293 citations as recorded by crossref.

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20. Pre-seismic anomalies from optical satellite observations: a review Z. Jiao et al. https://doi.org/10.5194/nhess-18-1013-2018
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26. 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
27. 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
28. 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
29. 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
30. 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
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33. 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
34. 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
35. 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
36. The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future L. Remer et al. https://doi.org/10.3390/rs12182900
37. 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
38. 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
39. Evaluation and comparison of MODIS and MISR aerosol products with ground-based monitoring stations in the Amazon Basin V. Schumacher et al. https://doi.org/10.1016/j.atmosenv.2024.120597
40. 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
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42. The influence and contribution of fine mode particles to aerosol optical properties during haze events at the foothills of Himalaya-Karakorum region S. Mohyuddin et al. https://doi.org/10.1016/j.atmosenv.2022.119388
43. Atmospheric Correction of Multispectral VNIR Remote Sensing Data: Algorithm and Inter‐sensor Comparison of Aerosol and Surface Reflectance Products M. Mishra et al. https://doi.org/10.1029/2019EA000710
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47. Himawari-8/AHI and MODIS Aerosol Optical Depths in China: Evaluation and Comparison T. Jiang et al. https://doi.org/10.3390/rs11091011
48. Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia N. Zaman et al. https://doi.org/10.3390/app11167326
49. Retrieval of High-Resolution Aerosol Optical Depth for Urban Air Pollution Monitoring R. Bai et al. https://doi.org/10.3390/atmos13050756
50. Spatial Variation and Relation of Aerosol Optical Depth with LULC and Spectral Indices V. Sharma et al. https://doi.org/10.3390/atmos13121992
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56. 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
57. 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
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67. A seasonal analysis of aerosol-cloud-radiation interaction over Indian region during 2000–2017 S. Kant et al. https://doi.org/10.1016/j.atmosenv.2018.12.044
68. 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
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116. Characteristics of PM2.5 concentrations in a tropical dry deciduous forest of Thailand: Forest fires and urban air pollution seriously affect air quality in forest ecosystems M. Xu et al. https://doi.org/10.1016/j.atmosenv.2025.121672
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