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Remote Sensing
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Advances in Surface Deformation Monitoring Using SAR Interferometry
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Advances in Surface Deformation Monitoring Using SAR Interferometry

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 29 July 2025 | Viewed by 1930

Special Issue Editors

Dr. Yuedong Wang

E-Mail Website
Guest Editor
School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Interests: InSAR; wide-area InSAR; deformation monitoring; geohazard modeling
Dr. Lijia He

E-Mail Website
Guest Editor
Earth Observatory of Singapore, Nanyang Technological University, Singapore 639798, Singapore
Interests: seismic cycle deformation observation and modeling; InSAR geodesy; space geodetic
Dr. Honglei Yang

E-Mail Website
Guest Editor
School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Interests: InSAR; ground-based radar interferometry; geological environment monitoring; geohazard analysis
Dr. Huiqiang Wang

E-Mail
Guest Editor
College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Interests: InSAR; PolInSAR; airborne and UAV-borne InSAR

Special Issue Information

Dear Colleagues,

We are pleased to announce the upcoming Special Issue, "Advances in Surface Deformation Monitoring Using SAR Interferometry". With the abundance of spaceborne SAR satellites and data and the continuous development of InSAR time series technology, InSAR plays an increasingly important role in regional, national, and global scale deformation monitoring and modeling. InSAR has been widely used in surface deformation monitoring in multiple fields and has achieved excellent results. This Special Issue will publish the important scientific and technological achievements of scholars worldwide in InSAR surface deformation monitoring methods and applications and promote the exchange of scholars in this field.

This Special Issue aims to explore advanced InSAR technology and its innovative algorithms and applications in multi-scale surface deformation monitoring, including the new generation SAR sensor and its surface deformation monitoring effect, intelligent algorithm deformation information mining, wide-area InSAR deformation monitoring, geohazards monitoring, integrated remote sensing geological disaster analysis, and InSAR geophysical monitoring and modeling. By presenting the latest progress of InSAR technology in surface deformation monitoring, this Special Issue will provide rich literature resources for geohazard monitoring and modeling analysis of geophysical phenomena and provide a valuable achievement exchange platform for scholars in related fields.

We welcome original research papers and review articles on a variety of topics within advanced InSAR surface deformation monitoring, including but not limited to the following:

  • Advanced InSAR deformation monitoring method and application;
  • Multisensor, multitrack, and multitemporal InSAR;
  • Wide-area InSAR deformation monitoring;
  • Intelligent identification of geohazard;
  • Deformation modeling and parameter inversion.

Dr. Yuedong Wang
Dr. Lijia He
Dr. Honglei Yang
Dr. Huiqiang 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 interferometry (InSAR)
  • wide-area InSAR
  • advanced methods for deformation monitoring
  • geohazards detection and analysis
  • geodetic and geophysical modeling
  • parameter inversion
  • deformation analysis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

