162 citations as recorded by crossref.

1. Deriving Aerosol Absorption Properties from Solar Ultraviolet Radiation Spectral Measurements at Thessaloniki, Greece I. Fountoulakis et al. https://doi.org/10.3390/rs11182179
2. 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
3. Radiative effects of absorbing aerosols over northeastern India: Observations and model simulations M. Gogoi et al. https://doi.org/10.1002/2016JD025592
4. 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
5. 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
6. An optimal-estimation-based aerosol retrieval algorithm using OMI near-UV observations U. Jeong et al. https://doi.org/10.5194/acp-16-177-2016
7. Ground-based retrievals of aerosol column absorption in the UV spectral region and their implications for GEMS measurements S. Go et al. https://doi.org/10.1016/j.rse.2020.111759
8. 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
9. Black Carbon and Brown Carbon and Selective Smoke Aerosol Absorption during Massive Wildfires in Alaska in 2019 and Canada in 2023 G. Gorchakov et al. https://doi.org/10.1134/S1024856025700058
10. Spatio-temporal variations of tropospheric nitrogen dioxide in South Mato Grosso based on remote sensing by satellite A. de Souza et al. https://doi.org/10.1007/s00703-021-00855-5
11. Potential drivers and consequences of regional phosphate depletion in the western subtropical North Pacific Z. Yuan et al. https://doi.org/10.1002/lol2.10314
12. Use of Decision Theory to Predict Dust Storms over New Delhi, India N. Desouza et al. https://doi.org/10.4236/ns.2014.68057
13. 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
14. Explicit Aerosol Correction of OMI Formaldehyde Retrievals Y. Jung et al. https://doi.org/10.1029/2019EA000702
15. 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
16. Effect of aerosol vertical distribution on aerosol-radiation interaction: A theoretical prospect A. Mishra et al. https://doi.org/10.1016/j.heliyon.2015.e00036
17. Factors for inconsistent aerosol single scattering albedo between SKYNET and AERONET P. Khatri et al. https://doi.org/10.1002/2015JD023976
18. Terrestrial aerosol retrieval over Beijing from Chinese GF-1 data based on the blue/red correlation. Y. Zhang et al. https://doi.org/10.1080/2150704X.2020.1856959
19. 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
20. Aerosol retrievals from the GOSAT-2/CAI-2 imager with the 2nd version of the multi-wavelength and multi-pixel method T. Nakajima et al. https://doi.org/10.1016/j.jqsrt.2026.109939
21. Potential of AOD Retrieval Using Atmospheric Emitted Radiance Interferometer (AERI) J. Seo et al. https://doi.org/10.3390/rs14020407
22. Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint Z. Fasnacht et al. https://doi.org/10.3389/frsen.2022.846174
23. 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
24. Aerosol single‐scattering albedo over the global oceans: Comparing PARASOL retrievals with AERONET, OMI, and AeroCom models estimates C. Lacagnina et al. https://doi.org/10.1002/2015JD023501
25. Primary Evaluation of the GCOM-C Aerosol Products at 380 nm Using Ground-Based Sky Radiometer Observations H. Hoque et al. https://doi.org/10.3390/rs12162661
26. Long-term variation of aerosol optical properties associated with aerosol types over East Asia using AERONET and satellite (VIIRS, OMI) data (2012–2019) S. Eom et al. https://doi.org/10.1016/j.atmosres.2022.106457
27. A 12-year long global record of optical depth of absorbing aerosols above the clouds derived from the OMI/OMACA algorithm H. Jethva et al. https://doi.org/10.5194/amt-11-5837-2018
28. Investigation on the role of aerosols on precipitation enhancement over Kerala during August 2018 M. Jasmine et al. https://doi.org/10.1016/j.atmosenv.2022.119101
29. Consistency of aerosols above clouds characterization from A-Train active and passive measurements L. Deaconu et al. https://doi.org/10.5194/amt-10-3499-2017
30. Satellite (GOSAT-2 CAI-2) retrieval and surface (ARFINET) observations of aerosol black carbon over India M. Gogoi et al. https://doi.org/10.5194/acp-23-8059-2023
