The Applications of Remote Sensing, Machine Learning, and Deep Learning in Atmospheric Radiative Transfer

Dear Colleagues,

Atmospheric radiative transfer (RT) plays a crucial role in climate change, weather forecasting, and environmental monitoring. Accurate simulation of the RT process is crucial for interpreting satellite observations, assessing greenhouse gas distribution, predicting radiative forcing, and monitoring changes in aerosols and clouds. Although considerable progress has been made in the development of RT theory and model construction, the latest advancements in remote sensing detection, machine learning (ML), and deep learning (DL) in recent years have provided new possibilities for breakthroughs in this field. This field is of great significance for enhancing climate models, optimizing satellite inversion, and supporting the formulation of environmental protection policies.

Remote sensing can provide high-resolution atmospheric data, while ML and DL can contribute to the construction of new RT models to improve the speed and accuracy of radiation simulation and help overcome challenges such as nonlinear interactions and sparse measurements. By combining cutting-edge computational methods with RT theory, new insights into the Earth's energy balance and atmospheric processes can be gained.

This Special Issue aims to study the applications of remote sensing and ML/DL in the field of atmospheric radiation. Submissions can cover anything related to the theoretical research of atmospheric radiation transfer, the construction of radiation models combined with ML/DL, and the remote sensing application of atmospheric radiation. Therefore, we welcome contributions exploring the application of ML/DL in the parameterization of radiation transfer models, the integration of radiation models and data-driven models, and the applications of remote sensing in the field of atmospheric radiation. Topics of interest include, but are not limited to, the following:

• Application of ML/DL in radiative transfer algorithms or models;
• Application of ML/DL in radiation process simulation;
• Role of remote sensing technology in the research of atmospheric radiation transfer;
• Cof multi-source radiation remote sensing data;
• Integration of radiation models and data-driven models;
• Observation of radiation physical quantities.

Research articles, review articles, and short communications are welcome.

Dr. Kun Wu
Dr. Panagiotis Kosmopoulos
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.