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InSAR Techniques for Landslide Study: A Review of Methods, Challenges, and Emerging Trends
Hanlu Zhang1,2, Bowen Liu3,4, Daming Zhu1,2
1Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650031, China.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
Interferometric Synthetic Aperture Radar (InSAR) technology offers precise, wide-area landslide monitoring, overcoming traditional method limitations. This review details InSAR advancements, including AI integration, for improved landslide detection and analysis.
Area of Science:
- Geosciences
- Remote Sensing
- Geotechnical Engineering
Background:
- Landslides pose significant global risks to ecosystems and infrastructure.
- Traditional landslide monitoring methods are often inefficient and unreliable.
- Interferometric Synthetic Aperture Radar (InSAR) offers high-precision, all-weather, wide-area monitoring capabilities.
Purpose of the Study:
- To systematically review the development and application of InSAR technologies for landslide monitoring.
- To analyze the applicability and potential of various InSAR methods.
- To identify solutions for challenges and discuss future research directions in landslide monitoring.
Main Methods:
- Comprehensive review of classical InSAR techniques: Differential InSAR (D-InSAR), Permanent Scatterer InSAR (PS-InSAR), Small Baseline Subset InSAR (SBAS-InSAR), and Distributed Scatterer InSAR (DS-InSAR).
- Summary of derivative techniques: Quasi-Permanent Scatterer InSAR (QPS-InSAR), Temporarily Coherent Point InSAR (TCP-InSAR), and Multiple Aperture InSAR (MAI).
- Highlighting recent advances in artificial intelligence (AI) and multi-source data fusion for landslide analysis.
Main Results:
- InSAR technology has evolved significantly, with various methods showing distinct applicability in landslide monitoring.
- Key challenges such as geometric distortion, decorrelation noise, atmospheric delay, and 3D deformation monitoring have been addressed.
- Integration of AI and data fusion shows promise for enhanced landslide monitoring frameworks.
Conclusions:
- InSAR technology has transitioned towards integrated and intelligent monitoring frameworks.
- Adaptability in complex terrains, processing efficiency, and model interpretability remain key challenges for InSAR.
- This review serves as a technical reference for advancing InSAR-based landslide monitoring research.
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