###### Abstract
Some methods that do not need phase unwrapping have been gradually used to identify landslide hazards [24; 25], but it is difficult to obtain enough monitoring points in low-coherence areas. Therefore, how to avoid phase unwrapping errors and how to detect landslides in both low coherence regions and large deformation regions are the main difficulties in landslide detection using InSAR. The phase gradient method is gradually being used to locate deformation regions. It has been used to detect the deformation information associated with seismic fault zones [26; 27] and faults [28]. The deformation range caused by these cases is relatively large, often extending for thousands or tens of kilometers in space, and these regions can be identified by the phase gradient method. Compared with large-scale deformation, these landslides are generally small, and most are located in a slope unit [29]. Some scholars have also made attempts at using the phase gradient for landslide recognition. First, for the identification of reservoir bank landslides, which tend to have obvious deformation before and after impounding, obvious signals are found in the differential interferogram, so the phase gradient can quickly locate them [30]. Secondly, earthquakes often lead to slope instability, and some slopes may move slowly or accelerate in earthquake areas, therefore such landslides are often dense [31]. Similarly, some scholars have used the NASA Uninhabited Aerial Vehicle SAR (UAVSAR) data and the phase gradient stacking method to explore the deformation boundary of monomer landslides [32]. Additionally, some scholars have applied the deep learning method to the phase gradient stacking results for the automatic identification of landslides [33; 34]. The current use of interferogram phase gradient stacking for the detection of deformation regions is more likely to use the results of phase gradient stacking in the range and azimuth directions. However, this approach does not make full use of the neighboring information, as a central pixel has at least eight neighbouring pixels. Thus, some landslides cannot be detected by this method due to the fact that the phase gradient stacking results in the range and azimuth directions are not obvious because of the direction of the slopes and the imaging parameters of the SAR satellites. Consequently, to ascertain landslides based on phase gradient information while leveraging the entirety of temporal signal sequences, a novel approach is proposed: a multi-direction phase gradient stacking technique tailored for the identification of slow-moving landslides. This method combines short baseline sets of interferograms over the entire time series, attenuating atmospheric effects by means of averaging over a large number of interferograms, with fast access to slow-moving landslides through the results. Subsequently, this method is applied to Zhouqu County, Gansu Province, China, to validate its efficacy. Furthermore, we employed the Small Baseline Subset (SBAS) method to determine the average deformation rate within this area, enabling a comparative analysis between the proposed method and the SBAS method in landslide detection, elucidating their disparities and respective advantages.