Methods and Impacts of Remote Sensing Aerosol–Cloud–Climate Interactions
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Air, Climate Change and Sustainability".
Deadline for manuscript submissions: 29 January 2027 | Viewed by 177
Editors
Interests: satellite remote sensing; urban carbon emission assessment; greenhouse gas detection; aerosol–cloud interaction
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing of aerosol and clouds; atmospheric radiative transfer; AI for earth science
Interests: aerosol–cloud–precipitation interactions; satellite and ground remote sensing; machine learning applications; assessment of extreme climate disasters
Special Issue Information
Dear Colleagues,
Advancing our understanding of aerosol–cloud–climate interactions critically hinges on innovations in observational and analytical methodologies. This Special Issue will focus on recent methodological developments in remote sensing—both satellite- and ground-based—that enhance the characterization, quantification, and prediction of these complex atmospheric processes. We invite original research and review articles that present novel retrieval algorithms, multi-source data fusion frameworks, machine learning applications, data assimilation techniques, and robust validation strategies for aerosol and cloud properties. Contributions that demonstrate how these methods improve the representation of aerosol–cloud interactions in models, or that link them to changes in extreme weather events, precipitation patterns, or radiative effects, are particularly encouraged. By highlighting cutting-edge methodologies and their scientific or societal impacts, this Special Issue aims to strengthen the technical foundations of climate prediction, environmental risk assessment, and disaster resilience planning.
Dr. Hongrong Shi
Dr. Qixiang Chen
Dr. Xinlei Han
Guest Editors
Manuscript Submission Information
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Keywords
- aerosols
- clouds
- satellite remote sensing
- ground-based remote sensing
- retrieval algorithms
- data fusion
- machine learning
- data validation
- extreme climate
- aerosol–cloud–climate interactions
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