.atmosenv.2025.121193
529. Validation and Accuracy Analysis of Global MODIS Aerosol Products over Land Q. Wang et al. https://doi.org/10.3390/atmos8080155
530. Daily and Hourly Surface PM2.5 Estimation From Satellite AOD H. Zhang & S. Kondragunta https://doi.org/10.1029/2020EA001599
531. Atmospheric precipitable water vapor over Iran using MODIS products: climatology and intercomparison M. Rezaei & M. Khazaei https://doi.org/10.1007/s00703-021-00854-6
532. Aerosol distribution during spring and summer and its relationship with Inter-Annual variability of summer monsoon rainfall over Indian region P. Das Mahapatra et al. https://doi.org/10.1080/10106049.2022.2058622
533. Enhanced net CO2 exchange of a semideciduous forest in the southern Amazon due to diffuse radiation from biomass burning S. Rodrigues et al. https://doi.org/10.5194/bg-21-843-2024
534. Interannual variation of the East Asia Jet Stream and its impact on the horizontal distribution of aerosol in boreal spring S. Lee et al. https://doi.org/10.1016/j.atmosenv.2020.117296
535. Spatiotemporal evolution of the remotely sensed global continental PM2.5 concentration from 2000-2014 based on Bayesian statistics J. Li et al. https://doi.org/10.1016/j.envpol.2018.03.050
536. Aerosol models from the AERONET database: application to surface reflectance validation J. Roger et al. https://doi.org/10.5194/amt-15-1123-2022
537. Spatiotemporal patterns of PM10 concentrations over China during 2005–2016: A satellite-based estimation using the random forests approach G. Chen et al. https://doi.org/10.1016/j.envpol.2018.07.012
538. Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations – Part 2: AOD time series for 1995–2017 combined from ATSR ADV and MODIS C6.1 and AOD tendency estimations L. Sogacheva et al. https://doi.org/10.5194/acp-18-16631-2018
539. XBAER-derived aerosol optical thickness from OLCI/Sentinel-3 observation L. Mei et al. https://doi.org/10.5194/acp-18-2511-2018
540. The 2025 Extreme Dust Events in China: Evidence, Attribution, and Implications for Regional Air Quality Assessment S. Wang et al. https://doi.org/10.3390/atmos17020213
541. Imputing Satellite-Derived Aerosol Optical Depth Using a Multi-Resolution Spatial Model and Random Forest for PM2.5 Prediction B. Kianian et al. https://doi.org/10.3390/rs13010126
542. Correction to: Long-term spatio-temporal trends in atmospheric aerosols and trace gases over Pakistan using remote sensing M. Javed et al. https://doi.org/10.1007/s11600-023-01173-7
543. Quantitative evaluation of uncertainties in satellite retrieval of dust-like aerosols induced by spherical assumption C. Li et al. https://doi.org/10.1016/j.jqsrt.2014.01.003
544. An assessment of aerosol optical properties from remote-sensing observations and regional chemistry–climate coupled models over Europe L. Palacios-Peña et al. https://doi.org/10.5194/acp-18-5021-2018
545. An Evaluation of Four MODIS Collection 6 Aerosol Products in a Humid Subtropical Region M. Zhang et al. https://doi.org/10.3390/rs9111173
546. Evaluation and comparison of MODIS aerosol optical depth retrieval algorithms over Brazil A. Rudke et al. https://doi.org/10.1016/j.atmosenv.2023.120130
547. First validation of GOME-2/MetOp absorbing aerosol height using EARLINET lidar observations K. Michailidis et al. https://doi.org/10.5194/acp-21-3193-2021
548. Characterization of released dust over open waters in the south of the Iran Plateau based on satellite and ground-based measurements F. Bayat & H. Khalesifard https://doi.org/10.1016/j.apr.2021.101208
549. Multi-decadal analysis of shoreline dynamics and coastal vulnerability along the Duba coast (Red Sea, Saudi Arabia): interaction of natural and anthropogenic factors in a context of rapid development E. Alamery et al. https://doi.org/10.3389/fmars.2026.1789802
550. Long-Term Variability of Dust Events in Southwestern Iran and Its Relationship with the Drought N. Hamzeh et al. https://doi.org/10.3390/atmos12101350
551. Effects of Aerosols and Clouds on the Levels of Surface Solar Radiation and Solar Energy in Cyprus I. Fountoulakis et al. https://doi.org/10.3390/rs13122319
552. Measurement report: An assessment of the impact of a nationwide lockdown on air pollution – a remote sensing perspective over India M. Pathakoti et al. https://doi.org/10.5194/acp-21-9047-2021
553. In situ measured seasonal characteristics of near‐surface aerosols over Bay of Bengal and MODIS‐retrieved columnar properties: A multicampaign analysis S. Aryasree et al. https://doi.org/10.1002/2015JD023418
554. An Ultra‐Broadband High Efficiency Polarization Beam Splitter for High Spectral Resolution Polarimetric Imaging in the Near Infrared H. Hsiao et al. https://doi.org/10.1002/advs.202201227
555. Performance evaluation for retrieving aerosol optical depth from the Directional Polarimetric Camera (DPC) based on the GRASP algorithm S. Jin et al. https://doi.org/10.5194/amt-15-4323-2022
556. Assessing COVID-19 lockdowns' impacts on global urban PM2.5 air quality with observations and modeling C. Yu et al. https://doi.org/10.5194/acp-25-14411-2025
557. Investigating the Long-Term Variation Trends of Absorbing Aerosols over Asia by Using Multiple Satellites D. Li et al. https://doi.org/10.3390/rs14225832
558. High-Resolution Aerosol Optical Depth Retrieval in Urban Areas Based on Sentinel-2 Y. Chen et al. https://doi.org/10.14358/PERS.22-00122R2
559. Quantifying the low bias of CALIPSO's column aerosol optical depth due to undetected aerosol layers M. Kim et al. https://doi.org/10.1002/2016JD025797
560. Satellite remote sensing of fine particulate air pollutants over Indian mega cities V. Sreekanth et al. https://doi.org/10.1016/j.asr.2017.08.008
561. Global trends in the aerosol optical, physical, and morphological properties obtained using multi-sensor measurements G. Gupta et al. https://doi.org/10.1016/j.atmosenv.2022.119569
562. Estimation of Surface PM2.5 Concentration from Satellite Aerosol Optical Depth Using a Constrained Observation-Based Model O. Fawole et al. https://doi.org/10.3390/cli14010001
563. Assessment of regional aerosol radiative effects under the SWAAMI campaign – Part 1: Quality-enhanced estimation of columnar aerosol extinction and absorption over the Indian subcontinent H. Pathak et al. https://doi.org/10.5194/acp-19-11865-2019
564. Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements X. Chen et al. https://doi.org/10.3390/rs14040980
565. Synergy of AERONET and MODIS AOD products in the estimation of PM2.5 concentrations in Beijing D. Fu et al. https://doi.org/10.1038/s41598-018-28535-2
566. Detecting layer height of smoke aerosols over vegetated land and water surfaces via oxygen absorption bands: hourly results from EPIC/DSCOVR in deep space X. Xu et al. https://doi.org/10.5194/amt-12-3269-2019
567. Seasonal Tropospheric Aerosol Classification Using AERONET Spectral Absorption Properties in African Locations S. Aladodo et al. https://doi.org/10.1007/s41810-022-00140-x
568. Trans-Pacific transport and evolution of aerosols: evaluation of quasi-global WRF-Chem simulation with multiple observations Z. Hu et al. https://doi.org/10.5194/gmd-9-1725-2016
569. Reducing multisensor satellite monthly mean aerosol optical depth uncertainty: 1. Objective assessment of current AERONET locations J. Li et al. https://doi.org/10.1002/2016JD025469
570. Temporal and spatial variations in dust activity in Australia based on remote sensing and reanalysis datasets Y. Che et al. https://doi.org/10.5194/acp-24-4105-2024
571. Daily Fine Resolution Estimates of the Influence of Wildfires on Fine Particulate Matter in California, 2011–2020 C. Jones-Ngo et al. https://doi.org/10.3390/atmos15060680
572. Assessment of AOD Simulation by Assimilating Himawari and MODIS Products Z. Wang et al. https://doi.org/10.1007/s13351-025-4220-4
573. Consistent signal of aerosol indirect and semi-direct effect on water clouds in the oceanic regions adjacent to the Indian subcontinent S. Rao & S. Dey https://doi.org/10.1016/j.atmosres.2019.104677
574. Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model T. Zhang et al. https://doi.org/10.3390/ijerph13100974
575. Extended validation and evaluation of the OLCI–SLSTR SYNERGY aerosol product (SY_2_AOD) on Sentinel-3 L. Sogacheva et al. https://doi.org/10.5194/amt-15-5289-2022
576. Remote sensing of aerosols due to biomass burning over Kanpur, Sao-Paulo, Ilorin and Canberra S. Tariq et al. https://doi.org/10.1007/s10874-022-09444-1
577. Assessment of PM2.5 concentrations over bright surfaces using MODIS satellite observations M. Sorek-Hamer et al. https://doi.org/10.1016/j.rse.2015.03.014
578. 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. https://doi.org/10.1016/j.atmosenv.2020.117788
579. An efficient transformer-based aerosol retrieval algorithm for the polarization crossfire (PCF) satellite senor suite: development and validation> H. Gu et al. https://doi.org/10.1016/j.jqsrt.2025.109793
580. Quantifying the impact of terrain–wind–governed close-effect on atmospheric polluted concentrations L. Wu et al. https://doi.org/10.1016/j.jclepro.2022.132995
581. A similarity distance-based space-time random forest model for estimating PM2.5 concentrations over China S. Guan et al. https://doi.org/10.1016/j.atmosenv.2023.120043
582. The Effects of Fireworks Discharge on Atmospheric PM2.5 Concentration in the Chinese Lunar New Year X. Zhang et al. https://doi.org/10.3390/ijerph17249333
583. Spatial-scale dependence of aerosol indirect effects over land in eastern China: a comparative analysis Y. Liu et al. https://doi.org/10.5194/acp-26-6351-2026
584. Estimation of Aerosol Optical Depth at 30 m Resolution Using Landsat Imagery and Machine Learning T. Liang et al. https://doi.org/10.3390/rs14051053
585. Comparative analysis of three key atmospheric correction techniques for bathymetric mapping in nearshore areas with Sentinel-2 Data - case study: Kish Island, Persian Gulf K. Kabiri https://doi.org/10.1590/2675-2824073.24077
586. Impact and Suggestion of Column-to-Surface Vertical Correction Scheme on the Relationship between Satellite AOD and Ground-Level PM2.5 in China W. Gong et al. https://doi.org/10.3390/rs9101038
587. Applying the Dark Target Aerosol Algorithm to MERSI-II: Retrieval and Validation of Aerosol Optical Depth over the Ocean X. Pei et al. https://doi.org/10.1007/s00376-024-4032-y
588. A review of coarse mineral dust in the Earth system A. Adebiyi et al. https://doi.org/10.1016/j.aeolia.2022.100849
589. Estimation of PM10 concentrations over Seoul using multiple empirical models with AERONET and MODIS data collected during the DRAGON-Asia campaign S. Seo et al. https://doi.org/10.5194/acp-15-319-2015
590. Revisiting the Impact of Sea Salt on Climate Sensitivity F. Paulot et al. https://doi.org/10.1029/2019GL085601
591. An Improved Aerosol Retrieval Algorithm for FY-4A/AGRI Data Based on the GRASP Framework H. Wang et al. https://doi.org/10.1109/TGRS.2025.3546614
592. Aerosol and cloud data processing and optical property retrieval algorithms for the spaceborne ACDL/DQ-1 G. Dai et al. https://doi.org/10.5194/amt-17-1879-2024
593. Analysis of global three-dimensional aerosol structure with spectral radiance matching D. Liu et al. https://doi.org/10.5194/amt-12-6541-2019
594. Understanding the drivers of marine liquid-water cloud occurrence and properties with global observations using neural networks H. Andersen et al. https://doi.org/10.5194/acp-17-9535-2017
595. Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees J. Wei et al. https://doi.org/10.5194/acp-20-3273-2020
596. Measurement report: Long-range transport patterns into the tropical northwest Pacific during the CAMP2Ex aircraft campaign: chemical composition, size distributions, and the impact of convection M. Hilario et al. https://doi.org/10.5194/acp-21-3777-2021
597. Machine learning data fusion for high spatio-temporal resolution PM2.5 A. Porcheddu et al. https://doi.org/10.5194/amt-18-4771-2025
598. Air pollution trends measured from Terra: CO and AOD over industrial, fire-prone, and background regions R. Buchholz et al. https://doi.org/10.1016/j.rse.2020.112275
599. The Aerosol Optical Characteristics in Different Dust Events Based on a 532 nm and 355 nm Polarization Lidar in Beijing Z. Chen et al. https://doi.org/10.3390/rs15143494
600. Interannual and Seasonal Variations in the Atmospheric Aerosol Optical Depth in the Region of Tomsk (1995–2018) D. Kabanov et al. https://doi.org/10.1134/S1024856019060071
601. The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2 A. Sinyuk et al. https://doi.org/10.5194/amt-13-3375-2020
602. Deep Spatial Prediction via Heterogeneous Multi-source Self-supervision M. Zhang et al. https://doi.org/10.1145/3605358
603. Air Pollution to Atmospheric Warming: Effects of Increasing Anthropogenic Aerosols on South Asia’s Climate S. Ramachandran et al. https://doi.org/10.1007/s41748-025-00732-w
604. Identifying sources of dust aerosol using a new framework based on remote sensing and modelling O. Rahmati et al. https://doi.org/10.1016/j.scitotenv.2020.139508
605. An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends Q. Zhao et al. https://doi.org/10.3390/rs14081863
606. Space‐Borne Estimation of Volcanic Sulfate Aerosol Lifetime C. Li & R. Cohen https://doi.org/10.1029/2020JD033883
607. Comparison of aerosol optical depth from satellite (MODIS), sun photometer and broadband pyrheliometer ground-based observations in Cuba J. Antuña-Marrero et al. https://doi.org/10.5194/amt-11-2279-2018
608. Impact of the choice of the satellite aerosol optical depth product in a sub-regional dust emission inversion J. Escribano et al. https://doi.org/10.5194/acp-17-7111-2017
609. Does the modern‐era retrospective analysis for research and applications‐2 aerosol reanalysis introduce an improvement in the simulation of surface solar radiation over China? F. Feng & K. Wang https://doi.org/10.1002/joc.5881
610. Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach T. Zheng et al. https://doi.org/10.1016/j.atmosenv.2020.117451
611. The impact of aerosol forcing on the statistical attribution of heatwaves F. Kraulich et al. https://doi.org/10.1016/j.wace.2025.100803
612. Uncertainty in Aqua-MODIS Aerosol Retrieval Algorithms During COVID-19 Lockdown M. Bilal et al. https://doi.org/10.1109/LGRS.2021.3077189
613. Effects of Nationwide COVID-19 Lockdown on Fossil Fuel and Biomass Burning Emissions in India A. Kumar et al. https://doi.org/10.1007/s41810-025-00322-3
614. Density, disparity, and dispersion: Unraveling the complex relationship between urban form and PM2.5 exposure in Accra, Ghana A. Alhassan et al. https://doi.org/10.1016/j.nexres.2026.101631
615. PM2.5 as a proxy for aerosol optical depth in night sky brightness models J. Petržala & M. Kocifaj https://doi.org/10.1093/mnras/stag712
616. JRAero: the Japanese Reanalysis for Aerosol v1.0 K. Yumimoto et al. https://doi.org/10.5194/gmd-10-3225-2017
617. Development of a model for particulate matter pollution in Australia with implications for other satellite-based models G. Pereira et al. https://doi.org/10.1016/j.envres.2017.07.044
618. Aerosol contamination survey during dust storm process in Northwestern China using ground, satellite observations and atmospheric modeling data M. Filonchyk et al. https://doi.org/10.1007/s00704-017-2362-8
619. Estimation of black carbon emissions from Siberian fires using satellite observations of absorption and extinction optical depths I. Konovalov et al. https://doi.org/10.5194/acp-18-14889-2018
620. Analysis of AOD from MODIS-Merged DT–DB Products Over the Arabian Peninsula M. Ali & M. Assiri https://doi.org/10.1007/s41748-019-00108-x
621. Features of the Extreme Fire Season of 2021 in Yakutia (Eastern Siberia) and Heavy Air Pollution Caused by Biomass Burning O. Tomshin & V. Solovyev https://doi.org/10.3390/rs14194980
622. Satellite Ocean Aerosol Retrieval (SOAR) Algorithm Extension to S‐NPP VIIRS as Part of the “Deep Blue” Aerosol Project A. Sayer et al. https://doi.org/10.1002/2017JD027412
623. Effect of COVID-19 shutdown on aerosol direct radiative forcing over the Indo-Gangetic Plain outflow region of the Bay of Bengal A. Thomas et al. https://doi.org/10.1016/j.scitotenv.2021.146918
624. Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate Matter B. Boys et al. https://doi.org/10.1021/es502113p
625. Evaluation of MODIS Dark Target AOD Product with 3 and 10 km Resolution in Amazonia R. Palácios et al. https://doi.org/10.3390/atmos13111742
626. Impact of aerosols on atmospheric processes and climate variability: A synthesis of recent research findings S. Perumpully & S. Gautam https://doi.org/10.1016/j.geogeo.2024.100317
627. APIFLAME v2.0 biomass burning emissions model: impact of refined input parameters on atmospheric concentration in Portugal in summer 2016 S. Turquety et al. https://doi.org/10.5194/gmd-13-2981-2020
628. Long-term variations of the PM2.5 concentration identified by MODIS in the tropical rain forest, Southeast Asia Y. Ma et al. https://doi.org/10.1016/j.atmosres.2018.12.024
629. A Deep Learning Approach to Increase the Value of Satellite Data for PM2.5 Monitoring in China B. Li et al. https://doi.org/10.3390/rs15153724
630. Above-cloud aerosol optical depth from airborne observations in the southeast Atlantic S. LeBlanc et al. https://doi.org/10.5194/acp-20-1565-2020
631. Himawari-8-Derived Aerosol Optical Depth Using an Improved Time Series Algorithm Over Eastern China D. Li et al. https://doi.org/10.3390/rs12060978
632. The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm M. Kim et al. https://doi.org/10.5194/amt-11-6107-2018
633. Influence of changes in anthropogenic and natural sources on global aerosol optical depth during COVID-19 lockdown: Ground-based observations, satellites, models S. Ramachandran & K. Ansari https://doi.org/10.1016/j.atmosenv.2024.120948
634. Evaluation and improvement of MODIS aerosol optical depth products over China Y. Li et al. https://doi.org/10.1016/j.atmosenv.2019.117251
635. Is positive correlation between cloud droplet effective radius and aerosol optical depth over land due to retrieval artifacts or real physical processes? H. Jia et al. https://doi.org/10.5194/acp-19-8879-2019
636. Tropospheric aerosols over the western North Atlantic Ocean during the winter and summer deployments of ACTIVATE 2020: life cycle, transport, and distribution H. Liu et al. https://doi.org/10.5194/acp-25-2087-2025
637. A data-driven method for aerosol FMF retrieval over land using single-view polarization satellite data Z. Shi et al. https://doi.org/10.1016/j.atmosenv.2025.121083
638. Spatiotemporal Variability of Remotely Sensed PM2.5 Concentrations in China from 1998 to 2014 Based on a Bayesian Hierarchy Model J. Li et al. https://doi.org/10.3390/ijerph13080772
639. Haze Pollution in the Unstable Atmospheric Boundary Layer Over the West Bank of Taiwan Strait Induced by Regional Transport of PM2.5 Y. Jiang et al. https://doi.org/10.1029/2022EA002505
640. A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data Y. Si et al. https://doi.org/10.3390/rs15020438
641. Identifying a transport mechanism of dust aerosols over South Asia to the Tibetan Plateau: A case study T. Wang et al. https://doi.org/10.1016/j.scitotenv.2020.143714
642. Comparison of Aerosol Optical Depth from MODIS Product Collection 6.1 and AERONET in the Western United States I. Eibedingil et al. https://doi.org/10.3390/rs13122316
643. 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. https://doi.org/10.1016/j.atmosenv.2018.11.049
644. Minimizing aerosol effects on the OMI tropospheric NO2 retrieval – An improved use of the 477 nm O2 − O2 band and an estimation of the aerosol correction uncertainty J. Chimot et al. https://doi.org/10.5194/amt-12-491-2019
645. Mineral dust optical properties for remote sensing and global modeling: A review P. Castellanos et al. https://doi.org/10.1016/j.rse.2023.113982
646. Dust in Western Iran: the emergence of new sources in response to shrinking water bodies A. Beyranvand et al. https://doi.org/10.1038/s41598-023-42173-3
647. Severe Biomass-Burning Aerosol Pollution during the 2019 Amazon Wildfire and Its Direct Radiative-Forcing Impact: A Space Perspective from MODIS Retrievals S. Yuan et al. https://doi.org/10.3390/rs14092080
648. Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5 T. Li et al. https://doi.org/10.1016/j.isprsjprs.2020.06.019
649. Relationships between Aerosols and Marine Clouds during the “Godzilla” Dust Storm: Perspective of Satellite and Reanalysis Products C. Chang & F. Hosseinpour https://doi.org/10.3390/atmos15010013
650. Global aerosol modeling with MADE3 (v3.0) in EMAC (based on v2.53): model description and evaluation J. Kaiser et al. https://doi.org/10.5194/gmd-12-541-2019
651. Satellite observed trends of global mean net atmospheric shortwave and longwave irradiances and diabatic heating by precipitation S. Kato et al. https://doi.org/10.1126/sciadv.adz1292
652. Impact of aerosols on surface ozone during COVID-19 pandemic in southern India: A multi-instrumental approach from ground and satellite observations, and model simulations R. Kalluri et al. https://doi.org/10.1016/j.jastp.2020.105491
653. The decomposition of cloud–aerosol forcing in the UK Earth System Model (UKESM1) D. Grosvenor & K. Carslaw https://doi.org/10.5194/acp-20-15681-2020
654. Dust Aerosol Optical Centroid Height (AOCH) Over Bright Surface: First Retrieval From TROPOMI Oxygen A and B Absorption Bands X. Chen et al. https://doi.org/10.1109/LGRS.2025.3601046
655. Known and unknown unknowns: uncertainty estimation in satellite remote sensing A. Povey & R. Grainger https://doi.org/10.5194/amt-8-4699-2015
656. A bulk-mass-modeling-based method for retrieving particulate matter pollution using CALIOP observations T. Toth et al. https://doi.org/10.5194/amt-12-1739-2019
657. A comprehensive review delineates advancements in retrieving particulate matter utilising satellite aerosol optical depth: Parameter consideration, data processing, models development and future perspectives S. Padimala & C. Matli https://doi.org/10.1016/j.atmosres.2024.107514
658. A Novel Atmospheric Correction Algorithm to Exploit the Diurnal Variability in Hypertemporal Geostationary Observations W. Wang et al. https://doi.org/10.3390/rs14040964
659. Analysis of aerosol cloud interactions with a consistent signal of meteorology and other influencing parameters K. Anwar et al. https://doi.org/10.1016/j.atmosres.2022.106241
660. Evaluating the effects of columnar NO2 on the accuracy of aerosol optical properties retrievals T. Drosoglou et al. https://doi.org/10.5194/amt-16-2989-2023
661. Aerosol Effects on Water Cloud Properties in Different Atmospheric Regimes P. Khatri et al. https://doi.org/10.1029/2023JD039729
662. Dissimilar roles of aerosols, nitrogen deposition and ozone on the terrestrial carbon sink in China during 2010–2020 N. Xie et al. https://doi.org/10.5194/acp-26-5925-2026
663. Bias induced by the AOD representation time scale in long-term solar radiation calculations. Part 1: Sensitivity of the AOD distribution to the representation time scale J. Ruiz-Arias et al. https://doi.org/10.1016/j.solener.2016.06.026
664. Aerosol emission reductions cause post-2011 rapid warming in the northwestern Pacific N. Yang et al. https://doi.org/10.1038/s43247-025-03015-4
665. K-Means Clustering for Improved Data-Driven Satellite Aerosol Retrieval S. Zhang et al. https://doi.org/10.1109/LGRS.2025.3631867
666. Aerosol Optical Properties over China from RAMS-CMAQ Model Compared with CALIOP Observations T. Wu et al. https://doi.org/10.3390/atmos8100201
667. Status of Solar-Energy Adoption in GCC, Yemen, Iraq, and Jordan: Challenges and Carbon-Footprint Analysis A. Farahat et al. https://doi.org/10.3390/cleantechnol6020036
668. Retrieval of gridded aerosol direct radiative forcing based on multiplatform datasets Y. Wang et al. https://doi.org/10.5194/amt-13-575-2020
669. Estimation of aerosol properties over the Chinese desert region with MODIS AOD assimilation in a global model X. Yin et al. https://doi.org/10.1016/j.accre.2016.04.001
670. Validation of the improved GOES-16 aerosol optical depth product over North America D. Fu et al. https://doi.org/10.1016/j.atmosenv.2023.119642
