Ground subsidence is a common urban geological hazard in several regions worldwide. The settlement of loess fill foundations exhibits more complex subsidence issues under the coupled effects of geomechanical and seepage-driven processes. This study selected 21 ascending Sentinel-1 A radar images from April 2023 to October 2024 to monitor the loess fill foundation in Shaanxi, China. To minimize errors caused by the orbital phase and residual flat-earth phase, this research combined PS-InSAR technology with the three-threshold method to improve the SBAS-InSAR processing workflow, thereby exploring time-series deformation of the loess fill foundation. Compared with conventional SBAS-InSAR technology, the improved SBAS-InSAR technique provided more consistent deformation time-series results with leveling data, effectively capturing the deformation characteristics of the fill foundation. Additionally, geographic information system (GIS) spatial analysis techniques and statistical methods were employed to analyze the overall characteristics and spatiotemporal evolution of the ground surface deformation in the study area. On the other hand, the major drivers of the subsidence in the study area were also discussed based on indoor experiments and engineering geological data. The results showed gradual and temporal shifts of the subsidence center toward areas with the maximum fill depths. In addition, two directions of uneven subsidence were observed within the fill foundation study area. The differences in the fill depth and soil properties caused by the building foundation construction were the main factors contributing to the uneven settlement of the foundations. Foundation deformation was also positively and negatively affected by surface water infiltration. This study integrates remote sensing and engineering geological data to provide a scientific basis for accurately monitoring and predicting loess fill foundation settlement. It also offers practical guidance for regional infrastructure development and geological hazard prevention.

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.

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.