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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".
Deadline for manuscript submissions: 31 October 2023 | Viewed by 5527
Special Issue Editors
Interests: air pollution; chemistry transport model; data assimilation; machine learning
Interests: air quality; satellite remote sensing; atmospheric chemistry; data science and machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: atmospheric composition; chemistry-transport model; data assimilation
Special Issue Information
Dear Colleagues,
Remotely sensed measurements provided by satellite instruments have been widely used in the field of atmospheric environment science and have led to dramatic improvements in our understanding of atmospheric pollutants. Chemistry transport models are powerful tools that are used to understand atmospheric pollutant sources and atmospheric fate. Data assimilation techniques, integrating models and observations, allow us to constrain the sources and sinks of atmospheric pollutants and provide better forecasts of air quality evolution. Recent advancements in data-driven machine learning techniques have provided new opportunities for the integration and extension of atmospheric observations, with the rapid rise in applications in atmospheric environment studies.
This Special Issue proposes to document recent advancements in the applications of satellite observations to monitor air pollution, methods to optimally combine satellite observations and chemical transport models, as well as the developments of inverse analyses, data assimilations and machine learning techniques.
Potential topics for this Special Issue include but are not limited to the following:
- Monitoring and analyses of air pollutants using satellite observations.
- Interpretation of atmospheric pollutants using satellite observations and chemistry transport models.
- Global and regional data assimilation of satellite observations of atmospheric composition.
- Inverse modeling to optimize fluxes by assimilating satellite observations.
- Applications of artificial intelligence and machine learning algorithms to extend and enhance satellite observations.
Dr. Zhe Jiang
Dr. Chi Li
Dr. Benjamin Gaubert
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
- satellite remote sensing
- air pollution
- chemistry transport models
- data assimilation
- machine learning
- emissions