671. Mapping 532 nm lidar ratios for CALIPSO-classified marine aerosols using MODIS AOD constrained retrievals and GOCART model simulations T. Toth et al. https://doi.org/10.5194/amt-18-6765-2025
672. Relationship Between MODIS-based Aerosol Optical Depth and PM10 over Sumatra to Overcome the Limitations of Air Quality Monitoring Data Availability S. Syafrijon et al. https://doi.org/10.13005/ojc/3404058
673. 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. https://doi.org/10.1016/j.scitotenv.2024.170593
674. Prediction of Solar Irradiance over the Arabian Peninsula: Satellite Data, Radiative Transfer Model, and Machine Learning Integration Approach S. Alwadei et al. https://doi.org/10.3390/app12020717
675. Impact of the Dust Aerosol Model on the VIIRS Aerosol Optical Depth (AOD) Product across China Y. Wang et al. https://doi.org/10.3390/rs12060991
676. Seasonal distribution and drivers of surface fine particulate matter and organic aerosol over the Indo-Gangetic Plain C. Mogno et al. https://doi.org/10.5194/acp-21-10881-2021
677. Utilizing deep learning techniques in operational system of geo-KOMPSAT-2A satellite for estimating solar radiation and aerosol optical depth J. Ha et al. https://doi.org/10.1080/15481603.2026.2658318
678. A New Satellite-Based Global Climatology of Dust Aerosol Optical Depth K. Voss & A. Evan https://doi.org/10.1175/JAMC-D-19-0194.1
679. Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1 P. Ma et al. https://doi.org/10.5194/gmd-15-2881-2022
680. Overview of atmospheric aerosol studies in Malaysia: Known and unknown K. Kanniah et al. https://doi.org/10.1016/j.atmosres.2016.08.002
681. Impacts of meteorological nudging on the global dust cycle simulated by NICAM coupled with an aerosol model T. Dai et al. https://doi.org/10.1016/j.atmosenv.2018.07.016
682. Evaluating MODIS and MISR aerosol optical depth retrievals over environmentally distinct sites in Pakistan G. Ali et al. https://doi.org/10.1016/j.jastp.2018.12.008
683. Aerosol meteorology of Maritime Continent for the 2012 7SEAS southwest monsoon intensive study – Part 2: Philippine receptor observations of fine-scale aerosol behavior J. Reid et al. https://doi.org/10.5194/acp-16-14057-2016
684. How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth A. Sayer & K. Knobelspiesse https://doi.org/10.5194/acp-19-15023-2019
685. Impact of mineral dust on shortwave and longwave radiation: evaluation of different vertically resolved parameterizations in 1-D radiative transfer computations M. Granados-Muñoz et al. https://doi.org/10.5194/acp-19-523-2019
686. 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. https://doi.org/10.5194/amt-12-4619-2019
687. The dominant factor in extreme dust events over the Gobi Desert is shifting from extreme winds to extreme droughts Q. Zhu & Y. Liu https://doi.org/10.1038/s41612-024-00689-z
688. Using satellite‐based measurements to explore spatiotemporal scales and variability of drivers of new particle formation R. Sullivan et al. https://doi.org/10.1002/2016JD025568
689. Pretrained Deep Learning Framework for Assessing Aerosol Pollution and Radiative Forcing from Satellite Polarization Measurements Z. Ji et al. https://doi.org/10.1021/acsestair.5c00445
690. 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. https://doi.org/10.1016/j.atmosenv.2019.02.029
691. Photochemical model evaluation of 2013 California wild fire air quality impacts using surface, aircraft, and satellite data K. Baker et al. https://doi.org/10.1016/j.scitotenv.2018.05.048
692. Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble Kalman filter Y. Cheng et al. https://doi.org/10.5194/acp-19-13445-2019
693. Investigating changes in atmospheric aerosols properties over the Indo-Gangetic Plain during different phases of COVID-19-induced lockdowns P. Singh et al. https://doi.org/10.1007/s11356-023-29449-x
694. A Physically Based PM$_{\text{2.5}}$ Estimation Method Using AERONET Data in Beijing Area G. Chen et al. https://doi.org/10.1109/JSTARS.2018.2817243
695. The value of satellite observations in the analysis and short-range prediction of Asian dust A. Benedetti et al. https://doi.org/10.5194/acp-19-987-2019
696. Retrieval of black carbon aerosol surface concentration using integrated MODIS and AERONET data X. Jiang et al. https://doi.org/10.5194/amt-18-4559-2025
697. COVID-19 lockdown air quality change implications for solar energy generation over China K. Choi & H. Brindley https://doi.org/10.1088/1748-9326/abd42f
698. Long-range transport of coarse mineral dust: an evaluation of the Met Office Unified Model against aircraft observations N. Ratcliffe et al. https://doi.org/10.5194/acp-24-12161-2024
699. Simulation of Spectral Albedo and Bidirectional Reflectance over Snow-Covered Urban Canyon: Model Development and Factor Analysis Q. Chen et al. https://doi.org/10.3390/rs16132340
700. Characteristic dissimilarities during high aerosol loading days between western and eastern Indo-Gangetic Plain K. Shukla et al. https://doi.org/10.1016/j.atmosenv.2021.118837
701. Spatiotemporal characteristics of aerosols and their trends over mainland China with the recent Collection 6 MODIS and OMI satellite datasets K. Hu et al. https://doi.org/10.1007/s11356-017-0715-6
702. Historical (1850–2014) Aerosol Evolution and Role on Climate Forcing Using the GISS ModelE2.1 Contribution to CMIP6 S. Bauer et al. https://doi.org/10.1029/2019MS001978
703. Air Pollution Dispersion over Durban, South Africa M. Jury & M. Buthelezi https://doi.org/10.3390/atmos13050811
704. High Resolution Aerosol Optical Depth Retrieval Using Gaofen-1 WFV Camera Data K. Sun et al. https://doi.org/10.3390/rs9010089
705. Spatial–Temporal Variations in NO2 and PM2.5 over the Chengdu–Chongqing Economic Zone in China during 2005–2015 Based on Satellite Remote Sensing K. Cai et al. https://doi.org/10.3390/s18113950
706. Comparison and Evaluation of Different MODIS Aerosol Optical Depth Products Over the Beijing-Tianjin-Hebei Region in China J. Wei & L. Sun https://doi.org/10.1109/JSTARS.2016.2595624
707. Daily Estimation of Ground-Level PM2.5 Concentrations over Beijing Using 3 km Resolution MODIS AOD Y. Xie et al. https://doi.org/10.1021/acs.est.5b01413
708. Improved merge schemes for MODIS Collection 6.1 Dark Target and Deep Blue combined aerosol products J. Wei et al. https://doi.org/10.1016/j.atmosenv.2019.01.016
709. Application of Topological Data Analysis to Multi-Resolution Matching of Aerosol Optical Depth Maps D. Ofori-Boateng et al. https://doi.org/10.3389/fenvs.2021.684716
710. Improving satellite-retrieved aerosol microphysical properties using GOCART data S. Li et al. https://doi.org/10.5194/amt-8-1157-2015
711. The Influence of Air‐Sea Fluxes on Atmospheric Aerosols During the Summer Monsoon Over the Tropical Indian Ocean A. Zavarsky et al. https://doi.org/10.1002/2017GL076410
712. Development of a daytime cloud and haze detection algorithm for Himawari‐8 satellite measurements over central and eastern China H. Shang et al. https://doi.org/10.1002/2016JD025659
713. Quantifying the single-scattering albedo for the January 2017 Chile wildfires from simulations of the OMI absorbing aerosol index J. Sun et al. https://doi.org/10.5194/amt-11-5261-2018
714. Observed Covariations of Aerosol Optical Depth and Cloud Cover in Extratropical Cyclones C. Naud et al. https://doi.org/10.1002/2017JD027240
715. Investigation of the Atmospheric Chemical Properties over Kuwait R. Alenezi & N. Aldaihani https://doi.org/10.1007/s11270-023-06235-6
716. Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980–2021) Y. Ouma et al. https://doi.org/10.1016/j.scitotenv.2023.169027
717. Orbital Observations of Dust Lofted by Daytime Convective Turbulence L. Fenton et al. https://doi.org/10.1007/s11214-016-0243-6
718. Spatiotemporal Variability of Extinction Coefficient of Aerosols and Their Subtypes Over Andhra Pradesh, South India From the CALIPSO Data P. Althaf et al. https://doi.org/10.1007/s41748-025-00936-0
719. Fengyun 4A Land Aerosol Retrieval: Algorithm Development, Validation, and Comparison With Other Datasets X. Su et al. https://doi.org/10.1109/TGRS.2023.3330544
720. Exploring the uncertainty associated with satellite-based estimates of premature mortality due to exposure to fine particulate matter B. Ford & C. Heald https://doi.org/10.5194/acp-16-3499-2016
721. Evaluation and uncertainty estimate of next-generation geostationary meteorological Himawari-8/AHI aerosol products J. Wei et al. https://doi.org/10.1016/j.scitotenv.2019.07.326
722. Challenges in the atmospheric characterization for the retrieval of spectrally resolved fluorescence and PRI region dynamics from space N. Sabater et al. https://doi.org/10.1016/j.rse.2020.112226
723. Nine-year spatial and temporal evolution of desert dust aerosols over South and East Asia as revealed by CALIOP E. Proestakis et al. https://doi.org/10.5194/acp-18-1337-2018
724. A New MODIS C6 Dark Target and Deep Blue Merged Aerosol Product on a 3 km Spatial Grid M. Bilal et al. https://doi.org/10.3390/rs10030463
725. Saharan dust and biomass burning aerosols during ex-hurricane Ophelia: observations from the new UK lidar and sun-photometer network M. Osborne et al. https://doi.org/10.5194/acp-19-3557-2019
726. Comprehensive Validation and Comparison of Three VIIRS Aerosol Products over the Ocean on a Global Scale W. Li et al. https://doi.org/10.3390/rs14112544
727. Biomass burning emission analysis based on MODIS aerosol optical depth and AeroCom multi-model simulations: implications for model constraints and emission inventories M. Petrenko et al. https://doi.org/10.5194/acp-25-1545-2025
728. Spatio-temporal distribution of AOD and its response to regional energy consumption and air pollution factors in China Y. Su et al. https://doi.org/10.1016/j.envres.2025.123393
729. Aerosol-induced atmospheric heating rate decreases over South and East Asia as a result of changing content and composition S. Ramachandran et al. https://doi.org/10.1038/s41598-020-76936-z
730. Improved Aerosol Optical Depth and Ångstrom Exponent Retrieval Over Land From MODIS Based on the Non-Lambertian Forward Model . Leiku Yang et al. https://doi.org/10.1109/LGRS.2014.2303317
731. Coupling the linear mixed effects model with random forest improves hourly PM2.5 estimation from Himawari-8 AOD over the Yangtze River Delta Y. Tan et al. https://doi.org/10.1016/j.apr.2023.101739
732. ReCuSum: A polyvalent method to monitor tropical forest disturbances Y. Bertrand et al. https://doi.org/10.1016/j.isprsjprs.2023.08.006
733. Aqua and Terra MODIS RSB Calibration Comparison Using BRDF Modeled Reflectance T. Chang et al. https://doi.org/10.1109/TGRS.2016.2641258
734. Evaluation of Cloud Fraction Data for Modelling Daily Surface Solar Radiation: Application to the Lake Baikal Region D. Golubets et al. https://doi.org/10.3390/atmos16121405
735. Aerosol Induced Changes in Sea Surface Temperature Over the Bay of Bengal Due to COVID-19 Lockdown T. Sarin et al. https://doi.org/10.3389/fmars.2021.648566
736. Satellite remote sensing of aerosol single scattering albedo: Instruments, algorithms, and challenges Y. Dong et al. https://doi.org/10.1016/j.jqsrt.2025.109802
737. The climatology of dust events over the European continent using data of the BSC-DREAM8b model F. Mandija et al. https://doi.org/10.1016/j.atmosres.2018.03.006
738. On the importance of the model representation of organic aerosol in simulations of the direct radiative effect of Siberian biomass burning aerosol in the eastern Arctic I. Konovalov et al. https://doi.org/10.1016/j.atmosenv.2023.119910
739. MISR Radiance Anomalies Induced by Stratospheric Volcanic Aerosols D. Wu et al. https://doi.org/10.3390/rs10121875
740. PM2.5 ∕ PM10 ratio prediction based on a long short-term memory neural network in Wuhan, China X. Wu et al. https://doi.org/10.5194/gmd-13-1499-2020
741. Developing automated methods to estimate spectrally resolved direct normal irradiance for solar energy applications K. Choi et al. https://doi.org/10.1016/j.renene.2021.03.127
742. In-Orbit Test of the Polarized Scanning Atmospheric Corrector (PSAC) Onboard Chinese Environmental Protection and Disaster Monitoring Satellite Constellation HJ-2 A/B Z. Li et al. https://doi.org/10.1109/TGRS.2022.3176978
743. Changes in column aerosol optical depth and ground-level particulate matter concentration over East Asia J. Nam et al. https://doi.org/10.1007/s11869-017-0517-5
744. From trees to rain: enhancement of cloud glaciation and precipitation by pollen J. Kretzschmar et al. https://doi.org/10.1088/1748-9326/ad747a
745. Transboundary transport of non-east and East Asian dust observed at Dunhuang, northwest China T. Zhou et al. https://doi.org/10.1016/j.atmosenv.2023.120197
746. Validation and Comparison of Long-Term Accuracy and Stability of Global Reanalysis and Satellite Retrieval AOD X. Su et al. https://doi.org/10.3390/rs16173304
747. A hybrid approach for integrating micro-satellite images and sensors network-based ground measurements using deep learning for high-resolution prediction of fine particulate matter (PM2.5) over an indian city, lucknow V. Jain et al. https://doi.org/10.1016/j.atmosenv.2024.120798
748. Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors A. van Donkelaar et al. https://doi.org/10.1021/acs.est.5b05833
749. Using 6.9 and 10.65 GHz From the AMSR2 and GMI Microwave Imagers in the ECMWF NWP System (IFS) T. Scanlon et al. https://doi.org/10.1109/TGRS.2025.3627448
750. Investigating multiple aerosol optical depth products from MODIS and VIIRS over Asia: Evaluation, comparison, and merging Y. Wang et al. https://doi.org/10.1016/j.atmosenv.2020.117548
751. Air pollution in the Gobi Desert region: Analysis of dust‐storm events M. Filonchyk et al. https://doi.org/10.1002/qj.3961
752. The ecological value of Neotropical forest landscapes through a multicriteria approach employing spatial models F. Amador-Cruz et al. https://doi.org/10.1177/03091333241248782
753. Sensitivity of soil dust emissions to driving factor variability in earth's main drylands X. Wang & C. Zhang https://doi.org/10.1016/j.geoderma.2024.116876
754. A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm P. Gupta et al. https://doi.org/10.5194/amt-9-3293-2016
755. Retrieval of aerosol optical properties from GOCI-II observations: Continuation of long-term geostationary aerosol monitoring over East Asia S. Lee et al. https://doi.org/10.1016/j.scitotenv.2023.166504
756. 基于MODIS观测的全球气溶胶与低云时空特征及其对海洋云增亮的启示 莉. 牛 et al. https://doi.org/10.1360/SSTe-2025-0229
757. Reconstruction of historical aerosol optical depth time series over Romania during summertime A. Dumitrescu et al. https://doi.org/10.1002/joc.5118
758. The First Validation of Aerosol Optical Parameters Retrieved from the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) and Its Application Y. Ren et al. https://doi.org/10.3390/rs16193689
759. Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model Y. Guo et al. https://doi.org/10.1016/j.rse.2017.06.001
760. Investigating the Influence of Galactic Cosmic Ray-Modulated Aerosol Optical Depth on Near-Surface Air Temperature Variability over the Past Two Decades F. Sarakhs et al. https://doi.org/10.3390/cli14030071
761. Collocation mismatch uncertainties in satellite aerosol retrieval validation T. Virtanen et al. https://doi.org/10.5194/amt-11-925-2018
762. Retrieval of Aerosol Optical Depth Over North China From Polarized Satellite Observations Using Re‐derived Surface Properties H. Wang et al. https://doi.org/10.1029/2019EA000903
763. Development of an Algorithm to Retrieve Aerosol Optical Properties Over Water Using an Artificial Neural Network Radiative Transfer Scheme: First Result From GOSAT-2/CAI-2 C. Shi et al. https://doi.org/10.1109/TGRS.2020.3038892
764. An Improved Aerosol Optical Depth Retrieval Algorithm for Multiangle Directional Polarimetric Camera (DPC) B. Ge et al. https://doi.org/10.3390/rs14164045
765. The influence of dusts on radiation and temperature over the eastern Asia with a regional climate model X. Liu et al. https://doi.org/10.1016/j.scitotenv.2021.148351
766. Evaluation of the MODIS C6 Aerosol Optical Depth Products over Chongqing, China G. Shi et al. https://doi.org/10.3390/atmos8110227
767. Wildfire-smoke-precipitation interactions in Siberia: Insights from a regional model study I. Konovalov et al. https://doi.org/10.1016/j.scitotenv.2024.175518
768. Performance and evaluation of remote sensing satellites for monitoring dust weather in East Asia Y. Zhang et al. https://doi.org/10.5194/amt-18-4885-2025
769. Estimating urban PM10 and PM2.5 concentrations, based on synergistic MERIS/AATSR aerosol observations, land cover and morphology data A. Beloconi et al. https://doi.org/10.1016/j.rse.2015.10.017
770. Polar Cloud Detection of FengYun-3D Medium Resolution Spectral Imager II Imagery Based on the Radiative Transfer Model S. Dong et al. https://doi.org/10.3390/rs15215221
771. Preflight Evaluation of the Performance of the Chinese Environmental Trace Gas Monitoring Instrument (EMI) by Spectral Analyses of Nitrogen Dioxide C. Zhang et al. https://doi.org/10.1109/TGRS.2018.2798038
772. Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India R. Singh et al. https://doi.org/10.1007/s10661-024-12631-0
773. Observation‐Based Study on Aerosol Optical Depth and Particle Size in Partly Cloudy Regions T. Várnai et al. https://doi.org/10.1002/2017JD027028
774. Dust induced radiative perturbations during an episode of long-range dust transport over Delhi, India: a high-resolution regional NWP model study T. Francis et al. https://doi.org/10.1007/s00703-020-00760-3
775. The sensitivity of smoke aerosol dispersion to smoke injection height and source-strength: a multi-model AeroCom study X. Pan et al. https://doi.org/10.5194/acp-26-171-2026
776. Impacts of climate and synoptic fluctuations on dust storm activity over the Middle East S. Namdari et al. https://doi.org/10.1016/j.atmosenv.2017.11.016
777. Can Forest Fires Be an Important Factor in the Reduction in Solar Power Production in India? U. Dumka et al. https://doi.org/10.3390/rs14030549
778. Solar UV radiation measurements in Marambio, Antarctica, during years 2017–2019 M. Aun et al. https://doi.org/10.5194/acp-20-6037-2020
779. Assessment and Improvement of MISR Angstrom Exponent and Single-Scattering Albedo Products Using AERONET Data in China Y. Si et al. https://doi.org/10.3390/rs9070693
780. High Summertime Aerosol Loadings Over the Arabian Sea and Their Transport Pathways Q. Jin et al. https://doi.org/10.1029/2018JD028588
781. Obtaining best parameterization scheme of RegCM 4.4 for aerosols and chemistry simulations over the CORDEX South Asia P. Ajay et al. https://doi.org/10.1007/s00382-018-4587-3
782. The Role of the Intertropical Discontinuity Region and the Heat Low in Dust Emission and Transport Over the Thar Desert, India: A Premonsoon Case Study U. Dumka et al. https://doi.org/10.1029/2019JD030836
783. Using infrared geostationary remote sensing to determine particulate matter ground-level composition and concentration M. Sowden & D. Blake https://doi.org/10.1007/s11869-021-01061-3
784. 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. https://doi.org/10.1080/01431161.2019.1601279
785. Remote Sensing Surveillance of NO2, SO2, CO, and AOD along the Suez Canal Pre- and Post-COVID-19 Lockdown Periods and during the Blockage G. Gamal et al. https://doi.org/10.3390/su15129362
786. Surface and Atmospheric Driven Variability of the Single‐Layer Urban Canopy Model Under Clear‐Sky Conditions Over London A. Tsiringakis et al. https://doi.org/10.1029/2019JD032167
787. Nonlinearity of the cloud response postpones climate penalty of mitigating air pollution in polluted regions H. Jia & J. Quaas https://doi.org/10.1038/s41558-023-01775-5
788. Effect of dust loadings, meteorological conditions, and local emissions on aerosol mixing and loading variability over highly urbanized semiarid countries: United Arab Emirates case study A. Abuelgasim & A. Farahat https://doi.org/10.1016/j.jastp.2020.105215
789. Examining Intrinsic Aerosol‐Cloud Interactions in South Asia Through Multiple Satellite Observations Z. Pan et al. https://doi.org/10.1029/2017JD028232
790. High-Spatial-Resolution Aerosol Optical Properties Retrieval Algorithm Using Chinese High-Resolution Earth Observation Satellite I F. Bao et al. https://doi.org/10.1109/TGRS.2016.2568246
791. Comparison of MODIS- and CALIPSO-Derived Temporal Aerosol Optical Depth over Yellow River Basin (China) from 2007 to 2015 Z. Zhang et al. https://doi.org/10.1007/s41748-020-00181-7
792. Aerosol optical properties over Europe: an evaluation of the AQMEII Phase 3 simulations against satellite observations L. Palacios-Peña et al. https://doi.org/10.5194/acp-19-2965-2019
793. Retrieval of aerosol optical properties using MERIS observations: Algorithm and some first results L. Mei et al. https://doi.org/10.1016/j.rse.2016.11.015
794. In situ identification of aerosol types in Athens, Greece, based on long-term optical and on online chemical characterization D. Kaskaoutis et al. https://doi.org/10.1016/j.atmosenv.2020.118070
795. New global aerosol fine-mode fraction data over land derived from MODIS satellite retrievals X. Yan et al. https://doi.org/10.1016/j.envpol.2021.116707
796. Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter A. van Donkelaar et al. https://doi.org/10.1289/ehp.1408646
797. Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM2.5 Temporal and Spatial Distributions J. Carmona et al. https://doi.org/10.3390/rs13163102
798. Temporal evolution of aerosols and their extreme events in polluted Asian regions during Terra's 20-year observations Z. Hu et al. https://doi.org/10.1016/j.rse.2021.112541
799. Effects of COVID-19 lockdowns on fine particulate matter concentrations M. Hammer et al. https://doi.org/10.1126/sciadv.abg7670
800. Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data W. Zhang et al. https://doi.org/10.3390/rs10010137
801. Opportunistic experiments to constrain aerosol effective radiative forcing M. Christensen et al. https://doi.org/10.5194/acp-22-641-2022
802. Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci) T. Popp et al. https://doi.org/10.3390/rs8050421
803. The Canadian Optimized Statistical Smoke Exposure Model (Canossem): A Machine Learning Approach to Estimate National Daily Fine Particulate Matter (Pm2.5) Exposure N. Paul et al. https://doi.org/10.2139/ssrn.4098551
804. The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation S. Callewaert et al. https://doi.org/10.5194/amt-12-3673-2019
805. Recent reductions in aerosol emissions have increased Earth’s energy imbalance Ø. Hodnebrog et al. https://doi.org/10.1038/s43247-024-01324-8
806. Cross-calibration of S-NPP VIIRS moderate-resolution reflective solar bands against MODIS Aqua over dark water scenes A. Sayer et al. https://doi.org/10.5194/amt-10-1425-2017
807. Changes in the shape of cloud ice water content vertical structure due to aerosol variations S. Massie et al. https://doi.org/10.5194/acp-16-6091-2016
808. A comparative time series analysis and modeling of aerosols in the contiguous United States and China X. Li et al. https://doi.org/10.1016/j.scitotenv.2019.07.072
809. Arctic amplification–induced decline in West and South Asia dust warrants stronger antidesertification toward carbon neutrality F. Wang et al. https://doi.org/10.1073/pnas.2317444121
810. 大气气溶胶尺度信息的卫星遥感反演：进展、挑战与展望（特邀） 晏. Yan Xing et al. https://doi.org/10.3788/AOS251947
811. The impact of three recent coal-fired power plant closings on Pittsburgh air quality: A natural experiment M. Russell et al. https://doi.org/10.1080/10962247.2016.1170738
812. Comparison of V4 and V3 CALIOP (L3) aerosol products: A global perspective V. Sreekanth & P. Kulkarni https://doi.org/10.1016/j.rsase.2020.100412
813. Comparison of hourly aerosol retrievals from JAXA Himawari/AHI in version 3.0 and a simple customized method W. Xu et al. https://doi.org/10.1038/s41598-020-77948-5
814. Observation and modeling of the historic “Godzilla” African dust intrusion into the Caribbean Basin and the southern US in June 2020 H. Yu et al. https://doi.org/10.5194/acp-21-12359-2021
815. 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. https://doi.org/10.1016/j.scitotenv.2023.164456
