The freeze-thaw (F-T) cycle of the active layer (AL) causes the frost heave and thaw settlement deformation of the terrain surface. Accurately identifying its amplitude and time characteristics is important for climate, hydrology, and ecology research in permafrost regions. We used Sentinel-1 SAR data and small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain the characteristics of F-T cycles in the Zonag Lake-Yanhu Lake permafrost-affected endorheic basin on the Qinghai-Tibet Plateau from 2017 to 2019. The results show that the seasonal deformation amplitude (SDA) in the study area mainly ranges from 0 to 60 mm, with an average value of 19 mm. The date of maximum frost heave (MFH) occurred between November 27th and March 21st of the following year, averaged in date of the year (DOY) 37. The maximum thaw settlement (MTS) occurred between July 25th and September 21st, averaged in DOY 225. The thawing duration is the thawing process lasting about 193 days. The spatial distribution differences in SDA, the date of MFH, and the date of MTS are relatively significant, but there is no apparent spatial difference in thawing duration. Although the SDA in the study area is mainly affected by the thermal state of permafrost, it still has the most apparent relationship with vegetation cover, the soil water content in AL, and active layer thickness. SDA has an apparent negative and positive correlation with the date of MFH and the date of MTS. In addition, due to the influence of soil texture and seasonal rivers, the seasonal deformation characteristics of the alluvial-diluvial area are different from those of the surrounding areas. This study provides a method for analyzing the F-T cycle of the AL using multi-temporal InSAR technology.

The Qilian Mountains, located on the northeastern edge of the Qinghai-Tibet Plateau, are characterized by unique high-altitude and cold-climate terrain, where permafrost and seasonally frozen ground are extensively distributed. In recent years, with global warming and increasing precipitation on the Qinghai-Tibet Plateau, permafrost degradation has become severe, further exacerbating the fragility of the ecological environment. Therefore, timely research on surface deformation and the freeze-thaw patterns of alpine permafrost in the Qilian Mountains is imperative. This study employs Sentinel-1A SAR data and the SBAS-InSAR technique to monitor surface deformation in the alpine permafrost regions of the Qilian Mountains from 2017 to 2023. A method for spatiotemporal interpolation of ascending and descending orbit results is proposed to calculate two-dimensional surface deformation fields further. Moreover, by constructing a dynamic periodic deformation model, the study more accurately summarizes the regular changes in permafrost freeze-thaw and the trends in seasonal deformation amplitudes. The results indicate that the surface deformation time series in both vertical and east-west directions obtained using this method show significant improvements in accuracy over the initial data, allowing for a more precise reflection of the dynamic processes of surface deformation in the study area. Subsidence is predominant in permafrost areas, while uplift mainly occurs in seasonally frozen ground areas near lakes and streams. The average vertical deformation rate is 1.56 mm/a, with seasonal amplitudes reaching 35 mm. Topographical (elevation; slope gradient; aspect) and climatic factors (temperature; soil moisture; precipitation) play key roles in deformation patterns. The deformation of permafrost follows five distinct phases: summer thawing; warm-season stability; frost heave; winter cooling; and spring thawing. This study enhances our understanding of permafrost deformation characteristics in high-latitude and high-altitude regions, providing a reference for preventing geological disasters in the Qinghai-Tibet Plateau area and offering theoretical guidance for regional ecological environmental protection and infrastructure safety.

