## 6 Conclusions  
In this paper, a multi-direction phase gradient stacking method based on differential interferograms is proposed for landslide detection, and landslides in Zhouqu County and its surrounding area are successfully detected. The method is based on the differential interferogram of a small baseline set, and the abnormal region is obtained by multi-direction phase gradient stacking. The landslides detected by this method are compared with that detected by SBAS. It is found that the landslide detected by this method is highly consistent with that detected by SBAS, indicating that landslides can be successfully detected by this method. At the same time, landslides can also be detected by this method in the incoherent region. More importantly, this method has great advantages in dealing with large deformation landslides, and can obtain complete information of the entire landslide. Additionally, the data processing efficiency of this method is much higher than that of the SBAS method, and the landslide location can be obtained efficiently in large-scale landslide detection. However, it should be noted that the method in this paper does not obtain the deformation variable of the landslide, and can only indicate whether the landslide is slippery or not, so the deformation characteristics of the landslide cannot be analyzed. The research results also show that the selection of the temporal baseline threshold will greatly affect the results of landslide detection. If the temporal baseline threshold is too small, some landslides with weak deformations cannot be detected, whereas if the temporal baseline threshold is too large, some landslide signals with large deformations will be weakened. For example, the Sentinel-1 data, with a 36-day temporal baseline threshold, proved to be the optimal temporal baseline threshold. However, whether a 36-day temporal baseline threshold is optimal for all regions is not clear and will be addressed in subsequent studies. The continuous development of SAR satellites will lead to the continuous surge of SAR data. Traditional temporal InSAR technology needs to re-process a large range of SAR data for newly acquired data in order to determine whether there are new geological hazard points based on deformation. Compared with the traditional temporal InSAR which needs to be untangled, this method can greatly improve the efficiency of identification. Therefore, in a large-scale landslide survey, this method can be used to quickly extract the landslide aggregation area, and the scale and aggregation degree of the landslide can be known, providing a guideline for landslide hazard evaluators to assess the hazards of landslides. Sentinel-1 data has a stable and short revisit period, which provides great convenience for our method, which can collect a large amount of data in a short time for the acquisition of results. However, the use of long-period data in this method is unknown. Follow-up research should utilize deep learning to realize large-scale automatic landslide detection. Conceptualization, Q.S. and T.X. ; methodology, Q.S. and T.X. ; software, J.H. ; validation, Q.S. and J.H. ; formal analysis, Q.S. and T.X. ; resources, Q.S. and J.H. ; writing--original draft preparation, Q.S. and T.X. ; writing--review and editing, all authors. ; supervision, Q.S. and J.H. ; funding acquisition, Q.S. and J.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Nature Science Foundation of Hunan Province (No. 2022J)30031), the Science and Technology Innovation Program of Hunan Province (No. 2022 RC3042), and the Science and Technology Innovation Program of Fujian Province (No. 2021Y3001). Not applicable. **Data Availability Statement:** Sentinel-1 data are provided by the European Space Agency (ESA) and downloaded from the Alaska Satellite Facility (ASF) ([[https://search.asf.alaska.edu/](https://search.asf.alaska.edu/)]([https://search.asf.alaska.edu/](https://search.asf.alaska.edu/)), accessed on 14 February 2014).