816. Validation of ESA Sentinel-2 L2A Aerosol Optical Thickness and Columnar Water Vapour during 2017–2018 M. Obregón et al. https://doi.org/10.3390/rs11141649
817. Adapting the Dark Target Algorithm to Advanced MERSI Sensor on the FengYun-3-D Satellite: Retrieval and Validation of Aerosol Optical Depth Over Land S. Jin et al. https://doi.org/10.1109/TGRS.2020.3021021
818. Highly anomalous fire emissions from the 2019–2020 Australian bushfires F. Li et al. https://doi.org/10.1088/2515-7620/ac2e6f
819. Aerosol optical depth retrieval over land from OCEANSAT-2/ OCM- 2 data – A simple physics based approach M. Mehta et al. https://doi.org/10.1016/j.apr.2022.101339
820. Spatial variability of aerosols over Greek archaeological sites using Space-Borne Remote Sensing A. Sakka et al. https://doi.org/10.1016/j.culher.2020.07.001
821. Estimation of nighttime aerosol optical depths using the ground-based microwave radiometer G. Liu et al. https://doi.org/10.5194/amt-18-6705-2025
822. Retrieval and validation of aerosol optical depth using GF-1 WFV cameras data X. Wang et al. https://doi.org/10.1016/j.asr.2019.10.023
823. Retrieval of the Fine-Mode Aerosol Optical Depth over East China Using a Grouped Residual Error Sorting (GRES) Method from Multi-Angle and Polarized Satellite Data Y. Zhang et al. https://doi.org/10.3390/rs10111838
824. Is the Awareness of the Aerosol State Useful in Predicting Enhanced Lightning for Lightning-Producing Storms over Northern Alabama? T. Ren et al. https://doi.org/10.1175/JAMC-D-17-0182.1
825. Enhancing the Applicability of Satellite Remote Sensing for PM2.5 Estimation Using MODIS Deep Blue AOD and Land Use Regression in California, United States H. Lee et al. https://doi.org/10.1021/acs.est.6b01438
826. Infusing satellite data into aerosol forecast for near real-time episode detection and diagnosis in East Asia S. Chen et al. https://doi.org/10.1016/j.scitotenv.2022.158797
827. Radiometric calibration and reflectivity inversion of spaceborne LiDAR X. Huang et al. https://doi.org/10.1364/AO.558017
828. A key study on spatial source distribution of PM2.5 based on the airflow trajectory model C. Wang et al. https://doi.org/10.1080/01431161.2016.1251628
829. Separating emission and meteorological contributions to long-term PM2.5 trends over eastern China during 2000–2018 Q. Xiao et al. https://doi.org/10.5194/acp-21-9475-2021
830. Evaluation of CNRM Earth System Model, CNRM‐ESM2‐1: Role of Earth System Processes in Present‐Day and Future Climate R. Séférian et al. https://doi.org/10.1029/2019MS001791
831. Exploring black carbon stratification in the urban mixed layer using UAV-based vertical profiles L. Villacura M. et al. https://doi.org/10.1016/j.uclim.2026.103012
832. Increased aerosol content in the atmosphere over Ukraine during summer 2010 E. Galytska et al. https://doi.org/10.5194/amt-11-2101-2018
833. On the Use of CALIPSO Land Surface Returns to Retrieve Aerosol and Cloud Optical Depths D. Josset et al. https://doi.org/10.1109/TGRS.2018.2796850
834. Estimating the effective spatial resolution of the operational BRDF, albedo, and nadir reflectance products from MODIS and VIIRS M. Campagnolo et al. https://doi.org/10.1016/j.rse.2015.12.033
835. PM2.5 Concentration Prediction: Ultrahigh Spatiotemporal Resolution Achieved by Combining Machine Learning and Low-Cost Sensors J. Li et al. https://doi.org/10.3390/s25175527
836. Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals T. Zhang et al. https://doi.org/10.1016/j.rse.2018.06.030
837. Model-enforced post-process correction of satellite aerosol retrievals A. Lipponen et al. https://doi.org/10.5194/amt-14-2981-2021
838. Satellite Aerosol Optical Depth Retrieval Based on Fully Connected Neural Network (FCNN) and a Combine Algorithm of Simplified Aerosol Retrieval Algorithm and Simplified and Robust Surface Reflectance Estimation (SREMARA) Y. Fan & L. Sun https://doi.org/10.1109/JSTARS.2023.3281777
839. Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over mainland China Y. Che et al. https://doi.org/10.1016/j.isprsjprs.2018.09.004
840. Two decades of aerosol observations by AATSR, MISR, MODIS and MERRA-2 over India and Indian Ocean J. Kuttippurath & S. Raj https://doi.org/10.1016/j.rse.2021.112363
841. Estimation of particulate matter concentrations in Türkiye using a random forest model based on satellite AOD retrievals G. Tuna Tuygun & T. Elbir https://doi.org/10.1007/s00477-023-02459-4
842. The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM2.5 Mass and Composition J. Belle et al. https://doi.org/10.3390/ijerph14101244
843. A Decade of Aerosol Optical Properties Measurements over Athens, Greece I. Raptis et al. https://doi.org/10.3390/atmos11020154
844. Exploring Aerosols Near Clouds With High‐Spatial‐Resolution Aircraft Remote Sensing During SEAC4RS R. Spencer et al. https://doi.org/10.1029/2018JD028989
845. A review of biomass burning: Emissions and impacts on air quality, health and climate in China J. Chen et al. https://doi.org/10.1016/j.scitotenv.2016.11.025
846. Forecasting dust impact on solar energy using remote sensing and modeling techniques A. Masoom et al. https://doi.org/10.1016/j.solener.2021.09.033
847. Retrieval of Aerosol Optical Depth over Arid Areas from MODIS Data X. Tian & L. Sun https://doi.org/10.3390/atmos7100134
848. Spatiotemporal aerosol prediction model based on fusion of machine learning and spatial analysis K. Lee et al. https://doi.org/10.1007/s44273-024-00031-2
849. High-Resolution Spatiotemporal Estimation of PM2.5 in Complex Terrain During the Heating Period: A Case Study in Shanxi, China X. Zhang et al. https://doi.org/10.1109/TGRS.2026.3672110
850. Global maps of aerosol single scattering albedo using combined CERES-MODIS retrieval A. Devi & S. Satheesh https://doi.org/10.5194/acp-22-5365-2022
851. Long-term trends in air quality in major cities in the UK and India: a view from space K. Vohra et al. https://doi.org/10.5194/acp-21-6275-2021
852. Long-term temporal analysis of the columnar and surface aerosol relationship with planetary boundary layer height at a southern coastal site of Turkey G. Tuna Tuygun & T. Elbir https://doi.org/10.1016/j.apr.2020.09.008
853. Assessment of the Coupled Model Intercomparison Project phase 6 (CMIP6) Model performance in simulating the spatial-temporal variation of aerosol optical depth over Eastern Central China X. Li et al. https://doi.org/10.1016/j.atmosres.2021.105747
854. Interdecadal variation in aerosol optical properties and their relationships to meteorological parameters over northeast China from 1980 to 2017 H. Zhao et al. https://doi.org/10.1016/j.chemosphere.2019.125737
855. Long-term three-dimensional distribution and transport of Saharan dust: Observation from CALIPSO, MODIS, and reanalysis data A. Merdji et al. https://doi.org/10.1016/j.atmosres.2023.106658
856. Remote sensing and model analysis of biomass burning smoke transported across the Atlantic during the 2020 Western US wildfire season X. Ceamanos et al. https://doi.org/10.1038/s41598-023-39312-1
857. The interactive global fire module pyrE (v1.0) K. Mezuman et al. https://doi.org/10.5194/gmd-13-3091-2020
858. Characterizing Weekly Cycles of Particulate Matter in a Coastal Megacity: The Importance of a Seasonal, Size‐Resolved, and Chemically Speciated Analysis M. Hilario et al. https://doi.org/10.1029/2020JD032614
859. Spatiotemporal Distribution of Major Aerosol Types over China Based on MODIS Products between 2008 and 2017 Q. Chen et al. https://doi.org/10.3390/atmos11070703
860. Global map of characterized dust sources using multisource remote sensing data A. Darvishi Boloorani et al. https://doi.org/10.1038/s41598-025-14794-3
861. GOCI-II geostationary satellite hourly aerosol optical depth obtained by data-driven methods: Validation and comparison Y. Fan et al. https://doi.org/10.1016/j.atmosenv.2023.119965
862. Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements Y. Zheng et al. https://doi.org/10.1016/j.atmosenv.2015.06.046
863. Chemically specific sampling bias: the ratio of PM2.5 to surface AOD on average and peak days in the U.S. S. Rosanka et al. https://doi.org/10.1039/D3EA00163F
864. Estimating ground-level particulate matter concentrations using satellite-based data: a review M. Shin et al. https://doi.org/10.1080/15481603.2019.1703288
865. Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. https://doi.org/10.1029/2019JD031465
866. A global perspective on CO2 satellite observations in high AOD conditions T. Virtanen et al. https://doi.org/10.5194/amt-18-929-2025
867. Improving the understanding between climate variability and observed extremes of global NO2 over the past 15 years W. Deng et al. https://doi.org/10.1088/1748-9326/abd502
868. Decadal evaluation of regional climate, air quality, and their interactions over the continental US and their interactions using WRF/Chem version 3.6.1 K. Yahya et al. https://doi.org/10.5194/gmd-9-671-2016
869. Can MODIS detect trends in aerosol optical depth over land? X. Fan et al. https://doi.org/10.1007/s00376-017-7017-2
870. Observational evidence of elevated smoke layers during crop residue burning season over Delhi: Potential implications on associated heterogeneous PM2.5 enhancements A. Mhawish et al. https://doi.org/10.1016/j.rse.2022.113167
871. Evaluation and Comparison of MODIS C6 and C6.1 Deep Blue Aerosol Products in Arid and Semi-Arid Areas of Northwestern China L. Yang et al. https://doi.org/10.3390/rs14081935
872. Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties F. Bender et al. https://doi.org/10.16993/tellusb.1868
873. Evaluation of Terra-MODIS C6 and C6.1 Aerosol Products against Beijing, XiangHe, and Xinglong AERONET Sites in China during 2004-2014 M. Bilal et al. https://doi.org/10.3390/rs11050486
874. Investigation of Aerosol Climatology, Optical Characteristics and Variability over Egypt Based on Satellite Observations and In-Situ Measurements I. Abou El-Magd et al. https://doi.org/10.3390/atmos11070714
875. Identification of dust aerosols, their sources, and the effect of soil moisture in Central Asia J. Liu et al. https://doi.org/10.1016/j.scitotenv.2023.161575
876. A generalized land surface reflectance reconstruction method for aerosol retrieval: Application to the Particulate Observing Scanning Polarimeter (POSP) onboard GaoFen-5 (02) satellite Z. Shi et al. https://doi.org/10.1016/j.rse.2023.113683
877. Satellite-Based Optimization and Planning of Urban Ventilation Corridors for a Healthy Microclimate Environment D. Gong et al. https://doi.org/10.3390/su152115653
878. Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations – Part 1: ATSR (1995–2011) and MODIS C6.1 (2000–2017) L. Sogacheva et al. https://doi.org/10.5194/acp-18-11389-2018
879. Preliminary Retrieval and Validation of Aerosol Optical Depths from FY-4B Advanced Geostationary Radiation Imager Images D. Zhou et al. https://doi.org/10.3390/rs16020372
880. Aerosol retrieval algorithm based on adaptive land–atmospheric decoupling for polarized remote sensing over land surfaces H. Wang et al. https://doi.org/10.1016/j.jqsrt.2018.08.011
881. 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. https://doi.org/10.3390/rs8030184
882. Trends in aerosol optical properties over Eastern Europe based on MODIS-Aqua M. Filonchyk et al. https://doi.org/10.1016/j.gsf.2020.03.014
883. Potential responses of vegetation to atmospheric aerosols in arid and semi-arid regions of Asia L. Jiao et al. https://doi.org/10.1007/s40333-021-0005-5
884. Evaluation of biomass burning aerosols in the HadGEM3 climate model with observations from the SAMBBA field campaign B. Johnson et al. https://doi.org/10.5194/acp-16-14657-2016
885. Systematic Evaluation of Four Satellite AOD Datasets for Estimating PM2.5 Using a Random Forest Approach J. Handschuh et al. https://doi.org/10.3390/rs15082064
886. Aerosol optical depth retrieval from Geostationary Environment Monitoring Spectrometer (GEMS): Advancing the first hyperspectral geostationary air quality mission using deep learning H. Choi et al. https://doi.org/10.1016/j.scitotenv.2025.180535
887. Estimation and Validation of Collection 6 Moderate Resolution Imaging Spectroradiometer Aerosol Products for East Asia K. Lee https://doi.org/10.5572/ajae.2018.12.3.193
888. A robust aerosol impact on clouds along the subtropical to tropical transition N. Yeheskel et al. https://doi.org/10.5194/acp-26-8765-2026
889. Spatial-Temporal Variation of AOD Based on MAIAC AOD in East Asia from 2011 to 2020 P. Wang et al. https://doi.org/10.3390/atmos13121983
890. Evaluation of the NDVI-Based Pixel Selection Criteria of the MODIS C6 Dark Target and Deep Blue Combined Aerosol Product M. Bilal & J. Nichol https://doi.org/10.1109/JSTARS.2017.2693289
891. Variability in the correlation between satellite-derived liquid cloud droplet effective radius and aerosol index over the northern Pacific Ocean S. Tan et al. https://doi.org/10.1080/16000889.2017.1391656
892. The impact of resolution on meteorological, chemical and aerosol properties in regional simulations with WRF-Chem P. Crippa et al. https://doi.org/10.5194/acp-17-1511-2017
893. Precipitable water vapor over oceans from the Maritime Aerosol Network: Evaluation of global models and satellite products under clear sky conditions D. Pérez-Ramírez et al. https://doi.org/10.1016/j.atmosres.2018.09.007
894. Radiative Impacts of Aerosols During COVID-19 Lockdown Period Over the Indian Region R. Bhawar et al. https://doi.org/10.3389/fenvs.2021.746090
895. Exploring the trend, prediction and driving forces of aerosols using satellite and ground data, and implications for climate change mitigation X. Li et al. https://doi.org/10.1016/j.jclepro.2019.03.121
896. Trends in the types and absorption characteristics of ambient aerosols over the Indo-Gangetic Plain and North China Plain in last two decades S. Ramachandran & M. Rupakheti https://doi.org/10.1016/j.scitotenv.2022.154867
897. Improving XCO2 retrieval under high aerosol loads with fused satellite aerosol Data: Advancing understanding of anthropogenic emissions H. Zhu et al. https://doi.org/10.1016/j.isprsjprs.2025.03.009
898. Investigating the Spatial and Temporal Limitations for Remote Sensing of Wildfire Smoke Using Satellite and Airborne Imagers During FIREX‐AQ Y. Shi et al. https://doi.org/10.1029/2023JD039085
899. A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula A. Alban et al. https://doi.org/10.1007/s41748-024-00398-w
900. A satellite-measured view of aerosol component content and optical property in a haze-polluted case over North China Plain L. Li et al. https://doi.org/10.1016/j.atmosres.2021.105958
901. Applying the Dark Target aerosol algorithm with Advanced Himawari Imager observations during the KORUS-AQ field campaign P. Gupta et al. https://doi.org/10.5194/amt-12-6557-2019
902. A hybrid method for reconstructing the historical evolution of aerosol optical depth from sunshine duration measurements W. Wandji Nyamsi et al. https://doi.org/10.5194/amt-13-3061-2020
903. Spatiotemporal analysis of hydrological and environmental changes in lake Urmia (2000–2024) using google earth engine H. Li et al. https://doi.org/10.1007/s12145-026-02089-8
904. Variability of mineral dust deposition in the western Mediterranean basin and south-east of France J. Vincent et al. https://doi.org/10.5194/acp-16-8749-2016
905. Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach T. Li et al. https://doi.org/10.1002/2017GL075710
906. Evaluation of WRF-Chem-RTFDDA dust forecasts over the MENA region using in-situ and remote-sensing observations D. Rostkier-Edelstein et al. https://doi.org/10.3389/fenvs.2022.981852
907. Aerosols under heatwaves: Interactions between pollution and heatwave extremes M. Caushaj et al. https://doi.org/10.1051/epjconf/202636504004
908. Study of optical scattering properties and direct radiative effects of high-altitude cirrus clouds in Barcelona, Spain, with 4 years of lidar measurements C. Gil-Díaz et al. https://doi.org/10.5194/acp-25-3445-2025
909. Assessing the performance of the Copernicus Atmosphere Monitoring Service reanalysis: a comprehensive analysis of aerosol optical depth over India K. Shukla et al. https://doi.org/10.1007/s11356-025-37286-3
910. Statistics and Meteorology of Air Pollution Episodes over the South African Highveld Based on Satellite–Model Datasets M. Jury https://doi.org/10.1175/JAMC-D-16-0354.1
911. Long-term Changes in Solar Radiation in Northern Eurasia during the Warm Season According to Measurements and Reconstruction Model E. Volpert & N. Chubarova https://doi.org/10.3103/S1068373921080021
912. ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data set A. Gkikas et al. https://doi.org/10.5194/amt-14-309-2021
913. Spaceborne observations of aerosol vertical distribution over Indian mainland (2009–2018) M. Mehta et al. https://doi.org/10.1016/j.atmosenv.2020.117902
914. Effects of COVID-19 pandemic lockdown: A satellite data-based appraisal of air quality in Guwahati, Assam J. Biswas & B. Pathak https://doi.org/10.1016/j.matpr.2022.06.218
915. Landscape determinants of spatio-temporal patterns of aerosol optical depth in the two most polluted metropolitans in the United States C. Wang et al. https://doi.org/10.1016/j.scitotenv.2017.07.273
916. Technical note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers – first steps toward assessing the feasibility of a global observation strategy D. Anderson et al. https://doi.org/10.5194/acp-23-6319-2023
917. Spatiotemporal variability of aerosol-cloud interactions in Northwestern Pacific and impact on NPP X. Qiu et al. https://doi.org/10.1016/j.atmosenv.2025.121151
918. Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models J. Yeom et al. https://doi.org/10.1109/TGRS.2021.3107542
919. An exploratory study on the aerosol height retrieval from OMI measurements of the 477  nm O2 − O2 spectral band using a neural network approach J. Chimot et al. https://doi.org/10.5194/amt-10-783-2017
920. CALIOP-Based Quantification of Central Asian Dust Transport Y. Han et al. https://doi.org/10.3390/rs14061416
921. Assessments of Satellite-Based Aerosol Optical Depth for Monitoring Air Quality of the Large Port of Busan, Korea U. Jeong et al. https://doi.org/10.3390/atmos16101123
922. Spatial and seasonal variations in atmospheric aerosols over Nigeria: Assessment of influence of intertropical discontinuity movement A. Ayanlade et al. https://doi.org/10.1177/1759313118820306
923. The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations Q. Yang et al. https://doi.org/10.1016/j.envpol.2019.02.071
924. Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing F. Bao et al. https://doi.org/10.1016/j.rse.2024.114275
925. Spatio-temporal variations in fine particulate matter and evaluation of associated health risk over Pakistan A. Mariam et al. https://doi.org/10.1002/ieam.4446
926. Analysis of the MODIS above-cloud aerosol retrieval algorithm using MCARS G. Wind et al. https://doi.org/10.5194/gmd-15-1-2022
927. Retrieval of global aerosol and surface properties from the Gaofen-5 Directional Polarimetric Camera measurements Z. Zhang et al. https://doi.org/10.5194/amt-19-2555-2026
928. Evaluation and utilization of MODIS and CALIPSO aerosol retrievals over a complex terrain in Himalaya A. Kumar et al. https://doi.org/10.1016/j.rse.2017.12.019
929. Impact of atmospheric circulation types on southwest Asian dust and Indian summer monsoon rainfall D. Kaskaoutis et al. https://doi.org/10.1016/j.atmosres.2017.11.002
930. Impacts of Sun Glint Off Ice Clouds on DSCOVR EPIC Cloud Products T. Várnai et al. https://doi.org/10.1109/TGRS.2024.3400253
931. Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation A. Sayer et al. https://doi.org/10.5194/amt-8-5277-2015
932. The Sensitivity of AOD Retrieval to Aerosol Type and Vertical Distribution over Land with MODIS Data Y. Wu et al. https://doi.org/10.3390/rs8090765
933. SI-traceable solar irradiance measurements for aerosol optical depth retrieval N. Kouremeti et al. https://doi.org/10.1088/1681-7575/ac6cbb
934. The regime of aerosol asymmetry parameter over Europe, the Mediterranean and the Middle East based on MODIS satellite data: evaluation against surface AERONET measurements M. Korras-Carraca et al. https://doi.org/10.5194/acp-15-13113-2015
935. Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh A. Gupta et al. https://doi.org/10.1007/s42452-020-03829-1
936. Long-term aerosol optical depth trend over Iran and identification of dominant aerosol types R. Yousefi et al. https://doi.org/10.1016/j.scitotenv.2020.137906
937. High-Resolution Satellite-Based PM2.5 Concentration Data Acquired During the COVID-19 Outbreak Throughout China: Model, Variations, and Reasons H. Guo et al. https://doi.org/10.1109/JSTARS.2021.3119383
938. SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm L. She et al. https://doi.org/10.3390/rs9030253
939. Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China Y. Che et al. https://doi.org/10.5194/acp-16-9655-2016
940. Global validation and comparison of MODIS DT and POLDER-3 GRASP aerosol products over ocean X. Pei et al. https://doi.org/10.1016/j.atmosres.2022.106480
941. Global and regional evaluation of a global model simulated AODs with AERONET and MODIS observations V. Sheel et al. https://doi.org/10.1002/joc.5367
942. Description and performance of a sectional aerosol microphysical model in the Community Earth System Model (CESM2) S. Tilmes et al. https://doi.org/10.5194/gmd-16-6087-2023
943. A critical view of long-term AVHRR aerosol data record in China: Retrieval frequency and heavy pollution M. Tao et al. https://doi.org/10.1016/j.atmosenv.2019.117246
944. Evaluation of MODIS columnar aerosol retrievals using AERONET in semi-arid Nevada and California, U.S.A., during the summer of 2012 S. Loría-Salazar et al. https://doi.org/10.1016/j.atmosenv.2016.08.070
945. Evaluation of the Aqua-MODIS C6 and C6.1 Aerosol Optical Depth Products in the Yellow River Basin, China M. Zhang et al. https://doi.org/10.3390/atmos10080426
946. Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm T. Zhang et al. https://doi.org/10.1080/15481603.2022.2060596
947. Increase of surface solar irradiance across East China related to changes in aerosol properties during the past decade J. Li et al. https://doi.org/10.1088/1748-9326/aaa35a
948. Models transport Saharan dust too low in the atmosphere: a comparison of the MetUM and CAMS forecasts with observations D. O'Sullivan et al. https://doi.org/10.5194/acp-20-12955-2020
949. Satellite-based PM2.5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm J. Liu et al. https://doi.org/10.1016/j.atmosenv.2019.04.002
950. 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. https://doi.org/10.5194/amt-11-385-2018
951. Dual‐Isotope Constraints on Seasonally Resolved Source Fingerprinting of Black Carbon Aerosols in Sites of the Four Emission Hot Spot Regions of China W. Fang et al. https://doi.org/10.1029/2018JD028607
952. Mitigating Satellite‐Based Fire Sampling Limitations in Deriving Biomass Burning Emission Rates: Application to WRF‐Chem Model Over the Northern sub‐Saharan African Region J. Wang et al. https://doi.org/10.1002/2017JD026840
953. Coupling meteorological variables with Moderate Resolution Imaging Spectroradiometer atmospheric products for estimating global solar radiation J. Chen et al. https://doi.org/10.1016/j.enconman.2019.112383
954. Accuracy assessment and climatology of MODIS aerosol optical properties over North Africa A. Merdji et al. https://doi.org/10.1007/s11356-022-22997-8
955. 准东煤田吸光气溶胶特性及辐射强迫模拟 张. Zhang Zikang et al. https://doi.org/10.3788/AOS241868
956. Changing air pollution scenario during COVID-19: Redefining the hotspot regions over India B. Tyagi et al. https://doi.org/10.1016/j.envpol.2020.116354
957. High‐Resolution Post‐Process Corrected Satellite AOD H. Taskinen et al. https://doi.org/10.1029/2022GL099733
958. Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak A. Rudke et al. https://doi.org/10.1016/j.rse.2023.113514
959. Optimal estimation of spectral surface reflectance in challenging atmospheres D. Thompson et al. https://doi.org/10.1016/j.rse.2019.111258
