Shenzhen, the first special economic zone of China, has witnessed earth-shaking changes since the late 1980s. In the past 35 years, about 80 km² of land has been reclaimed from the sea in Shenzhen. In order to investigate coastal vertical land motions associated with land reclamation, we proposed an elaborated Point Target (PT) based Small Baseline Subset InSAR (SBAS-InSAR) strategy to process an ENVISAT ASAR ascending and descending orbits dataset both acquired from 2007 to 2010. This new strategy can not only select high density PTs but also generate a reliable InSAR measurement with full spatial resolution. The inter-comparison between InSAR-derived deformation velocities from different orbits shows a good self-consistency of the results extracted by the elaborated PT-based SBAS-InSAR strategy. The InSAR measurements show that the reclaimed land is undergoing remarkable coastal subsidence (up to 25 mm/year), especially at the Shenzhen Airport, Bao’an Center, Qianhai Bay and Shenzhen Bay. By analyzing the results together with land reclamation evolution, we conclude that the ground deformation is expected to continue in the near future, which will amplify the regional sea level rise.

Subways have been an important method for relieving traffic pressures in urban areas, but ground subsidence, during construction and operation, can be a serious problem as it may affect the safety of its operation and that of the surrounding buildings. Thus, conducting long-term ground deformation monitoring and modeling for subway networks are essential. Compared with traditional geodetic methods, the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique offers wider coverage and denser measurements along subway lines. In this study, we mapped the surface deformation of the Guangzhou subway network with Advanced Synthetic Aperture Radar (ASAR) and Phased Array Type L-band Synthetic Aperture Radar (PALSAR) data using the Interferometric Point Target Analysis (IPTA) technique. The results indicate that newly excavated tunnels have regional subsidence with an average rate of more than 8 mm/year, as found on Lines Two, Three, Six, and GuangFo (GF). Furthermore, we determined the spatio-temporal subsidence behavior of subways with PALSAR in delta areas using Peck’s formula and the logistic time model. We estimated the tunneling-related parameters in soft soil areas, which had not been previously explored. We examined a section of line GF, as an example, to estimate the ground settlement trough development. The results showed the maximum settlement increased from −5.2 mm to −23.6 mm and its ground loss ratio ranged from 1.5–8.7% between 13 July 2008 and 19 January 2011. In addition, we found that the tunnels in line GF will become stable after a period of about 2300 days in peak subsidence areas. The results show that the proposed approach can help explain the dynamic ground subsidence along a metro line. This study can provide references for urban subway projects in delta areas, and for the risk assessment of nearby buildings and underground pipelines along metro lines.

The Generic Atmospheric Correction Online Service (GACOS) products for interferometric synthetic aperture radar (InSAR) are widely used near-real-time and global-coverage atmospheric delay products which provide a new approach for the atmospheric correction of repeat-pass InSAR. However, it has not been determined whether these products can improve the accuracy of InSAR deformation monitoring. In this paper, GACOS products were used to correct atmospheric errors in short baseline subset (SBAS)-InSAR. Southern California in the U.S. was selected as the research area, and the effect of GACOS-based SBAS-InSAR was analyzed by comparing with classical SBAS-InSAR results and external global positioning system (GPS) data. The results showed that the accuracy of deformation monitoring was improved in the whole study area after GACOS correction, and the mean square error decreased from 0.34 cm/a to 0.31 cm/a. The improvement of the mid-altitude (15–140 m) point was the most obvious after GACOS correction, and the accuracy was increased by about 23%. The accuracy for low- and high-altitude areas was roughly equal and there was no significant improvement. Additionally, GACOS correction may increase the error for some points, which may be related to the low accuracy of GACOS turbulence data.

Accurately predicting ground surface deformation is a crucial task in mining-related geohazards control. Interferometric synthetic aperture radar (InSAR) technique can widely detect historical line-of-sight (LOS) displacements with a high spatial resolution. By incorporating with spatio-temporal deformation models, InSAR can predict kinematic 3-D displacements due to underground mining. However, this method depends on the geometric parameters (at least seven generally) of underground mined-out areas, hindering its practical applications especially over a large area. To circumvent this, we proposed a new method for predicting kinematic 3-D mining displacements by incorporating InSAR with a temporal model, rather than spatio-temporal models used before, in this article. In doing so, much less prior parameters (only three and can be empirically given) are required, with respect to the previous InSAR-based methods. To achieve this, we first revealed that InSAR LOS displacements caused by underground longwall mining at a single point temporally follow an S-shaped growth pattern. Meanwhile, we also showed that a Weibull model can describe the temporal evolution well. Based on these findings, the proposed method first predicts, in a point-wise manner, the kinematic LOS mining displacements from historical InSAR measurements using the Weibull model and a Kalman filter. The kinematic 3-D displacements are then resolved from the predicted LOS displacements with the help of a common prior information relating to mining deformation. The proposed method was tested in the Datong coal mining area of north China. The results show an averaged accuracy of about 0.007 m of the resolved kinematic 3-D displacements.

Yunnan Province, China, has complex topography and geomorphology, many ravines and valleys, and frequent landslide geological disasters and is of great significance in the assessment of regional landslide geological disasters in Yunnan Province for disaster prevention and mitigation. In this study, Yunnan Province was selected as the research area, and the average annual deformation rate of radar line-of-sight in Yunnan Province over four years from 2018 to 2021 was obtained with SBAS-InSAR technology, which was used as one of the index factors for the susceptibility evaluation of Yunnan Province. The deformation rate reflects the slow movement of the land surface. In addition, elevation, slope, aspect, lithological classification, geological structure, rainfall, distance from roads, distance from rivers, topographic undulation, and NDVI were selected as evaluation index factors and combined with the annual mean deformation rate. A random forest model was used to evaluate and accurately analyze landslide geological disasters in Yunnan Province. The results showed that as an important index factor, the annual mean deformation rate of Yunnan Province can be added to the random forest model to improve the prediction accuracy. The area with high susceptibility accounted for 10% of the entire province, and the number of landslides in the region accounted for 68% of the province. Additionally, the results for prone zoning were highly correlated with the landslide distribution. The accuracy of the random forest model prediction was 0.80, and the AUC value was 0.87, indicating that the random forest model was a highly accurate and reliable evaluation method for studying landslide geological disasters. It is very suitable for the evaluation of landslide susceptibility in Yunnan Province.