The ancient city of Xi’an, China, has been suffering severe land subsidence and ground fissure hazards since the 1960s, mainly due to the over-withdrawal of groundwater and large-scale urban construction. This has threatened and will continue to threaten the stability of urban infrastructure, such as the construction and operation of high buildings and subway lines. It is necessary to map the spatiotemporal variations of land subsidence over Xi’an, and to analyze their causes and the correlation with underground water level changes and ground fissure deformation. Time series of land subsidence were observed with the interferometric synthetic aperture radar (InSAR) technique, using multi-sensor SAR datasets from 2012 to 2018. Four land subsidence rate maps over Xi’an city were retrieved from TerraSAR-X, ALOS/PALSAR2, and Sentinel-1 data, each with different tracks. The InSAR derived results were then cross-validated with three independent SAR data stacks, and calibrated with GPS and leveling observations. Next, the spatiotemporal evolutions of three main regional land subsidence zones were quantitatively analyzed in detail, and the surface deformation of the Xi’an subway network was spatially analyzed. Third, the correlations between land subsidence and ground water withdrawal, ground fissure deformation, landforms, and faults were intensively analyzed. Finally, a flat lying sill model with distributed contractions was implemented to model the InSAR deformation over one typical subsidence zone, which further suggested that the ground deformation was mainly caused by groundwater withdrawal. This systematic research can provide sound evidence to serve decision-making for land subsidence mitigation in Xi’an, and may also guide land subsidence research in other cities.

Interferometric synthetic aperture radar (InSAR) time series deformation monitoring plays an important role in revealing historical displacement of the Earth’s surface. Xi’an, China, has suffered from severe land subsidence along with ground fissure development since the 1960s, which has threatened and will continue to threaten the stability of urban artificial constructions. In addition, some local areas in Xi’an suffered from uplifting for some specific period. Time series deformation derived from multi-temporal InSAR techniques makes it possible to obtain the temporal evolution of land subsidence and rebound in Xi’an. In this paper, we used the sequential InSAR time series estimation method to map the ground subsidence and rebound in Xi’an with Sentinel-1A data during 2015 to 2019, allowing estimation of surface deformation dynamically and quickly. From 20 June 2015 to 17 July 2019, two areas subsided continuously (Sanyaocun-Fengqiyuan and Qujiang New District), while Xi’an City Wall area uplifted with a maximum deformation rate of 12 mm/year. Furthermore, Yuhuazhai subsided from 20 June 2015 to 14 October 2018, and rebound occurred from 14 October 2018 to 17 July 2019, which can be explained as the response to artificial water injection. In the process of artificial water injection, the rebound pattern can be further divided into immediate elastic recovery deformation and time-dependent visco-elastic recovery deformation.

Shandong peninsula, the largest peninsula of China, is prone to severe land subsidence hazards along the coastline. In this paper, we provide, for the first time, multi-scale and multi-dimensional time series deformation measurements of the entire Shandong peninsula with advanced time series Interferometric Synthetic Aperture Radar (InSAR) techniques. We derive the spatiotemporal evolutions of the land subsidence by integrating multi-track Sentinel-1A/B and RADARSAT-2 satellite images. InSAR measurements are cross validated by the independent deformation rate results generated from different SAR tracks, reaching a precision of less than 1.3 cm/a. Two-dimensional time series over the Yellow River Delta (YRD) from 2017 to 2019 are revealed by integrating time series InSAR measurements from both descending and ascending tracks. Land subsidence zones are mainly concentrated on the YRD. In total, twelve typical localized subsidence zones are identified in the cities of Dongying (up to 290 mm/a; brine and groundwater exploitation for industrial usage), Weifang (up to 170 mm/a; brine exploitation for industrial usage), Qingdao (up to 70 mm/a; aquaculture and land reclamation), Yantai (up to 50 mm/a; land reclamation) and Rizhao (up to 60 mm/a; land reclamation). The causal factors of localized ground deformation are discussed, encompassing groundwater and brine exploitation, aquaculture and land reclamation. Multi-scale surveys of spatiotemporal deformation evolution and mechanism analysis are critical to make decisions on underground fluid exploitation and land reclamation.

Land subsidence is a widespread geo-hazard, and it can be effectively monitored with the Interferometric Synthetic Aperture Radar (InSAR) technique. Assessing land subsidence plays a significant role in ensuring safety and enhancing disaster prevention. It requires not only focusing on the extent or rate of deformation but also evaluating the susceptibility and risk of subsidence. In this paper, we propose a new comprehensive subsidence susceptibility and risk assessment strategy by integrating InSAR observation and hybrid machine learning models, which is first and successfully employed over Xi'an area. In this study, four machine learning models are compared to determine the optimal model, and found that the Random Forest (RF) performs the best in predicting InSAR-derived spatial deformation (RMSE = 3.53 mm) and susceptibility (Area Under the Curve (AUC) = 0.97). Also, the authenticity and reliability of the susceptibility results from RF model are improved by further Isotonic regression calibration processes. Then, subsidence risk map is obtained from the hazard and vulnerability assessment using the Analytic Hierarchy Process (AHP) by combining multiple conditional factors. Remarkably, the results revealed that regions with the very high and high risk level of subsidence are 12.33 km2 and correspond to 1.34% of the total. It further found that the groundwater level and its changes are the domain factors in Xi'an from machine learning method. This work provides an integrated assessment approach for subsidence from a new perspective, and the findings can serve as theoretical support for early warning and disaster prevention.

Active compound jamming, particularly compound interrupted sampling repeater jamming (ISRJ), possesses excellent flexibility and jamming effectiveness, making it one of the major threats to radar systems. Accurately measuring the key parameters of each ISRJ component within the compound ISRJ can provide critical prior information for subsequent anti-jamming efforts. However, most of the existing ISRJ parameter measurement methods target a single ISRJ and lack in-depth research on the measurement of compound ISRJ parameters. Therefore, we propose a unified framework for compound ISRJ parameter measurement that contains a compound ISRJ separation network based on an encoder-decoder architecture as well as a parameter regression module for measuring the key parameters of each ISRJ component in the compound ISRJ. Experimental results indicate that the proposed framework achieves parameter measurement accuracies of over 89% and 85% for dual-compound and multi-compound ISRJ, respectively, significantly outperforming existing methods.

The ancient Xi’an, China, has been suffering severe land subsidence and ground fissure hazards since the 1960s, which has affected the safety of Subways. Multi-sensor SAR data are conducted to monitor the latest complex ground deformation and its influence on subway line No.3 over Xi’an. Annual deformation rates have been retrieved to reveal the spatiotemporal evolution of ground subsidence in Xi’an city from 2013 to 2017. Meanwhile, the correlation between land subsidence and ground fissures are analyzed by retrieving the deformation differences in both sides of the fissures. Besides, the deformation along subway line No. 3 is analyzed, and the fast deformation section is quantitatively studied. Finally, a flat lying sill model with distributed contractions is implemented to model the InSAR deformation over YHZ subsidence center, which manifests that the ground deformation is mainly caused by groundwater withdrawal.