960. The relationship between cloud condensation nuclei (CCN) concentration and light extinction of dried particles: indications of underlying aerosol processes and implications for satellite-based CCN estimates Y. Shinozuka et al. https://doi.org/10.5194/acp-15-7585-2015
961. Study of Chemical and Optical Properties of Biomass Burning Aerosols during Long-Range Transport Events toward the Arctic in Summer 2017 T. Zielinski et al. https://doi.org/10.3390/atmos11010084
962. Aerosols impact on the convective and non-convective rain distribution over the Indian region: Results from WRF-Chem simulation S. Kedia et al. https://doi.org/10.1016/j.atmosenv.2019.01.020
963. Long term trend in aerosol direct radiative effects over Indian Ocean region from multi- satellite observations S. Jose et al. https://doi.org/10.1080/2150704X.2021.1957509
964. An improved meteorological variables-based aerosol optical depth estimation method by combining a physical mechanism model with a two-stage model F. Li et al. https://doi.org/10.1016/j.chemosphere.2024.142820
965. Role of aerosol–cloud–radiation interactions in modulating summertime quasi-biweekly rainfall intensity over South China H. Chen et al. https://doi.org/10.5194/acp-25-15765-2025
966. Spatiotemporal Trends of Aerosols over Urban Regions in Pakistan and Their Possible Links to Meteorological Parameters G. Ali et al. https://doi.org/10.3390/atmos11030306
967. 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. https://doi.org/10.5194/amt-16-3039-2023
968. Verification of dust forecast over the Indian region with satellite and ground based observations P. Das et al. https://doi.org/10.1080/01431161.2016.1239285
969. Observations of the Interaction and Transport of Fine Mode Aerosols With Cloud and/or Fog in Northeast Asia From Aerosol Robotic Network and Satellite Remote Sensing T. Eck et al. https://doi.org/10.1029/2018JD028313
970. Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements A. Sayer et al. https://doi.org/10.1002/2017JD026934
971. Seasonality of the mass concentration and chemical composition of aerosols around an urbanized basin in East Asia C. Chou et al. https://doi.org/10.1002/2016JD025728
972. Large air quality and human health impacts due to Amazon forest and vegetation fires E. Butt et al. https://doi.org/10.1088/2515-7620/abb0db
973. Error characterization and analysis of aerosol products from the geostationary environment monitoring spectrometer (GEMS) G. Du et al. https://doi.org/10.1016/j.atmosres.2025.108682
974. Synchronous Atmospheric Correction of High Spatial Resolution Images from Gao Fen Duo Mo Satellite L. Xu et al. https://doi.org/10.3390/rs14174427
975. Evaluation of the MISR fine resolution aerosol product using MODIS, MISR, and ground observations over China Y. Si et al. https://doi.org/10.1016/j.atmosenv.2019.117229
976. Impact of boundary layer schemes in RegCM-Chem on East Asian climate and its response to the aerosol-radiation interaction H. Cao et al. https://doi.org/10.1016/j.atmosres.2026.108951
977. Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1) L. Zhang et al. https://doi.org/10.5194/gmd-15-5337-2022
978. Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation M. Tao et al. https://doi.org/10.1016/j.atmosenv.2019.06.004
979. Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter T. Dai et al. https://doi.org/10.1029/2018MS001475
980. Seasonality biases arise from the interplay of retrieval quality and solar zenith angle effects in passive sensor AOD products S. Smith et al. https://doi.org/10.5194/acp-25-14333-2025
981. The influence of simulated surface dust lofting and atmospheric loading on radiative forcing S. Saleeby et al. https://doi.org/10.5194/acp-19-10279-2019
982. A strengthened and southward-shifted westerly jet mitigates warming-induced drying across Asian drylands J. Jiang & T. Zhou https://doi.org/10.1126/sciadv.aed7890
983. Climatology of the aerosol optical depth by components from the Multi-angle Imaging SpectroRadiometer (MISR) and chemistry transport models H. Lee et al. https://doi.org/10.5194/acp-16-6627-2016
984. Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD S. Kim et al. https://doi.org/10.3390/app112210755
985. Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions G. Saponaro et al. https://doi.org/10.5194/acp-20-1607-2020
986. Investigating the relationship of aerosols with enhanced vegetation index and meteorological parameters over Pakistan S. Tariq et al. https://doi.org/10.1016/j.apr.2021.101080
987. Evaluation of the multi‐angle implementation of atmospheric correction (MAIAC) aerosol algorithm through intercomparison with VIIRS aerosol products and AERONET S. Superczynski et al. https://doi.org/10.1002/2016JD025720
988. A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI: Development and Verification over Asia D. Fu et al. https://doi.org/10.1016/j.eng.2023.09.023
989. Long-Term Trends in Aerosol Single Scattering Albedo Cause Bias in MODIS Aerosol Optical Depth Trends Z. Zhang et al. https://doi.org/10.1109/TGRS.2024.3424981
990. Impact of Holuhraun volcano aerosols on clouds in cloud-system-resolving simulations M. Haghighatnasab et al. https://doi.org/10.5194/acp-22-8457-2022
991. Bias in modeled solar radiation by non-resolved intra-daily AOD variability J. Ruiz-Arias https://doi.org/10.1016/j.solener.2020.04.082
992. Long-term MAX-DOAS measurements of NO2, HCHO, and aerosols and evaluation of corresponding satellite data products over Mohali in the Indo-Gangetic Plain V. Kumar et al. https://doi.org/10.5194/acp-20-14183-2020
993. Fusion of the Arctic Aerosol Optical Depth Based on Improved Bayesian Maximum Entropy Y. Han et al. https://doi.org/10.1109/TGRS.2026.3654145
994. Assessment of Aerosol Radiative Forcing with 1-D Radiative Transfer Modeling in the U. S. South-East E. Alston & I. Sokolik https://doi.org/10.3390/atmos9070271
995. Effect of Reduced Emissions from Thermal Power Plants in China on Local Air Quality Improvement K. Lee & S. Shin https://doi.org/10.5572/KOSAE.2022.38.2.304
996. Assessing the relationship between atmospheric aerosols and maximum surface air temperature over the Indian region T. Sarin & V. Vinoj https://doi.org/10.1038/s41598-026-40641-0
997. Extended aerosol and surface characterization from S5P/TROPOMI with GRASP algorithm. Part I: Conditions, approaches, performance and new possibilities P. Litvinov et al. https://doi.org/10.1016/j.rse.2024.114355
998. Global changes in aerosol single scattering albedo during COVID-19 K. Ansari & S. Ramachandran https://doi.org/10.1016/j.atmosenv.2024.120649
999. Long Temporal Analysis of 3-km MODIS Aerosol Product Over East China Q. Ma et al. https://doi.org/10.1109/JSTARS.2017.2650144
1000. Large Contribution of Coarse Mode to Aerosol Microphysical and Optical Properties: Evidence from Ground-Based Observations of a Transpacific Dust Outbreak at a High-Elevation North American Site E. Kassianov et al. https://doi.org/10.1175/JAS-D-16-0256.1
1001. A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances M. Penning de Vries et al. https://doi.org/10.5194/acp-15-10597-2015
1002. Impact of Chinese air pollutants on a record-breaking PMs episode in the Republic of Korea for 11–15 January 2019 H. Oh et al. https://doi.org/10.1016/j.atmosenv.2020.117262
1003. Aerosol optical depth trend over the Middle East K. Klingmüller et al. https://doi.org/10.5194/acp-16-5063-2016
1004. The Uncharacteristic Occurrence of the June 2013 Biomass-Burning Haze Event in Southeast Asia: Effects of the Madden-Julian Oscillation and Tropical Cyclone Activity Y. Oozeer et al. https://doi.org/10.3390/atmos11010055
1005. Assessing the radiative impacts of an extreme desert dust outbreak and the potential improvements on short-term weather forecasts: The case of February 2015 A. Gkikas et al. https://doi.org/10.1016/j.atmosres.2019.04.020
1006. Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols G. Wind et al. https://doi.org/10.5194/gmd-9-2377-2016
1007. Impact of environmental pollution on the retrieval of hourly aerosol products from Advanced Himawari Imager (AHI) over Beijing Q. Xu et al. https://doi.org/10.1016/j.apr.2020.03.007
1008. Improving aerosol optical depth retrievals from Himawari-8 with ensemble learning enhancement: Validation over Asia D. Fu et al. https://doi.org/10.1016/j.atmosres.2023.106624
1009. Monitoring cyanobacterial harmful algal blooms at high spatiotemporal resolution by fusing Landsat and MODIS imagery Y. Zhao et al. https://doi.org/10.1016/j.envadv.2020.100008
1010. Aerosol chemistry, transport, and climatic implications during extreme biomass burning emissions over the Indo-Gangetic Plain N. Singh et al. https://doi.org/10.5194/acp-18-14197-2018
1011. An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness, part 1: Algorithm development X. Yan et al. https://doi.org/10.1016/j.rse.2017.02.005
1012. Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning J. Fuchs et al. https://doi.org/10.5194/acp-18-16537-2018
1013. Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea S. Park et al. https://doi.org/10.5194/acp-19-1097-2019
1014. Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China W. Wang et al. https://doi.org/10.3390/rs9080858
1015. An Enhanced Aerosol Optical Depth Retrieval Algorithm for Particulate Observing Scanning Polarimeter (POSP) Data Over Land Z. Ji et al. https://doi.org/10.1109/TGRS.2024.3514170
1016. A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches X. Yan et al. https://doi.org/10.5194/essd-14-1193-2022
1017. Aerosol Optical Depth Over the Nepalese Cryosphere Derived From an Empirical Model B. Bhattarai et al. https://doi.org/10.3389/feart.2019.00178
1018. The global spatial-temporal distribution and EOF analysis of AOD based on MODIS data during 2003–2021 X. Tian et al. https://doi.org/10.1016/j.atmosenv.2023.119722
1019. Integration of GEMS and MODIS AOD for enhanced long-term aerosol monitoring over East Asia H. Jeon et al. https://doi.org/10.1080/01431161.2026.2632162
1020. Extreme levels of Canadian wildfire smoke in the stratosphere over central Europe on 21–22 August 2017 A. Ansmann et al. https://doi.org/10.5194/acp-18-11831-2018
1021. Nocturnal, seasonal and intra-annual variability of tropospheric aerosols observed using ground-based and space-borne lidars over a tropical location of India P. Prasad et al. https://doi.org/10.1016/j.atmosenv.2019.06.008
1022. Coupling Hourly Land Surface Models With Spatiotemporal Variability Characteristics: A Study on FY-4B/AGRI Land Aerosol Retrieval Algorithm Y. Si et al. https://doi.org/10.1109/TGRS.2025.3591646
1023. Annual and Seasonal Variations in Aerosol Optical Characteristics in the Huai River Basin, China from 2007 to 2021 X. Deng et al. https://doi.org/10.3390/rs16091571
1024. Climatological variations in aerosol optical depth and aerosol type identification in Liaoning of Northeast China based on MODIS data from 2002 to 2019 H. Zhao et al. https://doi.org/10.1016/j.scitotenv.2021.146810
1025. A synergic study on estimating surface downward shortwave radiation from satellite data D. Wang et al. https://doi.org/10.1016/j.rse.2021.112639
1026. A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images O. Hagolle et al. https://doi.org/10.3390/rs70302668
1027. A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing A. Sayer et al. https://doi.org/10.5194/amt-13-373-2020
1028. The impact of moderately absorbing aerosol on surface sensible, latent, and net radiative fluxes during the summer of 2015 in Central Europe K. Markowicz et al. https://doi.org/10.1016/j.jaerosci.2020.105627
1029. Scattering and absorbing aerosols in the climate system J. Li et al. https://doi.org/10.1038/s43017-022-00296-7
1030. Advanced Time series Multi-wavelength AOD Transformer (TMAT) model: joint retrieval of aerosol optical and microphysical properties using Himawari-8 AHI data L. She et al. https://doi.org/10.1016/j.jag.2026.105252
1031. A deep hybrid CNN–BiLSTM–attention framework with metaheuristic optimization and uncertainty quantification for solar power forecasting in dust-affected arid climates M. Bou-Rabee et al. https://doi.org/10.3389/fenrg.2026.1750871
1032. Towards gridded nighttime aerosol optical thickness retrievals using VIIRS day–night band observations over regions with artificial light sources J. Zhang et al. https://doi.org/10.5194/amt-18-1787-2025
1033. Climatology of Aerosols over the Caribbean Islands: Aerosol Types, Synoptic Patterns, and Transport A. Rodríguez Vega et al. https://doi.org/10.1175/JAMC-D-21-0015.1
1034. Aerosol Optical Depth Retrieval From Landsat 8 OLI Images Over Urban Areas Supported by MODIS BRDF/Albedo Data X. Tian et al. https://doi.org/10.1109/LGRS.2018.2827200
1035. Retrieval of nighttime aerosol optical depth by simultaneous consideration of artificial and natural light sources Y. Meng et al. https://doi.org/10.1016/j.scitotenv.2023.166354
1036. Characterizing Smoke Haze Events in Australia Using a Hybrid Approach of Satellite-Based Aerosol Optical Depth and Chemical Transport Modeling M. Sowden et al. https://doi.org/10.3390/rs16071266
1037. Analysis of spatial-temporal heterogeneity in remotely sensed aerosol properties observed during 2005–2015 over three countries along the Gulf of Guinea Coast in Southern West Africa M. Aklesso et al. https://doi.org/10.1016/j.atmosenv.2018.03.062
1038. 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. https://doi.org/10.1016/j.atmosenv.2021.118784
1039. Aerosol monitoring in Siberia using an 808 nm automatic compact lidar G. Ancellet et al. https://doi.org/10.5194/amt-12-147-2019
1040. Anthropogenic emissions from South Asia reverses the aerosol indirect effect over the northern Indian Ocean S. Jose et al. https://doi.org/10.1038/s41598-020-74897-x
1041. Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product A. Lipponen et al. https://doi.org/10.5194/amt-15-895-2022
1042. Identification of Aerosol Pollution Hotspots in Jiangsu Province of China Y. Wang et al. https://doi.org/10.3390/rs13142842
1043. Assessing the Challenges of Surface‐Level Aerosol Mass Estimates From Remote Sensing During the SEAC4RS and SEARCH Campaigns: Baseline Surface Observations and Remote Sensing in the Southeastern United States K. Kaku et al. https://doi.org/10.1029/2017JD028074
1044. Tracking Smoke from a Prescribed Fire and Its Impacts on Local Air Quality Using Temporally Resolved GOES-16 ABI Aerosol Optical Depth (AOD) A. Huff et al. https://doi.org/10.1175/JTECH-D-20-0162.1
1045. Unraveling the short-duration Delhi dust storm: insights from In-Situ, satellite, and reanalysis datasets K. Shukla et al. https://doi.org/10.1007/s11869-026-02043-z
1046. Aerosol optical depth retrieval over the city of Abu Dhabi, United Arab Emirates (UAE) using Landsat-8 OLI images K. Omari et al. https://doi.org/10.1016/j.apr.2019.01.015
1047. Consistency of Aerosol Optical Properties between MODIS Satellite Retrievals and AERONET over a 14-Year Period in Central–East Europe L. Deaconu et al. https://doi.org/10.3390/rs16101677
1048. Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns H. Lim et al. https://doi.org/10.5194/amt-14-4575-2021
1049. Comparison of aerosol optical depth between observation and simulation from MIROC-SPRINTARS: Effects of temporal inhomogeneous sampling S. Park et al. https://doi.org/10.1016/j.atmosenv.2018.05.021
1050. Performance of the NPP-VIIRS and aqua-MODIS Aerosol Optical Depth Products over the Yangtze River Basin L. He et al. https://doi.org/10.3390/rs10010117
1051. Change from aerosol-driven to cloud-feedback-driven trend in short-wave radiative flux over the North Atlantic D. Grosvenor & K. Carslaw https://doi.org/10.5194/acp-23-6743-2023
1052. Employing relaxed smoothness constraints on imaginary part of refractive index in AERONET aerosol retrieval algorithm A. Sinyuk et al. https://doi.org/10.5194/amt-15-4135-2022
1053. 基于风云四号B星AGRI数据的海洋气溶胶反演算法研究（特邀） 郭. Guo Shijiao et al. https://doi.org/10.3788/AOS251988
1054. Aerosol optical depth distribution in extratropical cyclones over the Northern Hemisphere oceans C. Naud et al. https://doi.org/10.1002/2016GL070953
1055. Irrigation-Induced Environmental Changes around the Aral Sea: An Integrated View from Multiple Satellite Observations Q. Jin et al. https://doi.org/10.3390/rs9090900
1056. Assessment of the Spatial Structure of Black Carbon Concentrations in the Near-Surface Arctic Atmosphere E. Nagovitsyna et al. https://doi.org/10.3390/atmos14010139
1057. Quantitative assessment of the potential of optimal estimation for aerosol retrieval from geostationary weather satellites in the frame of the iAERUS‐GEO algorithm A. Georgeot et al. https://doi.org/10.1002/asl.1199
1058. Impact of North American intense fires on aerosol optical properties measured over the European Arctic in July 2015 K. Markowicz et al. https://doi.org/10.1002/2016JD025310
1059. Sensitivity of WRF-Chem model to land surface schemes: Assessment in a severe dust outbreak episode in the Central Mediterranean (Apulia Region) U. Rizza et al. https://doi.org/10.1016/j.atmosres.2017.10.022
1060. Air pollution inputs to the Mojave Desert by fusing surface mobile and airborne in situ and airborne and satellite remote sensing: A case study of interbasin transport with numerical model validation I. Leifer et al. https://doi.org/10.1016/j.atmosenv.2019.117184
1061. Multiscale Validation and Trend Evolution of Global Aerosol Reanalysis Datasets: A Comprehensive Comparative Study of CAMS and MERRA-2 P. Wang et al. https://doi.org/10.3390/rs18101569
1062. The impact of using assimilated Aeolus wind data on regional WRF-Chem dust simulations P. Kiriakidis et al. https://doi.org/10.5194/acp-23-4391-2023
1063. The Tibetan Plateau as dust aerosol transit station in middle troposphere over northern East Asia: A case study J. Cao & S. Chen https://doi.org/10.1016/j.atmosres.2022.106416
1064. In-Flight Radiometric Calibration of Gas Absorption Bands for the Gaofen-5 (02) DPC Using Sunglint S. Zhu et al. https://doi.org/10.3390/rs17213558
1065. Multi-Satellite Detection of Long-Range Transport and Transformation of Atmospheric Emissions from the Hunga Tonga-Hunga Ha’apai Volcano Q. Liu et al. https://doi.org/10.3390/rs15102661
1066. Globally analysing spatiotemporal trends of anthropogenic PM2.5 concentration and population's PM2.5 exposure from 1998 to 2016 J. Li et al. https://doi.org/10.1016/j.envint.2019.04.026
1067. California wildfire smoke contributes to a positive atmospheric temperature anomaly over the western United States J. Gomez et al. https://doi.org/10.5194/acp-24-6937-2024
1068. Retrieving Aerosol Characteristics From the PACE Mission, Part 1: Ocean Color Instrument L. Remer et al. https://doi.org/10.3389/feart.2019.00152
1069. Evaluation of atmospheric aerosols in the metropolitan area of São Paulo simulated by the regional EURAD-IM model on high-resolution E. De Souza Fernandes Duarte et al. https://doi.org/10.1016/j.apr.2020.12.006
1070. Model simulations capture seasonal Arctic Haze and clean-air cycle better than satellite and reanalysis B. Swain et al. https://doi.org/10.1038/s41598-025-29188-8
1071. Arctic spring and summertime aerosol optical depth baseline from long-term observations and model reanalyses – Part 1: Climatology and trend P. Xian et al. https://doi.org/10.5194/acp-22-9915-2022
1072. Exploring the relationship between high-resolution aerosol optical depth values and ground-level particulate matter concentrations in the Metropolitan Area of São Paulo A. Damascena et al. https://doi.org/10.1016/j.atmosenv.2020.117949
1073. A new top-down approach for directly estimating biomass burning emissions and fuel consumption rates and totals from geostationary satellite fire radiative power (FRP) B. Mota & M. Wooster https://doi.org/10.1016/j.rse.2017.12.016
1074. Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product N. Loeb et al. https://doi.org/10.1175/JCLI-D-17-0208.1
1075. Spatio-temporal variations and trends of MODIS C6.1 Dark Target and Deep Blue merged aerosol optical depth over China during 2000–2017 G. Xie et al. https://doi.org/10.1016/j.atmosenv.2019.116846
1076. Long-term trends of satellite-based fine-mode aerosol optical depth over the Seto Inland Sea, Japan, over two decades (2001–2020) S. Itahashi et al. https://doi.org/10.1088/1748-9326/ac03db
1077. Evolution of aerosol optical depth over China in 2010–2024: increasing importance of meteorological influences C. Fan et al. https://doi.org/10.5194/acp-25-11951-2025
1078. Heavy aerosol loading over the Bohai Bay as revealed by ground and satellite remote sensing J. Zhang et al. https://doi.org/10.1016/j.atmosenv.2015.03.048
1079. Dust Storm Remote Sensing Monitoring Supported by MODIS Land Surface Reflectance Database K. Sun et al. https://doi.org/10.3390/rs11151772
1080. Estimating ground level PM2.5 concentrations and associated health risk in India using satellite based AOD and WRF predicted meteorological parameters S. Sahu et al. https://doi.org/10.1016/j.chemosphere.2020.126969
1081. Constraining global aerosol emissions using POLDER/PARASOL satellite remote sensing observations C. Chen et al. https://doi.org/10.5194/acp-19-14585-2019
1082. Assessment of regional aerosol radiative effects under the SWAAMI campaign – Part 2: Clear-sky direct shortwave radiative forcing using multi-year assimilated data over the Indian subcontinent H. Pathak et al. https://doi.org/10.5194/acp-20-14237-2020
1083. Optical and physical characteristics of aerosols over Asia: AERONET, MERRA-2 and CAMS K. Ansari & S. Ramachandran https://doi.org/10.1016/j.atmosenv.2024.120470
1084. Validation of Aerosol Products from AATSR and MERIS/AATSR Synergy Algorithms—Part 1: Global Evaluation Y. Che et al. https://doi.org/10.3390/rs10091414
1085. CESM/CAM5 improvement and application: comparison and evaluation of updated CB05_GE and MOZART-4 gas-phase mechanisms and associated impacts on global air quality and climate J. He et al. https://doi.org/10.5194/gmd-8-3999-2015
1086. Characteristics of Ethiopia Air Chemistry and Its Meteorological Context M. Jury https://doi.org/10.1175/EI-D-17-0009.1
1087. Influence of Spatial Resolution and Retrieval Frequency on Applicability of Satellite-Predicted PM2.5 in Northern China R. Li et al. https://doi.org/10.3390/rs12040736
1088. Air Quality Assessment over Sudan using NASA Remote Sensing Satellites Data and MERRA-2 Model I. Muntasir et al. https://doi.org/10.30799/jespr.207.20060302
1089. Long-Term (2005–2017) View of Atmospheric Pollutants in Central China Using Multiple Satellite Observations R. Li et al. https://doi.org/10.3390/rs12061041
1090. A gradual increase of aerosol pollution in the Third Pole during the past four decades: Implication for regional climate change B. Sharma et al. https://doi.org/10.1016/j.envres.2023.117105
1091. Variability, predictability, and uncertainty in global aerosols inferred from gap-filled satellite observations and an econometric modeling approach X. Li et al. https://doi.org/10.1016/j.rse.2021.112501
1092. Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic J. Kretzschmar et al. https://doi.org/10.5194/acp-20-13145-2020
1093. Investigation of air quality over the largest city in central China using high resolution satellite derived aerosol optical depth data K. Sun et al. https://doi.org/10.1016/j.apr.2017.12.011
1094. FY-4A/AGRI Aerosol Optical Depth Retrieval Capability Test and Validation Based on NNAeroG H. Ding et al. https://doi.org/10.3390/rs14215591
1095. Clouds Detection in Polar Icy Terrains: A Deformable Attention-Based Deep Neural Network for Multispectral Polar Scene Parsing S. Dong et al. https://doi.org/10.1109/JSTARS.2025.3540394
1096. Modeling Atmosphere-Ocean Radiative Transfer: A PACE Mission Perspective J. Chowdhary et al. https://doi.org/10.3389/feart.2019.00100
1097. A First-Order Assessment of Direct Aerosol Radiative Effect in the Southeastern U.S. Using over a Decade Long Multisatellite Data Record E. Alston & I. Sokolik https://doi.org/10.4137/ASWR.S39226
1098. Validation and Calibration of CAMS PM2.5 Forecasts Using In Situ PM2.5 Measurements in China and United States C. Wu et al. https://doi.org/10.3390/rs12223813
1099. A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data H. Bagheri https://doi.org/10.1016/j.asr.2022.02.032
1100. Unraveling pathways of elevated ozone induced by the 2020 lockdown in Europe by an observationally constrained regional model using TROPOMI A. Souri et al. https://doi.org/10.5194/acp-21-18227-2021
