Remote Sensing in China University of Geosciences: Celebrating 75th Anniversary
A Special Issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: 31 December 2027 | Viewed by 66
Editors
Interests: remote sensing of atmosphere; radiative transfer and particle scattering; air quality; climate change
Special Issues, Collections and Topics in MDPI journals
Interests: forest; remote sensing; lidar; hyperspectral
Special Issues, Collections and Topics in MDPI journals
Interests: high-performance remote sensing data processing; time seires analysis; cloud computing in remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing information models; geohazard prediction; ecological environment monitoring; multimodal data fusion
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In celebration of the 75th anniversary of China University of Geosciences (Wuhan), we are pleased to announce the launch of a Special Issue dedicated to geoscience remote sensing. Building on the outstanding success of the 70th-anniversary Issue (https://www.mdpi.com/journal/remotesensing/special_issues/CUG_70th), this volume will be led by Professor Minghui Tao, Dr. Jia Sun, Dr. Jining Yan and Dr. Tao Chen.
CUG's remote sensing strengths cut across multiple schools and research centers, covering fundamental atmospheric processes, terrestrial ecosystems, advanced data analytics, artificial intelligence, and geohazard applications. This Special Issue invites contributions across a wide spectrum of topics, including, but not limited to, the following:
- Atmospheric remote sensing, air quality, and climate change;
- Forest ecosystem monitoring using LiDAR and hyperspectral techniques;
- High-performance remote sensing data processing and time series analysis;
- AI and big data in remote sensing;
- Geohazard early warning and risk assessment.
This Special Issue seeks to highlight the latest research advances at China University of Geosciences (Wuhan), promote interdisciplinary collaboration, and address key national priorities such as carbon neutrality, disaster mitigation, and sustainable resource management. We invite contributions from academics currently engaged in scientific research at CUG, as well as distinguished alumni and anyone participating in collaborations with CUG. Both original research papers and comprehensive literature reviews with unique scientific insights are welcome.
Prof. Dr. Minghui Tao
Dr. Jia Sun
Dr. Jining Yan
Dr. Tao Chen
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.
Keywords
- atmospheric remote sensing
- environmental remote sensing
- geological remote sensing
- hydrological remote sensing
- urban remote sensing
- multi-source data processing
- image processing
- climate change
- machine learning
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