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Advances in SAR Remote Sensing for Landslide Monitoring and Hazard Assessment

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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 June 2026 | Viewed by 5295

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Special Issue Editors

Dr. Wanji Zheng

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Guest Editor

Department of Geography, The University of Hong Kong, Hong Kong, China
Interests: InSAR; multi-dimensional deformation; landslide kinematics

Prof. Dr. Jun Hu

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Guest Editor

School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: InSAR; PolInSAR; 3-D deformation mapping; geohazard monitoring and interpreting; earthquake; landslide
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Prof. Dr. Zhong Lu

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Guest Editor

School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
Interests: SAR InSAR volcano landslide and subsidence natural hazards
Special Issues, Collections and Topics in MDPI journals

Dr. Jiehua Cai

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Guest Editor Assistant

School of Aeronautic Engineering, Changsha University of Science and Technology, Changsha, China
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

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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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Published Papers (5 papers)

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Research

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26 pages, 6698 KB

Open AccessArticle

A Novel Decomposition-Prediction Framework for Predicting InSAR-Derived Ground Displacement: A Case Study of the XMLC Landslide in China

by Mimi Peng, Jing Xue, Zhuge Xia, Jiantao Du and Yinghui Quan

Remote Sens. 2026, 18(3), 425; https://doi.org/10.3390/rs18030425 - 28 Jan 2026

Viewed by 385

Abstract

Interferometric Synthetic Aperture Radar (InSAR) is an advanced imaging geodesy technique for detecting and characterizing surface deformation with high spatial resolution and broad spatial coverage. However, as an inherently post-event observation method, InSAR suffers from limited capability for near-real-time and short-term updates of deformation time series. In this paper, we proposed a data-driven adaptive framework for deformation prediction based on a hybrid deep learning method to accurately predict the InSAR-derived deformation time series and take the Xi’erguazi−Mawo landslide complex (XMLC) as a case study. The InSAR-derived time series was initially decomposed into trend and periodic components with a two-step decomposition process, which were thereafter modeled separately to enhance the characterization of motion kinematics and prediction accuracy. After retrieving the observations from the multi-temporal InSAR method, two-step signal decomposition was then performed using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD). The decomposed trend and periodic components were further evaluated using statistical hypothesis testing to verify their significance and reliability. Compared with the single-decomposition model, the further decomposition leads to an overall improvement in prediction accuracy, i.e., the Mean Absolute Errors (MAEs) and the Root Mean Square Errors (RMSEs) are reduced by 40–49% and 36–42%, respectively. Subsequently, the Radial Basis Function (RBF) neural network and the proposed CNN-BiLSTM-SelfAttention (CBS) models were constructed to predict the trend and periodic variations, respectively. The CNN and self-attention help to extract local features in time series and strengthen the ability to capture global dependencies and key fluctuation patterns. Compared with the single network model in prediction, the MAEs and RMSEs are reduced by 22–57% and 4–33%, respectively. Finally, the two predicted components were integrated to generate the fused deformation prediction results. Ablation experiments and comparative experiments show that the proposed method has superior ability. Through rapid and accurate prediction of InSAR-derived deformation time series, this research could contribute to the early-warning systems of slope instabilities. Full article

(This article belongs to the Special Issue Advances in SAR Remote Sensing for Landslide Monitoring and Hazard Assessment)

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23 pages, 40663 KB

Open AccessArticle

Time Series Analysis of Fucheng-1 Interferometric SAR for Potential Landslide Monitoring and Synergistic Evaluation with Sentinel-1 and ALOS-2

by Guangmin Tang, Keren Dai, Feng Yang, Weijia Ren, Yakun Han, Chenwen Guo, Tianxiang Liu, Shumin Feng, Chen Liu, Hao Wang, Chenwei Zhang and Rui Zhang