1101. 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 https://doi.org/10.3390/atmos14121715
1102. Testing the two‐layer model for correcting near‐cloud reflectance enhancement using LES/SHDOM‐simulated radiances G. Wen et al. https://doi.org/10.1002/2016JD025021
1103. New customized methods for improvement of the MODIS C6 Dark Target and Deep Blue merged aerosol product M. Bilal et al. https://doi.org/10.1016/j.rse.2017.05.028
1104. Dust impact on surface solar irradiance assessed with model simulations, satellite observations and ground-based measurements P. Kosmopoulos et al. https://doi.org/10.5194/amt-10-2435-2017
1105. Validation and diurnal variation evaluation of MERRA-2 multiple aerosol properties on a global scale X. Su et al. https://doi.org/10.1016/j.atmosenv.2023.120019
1106. Dust Activities Induced by Nocturnal Low‐Level Jet Over the Taklimakan Desert From WRF‐Chem Simulation Z. Han et al. https://doi.org/10.1029/2021JD036114
1107. Assessment of satellite-based aerosol optical depth using continuous lidar observation C. Lin et al. https://doi.org/10.1016/j.atmosenv.2016.06.012
1108. Diurnal time representation of MODIS, VIIRS, MISR, and AHI over Asia and Oceania Z. Yang et al. https://doi.org/10.1016/j.rse.2023.113878
1109. Evaluation of MODIS C6 and C6.1 Dark Target AOD products over Turkey based on NDVI and aerosol type G. Tuna Tuygun et al. https://doi.org/10.1016/j.apr.2020.07.003
1110. Spatial distribution and temporal variation of aerosol optical depth and radiative effect in South China and its adjacent area M. Zhang et al. https://doi.org/10.1016/j.atmosenv.2018.06.028
1111. Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm H. Zhang et al. https://doi.org/10.5194/amt-13-5955-2020
1112. Study of Aerosols’ Characteristics and Dynamics over the Kingdom of Saudi Arabia Using a Multisensor Approach Combined with Ground Observations A. Farahat et al. https://doi.org/10.1155/2015/247531
1113. Aerosol optical depth (AOD): spatial and temporal variations and association with meteorological covariates in Taklimakan desert, China J. Li et al. https://doi.org/10.7717/peerj.10542
1114. Mapping the Twilight Zone—What We Are Missing between Clouds and Aerosols K. Schwarz et al. https://doi.org/10.3390/rs9060577
1115. Inter-annual and seasonal variations in optical and physical characteristics of columnar aerosols over the Pokhara Valley in the Himalayan foothills S. Ramachandran & M. Rupakheti https://doi.org/10.1016/j.atmosres.2020.105254
1116. Forest-fire aerosol–weather feedbacks over western North America using a high-resolution, online coupled air-quality model P. Makar et al. https://doi.org/10.5194/acp-21-10557-2021
1117. Real-Time Production of High-Resolution, Gap-Free, 3-Hourly AOD over South Korea: A Machine Learning Approach Using Model Forecasts, Satellite Products, and Air Quality Data S. Kim et al. https://doi.org/10.3390/atmos17010019
1118. Aerosol Information Retrieval from GF-5B DPC Data over North China Using the Dark Dense Vegetation Algorithm R. Zhang et al. https://doi.org/10.3390/atmos14020241
1119. Global Dimming and Brightening Features during the First Decade of the 21st Century N. Hatzianastassiou et al. https://doi.org/10.3390/atmos11030308
1120. Dipole pattern in aerosol-induced atmospheric warming trends over the Indian subcontinent in the last two decades S. Srivastava & S. Dey https://doi.org/10.1088/1748-9326/adbb04
1121. An evaluation of the impact of aerosol particles on weather forecasts from a biomass burning aerosol event over the Midwestern United States: observational-based analysis of surface temperature J. Zhang et al. https://doi.org/10.5194/acp-16-6475-2016
1122. Assessment of spatio-temporal trends of satellite-based aerosol optical depth using Mann–Kendall test and Sen’s slope estimator model L. Mohammad et al. https://doi.org/10.1080/19475705.2022.2070552
1123. Characterization of aerosol size properties from measurements of spectral optical depth: a global validation of the GRASP-AOD code using long-term AERONET data B. Torres & D. Fuertes https://doi.org/10.5194/amt-14-4471-2021
1124. Uncertainty in Aerosol Optical Depth From Modern Aerosol‐Climate Models, Reanalyses, and Satellite Products A. Vogel et al. https://doi.org/10.1029/2021JD035483
1125. 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. https://doi.org/10.3390/rs14040938
1126. Initial assessment Himawari-9 AHI aerosol optical depth over land H. Lin et al. https://doi.org/10.1016/j.apr.2026.102930
1127. The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future L. Remer et al. https://doi.org/10.3390/rs12182900
1128. Aerosol Direct Radiative Effect Sensitivity Analysis T. Thorsen et al. https://doi.org/10.1175/JCLI-D-19-0669.1
1129. Characteristics of Haze Episode in 2019 December by Using Satellite and Ground Measurements with Trajectory Model K. Lee & S. Park https://doi.org/10.5572/KOSAE.2020.36.1.128
1130. MODIS Collection 6 MAIAC algorithm A. Lyapustin et al. https://doi.org/10.5194/amt-11-5741-2018
1131. Natural and anthropogenic contributions to long-term variations of SO2, NO2, CO, and AOD over East China H. Kang et al. https://doi.org/10.1016/j.atmosres.2018.09.012
1132. WRF-Chem model simulations of a dust outbreak over the central Mediterranean and comparison with multi-sensor desert dust observations U. Rizza et al. https://doi.org/10.5194/acp-17-93-2017
1133. First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia Y. Cho et al. https://doi.org/10.5194/amt-17-4369-2024
1134. Extreme Aerosol Events Over Eastern North America: 1. Characterizing and Simulating Historical Events Y. Guo et al. https://doi.org/10.1029/2020JD033758
1135. Aerosol Fine-Mode-Fraction Retrieval From GEO-KOMPSAT-2A/AMI Using a Deep Neural Network and Spectral Deconvolution Algorithm M. Kim et al. https://doi.org/10.1109/TGRS.2025.3591177
1136. Declining Summertime Local‐Scale Precipitation Frequency Over China and the United States, 1981–2012: The Disparate Roles of Aerosols J. Guo et al. https://doi.org/10.1029/2019GL085442
1137. Aerosol Optical Properties and Direct Radiative Effects over Central China M. Zhang et al. https://doi.org/10.3390/rs9100997
1138. 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. https://doi.org/10.3390/rs11192223
1139. Modeling dust sources, transport, and radiative effects at different altitudes over the Tibetan Plateau Z. Hu et al. https://doi.org/10.5194/acp-20-1507-2020
1140. Exploration of Machine Learning Techniques in Emulating a Coupled Soil–Canopy–Atmosphere Radiative Transfer Model for Multi-Parameter Estimation From Satellite Observations H. Shi et al. https://doi.org/10.1109/TGRS.2019.2921392
1141. Prediction of PM2.5 daily concentrations for grid points throughout a vast area using remote sensing data and an improved dynamic spatial panel model M. Fu et al. https://doi.org/10.1016/j.atmosenv.2020.117667
1142. Forecasting ultrafine particle concentrations from satellite and in situ observations P. Crippa et al. https://doi.org/10.1002/2016JD026021
1143. Five Years of Dust Episodes at the Southern Italy GAW Regional Coastal Mediterranean Observatory: Multisensors and Modeling Analysis C. Calidonna et al. https://doi.org/10.3390/atmos11050456
1144. Robust evidence for reversal of the trend in aerosol effective climate forcing J. Quaas et al. https://doi.org/10.5194/acp-22-12221-2022
1145. Understanding Haze: Modeling Size-Resolved Mineral Aerosol from Satellite Remote Sensing N. Sanwlani & R. Das https://doi.org/10.3390/rs14030761
1146. Wind‐Driven and Seasonal Effects on Marine Aerosol Production in the Bellingshausen Sea, Antarctica S. Dasarathy et al. https://doi.org/10.1029/2022GL099723
1147. Spectral and Spatial Dependencies in the Validation of Satellite-Based Aerosol Optical Depth from the Geostationary Ocean Color Imager Using the Aerosol Robotic Network M. Kim et al. https://doi.org/10.3390/rs15143621
1148. An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products B. Zou et al. https://doi.org/10.3390/rs12071102
1149. A global analysis of the temporal availability of PlanetScope high spatial resolution multi-spectral imagery D. Roy et al. https://doi.org/10.1016/j.rse.2021.112586
1150. Lidar-based daytime boundary layer height variation and impact on the regional satellite-based PM2.5 estimate S. Chen et al. https://doi.org/10.1016/j.rse.2022.113224
1151. Estimation of nighttime aerosol optical thickness from Suomi-NPP DNB observations over small cities in Korea G. Choo & M. Jeong https://doi.org/10.7780/kjrs.2016.32.2.1
1152. Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm Y. Zhan et al. https://doi.org/10.1016/j.atmosenv.2017.02.023
1153. An ensemble machine learning method to retrieve aerosol parameters from ground-based Sun-sky photometer measurements Q. Li et al. https://doi.org/10.5194/amt-19-2507-2026
1154. Climate Forcing and Trends of Organic Aerosols in the Community Earth System Model (CESM2) S. Tilmes et al. https://doi.org/10.1029/2019MS001827
1155. Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations J. Mulcahy et al. https://doi.org/10.5194/gmd-13-6383-2020
1156. Measurement Report: Determination of aerosol vertical features on different timescales over East Asia based on CATS aerosol products Y. Cheng et al. https://doi.org/10.5194/acp-20-15307-2020
1157. Global evaluation of sentinel 2 level 2A Sen2Cor aerosol optical thickness retrievals A. Kumar & M. Mehta https://doi.org/10.1080/01431161.2024.2421947
1158. Temporal and Spatial Assessment of Aerosol Optical Depths in Tizi-Ouzou, Algeria, Using Aqua-MODIS Product . Slimani Kahina et al. https://doi.org/10.1134/S0001433825700422
1159. A Climatological Satellite Assessment of Absorbing Carbonaceous Aerosols on a Global Scale N. Hatzianastassiou et al. https://doi.org/10.3390/atmos10110671
1160. Review: Strategies for using satellite-based products in modeling PM2.5 and short-term pollution episodes M. Sorek-Hamer et al. https://doi.org/10.1016/j.envint.2020.106057
1161. Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI F. Yin et al. https://doi.org/10.5194/gmd-15-7933-2022
1162. Role of the Madden‐Julian Oscillation in the Transport of Smoke From Sumatra to the Malay Peninsula During Severe Non‐El Niño Haze Events S. Koplitz et al. https://doi.org/10.1029/2018JD028533
1163. On the retrieval of aerosol optical depth over cryosphere using passive remote sensing L. Mei et al. https://doi.org/10.1016/j.rse.2020.111731
1164. Estimation of High-Resolution PM2.5 over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and Land Use Variables A. Mhawish et al. https://doi.org/10.1021/acs.est.0c01769
1165. Atmospheric aerosol measurements from the ATSR-SLSTR series of dual-view satellite instruments 1995–2022 K. Pearson et al. https://doi.org/10.1038/s41597-025-04694-6
1166. A decadal, hourly high-resolution satellite dataset of aerosol optical properties over East Asia J. Lee et al. https://doi.org/10.5194/essd-17-5761-2025
1167. Improved air quality during COVID-19 at an urban megacity over the Indo-Gangetic Basin: From stringent to relaxed lockdown phases A. Srivastava et al. https://doi.org/10.1016/j.uclim.2021.100791
1168. Aerosol Optical Properties and Type Retrieval via Machine Learning and an All-Sky Imager S. Logothetis et al. https://doi.org/10.3390/atmos14081266
1169. The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data E. Gryspeerdt et al. https://doi.org/10.5194/amt-15-3875-2022
1170. Estimation of global horizontal irradiance using satellite-derived data across Middle East-North Africa: The role of aerosol optical properties and site-adaptation methodologies I. Vamvakas et al. https://doi.org/10.1016/j.renene.2020.05.004
1171. Relationship between Remotely Sensed Ambient PM10 and PM2.5 and Urban Forest in Seoul, South Korea J. Park & P. Lee https://doi.org/10.3390/f11101060
1172. Retrieving high-resolution surface photosynthetically active radiation from the MODIS and GOES-16 ABI data H. Zhang et al. https://doi.org/10.1016/j.rse.2021.112436
1173. Enhanced Aerosol Estimations From Suomi-NPP VIIRS Images Over Heterogeneous Surfaces J. Wei et al. https://doi.org/10.1109/TGRS.2019.2927432
1174. A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode J. Reid et al. https://doi.org/10.3390/rs14132978
1175. Fusion of Multisource Satellite AOD Products via Bayesian Maximum Entropy With Explicit A Priori Knowledge H. Zhu et al. https://doi.org/10.1109/TGRS.2023.3285943
1176. Impact of Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depth and AirNow PM2.5 assimilation on Community Multi‐scale Air Quality aerosol predictions over the contiguous United States T. Chai et al. https://doi.org/10.1002/2016JD026295
1177. Impact of regional climate patterns on the biomass burning emissions and transport over Peninsular Southeast Asia, 2000-2019 H. Huang et al. https://doi.org/10.1016/j.atmosres.2023.107067
1178. Associations of long-term exposure to ambient PM2.5 with mortality in Chinese adults: A pooled analysis of cohorts in the China-PAR project X. Yang et al. https://doi.org/10.1016/j.envint.2020.105589
1179. Disentangling temperature and water stress contributions to trends in isoprene emissions using satellite observations of formaldehyde, 2005–2016 S. Strada et al. https://doi.org/10.1016/j.atmosenv.2022.119530
1180. Spatiotemporal patterns of correlation between atmospheric nitrogen dioxide and aerosols over South Asia Z. ul-Haq et al. https://doi.org/10.1007/s00703-016-0485-6
1181. Transport Patterns, Size Distributions, and Depolarization Characteristics of Dust Particles in East Asia in Spring 2018 Y. Tian et al. https://doi.org/10.1029/2019JD031752
1182. Projections of fire emissions and the consequent impacts on air quality under 1.5 °C and 2 °C global warming C. Tian et al. https://doi.org/10.1016/j.envpol.2023.121311
1183. A sparse representation-based fusion model for improving daily MODIS C6.1 aerosol products on a 3 km grid Y. Zhao et al. https://doi.org/10.1080/01431161.2020.1823040
1184. Role of Aerosols in Spring Blooms in the Central Yellow Sea During the COVID-19 Lockdown by China J. Baek et al. https://doi.org/10.3389/fmars.2022.911819
1185. Dramatic improvement of aerosol pollution status over the East Asian ocean: from the establishment of Japanese environmental quality standard for PM2.5 in 2009 to its achievement in 2021 S. Itahashi & I. Uno https://doi.org/10.1088/1748-9326/ad37ce
1186. Climatological aspects of size-resolved column aerosol optical properties over a rural site in the southern peninsular India B. Madhavan et al. https://doi.org/10.1016/j.atmosres.2020.105345
1187. MODIS Retrieval of Aerosol Optical Depth over Turbid Coastal Water Y. Wang et al. https://doi.org/10.3390/rs9060595
1188. A Regionally Robust High-Spatial-Resolution Aerosol Retrieval Algorithm for MODIS Images Over Eastern China J. Wei et al. https://doi.org/10.1109/TGRS.2019.2892813
1189. JEDI‐Based Three‐Dimensional Ensemble‐Variational Data Assimilation System for Global Aerosol Forecasting at NCEP B. Huang et al. https://doi.org/10.1029/2022MS003232
1190. Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling L. Li et al. https://doi.org/10.1016/j.rse.2019.111584
1191. Aerosol Optical Properties and Associated Direct Radiative Forcing over the Yangtze River Basin during 2001–2015 L. He et al. https://doi.org/10.3390/rs9070746
1192. A Simplified Method for Retrieving Aerosol Optical Thickness Using Visibility Data Between 1980 and 2014, A Case Study in China Z. Zhang & M. Wong https://doi.org/10.1109/JSTARS.2017.2723043
1193. 250-m Aerosol Retrieval from FY-3 Satellite in Guangzhou Z. Wang et al. https://doi.org/10.3390/rs13050920
1194. Forest 2200 nm/660 nm reflectance seasonality for improved satellite-image atmospheric correction M. Lang et al. https://doi.org/10.2478/fsmu-2025-0001
1195. The CAMS reanalysis of atmospheric composition A. Inness et al. https://doi.org/10.5194/acp-19-3515-2019
1196. Retrieval of Aerosol Single-Scattering Albedo from MODIS Data Using an Artificial Neural Network L. Qi et al. https://doi.org/10.3390/rs14246341
1197. Seasonal simulations of summer aerosol optical depth over the Arabian Peninsula using WRF‐Chem: Validation, climatology, and variability R. Karumuri et al. https://doi.org/10.1002/joc.7396
1198. Satellite retrievals of dust aerosol over the Red Sea and the Persian Gulf (2005–2015) J. Banks et al. https://doi.org/10.5194/acp-17-3987-2017
1199. Reversal of Aerosol Properties in Eastern China with Rapid Decline of Anthropogenic Emissions M. Tao et al. https://doi.org/10.3390/rs12030523
1200. Significant underestimation of radiative forcing by aerosol–cloud interactions derived from satellite-based methods H. Jia et al. https://doi.org/10.1038/s41467-021-23888-1
1201. Atmospheric aerosols in the South Pacific: A regional baseline and characterization of aerosol fractions A. Isaza et al. https://doi.org/10.1016/j.atmosenv.2025.121048
1202. Correlation-Based Evaluation of GEMS Satellite Products Using Environmental and Meteorological Measurements Y. Jeong et al. https://doi.org/10.7780/kjrs.2024.40.6.1.28
1203. Improved Bi-Angle Aerosol Optical Depth Retrieval Algorithm from AHI Data Based on Particle Swarm Optimization C. Jin et al. https://doi.org/10.3390/rs13224689
1204. 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. https://doi.org/10.4137/EHI.S19590
1205. Linkage between the absorbing aerosol-induced snow darkening effects over the Himalayas-Tibetan Plateau and the pre-monsoon climate over northern India S. Das et al. https://doi.org/10.1007/s00704-021-03871-y
1206. Ground‐based High Spectral Resolution Lidar observation of aerosol vertical distribution in the summertime Southeast United States J. Reid et al. https://doi.org/10.1002/2016JD025798
1207. Climate Benefits of Cleaner Energy Transitions in East and South Asia Through Black Carbon Reduction S. Ramachandran et al. https://doi.org/10.3389/fenvs.2022.842319
1208. A 13-year climatological study on the variations of aerosol and cloud properties over Kazakhstan from remotely sensed satellite observations K. Kumar et al. https://doi.org/10.1016/j.jastp.2018.06.014
1209. Satellite Observations of Cloud-Related Variations in Aerosol Properties T. Várnai & A. Marshak https://doi.org/10.3390/atmos9110430
1210. Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites H. Bai et al. https://doi.org/10.5194/acp-18-1763-2018
1211. Satellite AOD conversion into ground PM10, PM2.5 and PM1 over the Po valley (Milan, Italy) exploiting information on aerosol vertical profiles, chemistry, hygroscopicity and meteorology L. Ferrero et al. https://doi.org/10.1016/j.apr.2019.08.003
1212. Addressing the difficulties in quantifying droplet number response to aerosol from satellite observations H. Jia et al. https://doi.org/10.5194/acp-22-7353-2022
1213. 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. https://doi.org/10.1016/j.rse.2017.05.002
1214. Application of XBAER aerosol optical depth retrieval algorithm to hyperspectral EnMAP satellite data S. Laffoy et al. https://doi.org/10.5194/amt-19-293-2026
1215. High-speed rail opening and urban air quality Z. Qin et al. https://doi.org/10.1016/j.regsciurbeco.2025.104134
1216. Joint estimation of global daily 1 km surface radiation budget components from MODIS observations (2000−2023) using conservation-constrained deep neural networks J. Xu et al. https://doi.org/10.1016/j.rse.2025.115135
1217. A novel algorithm for full-coverage daily aerosol optical depth retrievals using machine learning-based reconstruction technique X. Yu et al. https://doi.org/10.1016/j.atmosenv.2023.120216
1218. Simulating Multi-Directional Narrowband Reflectance of the Earth’s Surface Using ADAM (A Surface Reflectance Database for ESA’s Earth Observation Missions) C. Bacour et al. https://doi.org/10.3390/rs12101679
1219. An experimental study on the characteristic pattern of internal solitary waves in optical remote-sensing images M. Zhang et al. https://doi.org/10.1080/01431161.2019.1597308
1220. Evolution of Pollution Levels from COVID-19 Lockdown to Post-Lockdown over India B. Tyagi et al. https://doi.org/10.3390/toxics10110653
1221. The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua S. Platnick et al. https://doi.org/10.1109/TGRS.2016.2610522
1222. An Automatic Radiometric Cross-Calibration Method for Wide-Angle Medium-Resolution Multispectral Satellite Sensor Using Landsat Data J. Lu et al. https://doi.org/10.1109/TGRS.2021.3067672
1223. Temporal variability of aerosol optical thickness vertical distribution observed from CALIOP T. Toth et al. https://doi.org/10.1002/2015JD024668
1224. Evaluation and comparison of MERRA-2 AOD and DAOD with MODIS DeepBlue and AERONET data in Australia Y. Che et al. https://doi.org/10.1016/j.atmosenv.2022.119054
1225. Observation and quantification of aerosol outflow from southern Africa using spaceborne lidar M. McGill et al. https://doi.org/10.17159/sajs.2020/6398
1226. Difference between global and regional aerosol model classifications and associated implications for spaceborne aerosol optical depth retrieval P. Zhou et al. https://doi.org/10.1016/j.atmosenv.2023.119674
1227. Investigations into the development of a satellite-based aerosol climate data record using ATSR-2, AATSR and AVHRR data over north-eastern China from 1987 to 2012 Y. Che et al. https://doi.org/10.5194/amt-12-4091-2019
1228. Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring C. Chen et al. https://doi.org/10.5194/essd-12-3573-2020
1229. Classification of key aerosol types and their frequency distributions based on satellite remote sensing data at an industrially polluted city in the Yangtze River Delta, China K. Kumar et al. https://doi.org/10.1002/joc.5178
1230. Angular properties of the solar light scattering in the terrestrial atmosphere observed by the ScanPol instrument of the AEROSOL-UA project V. Danylevsky https://doi.org/10.15407/knit2020.06.060
1231. A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations C. Wang et al. https://doi.org/10.5194/amt-13-2257-2020
1232. Strong reduction in near-surface turbulence due to aerosols in South and East Asia Ø. Hodnebrog et al. https://doi.org/10.1038/s44407-025-00009-6
1233. Spatiotemporal patterns of aerosol optical depth and PM2.5 concentrations derived from multi-source remote sensing Q. Mao & S. Liu https://doi.org/10.1016/j.jastp.2025.106689
1234. A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM2.5 levels R. Zhang et al. https://doi.org/10.1016/j.envpol.2018.09.052
1235. Characterizations of interregional transport on PM2.5 pollution in Central China with adaptive mixed effect models based on satellite observations and meteorological factors Y. Huang et al. https://doi.org/10.1016/j.uclim.2025.102701
1236. Fast yet accurate computation of radiances in shortwave infrared satellite remote sensing channels N. Chen et al. https://doi.org/10.1364/OE.25.00A649
1237. Background noise model of spaceborne photon-counting lidars over oceans and aerosol optical depth retrieval from ICESat-2 noise data J. Yang et al. https://doi.org/10.1016/j.rse.2023.113858
1238. Analysis of tropical cloud-to-ground and intra-cloud lightning activity in southern India C. Unnikrishnan et al. https://doi.org/10.1007/s12040-024-02420-1
1239. Evaluation of Aqua MODIS Collection 6 AOD Parameters for Air Quality Research over the Continental United States J. Belle & Y. Liu https://doi.org/10.3390/rs8100815
1240. Long-term validation and error analysis of MODIS and VIIRS dark target aerosol products in Africa X. Xue et al. https://doi.org/10.1016/j.atmosres.2025.108372
1241. 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. https://doi.org/10.1016/j.atmosenv.2019.117068
1242. Improving Fengyun-3D satellite atmospheric precipitable water vapor products through machine learning-based post-processing correction M. Li et al. https://doi.org/10.1016/j.atmosres.2025.108133
1243. Air quality trends of the Kathmandu Valley: A satellite, observation and modeling perspective P. Mahapatra et al. https://doi.org/10.1016/j.atmosenv.2018.12.043
1244. Summarizing multiple aspects of triple collocation analysis in a single diagram L. Siu et al. https://doi.org/10.3389/frsen.2024.1395442
