Interferometry Synthetic Aperture Radar (InSAR) provides unique capabilities to map regional/global topography and deformation of the Earth’s surface and has led to a broad spectrum of deformation monitoring applications. In order to adapt to various challenging monitoring environments, researchers have made tremendous innovations to deal with issues such as atmospheric and ionospheric effects, loss of coherence due to large displacements, geometric distortions and unwrapping errors. Owing to recent technical and methodological advances, the Earth’s surface deformation, ranging from earthquake ruptures, volcanic eruptions, landslides, glaciers, to groundwater storage variations, mining subsidence and infrastructure instability can now be mapped anywhere in the world at high spatial and temporal resolutions. This special issue received a set of contributions highlighting recent advances in methodologies and applications of InSAR to ground deformation monitoring. We aim to present overviews of both the state of the art of SAR/InSAR techniques and the next generation of applications across the broad range of deformation monitoring applications.

Synthetic Aperture Radar Interferometry (InSAR) has shown its potential on seismic deformation monitoring since it can achieve the accuracy of centimeter level or even the millimeter level. However, the irregular varieties of ionosphere can induce the additional phase delay on SAR interferometry, restricting its further application in high-precision deformation monitoring. Although several methods have been proposed to correct the ionospheric phase delay on SAR interferometry, the performances of them haven’t been evaluated and compared. In this study, three commonly used methods, including polynomial fitting, azimuth offset and split-spectrum are applied to L’Aquila Earthquake to correct the ionospheric phase delay on two Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite-1 (ALOS-1) images. The result indicates that these three methods can effectively correct the ionospheric phase delay error for SAR interferometry, where the standard deviations of the ionosphere-corrected results have decreased by almost a factor of 1.8 times for polynomial fitting method, 4.2 times for azimuth offset method and 2.5 times for split-spectrum method, compared to those of the original phase. Furthermore, the result of the sliding distribution inversion of the seismic fault shows the best performance for split-spectrum method.

An increasing interest in the use of low frequency Synthetic Aperture Radar (SAR) systems, e.g., L- and P-bands, makes the research of the ionospheric effects on SAR interferograms become urgent and significant. As the most pronounced signature in interferograms, the ionosphere-induced azimuth streak was thoroughly investigated in this study through processing of the 19 L-band Advanced Land-Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR)images over the Chongqing City, China. The investigations show that the visible ionosphere-induced stripe-shape azimuth shifts with the invariable direction of 26°E, 113°N are observed in some interferometric pairs. Relating these anomalous azimuth shifts to the International GNSS Service (IGS) final ionospheric products shows that the detected ionosphere-contaminated SAR images display the relatively large ionospheric variation with time during SAR satellite travelled through the study area, indicating a somewhat correlation between them. After detecting the ionosphere-contaminated interferograms, we estimated the Ionospheric Phase Streak (IPS) based on an approximate linear relationship between IPS and azimuth shift, and then removed them from the original interferograms. The corrected results show that ionospheric phase patterns are largely removed from the ionosphere-contaminated interferograms. The investigation indicates that the direction of the IPS keeps approximately constant in space and time, which provides the potential chance to develop methods to correct the ionospheric effect. Furthermore, this study once more proves that the ionospheric effect on SAR interferogram can be detected, estimated and corrected from azimuth shifts.

LuTan-1 (LT-1) is a constellation with two full-polarization L-band radar satellites designed by China, and the first satellite was scheduled to be launched in December 2021 and the second one in January 2022. The LT-1 will be operated for deformation monitoring in repeat-pass mode, and for DEM generation in bistatic mode, improving self-sufficiency of SAR data for the field of geology, earthquake, disaster reduction, geomatics, forestry and so on. In this paper, we focused on designing an algorithm for interferometric DEM generation using LT-1 bistatic satellites. The basic principle, main error sources and errors control of the DEM generation algorithm of LT-1 were systematically analyzed. The experiment results demonstrated that: ① The implemented algorithm had rigorous resolution with a theoretic accuracy better than 0.03m for DEM generation. ② The errors in satellite velocity and Doppler centroid had no obvious effect on DEM accuracy and they could be neglected. While the errors in position, baseline, slant range and interferometric phase had a significant effect on DEM accuracy. And the DEM error caused by baseline error was dominated, followed by the slant range error, interferometric phase error and satellite position error. ③ To obtain an expected DEM accuracy of 2m, the baseline error must be strictly controlled and its accuracy shall be 1.0mm or better for Cross-Track and Normal-Direction component, respectively. And the slant range error and interferometric phase error shall be reasonably controlled. The research results were of great significance for accurately grasping the accuracy of LT-1 data products and their errors control, and could provide a scientific auxiliary basis for LT-1 in promoting global SAR technology progress and the generation of high-precision basic geographic data.

