An improved multi‐model risk assessment of creep‐type landslide dynamics with multi‐temporal synthetic aperture radar (MT‐InSAR)

Abstract Creep‐type landslides in high‐mountain regions threaten ecosystems, communities and infrastructure due to a combination of several factors, including topography, glacial deposits and sensitive geological formations. Accurately monitoring these landslides is challenging due to complex, nonlinear causal relationships and limitations in capturing multiscale spatio‐temporal dynamics. To address these challenges, this study develops an improved multi‐model framework for landslide risk assessment that integrates Multi‐Temporal Interferometric Synthetic Aperture Radar (MT‐InSAR) from multi‐orbit Sentinel‐1 data (October 2014–May 2024) with joint persistent scatterer (PS) and distributed scatterer (DS) processing, multiscale geographically weighted regression (MGWR), K‐medoids clustering and wavelet analysis. Also, hydro‐climatic time‐series variables, including soil moisture content (SMC), precipitation, snow depth and soil temperature, and terrain‐derived geomorphometric factors (e.g., slope, Topographic Wetness Index [TWI] and irrigation channel distance) are included. This framework was implemented in the Parsan Valley in the Hindukush–Himalayan–Karakoram Range (HHK) region of Pakistan. The average line‐of‐sight (LOS) deformation results revealed a significant movement (−60 to 60 mm/year), concentrated in the central and south‐eastern valley. Wavelet analysis identified significant SMC and deformation correlations (Spearman's ρ = −0.66, p < 0.001) with lags of 2–142 days, reflecting seasonal influences and suggesting a complex interplay with several environmental processes. MGWR (adjusted R 2 = 0.676) highlighted TWI ( β = 0.580) and irrigation channel distance ( β = 0.584) as primary drivers, with slope ( β = 0.063) as a secondary factor, driven by a dual moisture regime of monsoon rainfall (June–September), spring snowmelt (April–June) and spring‐fed irrigation channels, triggering shallow, translational sliding. K‐medoids clustering delineated high‐risk zones, with an R 2 value of 0.75 between deformation and slope angle. This framework improves landslide monitoring and risk assessment, supporting targeted mitigation with potential application in other areas.