1245. Satellite-based investigation of immediate impact of forest fires on the spatial distribution of aerosol loading over India A. Ignatious et al. https://doi.org/10.1016/j.atmosenv.2025.121652
1246. Aerosol radiative forcing efficiency in the UV-B region over central Argentina G. Palancar et al. https://doi.org/10.1016/j.atmosres.2016.02.016
1247. Seamless Global AOD Reconstruction Based on Transformer Model and Fusion of Multisource Satellite Products M. Wu et al. https://doi.org/10.1109/TGRS.2026.3692215
1248. Recent cloud trends and extremes reaffirm established bounds on cloud feedback and aerosol-cloud interactions M. Zelinka et al. https://doi.org/10.1038/s43247-026-03461-8
1249. Subways and Urban Air Pollution N. Gendron-Carrier et al. https://doi.org/10.1257/app.20180168
1250. Weather specific evaluation of satellite retrieved aerosol optical depth over drylands of the Southwest United States H. Dhaliwal & S. Pal https://doi.org/10.1016/j.atmosres.2026.108932
1251. Adaptation of the CIMEL-318T to shipborne use: 3 years of automated AERONET-compatible aerosol measurements on board the research vessel Marion Dufresne B. Torres et al. https://doi.org/10.5194/amt-18-4809-2025
1252. Global evaluation of Fengyun-3 MERSI dark target aerosol retrievals over land L. Yang et al. https://doi.org/10.1080/17538947.2024.2344580
1253. 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. https://doi.org/10.1007/s10661-020-08299-x
1254. Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing–Tianjin–Hebei Region, China L. Li et al. https://doi.org/10.3390/rs10122006
1255. Assessment of the Representativeness of MODIS Aerosol Optical Depth Products at Different Temporal Scales Using Global AERONET Measurements Y. Tong et al. https://doi.org/10.3390/rs12142330
1256. Recent changes of trans-boundary air pollution over the Yellow Sea: Implications for future air quality in South Korea P. Bhardwaj et al. https://doi.org/10.1016/j.envpol.2019.01.048
1257. Unprecedented Temporary Reduction in Global Air Pollution Associated with COVID-19 Forced Confinement: A Continental and City Scale Analysis Z. Zhang et al. https://doi.org/10.3390/rs12152420
1258. Detection of delay in post-monsoon agricultural burning across Punjab, India: potential drivers and consequences for air quality T. Liu et al. https://doi.org/10.1088/1748-9326/abcc28
1259. Spatiotemporal variations and long term trends analysis of aerosol optical depth over the United Arab Emirates A. Abuelgasim et al. https://doi.org/10.1016/j.rsase.2021.100532
1260. Analysis of Near-Cloud Changes in Atmospheric Aerosols Using Satellite Observations and Global Model Simulations T. Várnai & A. Marshak https://doi.org/10.3390/rs13061151
1261. Exploring the Trends of Aerosol Optical Depth and Its Relationship with Climate Variables over Saudi Arabia M. Rahman et al. https://doi.org/10.1007/s41748-024-00452-7
1262. Critical evaluation of cloud contamination in the MISR aerosol products using MODIS cloud mask products Y. Shi et al. https://doi.org/10.5194/amt-7-1791-2014
1263. Aerosol Optical Depth Retrieval Over Ocean and Land From Fengyun-3F MERSI-III: First Results and Validation L. Yang et al. https://doi.org/10.1109/JSTARS.2025.3613696
1264. Aerosol-enhanced high precipitation events near the Himalayan foothills G. Choudhury et al. https://doi.org/10.5194/acp-20-15389-2020
1265. 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. https://doi.org/10.5194/acp-18-1573-2018
1266. Impacts of a double-moment bulk cloud microphysics scheme (NDW6-G23) on aerosol fields in NICAM.19 with a global 14 km grid resolution D. Goto et al. https://doi.org/10.5194/gmd-17-651-2024
1267. Global aerosol simulations using NICAM.16 on a 14 km grid spacing for a climate study: improved and remaining issues relative to a lower-resolution model D. Goto et al. https://doi.org/10.5194/gmd-13-3731-2020
1268. Evaluating the representation of aerosol optical properties using an online coupled model over the Iberian Peninsula L. Palacios-Peña et al. https://doi.org/10.5194/acp-17-277-2017
1269. Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite C. Wang et al. https://doi.org/10.3390/rs15010094
1270. Systematic satellite observations of the impact of aerosols from passive volcanic degassing on local cloud properties S. Ebmeier et al. https://doi.org/10.5194/acp-14-10601-2014
1271. Impact of Covid-19 lockdown on air quality in the Poland, Eastern Europe M. Filonchyk et al. https://doi.org/10.1016/j.envres.2020.110454
1272. An intercomparison of SEMARA high-resolution AOD and MODIS operational AODs M. Bagherinia et al. https://doi.org/10.1016/j.apr.2023.102023
1273. Satellite observation of pollutant emissions from gas flaring activities near the Arctic C. Li et al. https://doi.org/10.1016/j.atmosenv.2016.03.019
1274. Performance of MODIS Collection 6.1 Level 3 aerosol products in spatial-temporal variations over land J. Wei et al. https://doi.org/10.1016/j.atmosenv.2019.03.001
1275. Joint VSWIR-TIR retrievals of earth's surface and atmosphere J. Fahlen et al. https://doi.org/10.1016/j.rse.2021.112727
1276. First lunar-light mapping of nighttime dust season oceanic aerosol optical depth over North Atlantic from space M. Zhou et al. https://doi.org/10.1016/j.rse.2024.114315
1277. Retrieving instantaneous extinction of aerosol undetected by the CALIPSO layer detection algorithm F. Mao et al. https://doi.org/10.5194/acp-22-10589-2022
1278. Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China M. Ali et al. https://doi.org/10.1016/j.gsf.2021.101325
1279. A comprehensive reappraisal of long-term aerosol characteristics, trends, and variability in Asia S. Jin et al. https://doi.org/10.5194/acp-23-8187-2023
1280. Evaluation of MODIS aerosol retrieval algorithms over the Beijing‐Tianjin‐Hebei region during low to very high pollution events M. Bilal & J. Nichol https://doi.org/10.1002/2015JD023082
1281. Variations in aerosol optical characteristics from SKYNET measurements in Beijing X. Dong et al. https://doi.org/10.1016/j.atmosenv.2023.119747
1282. Validation of FY-3D MERSI-2 Precipitable Water Vapor (PWV) Datasets Using Ground-Based PWV Data from AERONET Y. Xie et al. https://doi.org/10.3390/rs13163246
1283. A First Case Study of CCN Concentrations from Spaceborne Lidar Observations A. Georgoulias et al. https://doi.org/10.3390/rs12101557
1284. A New Cloud and Haze Mask Algorithm From Radiative Transfer Simulations Coupled With Machine Learning Y. Jiao et al. https://doi.org/10.1109/TGRS.2023.3252264
1285. An Improved High‐Spatial‐Resolution Aerosol Retrieval Algorithm for MODIS Images Over Land J. Wei et al. https://doi.org/10.1029/2017JD027795
1286. Opposite effects of aerosols and meteorological parameters on warm clouds in two contrasting regions over eastern China Y. Liu et al. https://doi.org/10.5194/acp-24-4651-2024
1287. Has China been exporting less particulate air pollution over the past decade? J. Zhang et al. https://doi.org/10.1002/2017GL072617
1288. Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia Y. Kang et al. https://doi.org/10.1016/j.isprsjprs.2021.11.016
1289. Validation of POLDER GRASP aerosol optical retrieval over China using SONET observations Y. Wei et al. https://doi.org/10.1016/j.jqsrt.2020.106931
1290. Physics-Informed Spatiotemporal Learning for Dust AOD Nowcasting over the Taklimakan Desert Using FY-4B Observations C. Hu et al. https://doi.org/10.3390/rs18121953
1291. Biosphere–atmosphere related processes influence trace-gas and aerosol satellite–model biases E. Sands et al. https://doi.org/10.5194/acp-25-7269-2025
1292. Assessing the WRF-Solar Model Performance Using Satellite-Derived Irradiance from the National Solar Radiation Database P. Jiménez et al. https://doi.org/10.1175/JAMC-D-21-0090.1
1293. Application of the CHIMERE-WRF Model Complex to Study the Radiative Effects of Siberian Smoke Aerosol in the Eastern Arctic I. Konovalov et al. https://doi.org/10.1134/S1024856023040085
1294. 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. https://doi.org/10.1016/j.atmosenv.2019.117257
1295. Increasing aerosol optical depth spatial and temporal availability by merging datasets from geostationary and sun-synchronous satellites P. Gupta et al. https://doi.org/10.5194/amt-17-5455-2024
1296. A multiyear tropical Pacific cooling response to recent Australian wildfires in CESM2 J. Fasullo et al. https://doi.org/10.1126/sciadv.adg1213
1297. MODIS AOD sampling rate and its effect on PM2.5 estimation in North China Z. Song et al. https://doi.org/10.1016/j.atmosenv.2019.04.020
1298. Climatology, trend of aerosol-cloud parameters and their correlation over the Northern Indian Ocean H. Kumar & S. Tiwari https://doi.org/10.1016/j.gsf.2023.101563
1299. Quasi‐Global Maps of Daily Aerosol Optical Depth From a Ring of Five Geostationary Meteorological Satellites Using AERUS‐GEO X. Ceamanos et al. https://doi.org/10.1029/2021JD034906
1300. Efficient Multiangle Polarimetric Retrieval of Aerosols Using Data-Driven Deep Learning Method W. Man et al. https://doi.org/10.1109/TGRS.2025.3534465
1301. Remote Sensing of Aerosol Optical Depth Using an Airborne Polarimeter over North China H. Wang et al. https://doi.org/10.3390/rs9100979
1302. Aerosol optical depth regime over megacities of the world K. Papachristopoulou et al. https://doi.org/10.5194/acp-22-15703-2022
1303. A new aerosol type identification algorithm for the Geostationary Environment Monitoring Spectrometer (GEMS) instrument F. Wang et al. https://doi.org/10.1080/20964471.2026.2654984
1304. Large-scale hazes over Eurasia in July 2016: Siberian smoke haze evolution G. Gorchakov et al. https://doi.org/10.1088/1755-1315/231/1/012019
1305. How emissions uncertainty influences the distribution and radiative impacts of smoke from fires in North America T. Carter et al. https://doi.org/10.5194/acp-20-2073-2020
1306. Global Validation of MODIS C6 and C6.1 Merged Aerosol Products over Diverse Vegetated Surfaces M. Bilal et al. https://doi.org/10.3390/rs10030475
1307. Global evaluation and uncertainty analysis of MODIS Deep Blue and MAIAC aerosol products over bright surfaces P. Zhang et al. https://doi.org/10.1016/j.atmosenv.2026.121991
1308. Performance evaluation of MODIS and VIIRS satellite AOD products over the Indian subcontinent S. Payra et al. https://doi.org/10.3389/fenvs.2023.1158641
1309. Distinct Seasonality in Aerosol Responses to Emission Control Over Northern China Y. Liu et al. https://doi.org/10.1029/2022JD038377
1310. Estimation of 1 km all-sky erythemal ultraviolet radiation and daily dose based on MODIS data and ancillary information: algorithm development, global product generation, and accuracy assessment T. He et al. https://doi.org/10.1016/j.rse.2025.115021
1311. A consistent aerosol optical depth (AOD) dataset over mainland China by integration of several AOD products H. Xu et al. https://doi.org/10.1016/j.atmosenv.2015.05.023
1312. The Silk Road agenda of the Pan-Eurasian Experiment (PEEX) program H. Lappalainen et al. https://doi.org/10.1080/20964471.2018.1437704
1313. Satellite-Derived Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing Based on Bayesian Maximum Entropy X. Xia et al. https://doi.org/10.1109/TGRS.2021.3051799
1314. A Dark Target research aerosol algorithm for MODIS observations over eastern China: increasing coverage while maintaining accuracy at high aerosol loading Y. Shi et al. https://doi.org/10.5194/amt-14-3449-2021
1315. First high temporal resolution retrievals of AOD over shallow and turbid coastal waters for Himawari-8 Y. Wang et al. https://doi.org/10.1016/j.isprsjprs.2025.07.027
1316. 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. https://doi.org/10.1016/j.envpol.2019.113323
1317. Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia A. Mhawish et al. https://doi.org/10.1016/j.rse.2019.01.033
1318. Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface Y. Huang et al. https://doi.org/10.1029/2019EA000809
1319. Hourly Mapping of the Layer Height of Thick Smoke Plumes Over the Western U.S. in 2020 Severe Fire Season Z. Lu et al. https://doi.org/10.3389/frsen.2021.766628
1320. Polarization Calibration Verification of the Directional Polarimetric Camera on the Terrestrial Ecosystem Carbon Inventory Satellite D. Xie et al. https://doi.org/10.1109/TGRS.2025.3588331
1321. From Tropospheric Folding to Khamsin and Foehn Winds: How Atmospheric Dynamics Advanced a Record-Breaking Dust Episode in Crete S. Solomos et al. https://doi.org/10.3390/atmos9070240
1322. Improved aerosol correction for OMI tropospheric NO2 retrieval over East Asia: constraint from CALIOP aerosol vertical profile M. Liu et al. https://doi.org/10.5194/amt-12-1-2019
1323. Planetary albedo decline over Northwest India contributing to near surface warming S. Sai Krishna et al. https://doi.org/10.1016/j.scitotenv.2021.151607
1324. Description and applications of a mobile system performing on-road aerosol remote sensing and in situ measurements I. Popovici et al. https://doi.org/10.5194/amt-11-4671-2018
1325. 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. https://doi.org/10.1016/j.atmosenv.2019.117016
1326. Global validation of the Particulate Observing Scanning Polarimeter (POSP) Aerosol Optical Depth products over land Z. Ji et al. https://doi.org/10.5194/amt-18-5783-2025
1327. Evaluation and modification of SARA high-resolution AOD retrieval algorithm during high dust loading conditions over bright desert surfaces N. Karimi et al. https://doi.org/10.1016/j.apr.2019.01.008
1328. A New Neighboring Pixels Method for Reducing Aerosol Effects on the NDVI Images D. Wang et al. https://doi.org/10.3390/rs8060489
1329. Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic K. Han et al. https://doi.org/10.3390/app11041959
1330. Global Solar Radiation Transfer and Its Loss in the Atmosphere J. Bai & X. Zong https://doi.org/10.3390/app11062651
1331. Global Distribution of Column Satellite Aerosol Optical Depth to Surface PM2.5 Relationships S. Christopher & P. Gupta https://doi.org/10.3390/rs12121985
1332. Long-term changes in aerosol loading over the ‘BIHAR’ State of India using nineteen years (2001–2019) of high-resolution satellite data (1 × 1 km2) M. Nair et al. https://doi.org/10.1016/j.apr.2021.101259
1333. Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China Q. Wang et al. https://doi.org/10.3390/rs12203425
1334. The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images F. Bao et al. https://doi.org/10.1016/j.rse.2022.113426
1335. Enhanced continuous aerosol optical depth (AOD) estimation using geostationary satellite data: focusing on nighttime AOD over East Asia S. Song et al. https://doi.org/10.1016/j.atmosenv.2025.121365
1336. Correcting MODIS aerosol optical depth products using a ridge regression model R. Hang et al. https://doi.org/10.1080/01431161.2018.1439597
1337. On the representation of aerosol activation and its influence on model-derived estimates of the aerosol indirect effect D. Rothenberg et al. https://doi.org/10.5194/acp-18-7961-2018
1338. The impact of meteorological parameters on PM10 and visibility during the Middle Eastern dust storms H. Maleki et al. https://doi.org/10.1007/s40201-022-00795-1
1339. A Method to Account for the Impact of Water Vapor on Observation‐Based Estimates of the Clear‐Sky Shortwave Direct Radiative Effect of Mineral Dust A. Kuwano & A. Evan https://doi.org/10.1029/2022JD036620
1340. 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. https://doi.org/10.1016/j.rse.2020.112093
1341. Aerosol characteristics at the three poles of the Earth as characterized by Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations Y. Yang et al. https://doi.org/10.5194/acp-21-4849-2021
1342. A Cloud masking algorithm for the XBAER aerosol retrieval using MERIS data L. Mei et al. https://doi.org/10.1016/j.rse.2016.11.016
1343. Impact of Aerosol and Cloud on the Solar Energy Potential over the Central Gangetic Himalayan Region U. Dumka et al. https://doi.org/10.3390/rs13163248
1344. Analysis and evaluation of the global aerosol optical properties simulated by an online aerosol-coupled non-hydrostatic icosahedral atmospheric model T. Dai et al. https://doi.org/10.1007/s00376-014-4098-z
1345. Open cells exhibit weaker entrainment of free-tropospheric biomass burning aerosol into the south-east Atlantic boundary layer S. Abel et al. https://doi.org/10.5194/acp-20-4059-2020
1346. Decadal-scale relationship between measurements of aerosols, land-use change, and fire over Southeast Asia J. Cohen et al. https://doi.org/10.5194/acp-17-721-2017
1347. The simulation of mineral dust in the United Kingdom Earth System Model UKESM1 S. Woodward et al. https://doi.org/10.5194/acp-22-14503-2022
1348. GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign M. Choi et al. https://doi.org/10.5194/amt-9-1377-2016
1349. MODIS Aerosol Optical Depth Inversion Over Urban Areas Supported by BRDF/Albedo Products W. Zhang et al. https://doi.org/10.1007/s12524-020-01129-0
1350. Rising surface ozone in China from 2013 to 2017: A response to the recent atmospheric warming or pollutant controls? M. Li et al. https://doi.org/10.1016/j.atmosenv.2020.118130
1351. Retrieval of aerosol optical depth from INSAT-3DR for accurate geostationary monitoring of regional and temporal aerosol dynamics C. Tandule et al. https://doi.org/10.1016/j.atmosenv.2025.121730
1352. On the negative polarization's effects at top of atmosphere W. Chen et al. https://doi.org/10.1016/j.atmosenv.2021.118425
1353. Properties of aerosol and surface derived from OLCI/Sentinel-3A using GRASP approach: Retrieval development and preliminary validation C. Chen et al. https://doi.org/10.1016/j.rse.2022.113142
1354. Spatiotemporal variability and contribution of different aerosol types to the aerosol optical depth over the Eastern Mediterranean A. Georgoulias et al. https://doi.org/10.5194/acp-16-13853-2016
1355. Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model W. Choi et al. https://doi.org/10.3390/rs13132464
1356. Development of an improved aerosol product over the Indian subcontinent: Blending model, satellite, and ground‐based estimates R. Singh et al. https://doi.org/10.1002/2016JD025335
1357. Revised mineral dust emissions in the atmospheric chemistry–climate model EMAC (MESSy 2.52 DU_Astitha1 KKDU2017 patch) K. Klingmüller et al. https://doi.org/10.5194/gmd-11-989-2018
1358. Directional Polarimetric Camera (DPC): Monitoring aerosol spectral optical properties over land from satellite observation Z. Li et al. https://doi.org/10.1016/j.jqsrt.2018.07.003
1359. An Evaluation of the CHIMERE Chemistry Transport Model to Simulate Dust Outbreaks across the Northern Hemisphere in March 2014 B. Bessagnet et al. https://doi.org/10.3390/atmos8120251
1360. Spatio-Temporal Pattern Estimation of PM2.5 in Beijing-Tianjin-Hebei Region Based on MODIS AOD and Meteorological Data Using the Back Propagation Neural Network X. Ni et al. https://doi.org/10.3390/atmos9030105
1361. Surface ozone climatology of South Eastern Brazil and the impact of biomass burning events A. Targino et al. https://doi.org/10.1016/j.jenvman.2019.109645
1362. Retrieval of aerosol properties from Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) observations during ACEPOL 2017 A. Puthukkudy et al. https://doi.org/10.5194/amt-13-5207-2020
1363. Air Pollution Scenario over China during COVID-19 J. Nichol et al. https://doi.org/10.3390/rs12132100
1364. Simultaneous retrieval of aerosol optical depth, spectral absorption and layer height from DSCOVR EPIC using MAIAC algorithm A. Lyapustin et al. https://doi.org/10.3389/frsen.2025.1677438
1365. Bounding Global Aerosol Radiative Forcing of Climate Change N. Bellouin et al. https://doi.org/10.1029/2019RG000660
1366. Impact of CALIPSO profile data assimilation on 3-D aerosol improvement in a size-resolved aerosol model H. Ye et al. https://doi.org/10.1016/j.atmosres.2021.105877
1367. An assessment of the quality of aerosol retrievals over the Red Sea and evaluation of the climatological cloud-free dust direct radiative effect in the region H. Brindley et al. https://doi.org/10.1002/2015JD023282
1368. Evaluating the relative importance of northern African mineral dust sources using remote sensing N. Bakker et al. https://doi.org/10.5194/acp-19-10525-2019
1369. Aerosol Optical Depth Retrieval Based on Neural Network Model Using Polarized Scanning Atmospheric Corrector (PSAC) Data Z. Shi et al. https://doi.org/10.1109/TGRS.2022.3192908
1370. South American 2020 regional smoke plume: intercomparison with previous years, impact on solar radiation, and the role of Pantanal biomass burning season N. Rosário et al. https://doi.org/10.5194/acp-22-15021-2022
1371. Characterization of the on-orbit response versus scan angle for Terra MODIS SWIR bands in Collection 7 Q. Mu et al. https://doi.org/10.1117/1.JRS.16.024520
1372. Climatology of aerosol component concentrations derived from multi-angular polarimetric POLDER-3 observations using GRASP algorithm L. Li et al. https://doi.org/10.5194/essd-14-3439-2022
1373. Prediction of abnormal proliferation risk of Phaeocystis globosa based on correlation mining of PC concentration indicator and meteorological factors along Qinzhou Bay, Guangxi H. Chen et al. https://doi.org/10.1016/j.seares.2023.102365
1374. Revisiting the levels of Aerosol Optical Depth in south-southeast Asia, Europe and USA amid the COVID-19 pandemic using satellite observations P. Acharya et al. https://doi.org/10.1016/j.envres.2020.110514
1375. Retrieval uncertainty and consistency of Suomi-NPP VIIRS Deep Blue and Dark Target aerosol products under diverse aerosol loading scenarios over South Asia K. Aditi et al. https://doi.org/10.1016/j.envpol.2023.121913
1376. Evolving patterns of arctic aerosols and the influence of regional variations over two decades K. Lee et al. https://doi.org/10.1016/j.scitotenv.2024.177465
1377. Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East Asia E. Kang et al. https://doi.org/10.1016/j.atmosenv.2023.119951
1378. Evaluation of MAIAC aerosol retrievals over China Z. Zhang et al. https://doi.org/10.1016/j.atmosenv.2019.01.013
1379. Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China N. Liu et al. https://doi.org/10.5194/acp-19-8243-2019
1380. Satellite-driven prediction of fine particulate matter (PM2.5) concentrations: machine learning and explainable artificial intelligence T. Nguyen & T. Trinh https://doi.org/10.1088/2631-8695/ae7028
1381. High-accuracy full-coverage PM 2.5 retrieval from 2014 to 2023 over China based on satellite remote sensing and hierarchical deep learning model Y. Fan et al. https://doi.org/10.1080/17538947.2024.2392850
1382. A Random Forest-Based CO2 Profile Emulator for Real-Time Prior Profile Generation in TanSat XCO2 Retrieval S. Wu et al. https://doi.org/10.3390/rs17162764
1383. Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season Z. Pan et al. https://doi.org/10.3390/rs10040651
1384. Long-term trends in Aerosol Optical Depth obtained across the globe using multi-satellite measurements G. Gupta et al. https://doi.org/10.1016/j.atmosenv.2022.118953
1385. An Operational Atmospheric Correction Framework for Multi-Source Medium-High-Resolution Remote Sensing Data of China H. Zhang et al. https://doi.org/10.3390/rs14215590
1386. Long‐Term Trends of High Aerosol Pollution Events and Their Climatic Impacts in North America Using Multiple Satellite Retrievals and Modern‐Era Retrospective Analysis for Research and Applications version 2 Q. Jin & S. Pryor https://doi.org/10.1029/2019JD031137
1387. Climatological Characteristics and Aerosol Loading Trends from 2001 to 2020 Based on MODIS MAIAC Data for Tianjin, North China Plain Z. Wu et al. https://doi.org/10.3390/su14031072
1388. Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model C. Brokamp et al. https://doi.org/10.1021/acs.est.7b05381
1389. Strategizing emission cuts in China to mitigate short-term warming from clean air policies Y. Zhu et al. https://doi.org/10.1038/s41467-026-72190-5
1390. Estimation of ground-level dry PM2.5 concentrations at 3 km resolution over Beijing using Geostationary Ocean Colour Imager J. Wang & Z. Li https://doi.org/10.1080/2150704X.2020.1795298