31. Simulation and analysis of synoptic scale dust storms over the Arabian Peninsula S. Beegum et al. https://doi.org/10.1016/j.atmosres.2017.09.003
32. Modeling radiative and climatic effects of brown carbon aerosols with the ARPEGE-Climat global climate model T. Drugé et al. https://doi.org/10.5194/acp-22-12167-2022
33. Optical and radiative properties of aerosols over Abu Dhabi in the United Arab Emirates S. Beegum et al. https://doi.org/10.1007/s12040-016-0759-x
34. Implications of MODIS impression of aerosol loading over urban and rural settlements in Nigeria: Possible links to energy consumption patterns in the country N. Onyeuwaoma et al. https://doi.org/10.5094/APR.2015.054
35. Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model W. Choi et al. https://doi.org/10.3390/rs13132464
36. Aerosol Optical Depth Retrieval over East Asia Using Himawari-8/AHI Data W. Zhang et al. https://doi.org/10.3390/rs10010137
37. Simultaneously inferring above‐cloud absorbing aerosol optical thickness and underlying liquid phase cloud optical and microphysical properties using MODIS K. Meyer et al. https://doi.org/10.1002/2015JD023128
38. Variability in Dust Observed over China Using A-Train CALIOP Instrument H. Xu et al. https://doi.org/10.1155/2016/1246590
39. Pollution Trends in China from 2000 to 2017: A Multi-Sensor View from Space J. Li https://doi.org/10.3390/rs12020208
40. 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
41. Assessment of two intense dust storm characteristics over Indo – Gangetic basin and their radiative impacts: A case study S. Tiwari et al. https://doi.org/10.1016/j.atmosres.2019.05.011
42. Earth Observations from DSCOVR EPIC Instrument A. Marshak et al. https://doi.org/10.1175/BAMS-D-17-0223.1
43. Planetary boundary layer height from CALIOP compared to radiosonde over China W. Zhang et al. https://doi.org/10.5194/acp-16-9951-2016
44. What factors control the trend of increasing AAOD over the United States in the last decade? L. Zhang et al. https://doi.org/10.1002/2016JD025472
45. Improving Spatial Coverage of Satellite Aerosol Classification Using a Random Forest Model W. Choi et al. https://doi.org/10.3390/rs13071268
46. 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
47. First TROPOMI Retrieval of Aerosol Effective Height Using O₄ Absorption Band at 477 nm and Aerosol Classification W. Choi et al. https://doi.org/10.1109/TGRS.2020.3044050
48. The COVID-19 lockdown: a unique perspective into heterogeneous impacts of transboundary pollution on snow and ice darkening across the Himalayas Z. Hou et al. https://doi.org/10.1093/pnasnexus/pgad172
49. 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
50. 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
51. 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
52. 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
53. Light-absorbing black carbon and brown carbon components of smoke aerosol from DSCOVR EPIC measurements over North America and central Africa M. Choi et al. https://doi.org/10.5194/acp-24-10543-2024
54. Identification and Characterization of Dust Source Regions Across North Africa and the Middle East Using MISR Satellite Observations Y. Yu et al. https://doi.org/10.1029/2018GL078324
55. 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
56. Characterization of absorbing aerosol types using ground and satellites based observations over an urban environment S. Bibi et al. https://doi.org/10.1016/j.atmosenv.2016.11.052
57. A Climatological Assessment of Intense Desert Dust Episodes over the Broader Mediterranean Basin Based on Satellite Data M. Gavrouzou et al. https://doi.org/10.3390/rs13152895
58. Global changes in aerosol single scattering albedo during COVID-19 K. Ansari & S. Ramachandran https://doi.org/10.1016/j.atmosenv.2024.120649
59. 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
60. Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives X. Wei et al. https://doi.org/10.1080/10643389.2019.1665944
61. TROPOMI aerosol products: evaluation and observations of synoptic-scale carbonaceous aerosol plumes during 2018–2020 O. Torres et al. https://doi.org/10.5194/amt-13-6789-2020