Under the interference of climate warming and human engineering activities, the degradation of permafrost causes the frequent occurrence of geological disasters such as uneven foundation settlement and landslides, which brings great challenges to the construction and operational safety of road projects. In this paper, the spatial and temporal evolution of surface deformations along the Beihei Highway was investigated by combining the SBAS-InSAR technique and the surface frost number model after considering the vegetation factor with multi-source remote sensing observation data. After comprehensively considering factors such as climate change, permafrost degradation, anthropogenic disturbance, and vegetation disturbance, the surface uneven settlement and landslide processes were analyzed in conjunction with site surveys and ground data. The results show that the average deformation rate is approximately -16 mm/a over the 22 km of the study area. The rate of surface deformation on the pavement is related to topography, and the rate of surface subsidence on the pavement is more pronounced in areas with high topographic relief and a sunny aspect. Permafrost along the roads in the study area showed an insignificant degradation trend, and at landslides with large surface deformation, permafrost showed a significant degradation trend. Meteorological monitoring data indicate that the annual minimum mean temperature in the study area is increasing rapidly at a rate of 1.266 degrees C/10a during the last 40 years. The occurrence of landslides is associated with precipitation and freeze-thaw cycles. There are interactions between permafrost degradation, landslides, and vegetation degradation, and permafrost and vegetation are important influences on uneven surface settlement. Focusing on the spatial and temporal evolution process of surface deformation in the permafrost zone can help to deeply understand the mechanism of climate change impact on road hazards in the permafrost zone.

Quantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze-thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited hydraulic conductivity of the underlying permafrost, the freeze-thaw processes are largely confined to the active layer, allowing for predictable seasonal deformations. This study employed Independent Component Analysis to isolate large-scale seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) measurements taken from 2016 to 2020 in the Yangtze River Source Region (YRSR) of the Qinghai-Tibet Plateau (QTP), covering 18,500 km2. We developed dedicated machine learning (ML) models that integrate these InSAR-derived measurements with various environmental proxies. By applying these models to the YRSR, we generated a comprehensive, full-coverage deformation map for permafrost terrains, achieving an R2 value of 0.91 and an Root Mean Squared Error of approximately 0.5 cm, thus confirming the model's strong predictability of seasonal deformation in permafrost regions. Deformation magnitude varied from less than 1 cm to over 10 cm. Our analysis suggests that terrain attributes, influenced by climate and soil conditions, are the primary factors driving these deformations. This research provides valuable insights into quantifying permafrost-related seasonal deformation across expansive and rural landscapes. It also aids in assessing subsurface hydrological processes and the resilience and vulnerability of permafrost. The developed ML algorithm, with access to precise environmental data, is capable of forecasting seasonal deformations across the entire QTP and potentially throughout the Arctic. Seasonal ground deformation, including both subsidence and uplift, is common in areas with a layer of ground that freezes and thaws seasonally, underlain by permafrost-a type of ground that remains at or below 0 degrees C for at least 2 years. These deformations are crucial indicators of changes in water content and thickness of this layer, offering insights into the freeze-thaw dynamics of cold environments and their sensitivity to climate change. However, accurately mapping ground deformation over large areas has been challenging. In this study, we developed machine learning (ML) models that use radar remote sensing data, statistical methods, and a set of environmental variables to predict these seasonal ground movements. Our models can accurately forecast seasonal deformation using readily available environmental data. We find that slope of the terrain is the main factor influencing seasonal deformation, with climate and soil conditions also playing significant roles. This research offers new ways to measure and understand ground deformation in remote permafrost regions and demonstrates how ML can be used to predict such deformations on a continental or even global scale large. Our findings provide valuable insights for environmental scientists and could help inform strategies for managing these regions under changing climatic conditions. Our results underscore the predictability of seasonal deformation with high accuracy in permafrost terrains Machine learning models predict full-coverage seasonal deformation with high accuracy (R2 = 0.91, Root Mean Squared Error [RMSE] = 0.5 cm) Seasonal deformation is primarily determined by terrain slope and regulated by climate and soil conditions

