<p>The SAR pixel offset tracking (POT) technique, which exploits synthetic aperture radar (SAR) intensity images, is an effective tool for measuring large-gradient displacements by overcoming phase decorrelation and phase unwrapping errors that limit interferometric synthetic aperture radar (InSAR) measurements. However, the accuracy of POT depends on image resolution, and its performance degrades significantly when using medium-resolution SAR data such as Sentinel-1, often leading to mismatches, noisy displacement fields, and reduced reliability in capturing localized landslide deformation. To address this challenge, we propose an autofocusing-based adaptive time-series POT (AF-TSPOT), which integrates autofocusing-based temporal stacking into a time-series POT framework for landslide deformation retrieval from medium-resolution SAR image sequences. By refocusing temporally stacked SAR intensity images at the pixel level and integrating displacement estimates from multiple temporal baselines, AF-TSPOT suppresses matching noise and reconstructs a more stable displacement time series. Application to the pre-failure deformation of the 2018 Baige landslide along the Jinsha River demonstrates that AF-TSPOT retrieves deformation patterns comparable to those derived from high-resolution ALOS-2 data, reducing the root mean square error (RMSE) in stable areas by 54.16% compared with the conventional APOT + PO-SBAS workflow. The refined time-series results reveal distinct acceleration signals during the autumn periods of 2016 and 2017, suggesting the combined influences of precipitation, freeze–thaw processes, and cumulative hydrothermal effects on landslide evolution. These results indicate that AF-TSPOT can improve time-series landslide deformation retrieval from widely available medium-resolution Sentinel-1 imagery and provide a practical approach for detailed landslide monitoring in mountainous regions.</p>