Remote Sensing and Machine Learning Applications in Atmospheric Physics, Weather, and Air Quality
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: 31 December 2024 | Viewed by 669
Special Issue Editor
2. NASA Ames Research Area, BAERI, Moffett Field, CA, USA
Interests: remote sensing; aerosol and gas measurements; machine learning; clouds; airborne measurements; air-quality
Special Issue Information
Dear Colleagues,
AI and machine-learning applications have been the fastest-growing field in the past decade, contributing to many essential fields, including self-driving cars, consumer science, media, smart systems, and in various medical fields. In the Earth Sciences as well, machine learning is gaining popularity in land-use/land-cover detection and trends. However, only recently have machine-learning applications been applied to the field of atmospheric measurements and processes. While still challenging, this field has a wealth of data from satellites, airborne observations, or modeling with a very dynamic nature that can be tamed to produce new insights with the newest AI approaches and computational power available today.
This Special Issue seeks papers dedicated to remote sensing measurements, from ground-based, airborne, or space-borne platforms, in various spatial and temporal scales, and the utilization of established and new machine-learning and AI approaches to extract new and better observables, better understanding of dynamical processes, and improved predictions of air-quality, weather, and climate models. Specific topics include but are not limited to: (1) cloud and aerosol plume detection and identification, (2) prediction of fire smoke spread, (3) improved prediction of precipitation and cloud cover, (4) improved understanding of atmospheric dynamical processes, (5) implementing machine learning and observations to improve climate model parameterization schemes, (6) air quality and extreme pollution event identification and early warning, and (7) improved sets of satellite-based products, on high spatial and/or temporal resolutions from federal and commercial platforms.
Dr. Michal Segal-Rosenheimer
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
- Remote sensing
- Machine learning
- Air-quality
- Aerosols
- Weather
- Cloud dynamics
- Fires
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