Remote Sens. 2026, 18(2), 304; https://doi.org/10.3390/rs18020304 - 16 Jan 2026

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Abstract

Fucheng-1 is China’s first commercial synthetic aperture radar (SAR) satellite equipped with interferometric capabilities. Since its launch in 2023, it has demonstrated strong potential across a range of application domains. However, a comprehensive and systematic evaluation of its overall performance, including its time-series monitoring capability, is still lacking. This study applies the Small Baseline Subset (SBAS-InSAR) method to conduct the first systematic processing and evaluation of 22 Fucheng-1 images acquired between 2023 and 2024. A total of 45 potential landslides were identified and subsequently validated through field investigations and UAV-based LiDAR data. Comparative analysis with Sentinel-1 and ALOS-2 indicates that Fucheng-1 demonstrates superior performance in small-scale deformation identification, temporal-variation characterization, and maintaining a high density of coherent pixels. Specifically, in the time-series InSAR-based potential landslide identification, Fucheng-1 identified 13 small-scale potential landslides, whereas Sentinel-1 identified none; the number of identifications is approximately 2.17 times that of ALOS-2. For time-series subsidence monitoring, the deformation magnitudes retrieved from Fucheng-1 are generally larger than those from Sentinel-1, mainly attributable to finer spatial sampling enabled by its higher spatial resolution and a higher maximum detectable deformation gradient. Moreover, as landslide size decreases, the advantages of Fucheng-1 in deformation identification and subsidence estimation become increasingly evident. Interferometric results further show that the number of high-coherence pixels for Fucheng-1 is 7–8 times that of co-temporal Sentinel-1 and 1.1–1.4 times that of ALOS-2, providing more high-quality observations for time-series inversion and thereby supporting a more detailed and spatially continuous reconstruction of deformation fields. Meanwhile, the orbital stability of Fucheng-1 is comparable to that of Sentinel-1, and its maximum detectable deformation gradient in mountainous terrain reaches twice that of Sentinel-1. Overall, this study provides the first systematic validation of the time-series InSAR capability of Fucheng-1 under complex terrain conditions, offering essential support and a solid foundation for the operational deployment of InSAR technologies based on China’s domestic SAR satellite constellation. Full article

(This article belongs to the Special Issue Advances in SAR Remote Sensing for Landslide Monitoring and Hazard Assessment)

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36 pages, 27661 KB

Open AccessArticle

Analysis of Land Subsidence During Rapid Urbanization in Chongqing, China: Impacts of Metro Construction, Groundwater Dynamics, and Natural–Anthropogenic Environment Interactions

by Yuanfeng Li, Yuan Yao, Yice Deng, Jiazheng Ren and Keren Dai

Remote Sens. 2025, 17(21), 3539; https://doi.org/10.3390/rs17213539 - 26 Oct 2025

Cited by 1 | Viewed by 2057

Abstract

Urban land subsidence, a globally prevalent environmental problem and geohazard triggered by rapid urbanization, threatens ecological security and socioeconomic stability. Chongqing City in southwestern China, recognized as the world’s largest mountainous city, has encountered land subsidence challenges exacerbated by accelerated urban construction. This study proposes an effective method for extracting urbanization intensity by integrating Sentinel-1, Sentinel-2, and its derived synthetic aperture radar and spectral indices features, combined with texture features. The small baseline subset interferometric synthetic aperture radar technique was employed to monitor land subsidence in Chongqing between 2018 and 2024. Furthermore, the relationships among urbanization intensity, metro construction, groundwater dynamics, and land subsidence were systematically analyzed. Finally, geographical detector and multiscale geographically weighted regression models were employed to explore the interactive effects of anthropogenic, topographic, geological-tectonic, climatic, and land surface characteristic factors contributing to land subsidence. The findings reveal that (1) the method proposed in this paper can effectively extract urbanization intensity and provide an important approach to analyze the influence of urbanization on land subsidence. (2) Land subsidence along newly opened metro lines was more pronounced than along existing lines. The shorter the interval between metro construction completion and the start of operation, the greater the subsidence observed within the first 3 months of operation, which indicates that this interval influences land subsidence. (3) Overall, groundwater dynamics and land subsidence showed a clear correlation from June 2022 to June 2023, a phenomenon largely caused by the extreme summer high temperatures of 2022, triggering reduced precipitation and a notable groundwater decline. Beyond this period, however, only a weak correlation was observed between groundwater fluctuations and land subsidence trends, indicating that other factors likely dominated subsidence dynamics. (4) The anthropogenic factors have a higher relative influence on land subsidence than other drivers. In terms of q-value, the top six factors are road network density > precipitation > elevation > enhanced normalized difference impervious surface index > population density > nighttime light, while distance to fault exhibits the least explanatory power. Given Chongqing’s exemplary status as a mountainous city, this study offers a foundational reference for subsequent quantitative analyses of land subsidence and its drivers in other mountainous cities worldwide. Full article

