In permafrost regions, ground surface deformations induced by freezing and thawing threaten the integrity of the built environment. Mapping the surface displacement of the ground at a high spatial resolution is of practical importance for the construction and planning sectors. In central Yakutia (the Sakha Republic), the long-term trend displays a consistent mean annual air temperature (MAAT) increase from -9.6 ‰ to -6.7 ‰, with pronounced temperature anomalies in the last decade. We processed Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) data from 2017 to 2021, acquired over the area of Yakutsk city. We performed phase optimization using adaptive coherence estimation to reduce the impact of vegetation. We then used StaMPS technology to generate surface deformation time series, allowing us to analyze the spatiotemporal distribution of seasonal deformation during freezing and thawing. The research findings indicate the boreal forest region in central Yakutia displays InSAR displacement signal in association with surface uplift caused by freezing of the active layer while urban area shows resilience against the melting permafrost.

Interferometric synthetic aperture radar (InSAR) has been widely applied in geoscience. As a fundamental parameter, coherence provides a quantitative measure of the interferometric phase quality and ground surface change between two SAR acquisitions. Unfortunately, the sample-estimated coherence is often biased due to the signal inhomogeneity and the bias of the estimator, especially when the numbers of SAR images and spatial samples are limited. In this study, we develop a hybrid method to improve the accuracy of coherence estimation. The effect due to heterogeneous pixels is suppressed by homogeneous pixel selection using Kullback-Leibler divergence. The bias caused by the sample magnitude estimator is mitigated through an iterative M-estimation. Experimental results from both simulation and real data tests demonstrate that the new method works well at texture-significant areas with insufficient SAR images.

The application of multitemporal interferometric synthetic aperture radar (MTInSAR) technology in bridge structural health monitoring often encounters considerable challenges due to the intricate nature of bridge structures. Notably, the thermal expansion and contraction (TEC) of bridges can lead to prominent interferometric phase jumps at the expansion joints. When the magnitude of the phase jump exceeds$\pi $, the continuity assumption required for phase unwrapping is no longer valid. Consequently, classical phase unwrapping methods fail to accurately retrieve bridge deformation. To address this limitation, we propose an adaptive MTInSAR method that can partition the bridge into independent segments and concurrently estimate deformation from multiple reference points. The algorithm first identifies expansion joint locations using a mean square error threshold. Subsequently, reference point selection and segmental phase unwrapping are performed to derive displacement time series of persistent scatterers (PSs), where the mechanical properties of the bridge structure are considered. We validate the effectiveness of the method using 23 TerraSAR-X (TSX) images of the Shanghai Yangtze River Bridge. The results demonstrate the successful detection of expansion joints and reliable phase unwrapping in PS subnetworks. Moreover, a comparative analysis with the classical minimum cost flow (MCF) method highlights the superior adaptability and reliability of the proposed approach. Finally, threshold values for triggering conditions when phase jumps occur are quantified. The proposed work will enhance the robust monitoring of bridge motions, safeguarding the structural health of bridges.

Coherence estimation is crucial in Interferometric Synthetic Aperture Radar (InSAR) for various applications, including land cover classification, change detection, and multi-temporal InSAR techniques. However, estimation challenges often arise due to systematic phases caused by deformation and topography, leading to an underestimation. Existing FFT-based stripe correction methods have limitations in handling complex patterns and are noise-sensitive. We propose here a novel approach using local phase surface fitting to remove the trend component and thereby improve the accuracy of the coherence estimation. The algorithm involves adaptive window size selection based on varying phase patterns and joint estimation of surface model parameters using regional network adjustment. Comparative experiments with simulated and Sentinel-1A data demonstrate the superiority of the method in effectively removing trend components, especially from nonlinear stripe regions. This improvement is evident in the significantly enhanced average coherence, with increases of 37.4% and 60.1% observed in the two experimental areas, resulting in enhanced quality of interferogram coherence maps.

With well-controlled orbits and a more accurate timing system of the new-generation synthetic aperture radar (SAR) satellites, the ability of high-precision geolocation with multiple SAR acquisitions has been verified based on radar reflectors. In this letter, SAR absolute and differential geolocation methods are introduced and implemented to retrieve the coordinates of two different types of radar reflectors: corner reflectors (CRs) and multidirectional dihedral reflectors (MDRs). The experimental results demonstrate that decimeter- even centimeter-level positioning accuracy can be obtained by SAR stereo geolocation based on multiaspect SAR acquisitions from RadarSAT-2 (RS-2), COSMO-SkyMed (CSK), Sentinel-1 (S1), and TerraSAR-X (TSX), respectively.

