113 citations as recorded by crossref.

1. 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
2. Exploring systematic offsets between aerosol products from the two MODIS sensors R. Levy et al. https://doi.org/10.5194/amt-11-4073-2018
3. Neural network for aerosol retrieval from hyperspectral imagery S. Mauceri et al. https://doi.org/10.5194/amt-12-6017-2019
4. Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm for Himawari-8 Data L. She et al. https://doi.org/10.3390/rs11232771
5. 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
6. 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
7. 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
8. Spatial Variation and Relation of Aerosol Optical Depth with LULC and Spectral Indices V. Sharma et al. https://doi.org/10.3390/atmos13121992
9. Two-dimensional mineral dust radiative effect calculations from CALIPSO observations over Europe M. Granados-Muñoz et al. https://doi.org/10.5194/acp-19-13157-2019
10. 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
11. 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
12. 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
13. 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
14. 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
15. Global evaluation of sentinel 2 level 2A Sen2Cor aerosol optical thickness retrievals A. Kumar & M. Mehta https://doi.org/10.1080/01431161.2024.2421947
16. Evaluation and Comparison of Multi-Satellite Aerosol Optical Depth Products over East Asia Ocean Z. Cao et al. https://doi.org/10.3390/toxics11100813
17. Multiparameter Aerosol Simultaneous Retrieval Combining Satellite Remote Sensing and Atmospheric Simulation Using Space-Time Transformer (STTF) Model Y. Fan et al. https://doi.org/10.1109/TGRS.2025.3538164
18. An intercomparison of SEMARA high-resolution AOD and MODIS operational AODs M. Bagherinia et al. https://doi.org/10.1016/j.apr.2023.102023
19. 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
20. 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
21. High-Resolution Gridded Level 3 Aerosol Optical Depth Data from MODIS P. Gupta et al. https://doi.org/10.3390/rs12172847
22. Land Surface Phenology Retrieval through Spectral and Angular Harmonization of Landsat-8, Sentinel-2 and Gaofen-1 Data J. Lu et al. https://doi.org/10.3390/rs14051296
23. 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
24. 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
25. 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
26. Influence of Temporal Representativeness of Satellite Aerosol Products on Direct Aerosol Radiative Effects X. Yang et al. https://doi.org/10.1109/TGRS.2025.3576650
27. Spatiotemporal mixed effects modeling for the estimation of PM2.5from MODIS AOD over the Indian subcontinent S. Unnithan & L. Gnanappazham https://doi.org/10.1080/15481603.2020.1712101
28. 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
29. Why Rainfall Alone Cannot Mitigate Urban Pollution: Two Decades (2003–2024) of Satellite Observation over Delhi G. Balachandran & S. S https://doi.org/10.1016/j.jastp.2026.106936
30. Continuing the MODIS Dark Target Aerosol Time Series with VIIRS V. Sawyer et al. https://doi.org/10.3390/rs12020308
31. 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
32. 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
33. 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
34. Assessment of AOD Simulation by Assimilating Himawari and MODIS Products Z. Wang et al. https://doi.org/10.1007/s13351-025-4220-4
35. 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
36. 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
37. Aerosol retrievals derived from a low-cost Calitoo sun-photometer taken on board a research vessel R. García et al. https://doi.org/10.1016/j.atmosenv.2024.120888
38. Vertical distribution of smoke aerosols over upper Indo-Gangetic Plain K. Vinjamuri et al. https://doi.org/10.1016/j.envpol.2019.113377
39. 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
40. 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
41. Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China N. Luo et al. https://doi.org/10.3390/atmos15050564
42. On the role of aerosol radiative effect in the wet season onset timing over the Congo rainforest during boreal autumn S. Chakraborty et al. https://doi.org/10.5194/acp-21-12855-2021
43. 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
44. Global Distribution of Column Satellite Aerosol Optical Depth to Surface PM2.5 Relationships S. Christopher & P. Gupta https://doi.org/10.3390/rs12121985
45. Characterizing aerosols during forest fires over Uttarakhand region in India using multi-satellite remote sensing data S. Verma et al. https://doi.org/10.1016/j.asr.2022.05.051
46. 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
47. Improving the accuracy of AOD by using multi-sensors data over the Red Sea and the Persian Gulf M. Pashayi et al. https://doi.org/10.1016/j.apr.2023.101948
48. 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
49. 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
50. Aerosol radiation characteristics based on Himawari-8 and AERONET in Beijing city Q. Mao & X. Zhang https://doi.org/10.1016/j.jastp.2024.106411
51. 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
52. 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
