Integrated InSAR and Machine Learning Reveal Soil Type as Primary Control on Rainfall‐Triggered Landslide Susceptibility in Meizhou, China

ABSTRACT The spatial controls on rainfall‐triggered landslides remain elusive due to monitoring challenges in mountainous regions with frequent cloud cover. Here we fuse three complementary interferometric techniques—Small BAseline Subset (SBAS), Enhanced Small BAseline Subset (E‐SBAS), and storm‐pair Differential Interferometric Synthetic Aperture Radar (D‐InSAR)—with Sentinel‐2 imagery and seven machine learning classifiers to analyze the June 2024 landslide outbreak in mountainous Meizhou, Guangdong. Time‐series interferometry captures centimeter‐scale precursor motion, yet radar decorrelation in vegetated areas limits detection, underscoring the need for multisensor integration. After ingesting the full remote‐sensing stack, the gradient boosting decision tree reveals soil types—especially the clay‐rich red soils that mantle lower catchments—as the dominant control: within these zones, the model captures 69% of new failures inside just 18% of the landscape (AUC = 0.85), whereas slope angle and aspect rank second‐order. Support vector machine performs optimally for historical records, while gradient boosting decision tree excels under extreme rainfall, reflecting temporal shifts in factor importance. By coupling near‐real‐time InSAR with soil‐aware learning frameworks, our approach offers a practical route toward adaptive early warning and targeted mitigation across the red‐soil belts of southern China.