InSAR Techniques for Landslide Study: A Review of Methods, Challenges, and Emerging Trends.

Authors

Hanlu Zhang, Bowen Liu, Daming Zhu, Zhanfeng Liu, Mengyao Shi

Published in

Sensors (Basel, Switzerland). Volume 26. Issue 15. Jul 23, 2026. Epub Jul 23, 2026.

Abstract

Landslides occur frequently under complex and variable global geomorphological and meteorological conditions, posing serious threats to the ecological environment, human life and property. Traditional monitoring approaches are often inefficient and highly susceptible to external conditions, making them inadequate for large-scale rapid deformation monitoring. Owing to its advantages of high precision, wide-area coverage, and all-weather continuous observation, Interferometric Synthetic Aperture Radar (InSAR) technology has been widely applied in landslide monitoring. This paper systematically reviews the development and application of InSAR technologies in landslide monitoring. Classical methods, including Differential InSAR (D-InSAR), Permanent Scatterer InSAR (PS-InSAR), Small Baseline Subset InSAR (SBAS-InSAR), and Distributed Scatterer InSAR (DS-InSAR), as well as derivative techniques such as Quasi-Permanent Scatterer InSAR (QPS-InSAR), Temporarily Coherent Point InSAR (TCP-InSAR), and Multiple Aperture InSAR (MAI), are comprehensively summarized. In addition, recent advances in artificial intelligence (AI) and multi-source data fusion are highlighted. Through comparative analysis of related studies, this paper summarizes the applicability and potential of various methods in landslide monitoring, and reviews the main solutions to challenges, including geometric distortion, decorrelation noise, atmospheric delay, and difficulties in three-dimensional deformation monitoring. Future research directions are also discussed. Overall, InSAR technology has evolved from single-method approaches toward integrated and intelligent frameworks; however, challenges remain in terms of adaptability in complex terrain, data-processing efficiency, and model interpretability. This review provides a technical reference for future landslide-monitoring research.

PMID:
42590446
Bibliographic data and abstract were imported from PubMed on 13 Aug 2026.

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