## 6 Conclusions  
In this study, we used 66 scenes of ALOS data (2007-2009), 73 scenes of ALOS-2 data (2015-2020) and 284 scenes of Sentinel-1 data (2017-2021) based on SBAS-InSAR to reveal the spatial and temporal deformation of permafrost in the northern QTP, passing through the Tuotuohe, Beiluhe, Wudaoliang and Xidatan regions between 2007 and 2021. In addition, a coherence weighted least squares estimator without any hypothetical model was used to calculate the long-term deformation velocity (LTDV) and maximum seasonal deformation (MSD) without any prior knowledge. The conclusions are summarized as follows:  
1. Analysis of the ALOS results shows that the LTDV ranged from \(-20\) to \(+20\) mm/year during 2007-2009. For the ALOS-2 and Sentinel-1 results, the LTDV ranged from \(-30\) to \(30\) mm/year during 2015-2021. Over a 15-year observation period, permafrost degradation is accelerating. The expansion areas of permafrost degradation are concentrated on braided stream plains, thermokarst lakes and glacier outwash plains. In addition, the high MSDs are mainly concentrated in glacier outwash plains and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Benchmark** & **A** & **B** & **C** \\ \hline Leveling (unit: mm) & \(-2.2\) & \(8.8\) & \(16.6\) \\ InSAR results (unit: mm) & \(-5.3\) & \(14.5\) & \(9.8\) \\ Absolute error (unit: mm) & \(3.1\) & \(5.7\) & \(6.8\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The absolute error between the measured values of leveling points A, B, and C and the InSAR observed values. Figure 8: Geographic location map of leveling points along the QTR. braided stream plains, which may be due to glacial erosion and a large amount of ice deposits brought to low-lying areas, affecting the water storage capacity and ice melting capacity of permafrost. 2. For the geological disaster of thermal melting collapses, we compared the permafrost monitored by the L-band and the C-band sensors. The results show that the L-band ALOS-2 is more sensitive to the thermal collapses of permafrost than C-band sensor, and the detected collapse areas (LTDV \(<-10\) mm/year) are consistent with the GF-1/2 thermal collapses dataset. 3. We extracted the time series deformation directly from the interferograms without a preset deformation model. The long-term deformation velocity (LTDV) and maximum seasonal deformation (MSD) were calculated from time series deformation without any prior knowledge, demonstrating that our methods can be effectively used to extract the deformation features of permafrost. In the future, more SAR datasets and auxiliary datasets will be used to illuminate the freeze-thaw cycle and retrieve the active layer thickness on the QTP on a large scale. Conceptualization, L.Z., C.W. and Y.T. ; formal analysis, L.Z. ; methodology, L.Z. and C.W. ; software, L.Z., H.Z., B.Z. and L.D. ; validation, L.Z., C.W. and Y.T. ; writing--original draft, L.Z. ; writing--review and editing, C.W. ; Y.T. and H.Z. ; visualization, L.Z. ; supervision, C.W. ; project administration, H.Z. ; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript. This paper was funded by the National Natural Science Foundation of China, Grant No. 41930110. ALOS is provide by JAXA and available from the Alaska Satellite Facility (ASF) ([[https://vertex.daac.asf.alaska.edu](https://vertex.daac.asf.alaska.edu)]([https://vertex.daac.asf.alaska.edu](https://vertex.daac.asf.alaska.edu)), accessed on 14 February 2022). ALOS-2 data is provided and available from JAXA EORA2 program. Sentinel-1 data is provided by the European Space Agency (ESA) and available from the Alaska Satellite Facility (ASF) ([[https://vertex.daac.asf.alaska.edu](https://vertex.daac.asf.alaska.edu)]([https://vertex.daac.asf.alaska.edu](https://vertex.daac.asf.alaska.edu)), accessed on 14 February 2022).