Abstract: Traditional offset tracking primarily relies on normalized cross correlation tracking method based on the regular matching window. However, in the analysis of optical remote sensing images, pixels representing disturbance factors such as cloud layers, water bodies, shadows are often present within the regular window. When applied to landslide monitoring, these disturbance factors may lead to errors in the offset estimation. In order to address this issue, an adaptive offset tracking algorithm is presented. Prior to the offset estimation, a pre-processing step is carried out to identify the locations of these disturbance factors in the study area and generate the corresponding masks. During offset estimation process, the disturbance factors of cross-correlation window can be found from its masks, then pixels representing disturbance factors within the cross-correlation window are excluded, thereby improving the accuracy and reliability of offset estimation experimental validation on the Baige landslide , which has demonstrated that this method can significantly enhance the accuracy and reliability of offset tracking.