Interferometric Synthetic Aperture Radar (InSAR) has become a widely used and efficient tool for monitoring large-scale, long-term land subsidence. Most applications, especially those related to risky assessment, utilize only the mean deformation rate derived through a linear fit of the InSAR-derived time series deformations without considering the full-time sequence. In this way, the embedded information, such as the onset, duration, and patterns of abnormal deformations, is ignored. This information, however, should not be compromised as it is critical for assessing the risky deformations and recognizing the causal factors. Due to the large data volume, processing the full-time-series information derived by InSAR analysis can be a challenge. In this study, we propose to use the Time-series Anomaly Detection Generative Adversarial Network (TadGAN) to deal with the time series InSAR deformation and recognize the onset and duration of abnormal epochs. The proposed method has been tested with InSAR-derived results over the Hong Kong airport region. It performs better than conventional risk assessment methods, such as the one based on Mean Absolute Deviation (MAD) outlier detection.

Interferometric Synthetic Aperture Radar (InSAR) has been recognized as an effective remote sensing tool for Earth Observation (EO) thanks to its capacity to penetrate clouds and forests. In the field of InSAR deformation monitoring, coherence is used as an indicator for the stability of the interferometric phase. Actually, interferometric coherence itself contains valuable information about land surface and can be used to classify land cover types. Currently, there are few studies about the possibility of retrieving forest canopy height from interferometric coherence, especially its multitemporal variation. In this study, we emphasize the relationship between multi-temporal InSAR coherence and forest canopy height by investigating the InSAR coherence variation of multi-temporal Sentinel-1 data regarding the tree height measured by the NASA Global Ecosystem Dynamics Investigation (GEDI). Results show that forest canopy height can be estimated by using the Random Forest (RF) regression model trained by samples of GEDI height and InSAR coherence variation. InSAR coherence performs better in estimating the forest canopy height than using other remote sensing data, such as multispectral reflectance from Sentinel2 and SAR backscatter intensity.

Analyzing road network subsidence and sinkholes is crucial for ensuring urban traffic and people's safety. InSAR technique is a non-contact measurement technique with high spatiotemporal resolution, wide monitoring range, and unaffected by road conditions, which is of great significance for the analysis of deformation hazards of man-made linear infrastructures, such as road networks and high-speed railways. In this study, we adopt 44 Sentinel-1A images from January 2022 to July 2023 to study the deformation of the road network in Longgang District, Shenzhen, China based on PS-InSAR. The road deformation is further analyzed associated with sinkhole information from field investigation. The results show that the deformation rate of the road network is between -38.3 mm/year and 16.9 mm/year, and the correlation coefficient between the top burial depth of the sinkholes and the maximum deformation rate is 0.58.

Accurate automated extraction of coseismic deformation from Synthetic Aperture Radar (SAR) data can be challenging owing to interference from inherent atmospheric noise. Particularly, the limited displacement of small-to-moderate earthquakes (Mw<6.5) can easily be obscured by phase errors and/or noise. To address this issue, we developed an autoencoder model based on a deep learning framework (i.e., Pytorch) to automate the accurate extraction of coseismic displacement from Interferometric SAR (InSAR) interferograms. We constructed a training dataset using simulated interferograms. Our trained model performed well for interferograms with real noise. When applied to worldwide real earthquakes of various rupture styles, the model produced clear coseismic displacement with less noise and a better fit to coseismic fault models compared to the differential InSAR method without noise correction. Additionally, it achieved co-seismic deformation similar to popular InSAR time series and GNSS methods. The approach will enhance the proceduralization and popularization of InSAR applications in earthquake monitoring, providing improved constraints on the kinematic characteristics of earthquakes.

Ground-based interferometric radar (GBIR) is an innovative tool for monitoring land surface subsidence and urban infrastructure deformation caused by rapid urbanization. However, the interferograms of GBIR are often contaminated by severe atmospheric effects, especially in coastal areas. In this study, we use independent component analysis (ICA) to extract atmospheric effects for the interferograms of GBIR. Analysis of the performance of ICA and traditional surface fitting methods have been carried out. The results suggest that the average improved rate of ICA is 93.05%, which is 69.33% higher than that of surface fitting.