1391. The climate impact of aerosols on the lightning flash rate: is it detectable from long-term measurements? Q. Wang et al. https://doi.org/10.5194/acp-18-12797-2018
1392. Extended aerosol and surface characterization from S5P/TROPOMI with GRASP algorithm. Part II: Global validation and Intercomparison C. Chen et al. https://doi.org/10.1016/j.rse.2024.114374
1393. Understanding Top‐of‐Atmosphere Flux Bias in the AeroCom Phase III Models: A Clear‐Sky Perspective W. Su et al. https://doi.org/10.1029/2021MS002584
1394. MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison J. Wei et al. https://doi.org/10.1016/j.atmosenv.2018.12.004
1395. Aerosol Optical Depth Measurements from a Simulated Low-Cost Multi-Wavelength Ground-Based Camera: A Clear Case over a Peri-Urban Area V. Boulisset et al. https://doi.org/10.3390/rs16010140
1396. Satellite-based analysis of top of atmosphere shortwave radiative forcing trend induced by biomass burning aerosols over South-Eastern Atlantic C. Jouan & G. Myhre https://doi.org/10.1038/s41612-024-00631-3
1397. Variation of intra-daily instantaneous FAPAR estimated from the geostationary Himawari-8 AHI data Y. Zhang et al. https://doi.org/10.1016/j.agrformet.2021.108535
1398. Geographical coverage analysis and usage suggestions of temporal averaged aerosol optical depth product from GOES-R satellite data X. Jiang et al. https://doi.org/10.1080/01431161.2024.2331978
1399. Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation F. Patadia et al. https://doi.org/10.5194/amt-11-3205-2018
1400. Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method X. Chen et al. https://doi.org/10.1016/j.rse.2020.112006
1401. Development of an operational land water mask for MODIS Collection 6, and influence on downstream data products M. Carroll et al. https://doi.org/10.1080/17538947.2016.1232756
1402. Long-Term Evaluation of Aerosol Optical Properties in the Levantine Region: A Comparative Analysis of AERONET and Aqua/MODIS A. Isik et al. https://doi.org/10.3390/rs16142651
1403. An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness. Part 2: Application and validation in Asia X. Yan et al. https://doi.org/10.1016/j.rse.2018.12.012
1404. Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity A. Gumber et al. https://doi.org/10.5194/amt-16-2547-2023
1405. Aerosol optical properties of haze episodes in eastern China based on remote-sensing observations and WRF-Chem simulations Y. Ma et al. https://doi.org/10.1016/j.scitotenv.2020.143784
1406. Impact of precursor gases and meteorological variables on satellite-estimated near-surface sulfate and nitrate concentrations over the North China Plain Y. Si et al. https://doi.org/10.1016/j.atmosenv.2018.11.030
1407. Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors A. van Donkelaar et al. https://doi.org/10.1021/acs.est.8b06392
1408. Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods M. Kim et al. https://doi.org/10.5194/amt-17-4317-2024
1409. Impact of Desert Dust on Air Quality: What is the Meaningfulness of Daily PM Standards in Regions Close to the Sources? The Example of Southern Tunisia C. Bouet et al. https://doi.org/10.3390/atmos10080452
1410. Composition and radiative forcing of aerosols across the Indo-Gangetic Plain and Himalayas: A seasonal analysis S. Ramachandran & M. Rupakheti https://doi.org/10.1016/j.aeaoa.2026.100433
1411. Seasonal aerosol characteristics and their classification over Assam, India D. Barua & H. Goswami https://doi.org/10.1007/s00703-026-01166-3
1412. Aerosol remote sensing in polar regions C. Tomasi et al. https://doi.org/10.1016/j.earscirev.2014.11.001
1413. Bayesian geostatistical modelling of PM10 and PM2.5 surface level concentrations in Europe using high-resolution satellite-derived products A. Beloconi et al. https://doi.org/10.1016/j.envint.2018.08.041
1414. Evaluation of Himawari-8 version 2.0 aerosol products against AERONET ground-based measurements over central and northern China L. Wang et al. https://doi.org/10.1016/j.atmosenv.2020.117357
1415. Impact of assimilating NOAA VIIRS aerosol optical depth (AOD) observations on global AOD analysis from the Copernicus Atmosphere Monitoring Service (CAMS) S. Garrigues et al. https://doi.org/10.5194/acp-23-10473-2023
1416. MODIS/MAIAC C6.1 AOD evaluation across China using SONET data as reference G. de Leeuw et al. https://doi.org/10.1016/j.atmosres.2026.109133
1417. Characterization of a Regional Dust Storm Using RAMAN Lidar Over the Western Indian Region K. Shukla et al. https://doi.org/10.1007/s12524-023-01778-x
1418. GOES-16 ABI solar reflective channel validation for earth science application J. McCorkel et al. https://doi.org/10.1016/j.rse.2019.111438
1419. Dust Lidar Ratios Retrieved from the CALIOP Measurements Using the MODIS AOD as a Constraint M. Kim et al. https://doi.org/10.3390/rs12020251
1420. Cross-Border Sand and Dust Storms between Mongolia and Northern China in Spring and Their Driving Weather Systems A. Borjigin et al. https://doi.org/10.3390/rs16122164
1421. Monitoring atmospheric particulate matters using vertically resolved measurements of a polarization lidar, in-situ recordings and satellite data over Tehran, Iran H. Panahifar et al. https://doi.org/10.1038/s41598-020-76947-w
1422. Evaluation of satellite-based aerosol datasets and the CAMS reanalysis over the ocean utilizing shipborne reference observations J. Witthuhn et al. https://doi.org/10.5194/amt-13-1387-2020
1423. Smoke-charged vortex doubles hemispheric aerosol in the middle stratosphere and buffers ozone depletion C. Ma et al. https://doi.org/10.1126/sciadv.adn3657
1424. Effects of the Hunga Tonga‐Hunga Ha'apai Eruption on MODIS‐Retrieved Sea Surface Temperatures C. Jia & P. Minnett https://doi.org/10.1029/2023GL104297
1425. Optimized retrievals of aerosol optical properties from directional polarimetric camera using optimal linear mixture of basis aerosol models supported by the non-negative matrix factorization S. Jin et al. https://doi.org/10.1016/j.rse.2026.115504
1426. Insights into the aging of biomass burning aerosol from satellite observations and 3D atmospheric modeling: evolution of the aerosol optical properties in Siberian wildfire plumes I. Konovalov et al. https://doi.org/10.5194/acp-21-357-2021
1427. Towards improved retrieval of aerosol properties from the geostationary orbit with the new Meteosat Third Generation-Imager satellite A. Georgeot et al. https://doi.org/10.5194/amt-18-4665-2025
1428. Worldwide validation of CAMS and MERRA-2 reanalysis aerosol optical depth products using 15 years of AERONET observations C. Gueymard & D. Yang https://doi.org/10.1016/j.atmosenv.2019.117216
1429. Spatial Variability of AERONET Aerosol Optical Properties and Satellite Data in South Korea during NASA DRAGON-Asia Campaign H. Lee & Y. Son https://doi.org/10.1021/acs.est.5b04831
1430. Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information G. Chen et al. https://doi.org/10.1016/j.envpol.2017.10.011
1431. Simulating effects of aerosols on rainfall in southern Africa S. Henriksson et al. https://doi.org/10.1007/s11869-018-0619-8
1432. Time series retrieval of Multi-wavelength Aerosol optical depth by adapting Transformer (TMAT) using Himawari-8 AHI data L. She et al. https://doi.org/10.1016/j.rse.2024.114115
1433. Long term analysis of the columnar and surface aerosol relationship at an urban European coastal site S. Segura et al. https://doi.org/10.1016/j.atmosenv.2017.08.012
1434. A Two-Stage Machine Learning Algorithm for Retrieving Multiple Aerosol Properties Over Land: Development and Validation M. Cao et al. https://doi.org/10.1109/TGRS.2023.3307934
1435. Spatio-temporal and synoptic changes in dust at the three islands in the Persian Gulf region S. Karami et al. https://doi.org/10.1016/j.jastp.2021.105539
1436. Analysis of the urban turbidity island effect: a case study in Beijing City, China Q. Luan et al. https://doi.org/10.1088/1748-9326/acaf8f
1437. Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas M. Bilal et al. https://doi.org/10.3390/rs14020373
1438. Spatial-Temporal Analysis of the Relationship Between Aerosol Optical Depth and Seasonal Land and Ocean Temperature Around Shanghai Y. Li et al. https://doi.org/10.1051/e3sconf/202340604048
1439. Caribbean Air Chemistry and Dispersion Conditions M. Jury https://doi.org/10.3390/atmos8080151
1440. Accuracy and error cause analysis, and recommendations for usage of Himawari-8 aerosol products over Asia and Oceania L. Feng et al. https://doi.org/10.1016/j.scitotenv.2021.148958
1441. Frequency-Domain clustering of Non-Attainment cities of India using CEEMDAN decomposition of aerosols and Coupled meteorological drivers D. Dutta https://doi.org/10.1016/j.jag.2026.105171
1442. Aerosol single-scattering albedo derived by merging OMI/POLDER satellite products and AERONET ground observations Y. Dong et al. https://doi.org/10.5194/essd-17-3873-2025
1443. Artificial bias typically neglected in comparisons of uncertain atmospheric data M. Pitkänen et al. https://doi.org/10.1002/2016GL070852
1444. Long-Term Trends in Black Carbon and Aerosol Optical Depth Over the Central Himalayas: Potential Causes and Implications H. Joshi et al. https://doi.org/10.3389/feart.2022.851444
1445. Clear-Sky Surface Solar Radiation and the Radiative Effect of Aerosol and Water Vapor Based on Simulations and Satellite Observations over Northern China G. Zhang & Y. Ma https://doi.org/10.3390/rs12121931
1446. Observational constraint on cloud susceptibility weakened by aerosol retrieval limitations P. Ma et al. https://doi.org/10.1038/s41467-018-05028-4
1447. Performance of MODIS C6.1 Dark Target and Deep Blue aerosol products in Delhi National Capital Region, India: Application for aerosol studies V. Sharma et al. https://doi.org/10.1016/j.apr.2021.01.023
1448. A Global Climatology of Dust Aerosols Based on Satellite Data: Spatial, Seasonal and Inter-Annual Patterns over the Period 2005–2019 M. Gavrouzou et al. https://doi.org/10.3390/rs13030359
1449. Downwind Ozone Changes of the 2019 Williams Flats Wildfire: Insights From WRF‐Chem/DART Assimilation of OMI NO2, HCHO, and MODIS AOD Retrievals A. Pouyaei et al. https://doi.org/10.1029/2022JD038019
1450. Astronomical site selection for Antarctica with astro-meteorological parameters N. Aksaker et al. https://doi.org/10.1007/s10686-024-09951-x
1451. A practical set of miniaturized instruments for vertical profiling of aerosol physical properties H. Telg et al. https://doi.org/10.1080/02786826.2017.1296103
1452. The Sensitivity of SeaWiFS Ocean Color Retrievals to Aerosol Amount and Type R. Kahn et al. https://doi.org/10.1175/JTECH-D-15-0121.1
1453. Radiative characteristics of near-surface aerosols at a tropical site: An estimation based on concurrent measurements of their physico-chemical characteristics S. Aryasree et al. https://doi.org/10.1007/s12040-020-01444-7
1454. A Critical Evaluation of Deep Blue Algorithm Derived AVHRR Aerosol Product Over China L. Mei et al. https://doi.org/10.1029/2018JD029929
1455. Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm X. Su et al. https://doi.org/10.1016/j.envint.2022.107343
1456. Global Scale Inversions from MOPITT CO and MODIS AOD B. Gaubert et al. https://doi.org/10.3390/rs15194813
1457. Observed aerosol-induced radiative effect on plant productivity in the eastern United States S. Strada et al. https://doi.org/10.1016/j.atmosenv.2015.09.051
1458. Investigating Smoke Aerosol Emission Coefficients Using MODIS Active Fire and Aerosol Products: A Case Study in the CONUS and Indonesia X. Lu et al. https://doi.org/10.1029/2018JG004974
1459. Sensitivity Analysis Method for Spectral Band Adjustment between Hyperspectral Sensors: A Case Study Using the CLARREO Pathfinder and HISUI K. Obata https://doi.org/10.3390/rs11111367
1460. Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring Z. Li et al. https://doi.org/10.3390/rs11020122
1461. Three-dimensional spatiotemporal evolution of wildfire-induced smoke aerosols: A case study from Liangshan, Southwest China X. Zhang et al. https://doi.org/10.1016/j.scitotenv.2020.144586
1462. Influences of Meteorological Factors on Aerosol Optical Depth and Near-Surface Aerosol Extinction Coefficient G. Wei et al. https://doi.org/10.1007/s12524-025-02253-5
1463. Low-Cost Sensors for Air Quality Monitoring - the Current State of the Technology and a Use Overview P. Buček et al. https://doi.org/10.2478/cdem-2021-0003
1464. Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets C. Huang et al. https://doi.org/10.3390/rs13163116
1465. Retrieval of hourly aerosol single scattering albedo over land using geostationary satellite data X. Jiang et al. https://doi.org/10.1038/s41612-024-00690-6
1466. Comparison and evaluation of the simulated annual aerosol characteristics over China with two global aerosol models Y. Cheng et al. https://doi.org/10.1016/j.scitotenv.2020.143003
1467. Comparison of different methods of determining land surface reflectance for AOD retrieval Q. Wang et al. https://doi.org/10.1016/j.apr.2021.101143
1468. First Retrieval of AOD at Fine Resolution Over Shallow and Turbid Coastal Waters From MODIS Y. Wang et al. https://doi.org/10.1029/2021GL094344
1469. Development and assessment of a higher-spatial-resolution (4.4 km) MISR aerosol optical depth product using AERONET-DRAGON data M. Garay et al. https://doi.org/10.5194/acp-17-5095-2017
1470. A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning R. Houborg & M. McCabe https://doi.org/10.1016/j.isprsjprs.2017.10.004
1471. HY-1D/COCTS太阳反射波段的辐射定标及陆地气溶胶反演应用 谢. Xie Yanqing et al. https://doi.org/10.3788/AOS250559
1472. Three-dimensional dust aerosol distribution and extinction climatology over northern Africa simulated with the ALADIN numerical prediction model from 2006 to 2010 M. Mokhtari et al. https://doi.org/10.5194/acp-15-9063-2015
1473. Detection of cloud cover using dynamic thresholds and radiative transfer models from the polarization satellite image C. Li et al. https://doi.org/10.1016/j.jqsrt.2018.10.026
1474. Long-Range Transport of Aerosols and Regional Sources Using MODIS and NASA MERRA Reanalysis Over South Asia B. Sanatan et al. https://doi.org/10.1007/s41748-024-00478-x
1475. The health impacts of Indonesian peatland fires L. Hein et al. https://doi.org/10.1186/s12940-022-00872-w
1476. Himawari-8 High Temporal Resolution AOD Products Recovery: Nested Bayesian Maximum Entropy Fusion Blending GEO With SSO Satellite Observations T. Zhang et al. https://doi.org/10.1109/TGRS.2023.3262785
1477. Retrieval of black carbon aerosol surface concentration using satellite remote sensing observations F. Bao et al. https://doi.org/10.1016/j.rse.2019.03.036
1478. Development of a Regression Model for Estimating Daily Radiative Forcing Due to Atmospheric Aerosols from Moderate Resolution Imaging Spectrometers (MODIS) Data in the Indo Gangetic Plain (IGP) S. Shrestha et al. https://doi.org/10.3390/atmos9100405
1479. Estimation of PM_2.5 Concentrations over Beijing with MODIS AODs Using an Artificial Neural Network H. Lyu et al. https://doi.org/10.2151/sola.2018-003
1480. Evaluation of MODIS Collection 6 aerosol retrieval algorithms over Indo-Gangetic Plain: Implications of aerosols types and mass loading A. Mhawish et al. https://doi.org/10.1016/j.rse.2017.09.016
1481. Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 2. Bias Correction With Satellite Data for Rural Areas H. Zhang et al. https://doi.org/10.1029/2021JD035563
1482. Comparative analysis of meteorological and air quality variables in urban and semi-urban environments: a case study of Varanasi and Azamgarh S. Maurya et al. https://doi.org/10.1007/s11869-026-01931-8
1483. Coupled Dynamics of Aerosols and Greenhouse Gases at the Socheongcho Ocean Research Station During High-Concentration Episodes S. Ahn et al. https://doi.org/10.3390/rs18050816
1484. Interference of Heavy Aerosol Loading on the VIIRS Aerosol Optical Depth (AOD) Retrieval Algorithm Y. Wang et al. https://doi.org/10.3390/rs9040397
1485. Moisture and dust in motion: the dual role of integrated vapour transport over West Africa P. Awoleye et al. https://doi.org/10.1007/s00704-025-05913-1
1486. Variations and photochemical transformations of atmospheric constituents in North China J. Bai et al. https://doi.org/10.1016/j.atmosenv.2018.07.004
1487. Profiling of Dust and Urban Haze Mass Concentrations during the 2019 National Day Parade in Beijing by Polarization Raman Lidar Z. Wang et al. https://doi.org/10.3390/rs13163326
1488. A theoretical study of the effect of subsurface oceanic bubbles on the enhanced aerosol optical depth band over the southern oceans as detected from MODIS and MISR M. Christensen et al. https://doi.org/10.5194/amt-8-2149-2015
1489. Assessment of dominating aerosol properties and their long-term trend in the Pan-Third Pole region: A study with 10-year multi-sensor measurements T. Wang et al. https://doi.org/10.1016/j.atmosenv.2020.117738
1490. Development of an infrared pollution index to identify ground-level compositional, particle size, and humidity changes using Himawari-8 M. Sowden et al. https://doi.org/10.1016/j.atmosenv.2020.117435
1491. Retrievals of aerosol optical depth over the western North Atlantic Ocean during ACTIVATE L. Siu et al. https://doi.org/10.5194/amt-17-2739-2024
1492. Aerosol Optical Depth Over India L. David et al. https://doi.org/10.1002/2017JD027719
1493. Quantification of the dust optical depth across spatiotemporal scales with the MIDAS global dataset (2003–2017) A. Gkikas et al. https://doi.org/10.5194/acp-22-3553-2022
1494. Multi-Satellite Assessment of Factors Controlling Biomass Burning Aerosol Formation over the South China Sea L. Liang et al. https://doi.org/10.3390/rs18101462
1495. Extending XBAER Algorithm to Aerosol and Cloud Condition L. Mei et al. https://doi.org/10.1109/TGRS.2019.2919910
1496. Impact of meteorological parameterization schemes on CTM model simulations N. Srivastava & N. Blond https://doi.org/10.1016/j.atmosenv.2021.118832
1497. Influences of atmospheric reanalysis on the accuracy of clear-sky irradiance estimates: Comparing MERRA-2 and CAMS D. Fu et al. https://doi.org/10.1016/j.atmosenv.2022.119080
1498. A simplified aerosol retrieval algorithm for Himawari-8 Advanced Himawari Imager over Beijing Z. Zhang et al. https://doi.org/10.1016/j.atmosenv.2018.11.023
1499. Development of an aerosol optical depth retrieval algorithm based on an improved scattering angle scheme for Advanced Himawari Imager observations J. Jin et al. https://doi.org/10.1016/j.atmosres.2025.107944
1500. Exploring the Use of PlanetScope Data for Particulate Matter Air Quality Research J. le Roux et al. https://doi.org/10.3390/rs13152981
1501. Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images L. Sun et al. https://doi.org/10.3390/rs8010023
1502. Multisensor Data Synergy of Terra-MODIS, Aqua-MODIS, and Suomi NPP-VIIRS for the Retrieval of Aerosol Optical Depth and Land Surface Reflectance Properties S. Shi et al. https://doi.org/10.1109/TGRS.2018.2835508
1503. Climatology of aerosol optical depth over Eastern Europe based on 19 years (2000–2018) MODIS TERRA data M. Filonchyk et al. https://doi.org/10.1002/joc.6412
1504. High-Resolution Gridded Level 3 Aerosol Optical Depth Data from MODIS P. Gupta et al. https://doi.org/10.3390/rs12172847
1505. Validation of MODIS 3 km land aerosol optical depth from NASA's EOS Terra and Aqua missions P. Gupta et al. https://doi.org/10.5194/amt-11-3145-2018
1506. Evaluating the potential for application of MODIS satellite data for urban air quality monitoring in Vietnam B. Hieu et al. https://doi.org/10.1088/1757-899X/869/2/022035
1507. Spatial and temporal gradients in the rate of dust deposition and aerosol optical thickness in southwestern Iran M. Foroushani et al. https://doi.org/10.1007/s40333-020-0079-5
1508. China Surface Ozone Remote Sensing Dataset in 2021–2022 at 1 km Spatial Resolution S. Zhang et al. https://doi.org/10.1007/s44408-026-00119-0
1509. Smoke emissions from agricultural fires in Mexico and Central America B. Rios & G. Raga https://doi.org/10.1117/1.JRS.13.036509
1510. Assessment of urban aerosol pollution over the Moscow megacity by the MAIAC aerosol product E. Zhdanova et al. https://doi.org/10.5194/amt-13-877-2020
1511. The role of aerosol layer height in quantifying aerosol absorption from ultraviolet satellite observations J. Sun et al. https://doi.org/10.5194/amt-12-6319-2019
1512. The observational evidence of association between types of aerosol mode-cloud-precipitation interaction over Iran M. Rezaei et al. https://doi.org/10.1016/j.apr.2023.101760
1513. Evaluation and Comparison of Long-Term MODIS C5.1 and C6 Products against AERONET Observations over China A. Fan et al. https://doi.org/10.3390/rs9121269
1514. EARLINET evaluation of the CATS Level 2 aerosol backscatter coefficient product E. Proestakis et al. https://doi.org/10.5194/acp-19-11743-2019
1515. Comprehensive Evaluation of Spatial Distribution and Temporal Trend of NO2, SO2 and AOD Using Satellite Observations over South and East Asia from 2011 to 2021 M. Rahman et al. https://doi.org/10.3390/rs15205069
1516. Combining low-cost, surface-based aerosol monitors with size-resolved satellite data for air quality applications P. deSouza et al. https://doi.org/10.5194/amt-13-5319-2020
1517. Satellite Remote Sensing for Estimating PM2.5 and Its Components Y. Li et al. https://doi.org/10.1007/s40726-020-00170-4
1518. Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder L. Liang et al. https://doi.org/10.1038/s41597-023-02696-w
1519. Merging regional and global aerosol optical depth records from major available satellite products L. Sogacheva et al. https://doi.org/10.5194/acp-20-2031-2020
1520. Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis S. Falah et al. https://doi.org/10.1016/j.atmosenv.2021.118659
1521. Estimation of daily photosynthetically active radiation (PAR) in presence of low to high aerosol loads: application to OLCI-like satellite data T. Harmel & M. Chami https://doi.org/10.1364/OE.24.0A1390
1522. Change in precipitation pattern over South Asia in response to the trends in regional warming and free-tropospheric aerosol loading S. Jana et al. https://doi.org/10.1038/s41598-024-64842-7
1523. How Do Aerosol Properties Affect the Temporal Variation of MODIS AOD Bias in Eastern China? M. Tao et al. https://doi.org/10.3390/rs9080800
1524. Investigating the relationship between aerosol and cloud optical properties inferred from the MODIS sensor in recent decades over East China J. Huang et al. https://doi.org/10.1016/j.atmosenv.2020.117812
1525. Aerosol Optical Depth Retrieval Over Land from Particulate Observing Scanning Polarimeter (POSP) Using a New Look-Up Table (LUT) Method Z. Ji et al. https://doi.org/10.1007/s41810-024-00236-6
1526. Microphysics and Radiation Effect of Dust on Saharan Air Layer: An HS3 Case Study Z. Tao et al. https://doi.org/10.1175/MWR-D-17-0279.1
1527. Aerosol Optical Depth from MODIS satellite data above the Pierre Auger Observatory N. Borodai et al. https://doi.org/10.1051/epjconf/201919702011
1528. Retrieving hourly aerosol optical depth for geostationary satellite FY-4B/AGRI by surface-related dynamic spectral reflectance ratio method W. Wang et al. https://doi.org/10.1016/j.asr.2024.10.057
1529. Exploring the effect of waterbodies coupled with other environmental parameters to model PM2.5 over Delhi-NCT in northwest India B. Gayen et al. https://doi.org/10.1016/j.apr.2022.101614
1530. Global aerosol retrieval over land from Landsat imagery integrating Transformer and Google Earth Engine J. Wei et al. https://doi.org/10.1016/j.rse.2024.114404
1531. Particulate matter variability in Kathmandu based on in-situ measurements, remote sensing, and reanalysis data S. Becker et al. https://doi.org/10.1016/j.atmosres.2021.105623
1532. Multi-dimensional satellite observations of aerosol properties and aerosol types over three major urban clusters in eastern China Y. Liu et al. https://doi.org/10.5194/acp-21-12331-2021
1533. Characterisation of dust aerosols and their absorbing efficiency over South Asia using infrared observations from INSAT-3D Imager C. Lima et al. https://doi.org/10.1080/01431161.2024.2431176
1534. Spatiotemporal changes in aerosols over Bangladesh using 18 years of MODIS and reanalysis data M. Ali et al. https://doi.org/10.1016/j.jenvman.2022.115097