(This article belongs to the Special Issue Advances in SAR Remote Sensing for Landslide Monitoring and Hazard Assessment)

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23 pages, 13405 KB

Open AccessArticle

Landslide Displacement Intelligent Dynamic Inversion: Technical Framework and Engineering Application

by Yue Dai, Wujiao Dai, Chunhua Chen, Minsi Ao, Jiaxun Li and Qian Huang

Remote Sens. 2025, 17(16), 2820; https://doi.org/10.3390/rs17162820 - 14 Aug 2025

Cited by 1 | Viewed by 1143

Abstract

Displacement back-analysis is a crucial approach to enhance the effectiveness of landslide monitoring data. To improve the computational efficiency and reliability of large-scale three-dimensional landslide displacement inversion, this study develops a novel Landslide Displacement Intelligent Dynamic Inversion Framework (LDIDIF), which integrates the Bayesian displacement back-analysis (BBA) approach, a Long Short-Term Memory (LSTM) surrogate model, and the RANdom SAmple Consensus (RANSAC) algorithm. Specifically, BBA is employed to dynamically calibrate geotechnical parameters with uncertainty, the LSTM model replaces traditional numerical simulations to reduce computational cost, and RANSAC filters inlier observations to enhance the robustness of the inversion model. A case study of the Dawanzi GNSS landslide is conducted. Results show that the LSTM surrogate model achieves prediction errors below 2 mm and enhances computational efficiency by approximately 50,000 times. The RANSAC algorithm effectively identifies and removes GNSS outliers. Notably, LDIDIF significantly reduces the uncertainty of shear strength parameters within the slip zone, yielding a calibrated displacement precision better than 10 mm. The calibrated model reveals that the landslide is buoyancy-driven and that frontal failure may trigger progressive deformation in the rear slope. These findings offer valuable insights for landslide early warning and reservoir operation planning in the Dawanzi area. Full article

(This article belongs to the Special Issue Advances in SAR Remote Sensing for Landslide Monitoring and Hazard Assessment)

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Review

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34 pages, 20370 KB

Open AccessReview

Satellite-Based Differential Radar Interferometry in Landslide Research: An Overview of Applications and Challenges

by Roberto Tomás, María I. Navarro-Hernández, Juan M. Lopez-Sanchez, Cristina Reyes-Carmona and Xiaojie Liu

Remote Sens. 2026, 18(7), 1081; https://doi.org/10.3390/rs18071081 - 3 Apr 2026

Viewed by 278

Abstract

The use of satellite Differential Synthetic Aperture Radar Interferometry (DInSAR) has transformed the analysis of landslide dynamics by enabling detailed spatiotemporal monitoring of slow and subtle ground deformations. DInSAR enables comprehensive geomorphological characterization and identification of triggering factors. Retrospective applications of DInSAR provide valuable insights into past events and support causal analysis linked to rainfall episodes or piezometric fluctuations. Moreover, integration with numerical modeling enhances predictive capabilities and facilitates the calibration of geotechnical parameters. DInSAR is also instrumental in assessing infrastructure impacts and in the generation of susceptibility, hazard, vulnerability, and risk maps, which are key for land-use planning and risk management. Nevertheless, this technique has inherent limitations that must be carefully considered when interpreting results. Future developments, driven by the integration of artificial intelligence and enhanced computing capacities, are transforming the landscape of InSAR applications in landslide studies. These advancements, combined with upcoming satellite missions, are expected to significantly improve measurement accuracy, temporal resolution, and overall operational potential, paving the way for more robust quasi-early warning systems for landslide prevention. In this work, an overview of the current applications, future trends, and challenges of DInSAR in landslide studies is presented, with particular emphasis on the practical dimension of landslide studies and on the exploitation of DInSAR outcomes to support risk management and mitigation strategies. Full article

(This article belongs to the Special Issue Advances in SAR Remote Sensing for Landslide Monitoring and Hazard Assessment)

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