With the continuous development of machine learning and deep learning algorithms, their accuracy and generalization capabilities have significantly improved. However, they remain black-box models characterized by instability and poor interpretability. In the field of landslide susceptibility, some scholars have attempted to use SHapley Additive exPlanations (SHAP) to provide explanations for machine learning models. [PERSON] et al. [27] successfully elucidated the weightings of the internal features and prediction outcomes of a CNN model using SHAP. [PERSON] et al. [28] utilized SHAP to elucidate three ensemble learning models, namely XGBoost, NGBoost, and LightGBM. The work of numerous researchers has confirmed that SHAP offers superior interpretability and visual interpretability. Presently, landslide susceptibility assessments primarily consider large-scale static influencing factors such as elevation, slope, and lithology. Hindered by their scale and static nature, these assessments fail to capture timely and detailed susceptibility results [29]. Hence, there is a need for extensive monitoring of surface deformations to refine and amend susceptibility results, enhancing the accuracy and precision of landslide susceptibility assessments. In recent years, with the continuous advancement and development of remote sensing technology, InSAR has been widely employed for the early detection and monitoring of landslides. The primary monitoring methods include small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR), permanent scatterer interferometric synthetic-aperture radar (PS-InSAR), and differential interferometric synthetic-aperture radar (DInSAR). These diverse monitoring methods are applicable in varying situations. DInSARutilizes SAR data at different times in the same area to obtain surface deformation information, as exemplified by [PERSON] et al. [30], who utilized DInSAR and PS-InSAR to investigate surface deformations in their study area. PS-InSAR can capture long-term surface deformations with minimal DEM and spatial-temporal baseline requirements. This makes it well-suited for monitoring urban areas with permanent scatterers, as demonstrated in recent years. For example, [PERSON] et al. [31] employed PS-InSAR to assess surface deformations in the Sibari and Metaponto coastal plains and [PERSON] et al. [32] completed the deformation monitoring of Eboling Mountain using PS-InSAR. SBAS-InSAR, capable of capturing long-term surface deformations like PS-InSAR, has lower image requirements. Additionally, it exhibits higher computational efficiency and does not necessitate a large number of permanent scatterers, as demonstrated in [PERSON] et al. [33]. They employed SBAS-InSAR to monitor surface deformations in the Yan'an New District. These three methods have distinct functionalities. Given that landslides often occur in non-urban hilly areas and require long-term surface deformation results, SBAS-InSAR is frequently chosen for landslide monitoring. Integrating the accurate and detailed surface deformation information obtained through SBAS-InSAR into landslide susceptibility assessment models helps address model limitations and leads to more precise results. The integration of landslide susceptibility assessment and InSAR results has become a focal point of interest among numerous scholars. [PERSON] et al. [34] constructed a decision matrix based on landslide susceptibility classification and PS-InSAR results. By integrating the two according to different categories, they corrected some errors in susceptibility mapping. [PERSON] et al. [35] also developed a decision matrix using susceptibility classification and PS-InSAR results, making susceptibility mapping more suitable for the southwestern karst regions. Building upon [PERSON]'s work, [PERSON] et al. [36] multiplied susceptibility classification with SBAS-InSAR results and classified them based on different levels, enhancing the reliability of susceptibility mapping. [PERSON] et al.