Remote Sensing of Aerosols and Clouds: Current Status and Emerging Challenges

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Aerosols".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 790

Special Issue Editors


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Guest Editor
Earth Observation Directorate, South African National Space Agency, Pretoria 0001, South Africa
Interests: climate; emissions; remote sensing; air quality

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Guest Editor
Council for Scientific and Industrial Research, Pretoria 0001, South Africa
Interests: modeling; remote sensing; climate; air quality

Special Issue Information

Dear Colleagues,

Aerosols are fine solid or liquid particles suspended in the atmosphere, typically remaining there for days to weeks before either settling to the ground or being removed by rain or snow. They originate from both human activities, such as the burning of fossil fuels, biofuels, and vegetation, and from natural sources like desert dust, sea spray, and volcanic eruptions. Tiny aerosol particles are abundant and often consist of a mix of inorganic and organic materials, and atmospheric aerosol plumes can be seen in the form of smoke, smog, haze, and dust. Clouds play a crucial role in Earth’s energy balance by scattering and absorbing solar radiation, absorbing longwave radiation emitted by Earth’s surface and the cloud-free atmosphere, and emitting longwave radiation themselves. Clouds and atmospheric circulation are closely linked as clouds indicate the state of the circulation, a fact utilized by weather observers, sailors, and pilots, as well as in numerical weather prediction. The presence—or absence—of clouds in certain circulations is related to the need for environments with supersaturated water vapor levels.

Remote sensing data plays a crucial role in enhancing our understanding of atmospheric processes and in studying the life cycles of clouds and their interactions with aerosols and radiation. This improved understanding is vital for assessing atmospheric models. Additionally, satellite remote sensing enables the measurement of total aerosol concentration and offers valuable insights into aerosol properties such as size, light absorption characteristics, and type. It also allows for the detection, profiling, and characterization of clouds. This Special Issue invites discussions on the current state and emerging challenges in aerosol and cloud remote sensing.

Dr. Lerato Shikwambana
Dr. Nkanyiso Mbatha
Dr. Filomena Romano
Guest Editors

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Keywords

  • aerosol remote sensing
  • cloud remote sensing
  • radiative transfer
  • aerosol optical thickness
  • cloud optical thickness
  • atmospheric pollution
  • aerosol–cloud interactions
  • climate change
  • remote sensing

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Published Papers (1 paper)

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Research

16 pages, 4589 KB  
Article
Estimation of PM2.5 Concentration in Yangquan City from 2015 to 2024 Based on MODIS Image and Meteorological Data and Analysis of Spatial and Temporal Variation
by Qinfeng Yao, Jinjun Liu, Shenghua Chen, Yongxiang Ning and Sunwen Du
Atmosphere 2026, 17(3), 308; https://doi.org/10.3390/atmos17030308 - 18 Mar 2026
Viewed by 372
Abstract
This study employed Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth data meteorological data, Digital Elevation Model (DEM), Normalized Difference Vegetation Index (NDVI), and ground monitoring data for particulate matter (PM2.5) to construct a model for estimating the PM2.5 concentration in Yangquan City, Shanxi [...] Read more.
This study employed Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth data meteorological data, Digital Elevation Model (DEM), Normalized Difference Vegetation Index (NDVI), and ground monitoring data for particulate matter (PM2.5) to construct a model for estimating the PM2.5 concentration in Yangquan City, Shanxi Province, from 2015 to 2024. The spatial and temporal changes in the PM2.5 concentration were analyzed. The results revealed the following: (1) The random forest model was more accurate than the multiple linear regression model. The spring model R2 increased by 38.7%, and the Root Mean Square Error (RMSE) decreased by 92.6%. The summer model R2 increased by 65.1%, and the RMSE decreased by 92.5%. The autumn model R2 increased by 2.7%, and the RMSE decreased by 83.4%. The winter model R2 increased by 25.4%, and the RMSE decreased by 95.5%. (2) The PM2.5 concentration in Yangquan City showed an upward trend from 2015 to 2017, and then a downward trend from 2018 to 2024, with an average decrease of 18.3 μg/m3. The highest concentration of PM2.5 was 55–85 μg/m3 in winter, and the lowest concentration of PM2.5 was 25–40 μg/m3 in summer. In terms of spatial distribution, the PM2.5 concentration in Yangquan City exhibits a pattern of being lower in the northwest and higher in the southeast. The high values are primarily concentrated in the central urban areas and major industrial zones in the southeast. Full article
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