62. Analysis of a saline dust storm from the Aralkum Desert – Part 1: Consistency between multisensor satellite aerosol products X. Xi et al. https://doi.org/10.5194/acp-25-7403-2025
63. 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
64. AEROCOM and AEROSAT AAOD and SSA study – Part 1: Evaluation and intercomparison of satellite measurements N. Schutgens et al. https://doi.org/10.5194/acp-21-6895-2021
65. 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
66. Aerosol Retrieval Sensitivity and Error Analysis for the Cloud and Aerosol Polarimetric Imager on Board TanSat: The Effect of Multi-Angle Measurement X. Chen et al. https://doi.org/10.3390/rs9020183
67. 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
68. A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation W. Choi et al. https://doi.org/10.3390/rs13040609
69. 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
70. Interpreting the ultraviolet aerosol index observed with the OMI satellite instrument to understand absorption by organic aerosols: implications for atmospheric oxidation and direct radiative effects M. Hammer et al. https://doi.org/10.5194/acp-16-2507-2016
71. 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
72. Evaluation and Comparison of Multi-Satellite Aerosol Optical Depth Products over East Asia Ocean Z. Cao et al. https://doi.org/10.3390/toxics11100813
73. Intercomparison of Aerosol Types Reported as Part of Aerosol Product Retrieval over Diverse Geographic Regions S. Falah et al. https://doi.org/10.3390/rs14153667
74. Vertical distribution of smoke aerosols over upper Indo-Gangetic Plain K. Vinjamuri et al. https://doi.org/10.1016/j.envpol.2019.113377
75. 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
76. Remote sensing of air pollution due to forest fires and dust storm over Balochistan (Pakistan) S. Tariq et al. https://doi.org/10.1016/j.apr.2023.101674
77. Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis V. Buchard et al. https://doi.org/10.5194/acp-15-5743-2015
78. Detection of Absorbing Aerosol Using Single Near-UV Radiance Measurements from a Cloud and Aerosol Imager S. Go et al. https://doi.org/10.3390/rs9040378
79. Solar UV Irradiance in a Changing Climate: Trends in Europe and the Significance of Spectral Monitoring in Italy I. Fountoulakis et al. https://doi.org/10.3390/environments7010001
80. 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
81. Characterizing the 2015 Indonesia fire event using modified MODIS aerosol retrievals Y. Shi et al. https://doi.org/10.5194/acp-19-259-2019
82. 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
83. An improved air mass factor calculation for nitrogen dioxide measurements from the Global Ozone Monitoring Experiment-2 (GOME-2) S. Liu et al. https://doi.org/10.5194/amt-13-755-2020
84. 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
85. How well do satellite AOD observations represent the spatial and temporal variability of PM 2.5 concentration for the United States? J. Li et al. https://doi.org/10.1016/j.atmosenv.2014.12.010
86. Análise da Espessura ótica dos Aerossóis do Sensor OMI na America do Sul P. Calado et al. https://doi.org/10.1590/0102-77863550105
87. Characterization and radiative impact of dust aerosols over northwestern part of India: a case study during a severe dust storm A. Singh et al. https://doi.org/10.1007/s00703-016-0445-1
88. 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
89. Single Scattering Albedo’s Spectral Dependence Effect on UV Irradiance I. Raptis et al. https://doi.org/10.3390/atmos9090364
90. 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
91. Constraining global aerosol emissions using POLDER/PARASOL satellite remote sensing observations C. Chen et al. https://doi.org/10.5194/acp-19-14585-2019
92. Global detection of absorbing aerosols over the ocean in the red and near‐infrared spectral region F. Waquet et al. https://doi.org/10.1002/2016JD025163
93. Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements over Asia M. Kim et al. https://doi.org/10.3390/rs10020162
94. 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
