Assessing the Added Value of InSAR Deformation Features for Landslide Susceptibility Mapping Using Spatial Validation and SHAP

Conventional landslide susceptibility assessments rely primarily on static environmental factors, which provide limited information on actual slope movement. The additional predictive value of interferometric synthetic aperture radar (InSAR) deformation features, and the consistency of this value, remain insufficiently established. This study assessed their contribution to landslide susceptibility mapping in Luding County, Sichuan Province, China. Sixteen static environmental factors were combined with three features derived from small baseline subset InSAR (SBAS-InSAR): long-term mean deformation velocity (Vmean), velocity variability (Vstd), and velocity trend (Kv). Random forest (RF), XGBoost (XGB), LightGBM (LGBM), and CatBoost (CAT) models were evaluated using 30 repeated spatial holdout splits and paired ablation experiments. Adding Vmean yielded modest median paired AUC gains of 0.003–0.005 across the four models under the primary 10 km spatial validation scheme. XGB achieved the highest mean AUC (0.914) with the M1 feature set, comprising the static factors and Vmean. Adding Kv or Vstd did not yield consistent further gains. Out-of-sample Shapley additive explanations (OOS-SHAP) identified rainfall, the coefficient of variation in elevation (CV), distance to faults, and lithology as the leading predictors; Vmean also made an appreciable contribution. The main predictors exhibited nonlinear responses. Spatial SHAP mapping and Moran’s I analysis further revealed pronounced spatial variation and significant clustering in feature contributions. These findings indicate that long-term mean InSAR deformation velocity provides a consistent but modest complement to conventional static factors, primarily through local adjustments to predicted susceptibility and the identification of regional differences.