Atmospheric Environmental Remote Sensing, Data Assimilation and Numerical Simulation
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".
Deadline for manuscript submissions: 15 August 2026 | Viewed by 12
Special Issue Editors
Interests: air pollution; aerosol–meteorology–climate interactions; air quality observation and modeling; moisture cycle
Special Issues, Collections and Topics in MDPI journals
Interests: air pollution; aerosol–meteorology interactions; air quality modeling; urban heat island
Interests: atmospheric aerosols; cloud physics; aerosol–boundary layer interactions; aerosol–cloud–radiation interactions; air pollution
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Atmospheric environmental remote sensing, data assimilation, and numerical simulation have become essential tools for understanding and predicting air quality, weather, and climate processes. The rapid development of satellite observation technologies, ground-based networks, and advanced reanalysis datasets has enabled unprecedented insight into atmospheric composition and dynamics. Meanwhile, integrating these observations into numerical models through data assimilation and model coupling has significantly enhanced our ability to reproduce real-world atmospheric states, assess emission sources, and project environmental change.
This Special Issue aims to present cutting-edge research that leverages remote sensing data, assimilation techniques, and numerical simulations to address key scientific and practical challenges in atmospheric environment studies. We invite innovative contributions in retrieval algorithms, data assimilation methods, multi-source data fusion, coupled modeling systems, and the application of machine learning techniques across these areas to advance atmospheric environment studies. Applications to air quality simulation–forecasting, aerosol–cloud–radiation interactions, atmospheric composition monitoring, and regional or global pollution assessments are also encouraged.
Suggested themes and article types for submissions:
- Satellite and ground-based remote sensing of atmospheric constituents;
- Data assimilation in chemical transport and climate models;
- Multi-source data fusion and reanalysis;
- Numerical simulation of air quality and meteorology;
- Emission estimation and inverse modeling;
- Aerosol–cloud–radiation interactions;
- Machine learning in atmospheric observation and modeling.
Prof. Dr. Bin Zhu
Dr. Hanqing Kang
Dr. Yuying Wang
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-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
- atmospheric remote sensing
- data assimilation
- numerical simulation
- air quality
- aerosol–cloud–radiation interaction
- emission estimation
- data fusion
- machine learning
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