###### Abstract  
Landslides are a common geological disaster, which cause many economic losses and casualties in the world each year. Drawing up a landslide list and monitoring their deformations is crucial to prevent landslide disasters. Interferometric synthetic aperture radar (InSAR) can obtain millimeter-level surface deformations and provide data support for landslide deformation monitoring. However, some landslides are difficult to detect due to the low-coherence caused by vegetation cover in mountainous areas and the difficulty of phase unwrapping caused by large landslide deformations. In this paper, a method based on multi-direction phase gradient stacking is proposed. It employs the differential interferograms of small baseline sets to directly obtain the abnormal region, thereby avoiding the problem where part of landslide cannot be detected due to a phase unwrapping error. In this study, the Sentinel-1 satellite ascending and descending data from 2018 to 2020 are used to detect landslides around Zhouqu County, China. A total of 26 active landslides were detected in ascending data and 32 active landslides in the descending data using the method in this paper, while the SBAS-InSAR detected 19 active landslides in the ascending data and 25 active landslides in the descending data. The method in this paper can successfully detect landslides in areas that are difficult for the SBAS-InSAR to detect. In addition, the proposed method does not require phase unwrapping, so a significant amount of data processing time can be saved. Keywords:phase gradient stacking: InSAR; landslide; Sentinel-1 +
Footnote †: journal: Medical  
because of its ability to obtain millimeter-level deformation values [9; 10; 11]. InSAR mainly obtains surface deformation using phase information to find abnormal areas on the surface. The main steps are as follows: Long-term SAR data and DEM are collected, and the average deformation rate of the surface is acquired by InSAR time series analysis techniques [12; 13; 14; 15]. Then, the landslide in the region is comprehensively judged by combining related data (DEM, optical images). Finally, the temporal deformation characteristics of the single landslide are obtained and the concrete parameters of the landslide are calculated using multi-track data [16; 17; 18; 19]. There have been numerous studies on obtaining large-scale surface information through InSAR to identify landslide disasters [20; 21], but there are still some difficulties in interpreting the obtained deformation results in a large range. Therefore, some methods that can automatically extract the deformation region have been applied to the interpretation of deformation results. In 2017, [PERSON] [22] proposed a method to automatically update the deformation region by using Sentinel-1 data and the PS-InSAR method, which has been applied to various deformation monitoring scenarios. This method can solve the difficulty in interpreting the final results. However, since the specific location of the landslide cannot be known during the landslide survey, it is necessary to process a large range of data simultaneously during data processing, which not only has low computational efficiency but also leads to the redundancy of results in most areas without deformation. With the development of SAR satellites, SAR data has increased explosively, and therefore this disadvantage will become more obvious. To address this, some scholars proposed that the deformation results of a large range be obtained by D-InSAR or Stacking methods first, and that key areas be processed by refined timing methods [23]. However, this method still has some defects. First, D-InSAR is the deformation information obtained between two time periods, and atmospheric errors cannot be avoided. Secondly, the D-InSAR and Stacking methods also require phase unwrapping, which cannot avoid the difficulty of unwrapping caused by steep mountain terrain.