## 1 Introduction  
The Qinghai-Tibet Plateau (QTP) is known as the Asian water tower, with an average altitude of more than 4000 m [1]. It is bounded by the Pamir Plateau in the west, Hengduan Mountain in the east, the southern end of the Himalayas in the south and Kunlun Altun Mountain, Qilian Mountain in the north [2]. The QTP is a high terrain and thus receives more solar radiation energy than lower elevation areas [3]. The Chinese mainland climate is affected by the South Asian and East Asian monsoons, resulting in a diversity of climates in different regions, such as the rainy climate in China's southern part of the Yangtze River and drought in Northwest China [4]. In addition, the QTP has many glaciers, lakes, groundwater and surface rivers, making the QTP a super water tower in the plateau area,which affects the water system layout of all of Asia [5]. The QTP is a region with a large amount of permafrost at high latitudes [6]. As a key component of the Earth's cryosphere, permafrost plays an important role in the surface energy balance, carbon and water cycles, terrestrial ecosystem, and hydrological system [7]. In recent years, with global warming, permafrost degradation has accelerated [8], and degradation has had an impact on the environment and the energy and material balance. Therefore, it is very important to monitor the permafrost status on a large scale for a long time series [7]. Traditional measurement methods of permafrost deformation include GPS [9], leveling surveys [10], and drilling [11]. However, due to the harsh environment of the QTP, these methods cannot monitor permafrost on a large scale [8]. The multitemporal interferometric synthetic aperture radar (MT-InSAR) technique is a useful tool to map ground deformation [12]. MT-InSAR has been used to monitor the freeze-thaw cycle of permafrost [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30], and to retrieve the thickness of the active layer [31; 32; 33; 34; 35; 36] and permafrost degradation [37; 38; 39; 40]. In these studies, some researchers have been committed to monitoring permafrost for a long time. [PERSON] [8] used Sentinel-1, ENVISAT and ERS-1 data to evaluate the ground deformation of permafrost and the risk along the Qinghai-Tibet Railway (QTR) from 1997 to 2018. The results show that the estimated deformation rate ranged from \(-\)20 to +10 mm/year and most of the QTR appeared to be stable. [PERSON] [41] used ENVISAT and Sentinel-1 data to construct the spatial and temporal dynamics of permafrost deformation in the northeastern QTP from 2003 to 2019. The results show the pervasive subsidence of the permafrost of up to \(\sim\)2 cm/year, increasing by a factor of 2 to 5 from 2003 to 2019. However, because the C-Band SAR data are easily affected by the region's vegetation and the atmosphere, the results may be affected by spatial and temporal decorrelation. The ALOS Phased Array type L-band Synthetic Aperture Rada (PALSAR) is preferred for ground subsidence monitoring in areas covered by vegetation and where there is a high rate of ground deformation [42]. Therefore, in order to improve the coherence of targets, we used L-band datasets to monitor the ground deformation of permafrost from 2007 to 2021. The ground deformation process of permafrost is complex. With tectonic activity, erosion, and sedimentation all interacting in the QTP [43], it is difficult to accurately describe the freezing and thawing cycle of permafrost. Therefore, research has attempted to understand the deformation characteristics of permafrost. The sinusoidal model [44; 45] and degree-day model [8; 46] were used to describe the seasonal variation in the ground surface due to up-down deformation cycles of permafrost. However, it remains controversial which type of model is better at describing seasonal deformation [47]. To extract the temporal characteristics of permafrost directly from the SAR data, [PERSON] [47] directly converted the network of interferograms into a deformation time series without a preset deformation model.