## 1 Introduction  
Landslides are one of the most common types of natural hazards that cause serious economic losses, casualties, and damages to buildings, critical infrastructures and industrial settlements [1]. The impact of landslides is particularly high in rural mountainous areas, where land management is difficult to achieve, and risk mitigation is scarce due to the large distances, difficult logistics and harsh climatic conditions. As an example, on 2 May 2014, a large-scale landslide occurred in the Badakhshan Province in Northeastern Afghanistan due to continuous heavy rainfall. It resulted in nearly 2700 deaths and more than 300 houses were buried [2]. Again, a giant landslide occurred in the Himachal Pradesh region of Northern India on 13 August 2017 that killed 47 people and nearly a 300-m highway was completely destroyed. Monitoring and early warning systems are effective methods to reduce the risk of landslides [3], but the application of them on each single, potentially unstable slope is often impossible in mountain areas, due to the high spatial frequency of affected slopes. In such cases, remote sensing may help to reduce the cost and time for the application of mitigation measures, because it can provide a preliminary low-cost assessment of the severity of the slope instability and allow for the prioritization of critical cases. The use of remote-sensing data is one of the hotspots of landslide research at present. Among all the physical manifestations of a mass movement, the surface deformation is the most intuitive and comprehensive to measure and use for hazard assessment. It is a critical indicator for developing landslide early warning systems. Therefore, the implementation of deformation monitoring is of great significance for landslide prevention and mitigation [4; 5]. The majority of fatal landslide events occur in less developed countries, such as China, India, Nepal, Bangladesh, etc. [1]. Traditional deformation monitoring equipment, such as an inclinometer, global positioning system (GPS), etc., are well-suitable, but their high costs limit their applications in underdeveloped areas. Synthetic Aperture Radar Interferometry (InSAR) is an effective surface deformation monitoring method with a wide range and high accuracy. Commercial radar imaging can be expensive and mainly used for landslide hazard prevention in developed areas, including landslide identification [6; 7], monitoring and early warning [8; 9; 10] and risk assessment [11]. With the free distribution of Sentinel-1 satellite images, InSAR has become a low-cost landslide monitoring technique. Small Baselines Subset (SBAS) InSAR is a time series analysis method of InSAR and can achieve effective monitoring in rural and urban areas. It has been gradually applied in landslide-prone areas, which provides new technical means for landslide monitoring and early warning [12; 13; 14; 15; 16]. The Three Gorges Reservoir area (TGRA) has been impounded since 2003. The periodical scheduling of the reservoir water level from 145 m to 175 m has been carried out since 2009. Affected by heavy rainfall and reservoir water level scheduling, a large number of new landslides and reactivation of old landslides have occurred [17]. Until now, more than 500 landslides have undergone varying degrees of deformation. However, due to the high cost of dedicated landslide monitoring, the latter was carried out only in 254 landslides in the TGRA [18], which cannot meet the needs of disaster prevention and mitigation. The Muyubao landslide located in the TGRA was taken as a case study in this paper. Based on the Sentinel-1 radar images, the SBAS InSAR analysis method was applied to extract the information of landslide movement. The characterization of its temporal and spatial deformation was conducted as well. Moreover, the evolution mechanism of the Muyubao landslide was analyzed with the combined consideration of its engineering geological conditions and triggering factors, including rainfall and fluctuation of reservoir water level.