95. OMI tropospheric NO2 air mass factors over South America: effects of biomass burning aerosols P. Castellanos et al. https://doi.org/10.5194/amt-8-3831-2015
96. A comparative evaluation of Aura-OMI and SKYNET near-UV single-scattering albedo products H. Jethva & O. Torres https://doi.org/10.5194/amt-12-6489-2019
97. Three-dimensional nature of summertime aerosols over South Asia A. Singh et al. https://doi.org/10.1016/j.scitotenv.2022.156834
98. Retrieving Aerosol Characteristics From the PACE Mission, Part 1: Ocean Color Instrument L. Remer et al. https://doi.org/10.3389/feart.2019.00152
99. 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
100. 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
101. In-orbit Earth reflectance validation of TROPOMI on board the Sentinel-5 Precursor satellite L. Tilstra et al. https://doi.org/10.5194/amt-13-4479-2020
102. Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China Y. Wang et al. https://doi.org/10.3390/rs13183742
103. A Climatological Satellite Assessment of Absorbing Carbonaceous Aerosols on a Global Scale N. Hatzianastassiou et al. https://doi.org/10.3390/atmos10110671
104. MAIAC-based climatology of atmospheric iron-oxide dust species from DSCOVR EPIC observations S. Go et al. https://doi.org/10.3389/frsen.2025.1676851
105. 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
106. Aerosol and cloud retrieval algorithm of TANSO-3/GOSAT-GW: theoretical basis and validations during the 2024 ASIA-AQ campaign using the TROPOMI observations as a testbed H. Lim et al. https://doi.org/10.1186/s40645-026-00820-z
107. Identifying the natural and anthropogenic drivers of absorbing aerosols using OMI data and HYSPLIT model over South Asia H. Nawaz et al. https://doi.org/10.1007/s11869-023-01423-z
108. 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
109. 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
110. Biomass Burning Over the United States East Coast and Western North Atlantic Ocean: Implications for Clouds and Air Quality A. Mardi et al. https://doi.org/10.1029/2021JD034916
111. 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
112. Retrieval of UV–visible aerosol absorption using AERONET and OMI–MODIS synergy: spatial and temporal variability across major aerosol environments V. Kayetha et al. https://doi.org/10.5194/amt-15-845-2022
113. 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
114. Absorbing Aerosol Optical Depth From OMI/TROPOMI Based on the GBRT Algorithm and AERONET Data in Asia D. Li et al. https://doi.org/10.1109/TGRS.2022.3231699
115. 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
116. Simulation of the Ozone Monitoring Instrument aerosol index using the NASA Goddard Earth Observing System aerosol reanalysis products P. Colarco et al. https://doi.org/10.5194/amt-10-4121-2017
117. 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
118. 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
119. Sensitivity of formaldehyde (HCHO) column measurements from a geostationary satellite to temporal variation of the air mass factor in East Asia H. Kwon et al. https://doi.org/10.5194/acp-17-4673-2017
120. 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
121. 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
122. 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
123. 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
124. 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
125. Variation of aerosol parameters (AI, AOD) and SO2 over Indo-Gangetic basin during COVID-19 outbreaks A. Kumar et al. https://doi.org/10.1007/s12648-023-02802-z
126. Performance of DSCOVR/EPIC diurnal aerosol products over China: Ground validation and intercomparison L. Gui et al. https://doi.org/10.1016/j.atmosres.2024.107268
127. Retrieving High Temporal Resolution Aerosol Layer Height From EPIC/DSCOVR Using Machine Learning Method X. Tian et al. https://doi.org/10.1109/TGRS.2024.3405186
128. 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
129. The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies V. Buchard et al. https://doi.org/10.1175/JCLI-D-16-0613.1
130. 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