Climate warming can lead to permafrost degradation, potentially resulting in slope failures such as retrogressive thaw slumps (RTSs). The formation of and changes in RTSs could exacerbate the degradation of permafrost and the environment in general. The mechanisms of RTS progression and the potential consequences on the analogous freeze-thaw cycle are not well understood, owing partly to necessitating field work under harsh conditions and with high costs. Here, we used multi-source remote sensing and field surveys to quantify the changes in an RTS on Eboling Mountain in the Qilian Mountain Range in west-central China. Based on optical remote sensing and SBAS-InSAR measurements, we analyzed the RTS evolution and the underlying drivers, combined with meteorological observations. The RTS expanded from 56 m2 in 2015 to 4294 m2 in 2022, growing at a rate of 1300 m2/a to its maximum in 2018 and then decreasing. Changes in temperature and precipitation play a dominant role in the evolution of the RTS, and the extreme weather in 2016 may also be a primary contributor to the accelerated growth, with an average deformation of -8.3 mm during the thawing period, which decreased slope stability. The RTS evolved more actively during the thawing and early freezing process, with earthquakes having potentially contributed further to RTS evolution. We anticipate that the rate of RTS evolution is likely to increase in the coming years.

Terrain displacement due to the seasonal thaw of the active layer above permafrost can be sensitive to climate change; however, its accurate characterization remains a challenge. This study aimed to improve the measurement of the subsidence or vertical ground surface displacement using differential synthetic aperture radar interferometry (InSAR). Existing methods for reliable phase unwrapping are hindered by the decorrelation between time -series of SAR acquisitions that can result due to the heterogeneity and structural sensitivity of permafrost landscapes to external conditions. In this study, an advanced phase unwrapping method was proposed, in which three types of regions, namely non-residue, sparse residue, and dense residue objects, were obtained from wrapped interferogram and residue map using a segmentation method. Two variants of Polynomial-Based Region Growing Phase Unwrapping (PBRGPU) were developed, which are sparse-residue Object-based PBRGPU(SOP) and dense-residue Object-based PBRGPU(DOP). The results demonstrated that the proposed method outperformed the existing phase unwrapping methods by partially suppressing the decorrelation phase and enhancing robustness for complex terrain deformation in the absence of measured field data. Both the PBRGPU variants and segmentation strategies compose the object-based unwrapping method for permafrost, and also provide a new framework by combining the segmentations and scenarios for phase unwrapping for permafrost regions.

Due to the effects of global climate change, the permafrost temperature in the Qinghai-Tibet Plateau (QTP) has rapidly increased over the past decades. The development of thermokarst landforms is one distinctive indicator of permafrost degradation, while the change of the rate of permafrost degradation in recent 10 years has not been systematically investigated in QTP. In this paper, the annual average growth rate (AAGR) of ground deformation, the change of thaw slump areas, and the change of active layer thickness (ALT) of thermokarst landforms are monitored integrating SAR (synthetic aperture radar) and optical images for years 2007 to 2020 in Qilian Mountain, northern QTP. The ground deformation rate and seasonal amplitude were estimated by InSAR method, and the descending and ascending InSAR data are compared the validate the results. Based on the deformation results, AAGR was introduced to evaluate the permafrost degradation degree. Moreover, the ALT were estimated based on the seasonal deformation amplitude and Stefan model. The spatio-temporal characteristics of ground deformation and its relationship with thaw slump and temperature are explored. Experimental results show that the deformation rate increased about 150 % from 2007 to 10 to 2017-20. The maximum AAGR of deformation rate in the study area can reach 20.6 %. The thaw slump area has an obvious trend of expansion from 2009 to 2015, and its distribution agreed well with the deformation map. The ALT results ranged from 0.5 m to 2.8 m, indicating an obvious increase trend from 2007 to 2020. Based on the estimated increased ground deformation, thaw slump area, and ALT, it is inferred that frozen ground was undergoing serious degradation in the last 10 years. This study demonstrates the capability of multi-temporal InSAR in observing the accelerated permafrost thaw-freezing process and monitoring the permafrost parameters.

