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Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1113

Special Issue Editors

Dr. Yuying Wang

E-Mail Website
Guest Editor
Key Laboratory for Aerosol–Cloud Precipitation of China Meteorological Administration/Special Test Field of National Integrated Meteorological Observation, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: atmospheric aerosols; cloud physics; aerosol-boundary layer interactions; aerosol-cloud interactions; air pollution
Dr. Haofei Wang

E-Mail Website
Guest Editor
National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Interests: lidar remote sensing; polarimetric remote sensing; planetary boundary layer; atmospheric aerosols; cloud physics
Dr. Chunsong Lu

E-Mail Website
Guest Editor
Key Laboratory for Aerosol–Cloud Precipitation of China Meteorological Administration/Special Test Field of National Integrated Meteorological Observation, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: cloud and precipitation physics; aerosol–cloud interaction; cloud physical parameterization; weather modification
Dr. Xiquan Dong

E-Mail Website
Guest Editor
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ 85721, USA
Interests: atmospheric radiation; clouds; aerosols; precipitation; ground and satellite based sensing techniques; cloud physics and radiation

Special Issue Information

Dear Colleagues,

This Special Issue, “Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors”, will focus on advancing our understanding of aerosol–cloud interactions using remote sensing technologies. Aerosols and clouds play crucial roles in Earth’s climate system, influencing radiative forcing, precipitation patterns, and weather dynamics. Accurately measuring and characterizing these interactions is essential in improving climate models and weather forecasts.

For this Special Issue, we invite contributions that explore the use of optical and multispectral sensors, such as nephelometers, lidar, radiometers, and satellite-based imaging systems, to estimate the physical and chemical properties of aerosols and clouds. We encourage studies that develop new retrieval algorithms to improve the accuracy of aerosol optical depth (AOD), cloud microphysical properties, cloud condensation nuclei (CCN) concentrations, and so on. The integration of machine learning techniques into sensor data is also a key theme, with the aim of enhancing the interpretation of complex aerosol–cloud systems.

Contributors are encouraged to present case studies that demonstrate the application of remote sensing in different climatic regions, with a focus on high-resolution, multi-source data fusion. Studies that address global aerosol–cloud interaction sensitivity and provide observational evidence from diverse environments are particularly welcome.

The Special Issue seeks to bridge the gap between theoretical modeling and practical observations, providing a comprehensive platform for understanding how aerosols and clouds influence atmospheric processes, including their role in climate change and air quality management.

Dr. Yuying Wang
Dr. Haofei Wang
Dr. Chunsong Lu
Dr. Xiquan Dong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • aerosol–cloud interactions
  • remote sensing
  • multispectral sensors
  • aerosol optical depth (AOD)
  • cloud microphysical properties
  • cloud condensation nuclei (CCN)
  • lidar
  • radiometers
  • satellite
  • machine learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

23 pages, 7707 KiB  
Article
Unraveling Aerosol and Low-Level Cloud Interactions Under Multi-Factor Constraints at the Semi-Arid Climate and Environment Observatory of Lanzhou University
by Qinghao Li, Jinming Ge, Yize Li, Qingyu Mu, Nan Peng, Jing Su, Bo Wang, Chi Zhang and Bochun Liu
Remote Sens. 2025, 17(9), 1533; https://doi.org/10.3390/rs17091533 - 25 Apr 2025
Viewed by 183
Abstract
The response of low-level cloud properties to aerosol loading remains ambiguous, particularly due to the confounding influence of meteorological factors and water vapor availability. We utilize long-term data from Ka-band Zenith Radar, Clouds and the Earth’s Radiant Energy System, Modern-Era Retrospective analysis for [...] Read more.
The response of low-level cloud properties to aerosol loading remains ambiguous, particularly due to the confounding influence of meteorological factors and water vapor availability. We utilize long-term data from Ka-band Zenith Radar, Clouds and the Earth’s Radiant Energy System, Modern-Era Retrospective analysis for Research and Applications Version 2, and European Centre for Medium-Range Weather Forecasts Reanalysis v5 to evaluate aerosol’s effects on low-level clouds under the constrains of meteorological conditions and liquid water path (LWP) over the Semi-Arid Climate and Environment Observatory of Lanzhou University during 2014–2019. To better constrain meteorological variability, we apply Principal Component Analysis to derive the first principal component (PC1), which strongly correlates with cloud properties, thereby enabling more accurate assessment of aerosol–cloud interaction (ACI) under constrained meteorological conditions delineated by PC1. Analysis suggests that under favorable meteorological conditions for low-level cloud formation (low PC1) and moderate LWP levels (25–150 g/m2), ACI is characterized by a significantly negative ACI index, with the cloud effective radius (CER) increasing in response to rising aerosol concentrations. When constrained by both PC1 and LWP, the relationship between CER and the aerosol optical depth shows a distinct bifurcation into positive and negative correlations. Different aerosol types show contrasting effects: dust aerosols increase CER under favorable meteorological conditions, whereas sulfate, organic carbon, and black carbon aerosols consistently decrease it, even under high-LWP conditions. Full article
(This article belongs to the Special Issue Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors)
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21 pages, 8807 KiB  
Article
Retrieval of Cloud Optical Thickness During Nighttime from FY-4B AGRI Using a Convolutional Neural Network
by Daozhen Xia, Dongzhi Zhao, Kailin Li, Zhongfeng Qiu, Jiayu Liu, Jiaye Luan, Si Chen, Biao Song, Yu Wang and Jingyuan Yang
Remote Sens. 2025, 17(5), 737; https://doi.org/10.3390/rs17050737 - 20 Feb 2025
Viewed by 413
Abstract
Cloud optical thickness (COT) stands as a critical parameter governing the radiative properties of clouds. This study develops a convolutional neural network (CNN) model to retrieve the COT of single-layer non-precipitating clouds during nighttime using FY-4B satellite data. The model integrates multi-channel brightness, [...] Read more.
Cloud optical thickness (COT) stands as a critical parameter governing the radiative properties of clouds. This study develops a convolutional neural network (CNN) model to retrieve the COT of single-layer non-precipitating clouds during nighttime using FY-4B satellite data. The model integrates multi-channel brightness, temperature, and geographic and temporal features, without relying on auxiliary meteorological data, using the multi-point averaged 532 nm COT from CALIPSO as ground truth for training. Performance evaluation demonstrates robust retrieval accuracy, achieving coefficients of determination (R2) of 0.88 and 0.73 for satellite zenith angles (SAZAs) < 70° and >70°, respectively. Key advancements include the incorporation of temporal features, the Squeeze-and-Excitation (SE) module, and a multi-point averaging technique, each validated through ablation experiments to reduce bias and enhance stability. Meanwhile, a model error analysis experiment was conducted that further clarified the performance boundaries of the model. These findings underscore the model’s capability to retrieve the COT of single-layer non-precipitating clouds during nighttime with high precision. Full article
(This article belongs to the Special Issue Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors)
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