AI-Driven Hyperspectral Remote Sensing of Atmosphere and Land

Dear Colleagues,

Hyperspectral remote sensing provides rich spectral information that is critical for understanding atmospheric and land surface processes. Hyperspectral sounders contribute significantly to atmospheric profiling, enabling precise retrieval of temperature, humidity, and trace gases, while hyperspectral imagers are widely used for land surface characterization, including vegetation monitoring, soil analysis, and urban mapping. However, the high dimensionality and complexity of hyperspectral data presents challenges for efficient data processing, noise reduction, and feature extraction.

The emergence of artificial intelligence (AI), particularly deep learning, has revolutionized the analysis of hyperspectral remote sensing data. Deep learning models can efficiently extract complex features, improve retrieval accuracy, and enhance classification and predictive capabilities. By integrating AI-driven techniques with hyperspectral observations, researchers can unlock new possibilities for improving atmospheric and land surface monitoring.

This Special Issue aims to bring together state-of-the-art research on the application of AI, particularly deep learning, in processing and analyzing hyperspectral data for both atmospheric and land studies. It seeks to provide a platform for exploring novel methodologies, theoretical advancements, and practical applications of AI in hyperspectral remote sensing. The scope aligns with the journal’s focus on remote sensing technologies, data analysis, and geospatial applications, emphasizing innovative AI-driven solutions.

We invite original research articles, review papers, and case studies covering, but not limited to, the following topics:

• AI-based retrieval of atmospheric parameters (e.g., temperature, humidity, gases) from hyperspectral sounders;
• Deep learning methods for classification and characterization of cloud/aerosol and atmospheric corrections;
• Hyperspectral land surface applications, including vegetation analysis, soil moisture estimation, and environmental monitoring;
• AI-based algorithms using multiband and hyperspectral imagers;
• Data fusion techniques combining hyperspectral sounder and imager data with other remote sensing modalities;
• Domain adaptation, self-supervised learning, and uncertainty quantification in hyperspectral AI applications.

Dr. Chunqiang Wu
Dr. Yong Zhang
Dr. Xingfeng Chen
Dr. Chunshan Li
Dr. Fu 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.