41 pages, 109481 KiB  
Article
Production and Analysis of a Landslide Susceptibility Map Covering Entire China
by Guo Zhang, Yutao Liu, Zhenwei Chen, Zixing Xu, Yuan Yuan, Shunyao Wang, Weiqi Lian, Hang Xu, Zan Ding and Run Wang
Remote Sens. 2025, 17(9), 1615; https://doi.org/10.3390/rs17091615 - 1 May 2025
Viewed by 349
Abstract
China, with its complex geology and diverse climate, is highly prone to landslides, endangering public safety and infrastructure. To address disaster prevention needs, this study comprehensively assesses national landslide susceptibility. We divided China into 37 geomorphic districts, diverging from traditional methods. By using [...] Read more.
China, with its complex geology and diverse climate, is highly prone to landslides, endangering public safety and infrastructure. To address disaster prevention needs, this study comprehensively assesses national landslide susceptibility. We divided China into 37 geomorphic districts, diverging from traditional methods. By using a 2018–2022 surface deformation dataset, we introduced a rarely—considered dynamic aspect for more accurate mapping of landslide—prone areas. Nine key environmental factors were carefully considered, including terrain, geology, meteorology, hydrology, seismic activities, and engineering activities. Based on these innovative methods and data, we created a 40 m—resolution landslide susceptibility map (LSM) for the whole country. Our assessment showed high accuracy, with an AUC of 0.927, precision of 0.859, recall of 0.815, F1—score of 0.828 and Matthews correlation coefficient of 0.773. Seven high—risk regions, like the Tianshan Mountains and the southern Tibetan valleys, were analyzed. The study revealed regional differences in landslide occurrences and key influencing factors. The LSM and findings enrich landslide susceptibility theory and offer a valuable resource for engineering, disaster management, and mitigation in China, helping reduce potential landslide losses. Full article
(This article belongs to the Special Issue Advances in Surface Deformation Monitoring Using SAR Interferometry)
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21 pages, 21704 KiB  
Article
An Efficient PSInSAR Method for High-Density Urban Areas Based on Regular Grid Partitioning and Connected Component Constraints
by Chunshuai Si, Jun Hu, Danni Zhou, Ruilin Chen, Xing Zhang, Hongli Huang and Jiabao Pan
Remote Sens. 2025, 17(9), 1518; https://doi.org/10.3390/rs17091518 - 25 Apr 2025
Viewed by 347
Abstract
Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), with millimeter-level accuracy and full-resolution capabilities, is essential for monitoring urban deformation. With the advancement of SAR sensors in spatial and temporal resolution and the expansion of wide-swath observation capabilities, the number of permanent scatterers (PSs) [...] Read more.
Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), with millimeter-level accuracy and full-resolution capabilities, is essential for monitoring urban deformation. With the advancement of SAR sensors in spatial and temporal resolution and the expansion of wide-swath observation capabilities, the number of permanent scatterers (PSs) in high-density urban areas has surged exponentially. To address these computational and memory challenges in high-density urban PSInSAR processing, this paper proposes an efficient method for integrating regular grid partitioning and connected component constraints. First, adaptive dynamic regular grid partitioning was employed to divide monitoring areas into sub-blocks, balancing memory usage and computational efficiency. Second, a weighted least squares adjustment model using common PS points in overlapping regions eliminated systematic inter-sub-block biases, ensuring global consistency. A graph-based connected component constraint mechanism was introduced to resolve multi-component segmentation issues within sub-blocks to preserve discontinuous PS information. Experiments on TerraSAR-X data covering Fuzhou, China (590 km2), demonstrated that the method processed 1.4 × 107 PS points under 32 GB memory constraints, where it achieved a 25-fold efficiency improvement over traditional global PSInSAR. The deformation rates and elevation residuals exhibited high consistency with conventional methods (correlation coefficient ≥ 0.98). This method effectively addresses the issues of memory overflow, connectivity loss between sub-blocks, and cumulative merging errors in large-scale PS networks. It provides an efficient solution for wide-area millimeter-scale deformation monitoring in high-density urban areas, supporting applications such as geohazard early warning and urban infrastructure safety assessment. Full article
(This article belongs to the Special Issue Advances in Surface Deformation Monitoring Using SAR Interferometry)
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Graphical abstract

18 pages, 77535 KiB  
Article
Assessing the Landslide Identification Capability of LuTan-1 in Hilly Regions: A Case Study in Longshan County, Hunan Province
by Hesheng Chen, Zuohui Qin, Bo Liu, Renwei Peng, Zhiyi Yu, Tengfei Yao, Zefa Yang, Guangcai Feng and Wenxin Wang
Remote Sens. 2025, 17(6), 960; https://doi.org/10.3390/rs17060960 - 8 Mar 2025
Viewed by 859
Abstract
China’s first L-band fully polarimetric Synthetic Aperture Radar (SAR) constellation, LuTan-1 (LT-1), was designed for terrain mapping and geohazard monitoring. This study evaluates LT-1’s capability in identifying landslides in the southern hilly regions of China, focusing on Longshan County, Hunan Province. Using both [...] Read more.
China’s first L-band fully polarimetric Synthetic Aperture Radar (SAR) constellation, LuTan-1 (LT-1), was designed for terrain mapping and geohazard monitoring. This study evaluates LT-1’s capability in identifying landslides in the southern hilly regions of China, focusing on Longshan County, Hunan Province. Using both ascending and descending orbit data from LT-1, we conducted landslide identification experiments. First, deformation was obtained using Differential Interferometric SAR (D-InSAR) technology, and the deformation rates were derived through the Stacking technique. A landslide identification method that integrates C-index, slope, and ascending/descending orbit deformation information was then applied. The identified landslides were validated against existing geohazard points and medium-to-high-risk slope and gully unit data. The experimental results indicate that LT-1-ascending orbit data identified 88 landslide areas, with 39.8% corresponding to geohazard points and 65.9% within known slope units. Descending orbit data identified 90 landslide areas, with 37.8% matching geohazard points and 61.1% within known slope units. The identification results demonstrated good consistency with existing data. Comparative analysis with Sentinel-1 data revealed that LT-1’s combined ascending and descending orbit data outperformed Sentinel-1’s single ascending orbit data. LT-1’s L-band characteristics, comprehensive ascending and descending orbit coverage, and high-precision deformation detection make it highly promising for landslide identification in the southern hilly regions. This study underscores LT-1’s robust technical support for early landslide identification, highlighting its potential to enhance geohazard monitoring and mitigate risks in challenging terrains. Full article
(This article belongs to the Special Issue Advances in Surface Deformation Monitoring Using SAR Interferometry)
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