Abstract: Rainfall-induced rapid landslides triggered by short-duration heavy precipitation often exhibit undetectable precursory deformation in InSAR time series due to geometric distortions and limited temporal sampling. To address this limitation, this study develops an early identification framework that couples InSAR surface deformation monitoring with the physically based SINMAP stability index. Using long-term monitoring data from the Xishancun and Huangnibazi landslides (Lixian County, Sichuan Province), InSAR applicability was first quantitatively evaluated via visibility and measurement sensitivity analyses. In areas of poor InSAR performance, a spatiotemporal cross-validation strategy was established to integrate deformation trends with evolving stability index patterns for comprehensive hazard assessment. Results reveal that the Xishancun landslide is generally stable with only localized frontal instability under rainfall, whereas the Huangnibazi landslide is highly rainfall-sensitive yet challenging to monitor using InSAR alone due to layover and shadow effects. The approach was successfully validated on the 2019 Jichang rainfall-induced rapid landslide (Guizhou Province), effectively capturing pre-failure signals missed by InSAR. This coupled deformation–stability index method significantly enhances early detection of rainfall-triggered rapid landslides and offers a transferable technique for early warning in complex mountainous regions.