Due to high interferometric coherence in the Nevada region, Interferometric Synthetic Aperture Radar (InSAR) phase stacking is capable of mapping coseismic signals from the 27 January 1999, M_w 4.8 Frenchman Flat earthquake. This is one of the smallest earthquakes yet studied using InSAR with line-of-sight displacements as small as ~1.5cm. Modelling the event as dislocation in an elastic half space suggests that the fault centroid was located at (115.96°W, 36.81°N) with a precision of 0.2~0.3km (1σ) at a depth of 3.4 ± 0.2km. Despite the dense local seismic network in southern Nevada, differences as large as 2~5km were observed between our InSAR earthquake location and those estimated from seismic data. The InSAR-derived magnitude appeared to be greater than that from seismic data, which is consistent with other studies, and believed to be due to the relatively long time interval of InSAR data.

In March 2021, a seismic sequence including three Mw>5.5 events struck northern Thessaly, Greece. Owing to the high temporal resolution of Sentinel-1 images which were sampled every 6 days and recorded the three events separately, we are able to map individually the coseismic deformation fields of the three events. Based on their respective coseismic displacements, we determined the geometry of the fault plane for each earthquake with the method of multipeak particle swarm optimization and inverted the best-fitting slip distribution by linear least squares inversion. Modelling results show that the three events occurred successively on 3, 4 and 12 March 2021 were all dominated by normal-slip motions on previously unknown faults within the top 15km of the Earth’s crust. The 3 March 2021 Mw 6.3 earthquake ruptured a northeast-dipping fault with a strike angle of 301° (clockwise from the North) and a dip angle of 46°, producing the maximum slip of about 2.2m. The slip motion of the 4 March 2021 Mw 5.9 aftershock shows a similar fault geometry (striking 297° and dipping 42°) to the 3 March mainshock, but with a considerably smaller dip-slip component (~0.8m). The 12 March 2021 Mw 5.6 aftershock occurred on a southwest-dipping fault (striking 100° and dipping 40°) with a normal fault slip of up to 0.5m. Static Coulomb stress changes triggered by the earthquake sequence imply a promotion relationship between the first 3 March event and the two subsequent events. Due to the coseismic stress perturbation, more than 70% of aftershocks were distributed in areas with increased Coulomb stress and the northwest segment of the Larissa fault close to the seismic sequence was exposed to a relatively high seismic risk.

Tianjin is one of the inland cities with the most severe cases of subsidence hazard in China. The majority of the existing studies have mainly focused on Beijing-Tianjin-Hebei, and little attention has been given to Tianjin. In addition, these existing studies are short-term investigations, lacking long-term monitoring of surface subsidence. In the present study, we use the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to monitor the subsidence process in Tianjin between 2015 and 2020 and reveal its spatial and temporal variation. We divided the 44-view Sentinel-1A image data into three periods to avoid the effect of temporal and spatial decoherence by extracting the surface deformation field in Tianjin. We finally verified the accuracy and reliability of the inversion results using second-order leveling data. Results showed that the correlation coefficient r between the two reached 0.89, and the root mean square error was 4.84mm/y. Obvious subsidence funnels exist in Tianjin, mainly in the towns of Wangqingtuo and Shengfang. These subsidence funnels have a subsidence deformation rate of -136.2mm/y and a maximum cumulative settlement of -346.3mm within the study period. The subsidence area tends to extend to the southwest. The analysis of annual rainfall, groundwater resource extraction, spatial location distribution of industrial areas combined with SBAS-InSAR inversion results indicates that overextraction of groundwater resources is the main cause of land subsidence in the area. Therefore, strict control of groundwater extraction is the main approach to mitigate land subsidence effectively.

Low-Rank and Sparse Representation (LRSR) method has gained popularity in Hyperspectral Image (HSI) processing. However, existing LRSR models rarely exploited spectral-spatial classification of HSI. In this paper, we proposed a novel Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization (LRSR-ANR) method for HSI classification. In the proposed method, we first represent the hyperspectral data via LRSR since it combines both sparsity and low-rankness to maintain global and local data structures simultaneously. The LRSR is optimized by using a mixed Gauss-Seidel and Jacobian Alternating Direction Method of Multipliers (M-ADMM), which converges faster than ADMM. Then to incorporate the spatial information, an ANR scheme is designed by combining Euclidean and Cosine distance metrics to reduce the mixed pixels within a neighborhood. Lastly, the predicted labels are determined by jointly considering the homogeneous pixels in the classification rule of the minimum reconstruction error. Experimental results based on three popular hyperspectral images demonstrate that the proposed method outperforms other related methods in terms of classification accuracy and generalization performance.

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, change detection methods based on UAV images have been extensively studied. However, the imaging of UAV sensors is susceptible to environmental interference, which leads to great differences of same object between UAV images. Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection. To address this issue, a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model (GFLICM) was proposed in this study. Within this method, the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images. The local variation coefficient was introduced and a new fuzzy factor was reconstructed, after which the GFLICM algorithm was used to analyze difference images. Finally, change detection results were analyzed qualitatively and quantitatively. To measure the feasibility and robustness of the proposed method, experiments were conducted using two data sets from the cities of Yangzhou and Nanjing. The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.