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According to historical records, land subsidence has been occurring in Xi'an, China, since the 1960s, characterized by complex land subsidence patterns. This subsidence has the potential to cause serious societal and economic problems during the process of urbanization. Long-term, large-scale monitoring and dynamic high precision tracking of the evolution of surface deformation associated with geohazards is a prerequisite for effective prevention or advance warning of geological disasters. Synthetic aperture radar interferometry (InSAR), a satellite remote sensing technology, can facilitate such monitoring. Both currently operating and planned SAR satellites would provide extensive amounts of SAR data. In this article, we describe an approach for continuously updating long-term multisensor InSAR deformation time series using robust sequential least squares. It is successfully applied to near-real-time monitoring of the long-term evolution of surface deformation in Xi'an, China, from January 3, 2007 to February 12, 2021, using four SAR satellites: ALOS/PALSAR-1, TerraSAR-X, ALOS/PALSAR-2, and Sentinel-1A. In order to analyze deformation evolution, temporal independent component analysis was used to interpret deformation patterns. We found that land subsidence in Xi'an has slowed and even halted in some areas. However, large areas are uplifting, which presents a potential for geohazards. We conclude that the proposed approach using continuously updated deformation time series from multisensor InSAR can provide nearreal-time deformation measurements, which are necessary for an early warning system.

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The current and planned synthetic aperture radar (SAR) sensors mounted on satellite platforms will continue to operate over the coming years, providing unprecedented SAR data for monitoring wide-range surface deformations. The near real-time processing of SAR interferometry (InSAR) data for the retrieval of ground-deformation time series is urgently required in the current era of big data. The state-of-the-art Kalman filter (KF) and sequential least squares (SLS) algorithms have been proposed to update an InSAR-driven ground-deformation time series. As a contribution of this study, we customize the conventional KF and SLS for big InSAR data for near real-time processing. The development of an accurate prediction model for KF-based InSAR processing is a challenge owing to the large scale of the targets for surface monitoring. We developed a modified KF algorithm, abbreviated as npKF, that does not require any prediction information, abbreviated as npKF. In this context, to avoid occupying a large storage space in SLS-based InSAR processing, we developed a modified SLS algorithm with a truncated cofactor matrix, abbreviated as TSLS. Using both simulated and actual SAR data, we evaluated the performance of these methods under three different aspects: accuracy, computation, and storage performance. With big data, the proposed method can estimate the deformation time series in near real time. It will be a reliable and effective tool for producing near real-time InSAR deformation products in the coming era of processing big SAR data and will play a part in the geologic hazard routine monitoring and early warning system.

Loess landslides represent an important geohazard in relation to the deformation of unstable loess structures occurred on the slope of loess-covered area. It has become one of the important topics to accurately identify the distribution and activity of loess landslides and describe the spatio-temporal kinematics in the western-project construction in China. Interferometric synthetic aperture radar (InSAR) proves to be effective for landslides investigation. This study proposes an improved InSAR-based procedure for large-area landslide mapping in loess-hilly areas, including tropospheric-delay correction based on quadtree segmentation and automatic selection of interferograms based on minimum-error boundary. It is tested in Dingbian County in Shaanxi Province, China. More than 200 SAR images were processed and a total of 50 potential loess landslides were detected and mapped. Results show that the landslides are mainly distributed along the river basins and concentrated in areas with elevation ranging from 1450 m to 1650 m, and with slope angles of 10–40°. Then, a total of eight (16%) loess landslides are classified as active ones based on three parameters derived from InSAR-deformation rates: activity index (AI), mean deformation rate, and maximum deformation rate. Moreover, we characterize the segmentation of detected landslides and describe the discrepancy of local topography and deformation rates by coupling the peak in probability-density curves of deformation rates and profiles of the elevation and deformation rates. Finally, correlation between landslide deformation and rainfall is given through wavelet analysis.