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Remote Sensing
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Role of SAR/InSAR Techniques in Investigating Ground Deformation
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Role of SAR/InSAR Techniques in Investigating Ground Deformation

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: 30 April 2026 | Viewed by 1051

Special Issue Editors

Dr. Xue Chen

E-Mail Website
Guest Editor
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
Interests: SAR data; deformation; InSAR/GNSS; time series; seismic dynamics
Dr. Teng Wang

E-Mail Website
Guest Editor
School of Earth and Space Sciences, Peking University, Beijing 100871, China
Interests: InSAR; radar imaging; geodesy; natural hazards
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The study of ground deformation caused by natural and anthropogenic processes has always been a major focus of geodetic and geological research communities and is essential for understanding geodynamics and mitigating disaster. Synthetic Aperture Radar (SAR) and Interferometric Synthetic Aperture Radar (InSAR) techniques have emerged as powerful tools in this field, enabling the monitoring of the physical properties and processes of surface deformation with unprecedented accuracy at local and global scales with high resolution, a wide coverage area and all-weather capabilities.

We are pleased to announce the launch of a new Special Issue of Remote Sensing that aims to collect the latest research on SAR/InSAR techniques to monitor and understand ground deformation. Research topics include, but are not limited to, the application of optical, SAR and InSAR to study various geological processes including tectonic activity, volcanic eruptions, landslides and anthropogenic impacts such as mining and groundwater extraction.

We invite you to submit articles on your recent research, including, but not limited to, the following topics:

Integrated monitoring systems for measuring ground deformation (land subsidence, uplift and seasonal movement);
SAR/InSAR applications for monitoring surface deformation associated with earthquakes, volcanic eruptions, glaciers and landslides;

The integration of SAR/InSAR data with other geophysical and geological datasets for understanding deformation assessment;

The development of new algorithms and methods for processing and interpreting SAR/InSAR data, including data fusion and machine learning techniques;

Earthquake rupture mechanisms (slip rate, fault length, fault spacing, etc.).

Dr. Xue Chen
Dr. Teng 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

  • SAR/InSAR
  • ground deformation
  • tectonic activities
  • seismic activities
  • transient slow-slip processes
  • geological hazards
  • optical imagery
  • data fusion and machine learning

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Published Papers (2 papers)

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Research

21 pages, 12581 KB  
Article
An Efficient RMA with Chunked Nonlinear Normalized Weights and SNR-Based Multichannel Fusion for MIMO-SAR Imaging
by Jingjing Wang, Hao Chen, Haowei Duan, Rongbo Sun, Kehui Yang, Jing Fang, Huaqiang Xu and Pengbo Song
Remote Sens. 2025, 17(18), 3232; https://doi.org/10.3390/rs17183232 - 18 Sep 2025
Viewed by 281
Abstract
Millimeter-wave multiple-input multiple-output synthetic aperture radar (MIMO-SAR) has been widely used in many scenarios such as geological exploration, post-disaster rescue, and security inspection. When faced with large complex scenes, the signal suffers from distortion problems due to amplitude-phase nonlinear aberrations, resulting in undesired [...] Read more.
Millimeter-wave multiple-input multiple-output synthetic aperture radar (MIMO-SAR) has been widely used in many scenarios such as geological exploration, post-disaster rescue, and security inspection. When faced with large complex scenes, the signal suffers from distortion problems due to amplitude-phase nonlinear aberrations, resulting in undesired artifacts. Many previous studies eliminate artifacts but result in missing target structures. In this paper, we propose to use chunked nonlinear normalized weights in conjunction with signal-to-noise ratio-based (SNR-based) multichannel fusion to address the above-mentioned problems. The chunked nonlinear normalized weights make use of the scene’s characteristics to separately perform the optimization of different regions of the scene. This approach significantly mitigates the effects of amplitude-phase distortion on signal quality, thereby facilitating the effective suppression of noise and artifacts. Applying SNR-based multichannel fusion solves the problem of missing target structures caused by the chunked weights. With the proposed techniques, we can effectively suppress artifacts and noise while maintaining the target structures to enhance the robustness of system. Based on practical experiments, the proposed techniques achieve the image entropy (IE) value, which reduces by approximately 1, and the image contrast (IC) value is increased by approximately 2~4. Furthermore, the computational time is only about 1.3 times that needed by the latest reported algorithm. Consequently, imaging resolution and system robustness are improved by implementing these techniques. Full article
(This article belongs to the Special Issue Role of SAR/InSAR Techniques in Investigating Ground Deformation)
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30 pages, 26397 KB  
Article
Dynamic Landslide Susceptibility Assessment in the Yalong River Alpine Gorge Region Integrating InSAR-Derived Deformation Velocity
by Zhoujiang Li, Jianming Xiang, Guanchen Zhuo, Hongyuan Zhang, Keren Dai and Xianlin Shi
Remote Sens. 2025, 17(18), 3210; https://doi.org/10.3390/rs17183210 - 17 Sep 2025
Viewed by 354
Abstract
Dynamic susceptibility assessment is essential for mitigating evolving landslide risks in alpine gorge regions. To address the static limitations and unit mismatch issues in conventional landslide susceptibility assessments in alpine gorge regions, this study proposes a dynamic framework integrating time-series InSAR-derived deformation. Applied [...] Read more.
Dynamic susceptibility assessment is essential for mitigating evolving landslide risks in alpine gorge regions. To address the static limitations and unit mismatch issues in conventional landslide susceptibility assessments in alpine gorge regions, this study proposes a dynamic framework integrating time-series InSAR-derived deformation. Applied to the Xinlong–Kangding section of the Yalong River, annual surface deformation velocities were retrieved using SBAS-InSAR with Sentinel-1 data, identifying 24 active landslide zones (>25 mm/a). The Geodetector model quantified the spatial influence of 18 conditioning factors, highlighting deformation velocity as the second most significant (q = 0.21), following soil type. Incorporating historical landslide data and InSAR deformation zones, slope unit delineation was optimized to construct a refined sample dataset. A Random Forest model was then used to assess the contribution of deformation factors. Results show that integrating InSAR data substantially improved model performance: “Very High” risk landslides increased from 67.21% to 87.01%, the AUC score improved from 0.9530 to 0.9798, and the Kappa coefficient increased from 0.7316 to 0.8870. These results demonstrate the value of InSAR-based dynamic monitoring in enhancing landslide susceptibility mapping, particularly for spatial clustering, classification precision, and model robustness. This approach offers a more efficient dynamic evaluation pathway for dynamic assessment and early warning of landslide hazards in mountainous regions. Full article
(This article belongs to the Special Issue Role of SAR/InSAR Techniques in Investigating Ground Deformation)
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