Abstract: Objectives: Landslides are among the most frequent and destructive geological hazards worldwide, posing significant threats to human lives, property, and natural environments. Accurate landslide susceptibility modeling is critical for disaster early warning and risk management. However, a major challenge in this process is the selection of non-landslide samples, which is often arbitrary and overlooks the fact that these samples might originate from potentially unstable areas, leading to reduced model accuracy. Methods: To address this, a novel negative sample selection strategy based on InSAR-derived ground deformation data is proposed. Using Hanyuan County, Sichuan Province as a case study, annual average ground deformation rates were generated using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. Negative samples were extracted from regions exhibiting extremely low deformation rates, representing areas unlikely to experience landslides. Results: Results demonstrate that this InSAR-based sampling method significantly improves predictive performance in both extreme gradient boosting (XGBoost) and random forest (RF) models. The InSAR-RF model achieved the highest accuracy, with an AUC of 0.847 and an overall accuracy of 89.8%, representing a 4.4% and 5.7% improvement, respectively, over traditional buffer-based sampling approaches. Additionally, to address the lack of dynamic features in conventional landslide susceptibility assessment, this study integrates InSAR data with machine learning models, constructing an evaluation matrix to refine susceptibility mapping. The decision mechanisms within the models were further analyzed using shapley additive explanations (SHAP) to enhance interpretability. Conclusions: This InSAR-based negative sampling strategy not only improves model accuracy but also strengthens the scientific reliability of landslide susceptibility assessments by integrating real-world deformation data. It offers a new approach for landslide hazard early warning and risk management.