Abstract: Affected by global climate change, the degradation of permafrost and surface instability in the Qinghai-Xizang Plateau have been continuously intensifying, posing obstacles to the construction and maintenance of infrastructure as well as regional socio-economic development. In recent years, SBAS-InSAR technology has been widely applied in monitoring surface deformation of permafrost. However, due to severe decorrelation phenomena in some areas of the Qinghai-Xizang Plateau, the deformation monitoring results exhibit spatial discontinuity, making it difficult to obtain comprehensive and detailed monitoring outcomes. To address these issues, this paper proposes a method for monitoring permafrost deformation that integrates machine learning with SBAS-InSAR. Taking the Menshi Township in Ali, Xizang, as the study area, a total of 43 descending Sentinel-1A images from January 7, 2020, to June 6, 2021, are used to extract surface deformation information. After generating a training dataset by integrating multiple environmental factor data, a machine learning model is introduced to fit the intrinsic relationship between SBAS-InSAR monitoring results and environmental factors, thereby obtaining a continuous deformation rate map of the study area. The results indicate that the method combining the random forest model with SBAS-InSAR performs optimally. By interpolating the missing regions of permafrost deformation using this method, the monitoring coverage of the original SBAS-InSAR method can be significantly improved, with an average error and root mean square error of 0.459 and 0.739 mm/a, respectively, for the interpolation results.

Key words: SBAS-InSAR, machine learning, surface deformation, frozen soil, Sentinel-1A