131. 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
132. Stratospheric Injection of Massive Smoke Plume From Canadian Boreal Fires in 2017 as Seen by DSCOVR‐EPIC, CALIOP, and OMPS‐LP Observations O. Torres et al. https://doi.org/10.1029/2020JD032579
133. Constraining black carbon aerosol over Asia using OMI aerosol absorption optical depth and the adjoint of GEOS-Chem L. Zhang et al. https://doi.org/10.5194/acp-15-10281-2015
134. Radiative effect of dust aerosols on cloud microphysics and meso-scale dynamics during monsoon breaks over Arabian sea G. Harikishan et al. https://doi.org/10.1016/j.atmosenv.2015.01.037
135. 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
136. 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
137. Retrievals of Aerosol Optical Depth and Spectral Absorption From DSCOVR EPIC A. Lyapustin et al. https://doi.org/10.3389/frsen.2021.645794
138. Comparisons of spectral aerosol single scattering albedo in Seoul, South Korea J. Mok et al. https://doi.org/10.5194/amt-11-2295-2018
139. Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data J. Yorks et al. https://doi.org/10.3390/atmos12050606
140. 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
141. Inter-comparison of multi-satellites and Aeronet AOD over Indian Region R. Mangla et al. https://doi.org/10.1016/j.atmosres.2020.104950
142. 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
143. 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
144. Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land W. Zhang et al. https://doi.org/10.3390/rs11091108
145. 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
146. 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
147. Retrieving UV–Vis spectral single-scattering albedo of absorbing aerosols above clouds from synergy of ORACLES airborne and A-train sensors H. Jethva et al. https://doi.org/10.5194/amt-17-2335-2024
148. Spatiotemporal variations and relationships of absorbing aerosol-radiation-gross primary productivity over China L. Li et al. https://doi.org/10.1007/s10661-022-10775-5
149. Direct radiative effect of aerosols based on PARASOL and OMI satellite observations C. Lacagnina et al. https://doi.org/10.1002/2016JD025706
150. Validation of OMI HCHO Products Using MAX-DOAS observations from 2010 to 2016 in Xianghe, Beijing: Investigation of the Effects of Aerosols on Satellite Products Y. Wang et al. https://doi.org/10.3390/rs11020203
151. Aerosol Layer Height Retrieval Over Ocean From the Advanced Himawari Imager Using Spectral Reflectance Sensitivity H. Lim et al. https://doi.org/10.1109/LGRS.2023.3236299
152. Photocatalytic Azo Dye Degradation Using Graphite Carbon Nitride Photocatalyst and UV-A Irradiation S. Al-Zahrani et al. https://doi.org/10.3390/cryst13040577
153. 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
154. 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
155. 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
156. Identifying Absorbing Aerosols Above Clouds From the Spinning Enhanced Visible and Infrared Imager Coupled With NASA A-Train Multiple Sensors I. Chang & S. Christopher https://doi.org/10.1109/TGRS.2015.2513015
157. An algorithm for hyperspectral remote sensing of aerosols: 2. Information content analysis for aerosol parameters and principal components of surface spectra W. Hou et al. https://doi.org/10.1016/j.jqsrt.2017.01.041
158. Shortwave direct radiative effects of above-cloud aerosols over global oceans derived from 8 years of CALIOP and MODIS observations Z. Zhang et al. https://doi.org/10.5194/acp-16-2877-2016
159. 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
160. The Ozone Monitoring Instrument: overview of 14 years in space P. Levelt et al. https://doi.org/10.5194/acp-18-5699-2018
161. Changing Trends of Aerosol Loadings Over Three Major Zones of Indian Region During the Last Seventeen Years (2005–2021) M. Mehta et al. https://doi.org/10.1007/s12524-022-01533-8
162. A novel method for estimating shortwave direct radiative effect of above-cloud aerosols using CALIOP and MODIS data Z. Zhang et al. https://doi.org/10.5194/amt-7-1777-2014