Synthetic Aperture Radar Interferometry (InSAR) provides an effective tool to study slow-moving landslides. However, InSAR observations are often contaminated by tropospheric artefacts due to spatial and temporal variations of atmospheric refractivity. Particularly, the topography-dependent stratified delays may introduce seasonal oscillation biases into InSAR-measured deformation time series under steep terrains, which cannot be removed by conventional spatial and temporal filtering. In this study we proposed two complementary approaches to correct the stratified tropospheric delays for time series InSAR analysis when studying single landslides. One is the Iterative Linear Model (ILM) as an improved version of the traditional Linear Model (LM). The other is to fuse tropospheric delays predicted by several global weather models (FDWM) with different temporal intervals and spatial resolutions. Both methods are integrated into the standard Small BAseline Subset (SBAS) time series analysis procedure. We evaluated the proposed methods in three landslide-prone areas in southwest China using Sentinel-1 datasets. The experimental results demonstrated that the ILM method removed the seasonal stratified delays mixed in deformation time series, unaffected by the deforming points. The FDWM method achieved an optimal combination of tropospheric delay predictions by four weather models, i.e. ERA-Interim, ERA5, HRES ECMWF, and MERRA-2. Validations using in-situ GPS measurements suggested that the original Root Mean Squared (RMS) values of interferometric phases declined by more than 35% after both ILM and FDWM corrections. The ILM had better performances than the FDWM to correct stratified delay for single landslides, whereas the FDWM can be an effective alternative when the ILM is inapplicable in case of limited coherent points. •We found seasonal tropospheric delay signals in InSAR results over steep terrains.•The iterative linear fitting method is robust to the adverse impacts of moving points.•The fusion method optimally combines delays predicted by multiple weather models.•The modified SBAS approach removes seasonal fluctuations in deformation time series.•The GPS measurements validate the reliability and accuracy of the proposed methods.