Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements (Second Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 10
Special Issue Editor
Interests: cloud and aerosol remote sensing; lidar remote sensing; radiative transfer
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Due to the overwhelming support for and interest in the previous Special Issue, we are introducing a second edition on “Recent Developments in Remote Sensing Instruments, Technologies, and Results for Aerosol and Cloud Measurements”. We would like to thank all the authors and co-authors who contributed to the success of the first edition of this Special Issue.
Clouds are the primary modifier of the Earth’s surface temperature. Aerosols, especially dense aerosol emissions from fires, volcanic eruptions, and dust storms, also provide a modulating effect on the Earth’s temperature. Acting as cloud condensation nuclei, aerosols provide sources for cloud formation, leading to complex interactions between clouds and aerosols that are still poorly understood. In addition to radiative impacts, aerosols impact air quality, especially in the planetary boundary layer (PBL). The remote sensing of clouds and aerosols, both active and passive, provides a means to study clouds, aerosols, and their interactions on both local and global scales.
The objective of this Special Issue is to highlight emerging concepts, new instruments and technologies, and scientific results related to remotely sensed measurement of clouds and aerosols in the Earth’s atmosphere. The Special Issue will highlight the following topics:
- Emerging concepts that can provide improved measurements and understanding of cloud and aerosol processes in the Earth’s atmosphere;
- Recent sensor and technology developments that enable new or enhanced measurements and understanding of cloud and aerosol properties including distributions, radiative properties, and interactions;
- Original scientific results from the analysis of data, with emphasis on (1) the diurnal variability of clouds and aerosols, (2) the application of advanced machine learning techniques, and (3) the synergy of active and passive remote sensing techniques.
Prof. Dr. Matthew McGill
Guest Editor
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
- clouds
- cloud radiative effects
- aerosols
- aerosol radiative effects
- cloud–aerosol interactions
- aerosol transport
- diurnal variability
- remote sensing
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