We propose a new method for retrieving 3-D deformation maps by combining the single Interferometric Synthetic Aperture Radar (InSAR)-line of sight (LOS) data and the physical property of crustal deformation, where the spatial coherence of the crustal deformation direction in elastic senses caused by fault slip is considered. First, fault slip and geometry parameters are inverted based on a simplified elastic dislocation model using the InSAR-LOS data. The continuous 3-D surface deformation direction vectors are then estimated by the model parameters. Finally, with the constraints of direction, InSAR-LOS is transformed into the 3-D components, and the 3-D coseismic deformation field is obtained. We implement the L-band SAR data from Advanced Land Observing Satellite (ALOS)/Phased Array L-band SAR (PALSAR) for the 2008 Wenchuan earthquake to testify our method. For the purpose of comparison, four representative models are constructed, ranging from rough to fine. The experimental results explain that the spatial consistency of our method agrees well with the field investigation and Global Navigation Satellite System (GNSS) observations and shows more application potentials than forward modeling.

Synthetic aperture radar (SAR) missions with short repeat times enable opportunities for near real-time deformation monitoring. Traditional multitemporal interferometric SAR (MT-InSAR) is able to monitor long-term and periodic deformation with high precision by time-series analysis. However, as time series lengthen, it is time-consuming to update the current results by reprocessing the whole dataset. Additionally, the number of coherent scatterers varies over time due to disappearing and emerging scatterers due to inevitable changes in surface scattering, and potential deformation anomalies require changes in the prevailing deformation model. Here, we propose a novel method to analyze InSAR time series recursively and detect both significant changes in scattering as well as deformation anomalies based on the new acquisitions. Sequential change detection is developed to identify temporary coherent scatterers (TCSs) using amplitude time series. Based on the predicted phase residuals, scatterers with abnormal deformation displacements are identified by a generalized ratio test, while the parameters of stable scatterers are updated using Kalman filtering. The quality of the anomaly detection is assessed based on the detectability power and the minimum detectable deformation. This facilitates (near) real-time data processing and decreases the false alarm likelihood. Experimental results show that the technique can be used for the real-time evaluation of deformation risks.

For delta area once groundwater is over-exploited, it may trigger a series of geological disasters, especially ground subsidence, which made the city along coastal regions have an increased risk of sea-level rise. During recent years at Yangtze Delta ground subsidence has got been controlled primarily via re-pumping of seawater into land region, but the subsidence is still continuous. TCPInSAR(Temporarily Coherent Point Synthetic Aperture Radar Interferometry) technique as an improvement of PSInSAR can generate the settlement time series with high accuracy. We adopted this technique to process ENVISAT ASAR images spanning from 2007 to 2009 and TerraSAR-X images spanning from 2009 to 2010 to obtain the land surface subsidence in Shanghai. And then we compared the settlement of this area with the groundwater level measured by the in-situ wells, and found the two are in good agreement. Finally, we estimated the storage coefficients of the aquifer. This study showed that InSAR technique is very useful to monitor and model groundwater efficiently.

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) is used for many applications in earth observation. Most MT-InSAR methods select scatterers with high coherence throughout the entire time series. However, as time series lengthen, inevitable changes in surface scattering lead to decorrelation, which systematically decreases the number of coherent scatterers. Here, we propose a novel method to detect and process temporary coherent scatterers (TCS) by subsequently analyzing the amplitude and the interferometric phase. Two hypothesis tests are developed for amplitude analysis in order to identify the moments of appearing and/or disappearing coherent scatterers. Based on the amplitude analysis, the parameters of interest are then estimated using the interferometric phase. An optimized adaptive temporal subset approach is proposed to improve the precision of the estimated parameters. If the scatterers are not evenly distributed over the area, a secondary (support) network is designed to improve the spatial point distribution. The main advantage of this method is the reliable extraction of a subset of time series without using any contextual information. Experimental results show that the TCSs significantly increase the number of observations for displacement monitoring and improve the change detection capability in urban construction areas.

In the multi-temporal InSAR technique, a deformation map is obtained by arc integration. Therefore, the reliability of an arc solution directly influences the precision of the final result. Many methods have been proposed to estimate arc parameters, but few have evaluated the arc quality. In this study, the influence of phase ambiguity is analyzed and two indicators are defined to identify the arcs with ambiguity. Subsequently, phase unwrapping is applied and bad arcs are removed according to other two indicators. In addition, the network is reconstructed based on a simple star network to link most points. Finally, the result obtained by this method is compared with the result by GAMMA software, which generally exhibit good agreement.

In this letter, the permanent scatterer interferometric synthetic aperture radar (PS-InSAR) method is applied to extract ground subsidence using X-band TerraSAR-X images. A zone of 1 km width on both sides of the Shanghai maglev was selected, and the ground subsidence rates were obtained on all permanent scatters or highly coherent targets. The results show that permanent scatters are distributed on the rail of the maglev, and the subsidence rates of these PS points are mostly less than 3 mm/a. A KS-test is formulated to judge the agreements between the subsidence results obtained by PS-InSAR and those obtained by spirit leveling, which indicated that PS-InSAR results are acceptable statistically. In addition, the PS-InSAR results show some ground subsiding troughs near the maglev track, with subsidence rates of more than 10 mm/a.