Advances in SAR Remote Sensing for Landslide Monitoring and Hazard Assessment
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".
Deadline for manuscript submissions: 28 November 2025 | Viewed by 84
Special Issue Editors
Interests: InSAR; multi-dimensional deformation; landslide kinematics
Interests: InSAR; PolInSAR; 3-D deformation mapping; geohazard monitoring and interpreting; earthquake; landslide
Special Issues, Collections and Topics in MDPI journals
Interests: SAR InSAR volcano landslide and subsidence natural hazards
Interests: InSAR; geological disaster monitoring; deep learning
Special Issue Information
Dear Colleagues,
Landslides are widely distributed across the globe and pose serious threats to infrastructure and human safety, especially under the increasing influence of extreme climate events. In recent years, Synthetic Aperture Radar (SAR) remote sensing has shown remarkable progress in landslide identification, long-term deformation monitoring, and hazard risk assessment, thanks to its all-weather, day-and-night, high-precision, and wide-area observation capabilities, along with the growing availability of SAR data. SAR has become an indispensable tool in landslide early warning, risk analysis, and the study of dynamic slope processes.
This Special Issue aims to bring together cutting-edge research and innovative applications of SAR technologies in landslide monitoring and hazard assessment. We welcome original research articles focusing on novel algorithms, modeling approaches, multi-sensor data fusion, and representative case studies. Contributions addressing artificial intelligence-assisted analysis, regional to global-scale monitoring, and near-real-time applications are especially encouraged.
Topics of interest include, but are not limited to, the following:
- InSAR-based techniques for landslide detection and deformation monitoring;
- Analysis of landslide dynamics and disaster mechanisms using multi-temporal SAR data;
- Integration of SAR with geological, hydrological, and meteorological data;
- Fusion of SAR with optical imagery, LiDAR, and GNSS datasets;
- Real-time or near-real-time SAR techniques for landslide monitoring;
- Deep learning applications in SAR-based landslide remote sensing;
- Case studies on operational landslide monitoring and early warning systems.
Dr. Wanji Zheng
Prof. Dr. Jun Hu
Prof. Dr. Zhong Lu
Guest Editors
Dr. Jiehua Cai
Guest Editor Assistant
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
- Synthetic Aperture Radar (SAR)
- Interferometric Synthetic Aperture Radar (InSAR)
- landslide monitoring
- ground deformation
- multi-temporal analysis
- data fusion
- deep learning
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