Multitemporal InSAR is a widely used geodetic technique for measuring ground deformation. However, assessing the accuracy of InSAR deformation results is challenging, especially when field measurements such as leveling are limited in coverage or unavailable. While many studies have attempted to calculate the uncertainty of deformation using a priori InSAR stochastic models to assess the deformation reliability, these models are often biased by various factors. In this letter, we propose a new method called the Sparse Parameter Model (SPM) for InSAR deformation retrieval and uncertainty assessment when instantaneous deformation is not the focus. The method estimates the sparser deformation time series and leverages redundant SAR observations for the deformation uncertainty assessment and decorrelation noise suppression. The proposed model is tested by both simulated and real Sentinel-1 datasets and the derived deformation was validated with GPS measurements in the real application. The results demonstrated that the overall uncertainty of InSAR deformation, as estimated by the SPM, is 5.4 mm, falling well within the expected range of uncertainty, which highlights the effectiveness of the SPM in retrieving InSAR deformation and assessing uncertainty.

The construction of the metro system alleviates the traffic congestion caused by urban expansion. However, at the same time, improper construction and operation activities would cause ground deformation that might lead to the urban hazards of ground cracks, building tilting, and ground collapse. Such ground deformation along the metro system can be generally measured by the interferometric synthetic aperture radar (InSAR) technique owning to the advantages of wide ground coverage and high accuracy of measurements. In this study, a general survey of the ground stability along the Shenzhen metro system has been carried out with the PS-InSAR technique using the 119 Sentinel-1A images from March 2017 to March 2021. The results show that, during the observation period, the ground deformation rate along the Shenzhen metro system is ranged from -26.2 mm/year to 9.3 mm/year. The deformation is mainly due to the consolidation of the reclamation land, construction activities of new metro lines, and geological changes.

Interferometric synthetic aperture radar (InSAR) has been more and more applied in acquiring long-term deformation of land surface in a large coverage and is becoming a routine investigation technique. Validation of the InSAR results depends largely on thein situmeasurements. These measurements are usually point-wise and of high cost, as many sensors need to be set up for the long-term monitoring. In applications associated with a large area, qualitative and low-cost validation may be more necessary at the first place, which is still a challenge. In the recent decade, the crowdsourcing and volunteered geographic information (VGI) have been more and more accepted in the field of geoinformatics. Inspired by these, this letter proposes an InSAR crowdsourcing annotation system to integrate the InSAR displacements and the photographs uploaded by volunteers. We processed 119 Sentinel-1A data ranging from 2017 to 2021 to derive long-term displacements. Then, based on the InSAR displacements, task areas were selected and published to public via the system. Volunteers online accepted the task and uploaded photographs, indicating land displacements. In a coastal city, Shenzhen of China, 135 task areas were selected and published in total, and 1742 useful photographs were uploaded. The uploaded photographs were then inspected to validate and analyze the InSAR results. Post-analysis found some high correlation between the uploaded photographs with the InSAR alerting displacements. The proposed system is a prototype, and its interface and functions can be further extended in the future toward an effective and efficient alerting system for risking land deformations.

The proper use of distributed scatterer (DS) can improve both the density and quality of synthetic aperture radar (SAR) interferometry (InSAR) measurements. A critical step in DS interferometry (DSI) is the restoration of a consistent phase series from SAR interferogram stacks. Most state-of-the-art algorithms adopt an approximate likelihood function to calculate the likelihood by replacing the true coherence matrix with its estimation, more specifically, the sample coherence matrix (SCM). However, this approximation has a drawback in that the coherence estimates are greatly biased when the coherence is low. In this study, we derive a new likelihood function without such an approximation. Accordingly, a DSI framework using this function for phase estimation and point selection is provided. In this framework, the new likelihood function serves as a cost function for phase estimation and a quality measure for DS selection. Its performance is investigated by experiments in a simulation study and a real-world case study using Sentinel-1 data over Shenzhen airport in China. The results reveal that the proposed DSI framework outperforms the existing state-of-the-art approaches in different scenarios, in terms of providing a more accurate estimation and improving DS density and coverage.