53. The Difference in MODIS Aerosol Retrieval Accuracy over Chinese Forested Regions M. Ahmed et al. https://doi.org/10.3390/rs17142401
54. 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
55. 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
56. Ambient air particulate matter (PM10) satellite monitoring and respiratory health effects assessment M. Mohebbichamkhorami et al. https://doi.org/10.1007/s40201-020-00542-4
57. K-Means Clustering for Improved Data-Driven Satellite Aerosol Retrieval S. Zhang et al. https://doi.org/10.1109/LGRS.2025.3631867
58. 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
59. 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
60. 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
61. 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
62. 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
63. Multisensor and Multimodel Monitoring and Investigation of a Wintertime Air Pollution Event Ahead of a Cold Front Over Eastern China X. Hu et al. https://doi.org/10.1029/2020JD033538
64. 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
65. 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
66. 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
67. Significant contribution of primary brown carbon to the light absorption at Delhi, India: An implication for incomplete oxidation A. Pipal et al. https://doi.org/10.1016/j.apr.2025.102722
68. MAIAC AOD profiling over the Persian Gulf: A seasonal-independent machine learning approach M. Pashayi et al. https://doi.org/10.1016/j.apr.2024.102128
69. 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
70. 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
71. 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
72. Operational AOD Retrieval at Subkilometer Resolution Using OceanSat‐2 OCM Over Land: SAER Algorithm, Uncertainties, Validation & Inter‐Sensor Comparison M. Mishra et al. https://doi.org/10.1029/2023EA002896
73. Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations S. Ahn et al. https://doi.org/10.3390/rs13061096
74. A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges A. Ranjan et al. https://doi.org/10.1007/s13143-020-00215-0
75. 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
76. Validation and Analysis of MISR and POLDER Aerosol Products over China S. Jiao et al. https://doi.org/10.3390/rs14153697
77. 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
78. 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
79. 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
80. 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
81. Evaluation of MAIAC aerosol retrievals over China Z. Zhang et al. https://doi.org/10.1016/j.atmosenv.2019.01.013
82. 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
83. 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
84. 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
85. Assessment of aerosol burden over Ghana K. Fosu-Amankwah et al. https://doi.org/10.1016/j.sciaf.2021.e00971
86. A High-Precision Aerosol Retrieval Algorithm (HiPARA) for Advanced Himawari Imager (AHI) data: Development and verification X. Su et al. https://doi.org/10.1016/j.rse.2020.112221
87. 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
88. 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
89. 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
90. The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future L. Remer et al. https://doi.org/10.3390/rs12182900
91. A global perspective on CO2 satellite observations in high AOD conditions T. Virtanen et al. https://doi.org/10.5194/amt-18-929-2025
92. 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
93. 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
94. Himawari-8/AHI and MODIS Aerosol Optical Depths in China: Evaluation and Comparison T. Jiang et al. https://doi.org/10.3390/rs11091011
95. On-orbit cross-calibration of the DPC and POSP sensors onboard the GaoFen-5 (02) satellite H. Zhang et al. https://doi.org/10.1364/AO.576062
96. 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
97. 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
98. 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
99. Aerosol optical depth over Northeastern Brazil: A multi-platform intercomparison study G. Münchow et al. https://doi.org/10.1016/j.atmosres.2024.107864
100. 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
101. 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
102. Machine learning-driven characterization and prescription of aerosol optical properties for atmospheric models N. do Rosário et al. https://doi.org/10.5194/gmd-19-2691-2026
103. 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
104. 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
105. 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
106. 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
107. 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
108. 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
109. A Robust Atmospheric Correction Procedure for Determination of Spectral Reflectance of Terrestrial Surfaces from Satellite Spectral Measurements I. Katsev et al. https://doi.org/10.3390/rs13091831
110. 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
111. 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
112. 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
113. Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019 W. Wang et al. https://doi.org/10.1016/j.apr.2022.101579