AI-Driven Hyperspectral Remote Sensing of Atmosphere and Land
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 91
Special Issue Editors
2. State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, China
Interests: infrared interferometer data validation and calibration
Interests: atmospheric and land remote sensing; remote sensing calibration and validation; environmental science
Special Issues, Collections and Topics in MDPI journals
Interests: quantitatively intelligent remote sensing and atmospheric correction of high resolution imagery
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; multimodal remote sensing; big data computing
2. State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, China
Interests: satellite data assimilation; AI for atmospheric application; aerosol–cloud interaction; air pollution
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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 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
- deep learning for remote sensing
- hyperspectral remote sensing
- retrieval of atmospheric parameter
- land surface monitoring hyperspectral sounder and imager applications
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue policies can be found here.