Tatun volcano group, the last active volcano in Taiwan, is located in northern Taipei Basin, only 15 km north of the Taipei City. It was previously thought to be a dead volcano, but recent studies show that the last magmatic eruption happened about 5000 to 6000 years ago. The geothermal and seismic activities over the Tatun volcanic area have been highly active in recent years. The geochemical analysis implies the potential existing of the magma chamber under the ground surface of northern Taiwan which has the possibility of re-eruption in the future. In this study, we use ALOS-1/PALSAR images to monitor the surface deformation at the Tatun volcanic area. Stratified atmospheric delay and orbit errors are well considered and corrected by an adaptive patch-based method. The derived displacement history provides a detailed map of surface change with large spatial extent, which is validated by GPS measurements. The results demonstrate the capability of InSAR technique to monitor surface deformation over the volcanic zones.

Multi-temporal interferometric synthetic aperture radar (MT-InSAR), as one of the geodetic techniques, is widely used in geological disaster monitoring and engineering applications. However, modern satellites provide an endless array of SAR imagery for InSAR applications, which has prompted us in exploring more efficient and accurate MT-InSAR methods to monitor the ground stability continuously and early detect the ground hazards. We proposed in this paper a step-by-step estimator for continuous deformation monitoring, aiming to study the ground surface deformation that has occurred in the past few years and monitor the latest developments in the same location continuously. Associated with the existing results of MT-InSAR, when a new SAR image is available, the unwrapped phase vector is sequentially estimated with the ambiguity detection, and then the deformation parameters are updated timely. Through experimental verification over Hong Kong, the proposed estimator shown its capability to detect the abnormal changes and update the deformation sequentially without being affected by the complex deformation histories.

The last two decades have witnessed a dramatic development of the satellite interferometric synthetic aperture radar (InSAR) technology in both the theoretical methods and operational platforms. The propagation errors of SAR signal in the ionosphere is one of the problematic issues for the low-frequency SAR systems, such as L-band and P-band, and can also seriously degrade the accuracy of InSAR. Recently, there has been renewed interest in the range split-spectrum method for the ionopheric correction after some critical issues are resolved. In this study, we used the ultra-fine observation mode (80 MHz) of the Advanced Land Observation Satellite-2 (ALOS-2) phased array-type L-band synthetic aperture radar-2 (PALSAR-2) data over the 2016 Kumamoto earthquake to evaluate the performance of an asymmetric split-spectrum method for the ionospheric correction. The presented works demonstrate the effectiveness of two types of the asymmetric split-spectrum method for mitigating the ionospheric artifact in InSAR, as compared with the conventional split-spectrum method. Additionally, this study provides practical insight into the ionospheric correction strategies of the scheduled NASA-ISRO synthetic aperture radar (NISAR) mission.

Interferometric synthetic aperture radar (InSAR) is one of the most popular geodetic techniques for studying earthquake-related crustal displacements. Satellite SAR signals interact with the ionosphere when they travel through it during the synthetic aperture time. The condition of the ionosphere and its variation can significantly affect spaceborne InSAR measurements. In this letter, we use the Advanced Land Observation Satellite Phase Array-Type L-band SAR data from the 2009 southern Sumatra earthquake to evaluate the effects of the ionospheric artifacts on the slip distribution inversion of earthquake. The split-spectrum method is used to estimate and correct the ionospheric artifacts in the InSAR results. This letter shows that the long-wavelength ionospheric artifacts in the coseismic interferograms can be effectively mitigated. The slip distribution of the earthquake derived from the interferograms corrected for the ionospheric artifacts is presented. The slip distribution pattern and the magnitude of the slip are significantly refined after correcting the ionospheric artifacts.

Ground based interferometric radar (GBIR) is a revolutionary advanced measurement technique for geoscience and engineering geodesy. It is powerful for temporally and spatially dense measurements of highly dynamic target with sub-millimetric accuracy, especially in man-made structures, e.g. buildings, towers, dams and bridges. In this case study, we use a real aperture radar system, the Gamma Portable Radar Interferometer (GPRI-II), to perform near-real-time deformation monitoring of the deck (back side) of a cable-stayed bridge. As a test site, the Ting Kau Bridge at Tsuen Wan, Hong Kong, was continuously measured from two modes of observation, rotated azimuth scanning (RAS) and fixed azimuth scanning (FAS). The results reveal the wind-driven and vehicle-driven non-uniform oscillation of the bridge. The presented works demonstrate the ability of GPRI-II in bridge deformation or oscillation monitoring, which provide a new way for structural health monitoring of bridge.