Novel Remote Sensing and Machine Learning Approaches for Aerosol and Cloud Physics Retrieval
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Atmospheric Remote Sensing".
Deadline for manuscript submissions: 16 October 2026 | Viewed by 728
Editors
Interests: remote sensing; deep learning; cloud; aerosol; atmospheric pollution
Interests: optical and laser remote sensing; remote sensing of atmospheric environment
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; deep learning; Lidar; point cloud
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue explores aerosol and cloud remote-sensing methods that couple machine learning with physical constraints. Such integration addresses the trade-off between computational cost and retrieval accuracy, pointing toward more realistic atmospheric characterization. The real atmosphere is characterized by vertically stratified aerosols and clouds, significant 3D radiative transfer effects and pervasive sub-pixel heterogeneity. Researchers have made headway in resolving aerosol and cloud vertical structure and microphysics, with direct implications for climate processes and air quality management. Still, much remains to be done in fusing satellite sensors, capturing extreme events and grounding results in field observations and radiative transfer models.
We seek to highlight how combining machine learning with physical retrieval can overcome existing observational and modeling challenges. The contributions to this issue are expected to enhance the accuracy and reliability of aerosol and cloud retrieval.
We invite submissions of research articles, review articles and application-oriented papers on the following topics:
- Development of hybrid retrieval algorithms that combine machine learning with physical models to enhance accuracy, interpretability and uncertainty quantification.
- Use of machine learning to parameterize complex processes in aerosol and cloud physics that are not fully captured by current models.
- Novel methods for retrieving the three-dimensional distribution of aerosols and clouds using advanced remote-sensing techniques.
- Exploration of innovative observational techniques and new equipment for aerosol and cloud measurements.
- Assimilation of long-term satellite observations into models, focusing on improving the accuracy and consistency of long-term historical data records.
- Multiple source fusion techniques (satellite, ground-based and airborne), leveraging active-passive synergy and geostationary–high orbit combination.
- Real-time processing and edge computing architectures for operational aerosol and cloud monitoring systems.
Dr. Jie Yang
Prof. Dr. Wei Wang
Dr. Jian Yang
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized 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
- remote sensing
- machine learning
- hybrid algorithms
- physical constraints
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