The Qinghai-Tibet Railway (QTR) is the highest plateau artificial facility, connecting Lhasa and Golmud over Qinghai-Tibet Plateau. Climate change and anthropogenic activities are changing the condition of plateau, with potential influences on the stabilities of QTR. Synthetic aperture radar interferometry (InSAR) technique could retrieve ground millimeter scale deformation utilizing phase information from SAR images. In this study, the structure and deformation features of QTR are retrieved and analyzed using time-series interferometry with Sentinel-1A and TerraSAR-X images. The backscattering and coherence features of QTR are analyzed in medium and very high-resolution SAR images. Then, the deformation results from different SAR datasets are estimated and analyzed. Experimental results show that some of the QTR sections undergo serious deformation, with the maximum deformation rate of -20 mm/year. Moreover, the detailed deformation feature in the Beiluhe has been analyzed as well as the effects of different cooling measurements underline QTR embankment. It is also found that embankment-bridge transition along QTR is prone to undergo deformation. Our study demonstrates the application potential of high-resolution InSAR in deformation monitoring of QTR.

Widespread permafrost thaw in response to changing climate conditions has the potential to dramatically impact ecosystems, infrastructure, and the global carbon budget. Ambient seismic noise techniques allow passive subsurface monitoring that could provide new insights into permafrost vulnerability and active-layer processes. Using nearly 2 years of continuous seismic data recorded near Fairbanks, Alaska, we measured relative velocity variations that showed a clear seasonal cycle reflecting active-layer freeze and thaw. Relative to January 2014, velocities increased up to 3% through late spring, decreased to -8% by late August, and then gradually returned to the initial values by the following winter. Velocities responded rapidly (over similar to 2 to 7 days) to discrete hydrologic events and temperature forcing and indicated that spring snowmelt and infiltration events from summer rainfall were particularly influential in propagating thaw across the site. Velocity increases during the fall zero-curtain captured the refreezing process and incremental ice formation. Looking across multiple frequency bands (3-30 Hz), negative relative velocities began at higher frequencies earlier in the summer and then shifted lower when active-layer thaw deepened, suggesting a potential relationship between frequency and thaw depth; however, this response was dependent on interstation distance. Bayesian tomography returned 2-D time-lapse images identifying zones of greatest velocity reduction concentrated in the western side of the array, providing insight into the spatial variability of thaw progression, soil moisture, and drainage. This study demonstrates the potential of passive sei(s)mic monitoring as a new tool for studying site-scale active-layer and permafrost thaw processes at high temporal and spatial resolution.

Qinghai-Tibet plateau (QTP) is closely related to global climate change, and it has undergone serious permafrost degradation due to global warming in the last decades. It is crucial to measure the active layer thickness (ALT) for characterizing and monitoring the permafrost degradation of QTP. In this paper, an ALT retrieval model based on ground subsidence derived from synthetic aperture radar interferometry (InSAR), land cover types, and groundwater information is proposed. In particular, the surface subsidence is retrieved using the time-series InSAR technique with TerraSAR-X ST mode images. Moreover, groundwater content models with different land covers are constructed based on multilayered assumptions and in situ data. By taking into account the groundwater content profile and land cover types, the ALT is retrieved from deformation with the full season cycle derived by InSAR technique. The experimental results in Beiluhe indicate that the estimated ALT is consistent with field-measured data. The estimated ALT map shows the difference between the alpine meadow and alpine desert areas, with mean ALT of approximately 1.5 m in alpine meadow area and approximately 3 m in alpine desert area. Our results demonstrate that the InSAR technique with high-resolution SAR images can be of great importance for the study of permafrost environments.

The Qinghai-Tibet Plateau (QTP) is heavily affected by climate change and has been undergoing serious permafrost degradation due to global warming. Synthetic aperture radar interferometry (InSAR) has been a significant tool for mapping surface features or measuring physical parameters, such as soil moisture, active layer thickness, that can be used for permafrost modelling. This study analyzed variations of coherence in the QTP area for the first time with high-resolution SAR images acquired from June 2014 to August 2016. The coherence variation of typical ground targets was obtained and analyzed. Because of the effects of active-layer (AL) freezing and thawing, coherence maps generated in the Beiluhe permafrost area exhibits seasonal variation. Furthermore, a temporal decorrelation model determined by a linear temporal-decorrelation component plus a seasonal periodic-decorrelation component and a constant component have been proposed. Most of the typical ground targets fit this temporal model. The results clearly indicate that railways and highways can hold high coherence properties over the long term in X-band images. By contrast, mountain slopes and barren areas cannot hold high coherence after one cycle of freezing and thawing. The possible factors (vegetation, soil moisture, soil freezing and thawing, and human activity) affecting InSAR coherence are discussed. This study shows that high-resolution time series of TerraSAR-X coherence can be useful for understanding QTP environments and for other applications.

Cross correlations of seismic noise can potentially record large changes in subsurface velocity due to permafrost dynamics and be valuable for long-term Arctic monitoring. We applied seismic interferometry, using moving window cross-spectral analysis (MWCS), to 2years of ambient noise data recorded in central Alaska to investigate whether seismic noise could be used to quantify relative velocity changes due to seasonal active-layer dynamics. The large velocity changes (>75%) between frozen and thawed soil caused prevalent cycle-skipping which made the method unusable in this setting. We developed an improved MWCS procedure which uses a moving reference to measure daily velocity variations that are then accumulated to recover the full seasonal change. This approach reduced cycle-skipping and recovered a seasonal trend that corresponded well with the timing of active-layer freeze and thaw. This improvement opens the possibility of measuring large velocity changes by using MWCS and permafrost monitoring by using ambient noise.