1535. Trends in AOD, Clouds, and Cloud Radiative Effects in Satellite Data and CMIP5 and CMIP6 Model Simulations Over Aerosol Source Regions R. Cherian & J. Quaas https://doi.org/10.1029/2020GL087132
1536. Evaluation of Aerosol Optical Depth and Aerosol Models from VIIRS Retrieval Algorithms over North China Plain J. Zhu et al. https://doi.org/10.3390/rs9050432
1537. Spatial and temporal variability in dust storms in the Middle East, 2002–2018: three case studies in July 2009 N. Hamzeh et al. https://doi.org/10.1007/s12517-021-06859-0
1538. AeroTrans: Hourly AOD retrieval over land from MSG-1/SEVIRI imagery integrating Transformer and transfer learning Y. Fan et al. https://doi.org/10.1016/j.rse.2026.115268
1539. Assimilation of INSAT-3D imager retrieved aerosol optical depth for enhanced dust storm prediction using WRF-Chem model A. Khan et al. https://doi.org/10.1088/2515-7620/ad9712
1540. Characterization of Historical Aerosol Optical Depth Dynamics Using LSTM and Peak Enhancement Techniques H. Cămărășan et al. https://doi.org/10.3390/atmos16060743
1541. Description and evaluation of the tropospheric aerosol scheme in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS-AER, cycle 45R1) S. Rémy et al. https://doi.org/10.5194/gmd-12-4627-2019
1542. Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation M. Grzegorski et al. https://doi.org/10.3390/rs14010085
1543. Satellite-Based Spatiotemporal Trends in PM 2.5 Concentrations: China, 2004–2013 Z. Ma et al. https://doi.org/10.1289/ehp.1409481
1544. Improved simulations of biomass burning aerosol optical properties and lifetimes in the NASA GEOS Model during the ORACLES-I campaign S. Das et al. https://doi.org/10.5194/acp-24-4421-2024
1545. Investigating the frequency and interannual variability in global above-cloud aerosol characteristics with CALIOP and OMI R. Alfaro-Contreras et al. https://doi.org/10.5194/acp-16-47-2016
1546. Satellite-Derived Correlation of SO2, NO2, and Aerosol Optical Depth with Meteorological Conditions over East Asia from 2005 to 2015 C. Lin et al. https://doi.org/10.3390/rs11151738
1547. Simultaneous Monitoring of Wildfire and Smoke Using Geostationary Satellite Infrared Channel Observations K. Lee et al. https://doi.org/10.5572/KOSAE.2024.40.3.337
1548. Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation Y. Wang et al. https://doi.org/10.1016/j.isprsjprs.2019.08.017
1549. On the build-up of dust aerosols and possible indirect effect during Indian summer monsoon break spells using recent satellite observations of aerosols and cloud properties V. Arya et al. https://doi.org/10.1007/s12040-020-01526-6
1550. Performance of the Semi-Empirical Precipitable Water Vapor Retrieval Algorithm Developed for Polarized Scanning Atmospheric Corrector (PSAC) in the Presence of Sensor Decay Y. Xie et al. https://doi.org/10.1109/TGRS.2023.3304129
1551. Mapping PM2.5 concentration from the top-of-atmosphere reflectance of Himawari-8 via an ensemble stacking model X. Chen et al. https://doi.org/10.1016/j.atmosenv.2024.120560
1552. The Global Atmosphere‐aerosol Model ICON‐A‐HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness M. Salzmann et al. https://doi.org/10.1029/2021MS002699
1553. Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS) M. Kim et al. https://doi.org/10.5194/acp-16-1789-2016
1554. Dust emission and transport in Northwest China: WRF-Chem simulation and comparisons with multi-sensor observations J. Zhao et al. https://doi.org/10.1016/j.atmosres.2020.104978
1555. Surface Reflectance and Aerosol Retrieval from SPOT-VGT and PROBA-V in the Mission Exploitation Platform Environment M. Luffarelli et al. https://doi.org/10.3390/rs15215109
1556. Circular polarization in atmospheric aerosols S. Gassó & K. Knobelspiesse https://doi.org/10.5194/acp-22-13581-2022
1557. Continuing the MODIS Dark Target Aerosol Time Series with VIIRS V. Sawyer et al. https://doi.org/10.3390/rs12020308
1558. Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa W. Tang et al. https://doi.org/10.5194/gmd-16-6001-2023
1559. Retrieval of Aerosol Optical Depth in the Arid or Semiarid Region of Northern Xinjiang, China X. Tian et al. https://doi.org/10.3390/rs10020197
1560. Effect of Aerosols, Tropospheric NO2 and Clouds on Surface Solar Radiation over the Eastern Mediterranean (Greece) G. Alexandri et al. https://doi.org/10.3390/rs13132587
1561. Emission Reductions Significantly Reduce the Hemispheric Contrast in Cloud Droplet Number Concentration in Recent Two Decades Y. Cao et al. https://doi.org/10.1029/2022JD037417
1562. Results From the Deep Convective Clouds-Based Response Versus Scan-Angle Characterization for the MODIS Reflective Solar Bands A. Angal et al. https://doi.org/10.1109/TGRS.2017.2759660
1563. Estimation of Net Surface Shortwave Radiation From Remotely Sensed Data Under Dust Aerosol Conditions Y. Sun & B. Tang https://doi.org/10.1109/ACCESS.2021.3069791
1564. Sources, seasonality, and trends of southeast US aerosol: an integrated analysis of surface, aircraft, and satellite observations with the GEOS-Chem chemical transport model P. Kim et al. https://doi.org/10.5194/acp-15-10411-2015
1565. Clouds and the Earth’s Radiant Energy System (CERES) Cloud Radiative Swath (CRS) Edition 4 Data Product R. Scott et al. https://doi.org/10.1175/JTECH-D-22-0021.1
1566. Contrasting changes in snow cover and its sensitivity to aerosol optical properties in Hindukush-Karakoram-Himalaya region M. Ahmad et al. https://doi.org/10.1016/j.scitotenv.2019.134356
1567. Satellite-derived spatiotemporal PM2.5 concentrations and variations from 2006 to 2017 in China W. Xue et al. https://doi.org/10.1016/j.scitotenv.2019.134577
1568. A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data Z. Ma et al. https://doi.org/10.1016/j.rse.2021.112827
1569. Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum J. Zhang et al. https://doi.org/10.5194/gmd-14-27-2021
1570. Impacts of dust aerosol and adjacency effects on the accuracy of Landsat 8 and RapidEye surface reflectances R. Houborg & M. McCabe https://doi.org/10.1016/j.rse.2017.03.013
1571. Accuracy assessment of MODIS land aerosol optical thickness algorithms using AERONET measurements over North America H. Jethva et al. https://doi.org/10.5194/amt-12-4291-2019
1572. Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS L. Wang et al. https://doi.org/10.1016/j.isprsjprs.2024.06.022
1573. Two decades of high-resolution aerosol product over the Sierra Nevada Mountain region (SE Spain): Spatio-temporal distribution and impact on ecosystems A. del Águila et al. https://doi.org/10.1016/j.atmosres.2024.107515
1574. Investigation on spatiotemporal distribution of aerosol optical properties over two oceanic regions surrounding Indian subcontinent during summer monsoon season C. Vachaspati et al. https://doi.org/10.1007/s11356-018-2682-y
1575. Lower tropospheric ozone over the North China Plain: variability and trends revealed by IASI satellite observations for 2008–2016 G. Dufour et al. https://doi.org/10.5194/acp-18-16439-2018
1576. Advancing the quantification of aerosol-cloud interactions with the CALIPSO-CloudSat-Aqua/MODIS record Z. Li et al. https://doi.org/10.5194/acp-26-7705-2026
1577. Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion G. Geng et al. https://doi.org/10.1021/acs.est.1c01863
1578. Physical-Guided Transfer Deep Neural Network for High-Resolution AOD Retrieval D. Chen et al. https://doi.org/10.3390/rs17213606
1579. Analysis of Atmospheric Aerosol Optical Properties in the Northeast Brazilian Atmosphere with Remote Sensing Data from MODIS and CALIOP/CALIPSO Satellites, AERONET Photometers and a Ground-Based Lidar A. de Oliveira et al. https://doi.org/10.3390/atmos10100594
1580. Decreased dust particles amplify the cloud cooling effect by regulating cloud ice formation over the Tibetan Plateau J. Chen et al. https://doi.org/10.1126/sciadv.ado0885
1581. Seasonal evolution of aerosol loading and its vertical distribution in northeastern China from long-term satellite observations and model reanalysis H. Zhao et al. https://doi.org/10.1016/j.atmosenv.2023.119720
1582. Cloud condensation nuclei concentrations derived from the CAMS reanalysis K. Block et al. https://doi.org/10.5194/essd-16-443-2024
1583. Analysis and Validation of the Aerosol Optical Depth of MODIS Products in Gansu Province, Northwest China F. Huang et al. https://doi.org/10.3390/rs15122972
1584. Impact of California Fires on Local and Regional Air Quality: The Role of a Low‐Cost Sensor Network and Satellite Observations P. Gupta et al. https://doi.org/10.1029/2018GH000136
1585. Evaluating the consistency between Sentinel-2 and Planet constellations at field scale: illustration over winter wheat Y. Ma et al. https://doi.org/10.1007/s11119-025-10226-4
1586. 融合MODIS和OMI卫星数据的准东煤田吸光气溶胶辐射强迫估算研究 王. Wang Shuo et al. https://doi.org/10.3788/AOS250830
1587. Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. X. Ye et al. https://doi.org/10.3390/rs14236113
1588. Deriving a Global and Hourly Data Set of Aerosol Optical Depth Over Land Using Data From Four Geostationary Satellites: GOES-16, MSG-1, MSG-4, and Himawari-8 Y. Xie et al. https://doi.org/10.1109/TGRS.2019.2944949
1589. Temporal variation of surface reflectance and cloud fraction used to identify background aerosol retrieval information over East Asia S. Park et al. https://doi.org/10.1016/j.atmosenv.2023.119916
1590. Spatial downscaling of surface ozone concentration calculation from remotely sensed data based on mutual information X. Wang et al. https://doi.org/10.3389/fenvs.2022.925979
1591. A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements L. Clarisse et al. https://doi.org/10.1029/2018JD029701
1592. The Sub-Daily Variability of Aerosol Loading and Associated Radiative Forcing Over the Indian Region T. Mukherjee & V. Vinoj https://doi.org/10.3389/feart.2021.727169
1593. PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models H. Chu & M. Bilal https://doi.org/10.1007/s11356-018-3763-7
1594. Long-term study of desert dust deposition effects on phytoplankton biomass in the Persian Gulf using Google Earth Engine H. Asgari & A. Soleimany https://doi.org/10.1016/j.marpolbul.2023.115564
1595. Global distribution of maritime low clouds with an emphasis on different aerosol types and meteorological parameters inferred from multi-satellite and reanalysis data during 2007–2016 B. Pan et al. https://doi.org/10.1016/j.atmosenv.2020.118082
1596. An Ensemble Machine-Learning Model To Predict Historical PM2.5Concentrations in China from Satellite Data Q. Xiao et al. https://doi.org/10.1021/acs.est.8b02917
1597. The role of cloud contamination, aerosol layer height and aerosol model in the assessment of the OMI near-UV retrievals over the ocean S. Gassó & O. Torres https://doi.org/10.5194/amt-9-3031-2016
1598. High resolution aerosol optical depth retrieval over urban areas from Landsat-8 OLI images H. Lin et al. https://doi.org/10.1016/j.atmosenv.2021.118591
1599. Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM2.5 concentrations in Taiwan from 2005 to 2015 C. Jung et al. https://doi.org/10.1016/j.envpol.2017.11.016
1600. A study of 15-year aerosol optical thickness and direct shortwave aerosol radiative effect trends using MODIS, MISR, CALIOP and CERES R. Alfaro-Contreras et al. https://doi.org/10.5194/acp-17-13849-2017
1601. Improving aerosol retrieval from FY-3D/MERSI by parameterizing a binary quadratic spectral surface reflectance model with urban percentage and vegetation index W. Ji et al. https://doi.org/10.1016/j.atmosres.2025.108291
1602. Source-Specific PM2.5 Exposure and Associated Health Risks During Beijing Winter X. Liu et al. https://doi.org/10.3390/toxics13121081
1603. Large contrast in the vertical distribution of aerosol optical properties and radiative effects across the Indo-Gangetic Plain during the SWAAMI–RAWEX campaign A. Vaishya et al. https://doi.org/10.5194/acp-18-17669-2018
1604. Can We Detect the Brownness or Greenness of the Congo Rainforest Using Satellite-Derived Surface Albedo? A Study on the Role of Aerosol Uncertainties S. Moparthy et al. https://doi.org/10.3390/su11051410
1605. MISR empirical stray light corrections in high-contrast scenes J. Limbacher & R. Kahn https://doi.org/10.5194/amt-8-2927-2015
1606. Top-of-atmosphere hyper and multispectral signatures of submerged plastic litter with changing water clarity and depth S. Garaba & T. Harmel https://doi.org/10.1364/OE.451415
1607. A New Chemistry-Climate Model GRIMs-CCM: Model Evaluation of Interactive Chemistry-Meteorology Simulations S. Lee et al. https://doi.org/10.1007/s13143-022-00281-6
1608. Impact of mineral dust photocatalytic heterogeneous chemistry on the formation of the sulfate and nitrate: A modelling study over East Asia X. Li et al. https://doi.org/10.1016/j.atmosenv.2023.120166
1609. In-Flight Relative Radiometric Calibration of a Wide Field of View Directional Polarimetric Camera Based on the Rayleigh Scattering over Ocean S. Zhu et al. https://doi.org/10.3390/rs14051211
1610. Terra and Aqua MODIS TEB intercomparison using Himawari-8/AHI as reference T. Chang et al. https://doi.org/10.1117/1.JRS.13.017501
1611. Decadal simulation and comprehensive evaluation of CESM/CAM5.1 with advanced chemistry, aerosol microphysics, and aerosol‐cloud interactions J. He et al. https://doi.org/10.1002/2014MS000360
1612. Trends and Variability in Aerosol Optical Depth over North China from MODIS C6 Aerosol Products during 2001–2016 P. Wang et al. https://doi.org/10.3390/atmos8110223
1613. Southern Hemisphere mid- and high-latitudinal AOD, CO, NO2, and HCHO: spatiotemporal patterns revealed by satellite observations D. Ahn et al. https://doi.org/10.1186/s40645-019-0277-y
1614. Spatial Variation and Relation of Aerosol Optical Depth with LULC and Spectral Indices V. Sharma et al. https://doi.org/10.3390/atmos13121992
1615. Extending “Deep Blue” aerosol retrieval coverage to cases of absorbing aerosols above clouds: Sensitivity analysis and first case studies A. Sayer et al. https://doi.org/10.1002/2015JD024729
1616. The Influences of Indian Monsoon Phases on Aerosol Distribution and Composition over India P. Khan et al. https://doi.org/10.3390/rs16173171
1617. Variability in air-pollutants, aerosols, and associated meteorology over peninsular India and neighboring ocean regions during COVID-19 lockdown to unlock phases A. Sarkar et al. https://doi.org/10.1016/j.apr.2021.101231
1618. Understanding MODIS dark-target collection 5 and 6 aerosol data over China: Effect of surface type, aerosol loading and aerosol absorption L. Mei et al. https://doi.org/10.1016/j.atmosres.2019.05.023
1619. Satellites See the World’s Atmosphere S. Ackerman et al. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0009.1
1620. 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. https://doi.org/10.3390/rs10040573
1621. Dust Detection and Intensity Estimation Using Himawari-8/AHI Observation L. She et al. https://doi.org/10.3390/rs10040490
1622. Numerical simulations of dust storms originated from dried lakes in central and southwest Asia: The case of Aral Sea and Sistan Basin S. Karami et al. https://doi.org/10.1016/j.aeolia.2021.100679
1623. Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China C. Ma et al. https://doi.org/10.1029/2019JD030421
1624. Transport of aerosols over the French Riviera – link between ground-based lidar and spaceborne observations P. Chazette et al. https://doi.org/10.5194/acp-19-3885-2019
1625. Impact of satellite viewing-swath width on global and regional aerosol optical thickness statistics and trends P. Colarco et al. https://doi.org/10.5194/amt-7-2313-2014
1626. The Evaluation of the North Atlantic Climate System in UKESM1 Historical Simulations for CMIP6 J. Robson et al. https://doi.org/10.1029/2020MS002126
1627. 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. https://doi.org/10.5194/amt-16-2575-2023
1628. April 2008 Saharan dust event: Its contribution to PM10 concentrations over the Anatolian Peninsula and relation with synoptic conditions B. Kabatas et al. https://doi.org/10.1016/j.scitotenv.2018.03.150
1629. Description and evaluation of the tropospheric aerosol scheme in the Integrated Forecasting System (IFS-AER, cycle 47R1) of ECMWF S. Rémy et al. https://doi.org/10.5194/gmd-15-4881-2022
1630. Evaluating Aerosol Optical Depth From Himawari‐8 With Sun Photometer Network W. Wang et al. https://doi.org/10.1029/2018JD028599
1631. Assimilation and Forecasting Experiment for Heavy Siberian Wildfire Smoke in May 2016 with Himawari-8 Aerosol Optical Thickness K. YUMIMOTO et al. https://doi.org/10.2151/jmsj.2018-035
1632. Urban PM2.5 concentration monitoring: A review of recent advances in ground-based, satellite, model, and machine learning integration S. Lolli https://doi.org/10.1016/j.uclim.2025.102566
1633. Synergistic Use of Remote Sensing and Modeling to Assess an Anomalously High Chlorophyll-a Event during Summer 2015 in the South Central Red Sea W. Li et al. https://doi.org/10.3390/rs9080778
1634. Evaluation of the Consistency of Several XAERDT Aerosol Products Over Land and Ocean X. Pei et al. https://doi.org/10.1109/TGRS.2026.3694737
1635. Impact of Assimilating GEMS Aerosol Optical Depth on Asian Dust Storm Prediction: Comparative Assessment with MODIS Observation E. Lee et al. https://doi.org/10.1007/s13143-025-00407-6
1636. Improved estimation of fire particulate emissions using a combination of VIIRS and AHI data for Indonesia during 2015–2020 X. Lu et al. https://doi.org/10.1016/j.rse.2022.113238
1637. Decadal variations in natural and anthropogenic aerosol optical depth over the Bay of Bengal: the influence of pollutants from Indo-GangeticPlain K. Yadav et al. https://doi.org/10.1007/s11356-021-14703-x
1638. Retrievals of aerosol layer height during dust events over the taklimakan and gobi desert S. Chen et al. https://doi.org/10.1016/j.jqsrt.2020.107198
1639. An updated VIIRS dark target aerosol product for continuity with MODIS: assessing regional aerosol trends V. Sawyer et al. https://doi.org/10.3389/fenvs.2025.1602145
1640. Monitoring and tracking the trans-Pacific transport of aerosols using multi-satellite aerosol optical depth composites A. Naeger et al. https://doi.org/10.5194/amt-9-2463-2016
1641. Aerosol Optical Depth over the Andhra Pradesh State in South India from Reanalysis Data: Spatiotemporal Variabilities and Machine Learning Approach P. Althaf et al. https://doi.org/10.1007/s41748-024-00465-2
1642. Comparison of total ozone column observations from space-borne Ozone Monitoring Instrument with ground-based Dobson Ozone Spectrophotometer measurements at an urban location in Indo-Gangetic Basin Z. Ul-Haq et al. https://doi.org/10.1080/01431161.2017.1387306
1643. Screening for snow/snowmelt in SNPP VIIRS aerosol optical depth algorithm J. Huang et al. https://doi.org/10.5194/amt-11-5813-2018
1644. Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations W. Marais et al. https://doi.org/10.5194/amt-13-5459-2020
1645. Evaluation of MODIS and two reanalysis aerosol optical depth products over AERONET sites H. Shi et al. https://doi.org/10.1016/j.atmosres.2019.01.009
1646. Observational Constraints on the Aerosol Optical Depth–Surface PM2.5 Relationship during Alaskan Wildfire Seasons T. Zhao et al. https://doi.org/10.1021/acsestair.4c00120
1647. Validation and Accuracy Assessment of MODIS C6.1 Aerosol Products over the Heavy Aerosol Loading Area X. Tian & Z. Gao https://doi.org/10.3390/atmos10090548
1648. Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols O. Hasekamp et al. https://doi.org/10.5194/amt-17-1497-2024
1649. Simulating and Evaluating Global Aerosol Distributions With the Online Aerosol‐Coupled CAS‐FGOALS Model H. Wang et al. https://doi.org/10.1029/2019JD032097
1650. AOD Derivation from SDGSAT-1/GLI Dataset in Mega-City Area N. Wang et al. https://doi.org/10.3390/rs15051343
1651. Recent Increase in Winter Hazy Days over Central India and the Arabian Sea A. Thomas et al. https://doi.org/10.1038/s41598-019-53630-3
1652. Aerosol optical depth retrieval from visibility in China during 1973–2014 Z. Zhang et al. https://doi.org/10.1016/j.atmosenv.2017.09.004
1653. Impact of satellite AOD data on top-down estimation of biomass burning particulate matter emission X. Ye et al. https://doi.org/10.1016/j.scitotenv.2022.161055
1654. Using Multi-Platform Satellite Observations to Study the Atmospheric Evolution of Brown Carbon in Siberian Biomass Burning Plumes I. Konovalov et al. https://doi.org/10.3390/rs14112625
1655. Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements C. Fan et al. https://doi.org/10.3390/rs11232877
1656. Passive remote sensing of altitude and optical depth of dust plumes using the oxygen A and B bands: First results from EPIC/DSCOVR at Lagrange‐1 point X. Xu et al. https://doi.org/10.1002/2017GL073939
1657. Differences between the MODIS Collection 6 and 5.1 aerosol datasets over the greater Mediterranean region A. Georgoulias et al. https://doi.org/10.1016/j.atmosenv.2016.10.014
1658. Deciphering Long-Term Variation of Aerosol Characteristics and Their Climatic Implication over the World’s Wettest Place: Mawsynram M. Devi et al. https://doi.org/10.1007/s12524-025-02374-x
1659. Northward shift of transboundary air pollution pathways over the western Pacific under climate change Y. Cai et al. https://doi.org/10.1186/s40562-026-00488-9
1660. Research agenda for the Russian Far East and utilization of multi-platform comprehensive environmental observations T. Petäjä et al. https://doi.org/10.1080/17538947.2020.1826589
1661. Impact of vegetation greening on TOA clear-sky shortwave radiation in Northwest India M. Mishra et al. https://doi.org/10.1007/s00382-024-07321-z
1662. Regional sulfate drives long-term rise in AOD over megacity Kolkata, India P. Rawat et al. https://doi.org/10.1016/j.atmosenv.2019.04.031
1663. Pollution Trends in China from 2000 to 2017: A Multi-Sensor View from Space J. Li https://doi.org/10.3390/rs12020208
1664. An Improved Aerosol Retrieval Algorithm Based on Nonlinear Surface Model From FY-3D/MERSI-II Remote Sensing Data Y. Si et al. https://doi.org/10.1109/TGRS.2024.3367883
1665. Multi-Source Remote Sensing-Constrained Evaluation of CMAQ Aerosol Optical Depth over Major Urban Clusters in China Z. Peng et al. https://doi.org/10.3390/rs18081134
1666. Investigating the applicability of a simple iterative approach for aerosol optical depth (AOD) retrieval over diverse land surface types from Landsat 8 and Sentinel 2 using visible and near-infrared (VNIR) spectral bands A. Kumar & M. Mehta https://doi.org/10.1016/j.atmosenv.2023.120082
1667. Spatiotemporal variability and long-term trends of aerosol optical depth over Ethiopia: Insights from satellite and reanalysis data B. Wateso et al. https://doi.org/10.1007/s00703-026-01131-0
1668. Variation of Atmospheric Properties over the Central Himalaya During a Low-Intensity Dust Episode: Insights from Lidar and Auxiliary Observations S. Singh et al. https://doi.org/10.1007/s41810-026-00395-8
1669. MODIS high-resolution MAIAC aerosol product: Global validation and analysis W. Qin et al. https://doi.org/10.1016/j.atmosenv.2021.118684
1670. An analysis of particulate pollution using urban aerosol pollution island intensity over Delhi, India J. Singh et al. https://doi.org/10.1007/s10661-022-10573-z
1671. Which dual-band infrared indices are optimum for identifying aerosol compositional change using Himawari-8 data? M. Sowden & D. Blake https://doi.org/10.1016/j.atmosenv.2020.117620
1672. Validation of POLDER-3/GRASP aerosol products using AERONET measurements over China Y. Tan et al. https://doi.org/10.1016/j.atmosenv.2019.116893
1673. An Evaluation of MODIS-Retrieved Aerosol Optical Depth over AERONET Sites in Alaska A. McPhetres & S. Aggarwal https://doi.org/10.3390/rs10091384
1674. Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product M. Garay et al. https://doi.org/10.5194/amt-13-593-2020
1675. Evaluating PM2.5 Exposure Disparities Through Agent-Based Geospatial Modeling in an Urban Airshed D. Johnson et al. https://doi.org/10.3390/air3040033
1676. Numerical study of aerosol radiative forcing over East Asia and the impacts of cloud coverage and relative humidity J. Guo et al. https://doi.org/10.1016/j.atmosres.2022.106168