Permafrost is an important but poorly known carbon reservoir which is vulnerable to the high latitude accelerated warming. The projected thickening of its superficial seasonally thawed active layer and its induced spatial reorganization will hasten Carbon release in the atmosphere while impacting hydrology, geochemical transfers, vegetation repartition and ground stability. Active layer thickness (ALT) is only assessed by northern stations, therefore, its spatial distribution remains unknown and lacks for model evaluation, especially under the boreal forest. The all-weather spaceborne InSAR technique has shown only partial sensitivity to ALT through ground movements and remained restricted to non-forested areas. To overcome these limitations, we generalized the ground movement estimation under the omnipresent forest by exploiting the SAR polarimetric information, on the one side, and we isolated the thermodynamical component from the hydrological one during freezeback using a land surface model, on the other side, to extract ALT. Based on a one year TerraSAR-X time serie acquired over the region of Yakustk, we obtained a first high resolution ALT image which reveals unexpected short scale spatial heterogeneity, arranged along anisotrotopic patterns. Its poor comparison with the ALT simulated by the ISBA land surface model, currently used in climate modeling, highlights that climate models, and thus their simulations of greenhouse gas emissions, remain very uncertain over northern high latitudes in absence of regionalized ALT information under the boreal forest. This novel approach, operable using current and future sensors over wide areas, offers a new way forward to improve modeling as well as to optimally monitor global warming from the high latitudes.

The Arctic is experiencing rapid climate change, and the effect on hydrologic processes and resulting geomorphic changes to hillslopes and channels is unclear because we lack quantitative models and theory for rapid changes resulting from thawing permafrost. The presence of permafrost modulates water flow and the stability of soil-mantled slopes, implying that there should be a signature of permafrost processes, including warming-driven disturbance, in channel network extent. To inform understanding of hillslope-channel dynamics under changing climates, we examined soil-mantled hillslopes within a & SIM;300 km2 area of the Seward Peninsula, western Alaska, where discontinuous permafrost is particularly susceptible to thaw and rapid landscape change. In this study, we pair high-resolution topographic and satellite data to multi-annual observations of InSAR-derived surface displacement over a 5-year period to quantify spatial variations in topographic change across an upland landscape. We find that neither the basin slope nor the presence of knickzones controls the magnitude of recent surface displacements within the study basin, as may be expected under conceptual models of temperate hillslope evolution. Rather, the highest displacement magnitudes tended to occur at the broad hillslope-channel transition zone. In this study area, this zone is occupied by water tracks, which are zero-order ecogeomorphic features that concentrate surface and subsurface flow paths. Our results suggest that water tracks, which appear to occupy hillslope positions between saturation and incision thresholds, are vulnerable to warming-induced subsidence and incision. We hypothesize that gullying within water tracks will outpace infilling by hillslope processes, resulting in the growth of the channel network under future warming.

The increase in temperatures and changing precipitation patterns resulting from climate change are accelerating the occurrence and development of landslides in cold regions, especially in permafrost environments. Although the boundary regions between permafrost and seasonally frozen ground are very sensitive to climate warming, slope failures and their kinematics remain barely characterized or understood in these regions. Here, we apply multisource remote sensing and field investigation to study the activity and kinematics of two adjacent landslides (hereafter referred to as twin landslides) along the Datong River in the Qilian Mountains of the Qinghai-Tibet Plateau. After failure, there is no obvious change in the area corresponding to the twin landslides. Based on InSAR measurements derived from ALOS PALSAR-1 and -2, we observe significant downslope movements of up to 15 mm/day within the twin landslides and up to 5 mm/day in their surrounding slopes. We show that the downslope movements exhibit distinct seasonality; during the late thaw and early freeze season, a mean velocity of about 4 mm/day is observed, while during the late freeze and early thaw season the downslope velocity is nearly inactive. The pronounced seasonality of downslope movements during both pre- and post-failure stages suggest that the occurrence and development of the twin landslide are strongly influenced by freeze-thaw processes. Based on meteorological data, we infer that the occurrence of twin landslides are related to extensive precipitation and warm winters. Based on risk assessment, InSAR measurements, and field investigation, we infer that new slope failure or collapse may occur in the near future, which will probably block the Datong River and cause catastrophic disasters. Our study provides new insight into the failure mechanisms of slopes at the boundaries of permafrost and seasonally frozen ground.