## 1 Introduction  
Landslides are a common geological hazard in which soil or rock slides downhill along a slope, driven by gravity, under the influence of factors such as rain erosion and soaking [1]. Landslides are highly impactful geological hazards, characterized by their widespread occurrence and severe consequences, causing economic losses of nearly USD 10 billion and thousands of casualties each year in many countries around the world [2]. Landslides account for more than 70% of the total number of common geological hazards in China. In recent years, global climate change and harmful human activities have exacerbated the occurrence of landslide hazards, which pose a significant threat to the lives and property of residents in vulnerable areas [3]. In the face of the frequent landslide hazards in China, the construction of a monitoring, early warning, and forecasting system to mitigate or even prevent landslide-induced devastation is crucial [4]. The average number of successful geohazard forecasts in China between 2019 and 2021 was 796, preventing possibly 22,748 fatalities and direct economic losses of CNY 1.07 billion [5]. This indicates that successful forecasts of geological hazards, such as landslides, are effective in avoiding casualties and reducing the direct economic losses. However, accurate and comprehensive deformation monitoring data are the basis for successful forecasts. Therefore, the long-term monitoring of landslide deformation is crucial to understand the evolution of landslide hazards and to provide data support for the prevention and control of landslides [6]. Traditional methods for the monitoring of surface deformation include Global Positioning Systems (GPSs) and other elevation measurement approaches. However, these methods not only consume significant manpower and resources but also have a very limited measurement range; thus, they cannot meet the needs of large-scale landslide monitoring [7]. With the advancement of geographic information systems (GISs), remote sensing techniques have been widely used in monitoring and surveillance and have become an effective means of recognizing and understanding terrain deformation [8]. At present, the mainstream remote sensing techniques are categorized into optical remote sensing and radar remote sensing. Optical remote sensing can only provide the results of interpreted optical images, and it cannot reflect the specific values of ground deformation. Therefore, optical remote sensing is often used as a tool in identifying and interpreting topographic features, as well as determining the type of landslide, and it cannot provide strong evidence in the early warning stage [9; 10; 11]. InSAR-based radar remote sensing can be used to directly obtain terrain information and ground deformation information, which plays a significant role in hazard monitoring [12; 13]. In 2018, [PERSON] et al. [14] used Radarsat-2 imagery from 2013 to 2015 to obtain surface deformation information for permafrost conditions via D-InSAR. The occurrence of large-scale landslide hazards is characterized by uncertainty and suddenness. In 2020, [PERSON] et al. [15] used the PS-InSAR technique to obtain surface subsidence data for Isfahan, Iran, in response to the ongoing drought events and groundwater over-exploitation. They analyzed the causes of the outcomes in relation to geomorphological factors. In 2021, [PERSON] et al. [16] used the SBAS method to monitor the extent and rate of ground subsidence in the Yellow River Delta in 2018-2019, and they concluded that the most severe subsidence area was located in Xianhe Township, with an average annual subsidence rate of 204.7 mm/a. In 2021, [PERSON] et al. [17] used PSI technology to monitor the long-term temporal deformation in the area around Dongting Lake. They also used a model considering thermal expansion and seasonal environmental factors to simulate the time-varying characteristics of the deformation and obtained the temporal deformation and thermal expansion parameters of the area. In 2024, [PERSON] et al.