## 1 Introduction  
Landslide refers to the movement of rock, earth, or debris down a slope due to gravity, often triggered by rainfall, earthquakes, volcanic activity, or human actions [1]. According to the 2022 Global Natural Disaster Assessment Report [2], landslides accounted for 5.3% of all natural disaster events, with a decline in frequency, deaths, affected population, and economic losses compared to the 30-year average. Despite these reductions, the threat of massive landslides to life and property remains significant and non-negligible. For instance, during the preparation of this review, a landslide event in Enga Province, Papua New Guinea, on 27 May 2024, resulted in approximately 670 people missing [3]. In the context of global climate change, implementing landslide monitoring and creating landslide susceptibility maps are crucial for managing landslide risks and disaster reduction. This approach not only aligns with the Sendai Framework for Disaster Risk Reduction 2015-2030 goal of reducing existing disaster risk [4] but also contributes to achieving the United Nations' Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action) [5]. Deformation monitoring is a crucial approach for the effective identification and quantitative assessment of landslide movements. Conventional monitoring methods, such as the global navigation satellite system (GNSS) [6; 7; 8] and geotechnical sensors [9; 10; 11], are widely employed. Despite providing high precision at specific points, they have considerable limitations, such as restricted spatial coverage with sparse measurement points and deployment challenges in remote mountainous regions with steep slopes resulting from the expense and difficulty of access. Over the past few decades, the proliferation of satellite remote sensing techniques has demonstrated its substantial capability in offering a comprehensive perspective often unattainable by ground-based methods. Optical remote sensing and synthetic aperture radar (SAR) are the primary remote sensing platforms employed in landslide studies. Optical remote sensing, which leverages visible, infrared, and thermal sensors, plays a significant role in landslide detection and inventory mapping by analyzing the interpretation features of images, such as color, shape, and texture [12; 13; 14]. Some scholars have also attempted to conduct landslide monitoring based on optical images when sufficient ground control points are available for orthorectification. For instance, [PERSON] et al. [15] employed a multiple pairwise image correlation (MPIC) technique to investigate the landslide-prone fields in the Ubaye Valley from a sequence of multi-temporal Pleiades images. Similarly, [PERSON] et al. [16] generated a digital surface model (DSM) from GF-1 across-track stereo image pairs, and extracted the landslide area, displaced volume, and average sliding direction through the calculation of differences between the pre- and post-landslide DSM. However, optical remote sensing images have relatively poor precision in monitoring slow deformation rates and are susceptible to weather conditions such as clouds and rain, which may hinder monitoring in target areas or lead to missing monitoring time series. On the other hand, SAR satellites operate using microwave signals and are impervious to weather conditions such as clouds and water vapor. Interferometric SAR (InSAR), which utilizes SAR images to detect and measure subtle ground movements with high precision by capturing the phase difference between radar signals from consecutive passes over the same area, has attracted widespread attention. [PERSON] and [PERSON] [17] utilized InSAR to extract the digital elevation model (DEM) for the San Francisco Bay area, marking the first application of InSAR for topographic mapping. [PERSON] et al.