Multi-temporal InSAR Research Articles

InSAR-Based Active Landslide Detection and Characterization Along the Upper Reaches of the Yellow River

Landslides have attracted extensive attention and research worldwide because of their tremendous destructiveness, and many catastrophic landslides have occurred along river corridors and reservoir bank slopes. The detection and detailed monitoring of potential landslides are prerequisites for landslide disaster prevention, with recent advance in spaceborne Interferometric Synthetic Aperture Radar (InSAR) having effectively addressed this challenge. In this study, a wide-area landslide detection and monitoring framework, combining multiple InSAR techniques, is proposed to investigate active landslides along the upper reaches of the Yellow River from Longyang Gorge to Liujia Gorge (UYRLL), north-west China. A total of 597 active landslides have been mapped in this region for the first time. Our analyses suggest that a large percentage of the detected landslides are distributed at an elevation of 2,000-3,000 m with slope angles of 5-30°and their spatial distribution density is correlated with regional tectonic activity. Multi-Temporal InSAR (MTI) techniques have also been adopted to analyze the surface motion characteristics of two typical landslides (i.e., the Lijia landslide and the Xijitan landslide), showing that these two landslides are dominated by linear motion. The landslide movements also contain slight non-linear oscillations, inferred from wavelet analysis to be associated with reservoir water area changes and seasonal rainfall. The proposed framework herein can be extended to the detection and monitoring of active landslides in other areas with similar geomorphic features, and the above findings on landslide characteristics can also contribute to landslide prevention and mitigation in the upper Yellow River area.

MT-InSAR Unveils Dynamic Permafrost Disturbances in Hoh Xil (Kekexili) on the Tibetan Plateau Hinterland

Hoh Xil is an uninhabited extremity secluded on the Tibetan Plateau hinterland. A complete mapping of ground motion variation in Hoh Xil is essential for in-depth understanding of terrain’s responses to climate change on the Tibetan Plateau. However, the inaccessibility and extremely harsh environment impeded extensive field investigations on landform alteration and its formative process. Such difficulty can be resolved by Interferometric Synthetic Aperture Radar (InSAR), which enables a broad detection of subtle permafrost motions at millimeter precision. This study, for the first time, accomplished a Multi-temporal InSAR (MT-InSAR) deformation mapping from 2015 to 2020 in Hoh Xil, with a wide coverage of about 200,000 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . 1,592 Sentinel-1 images were processed based on the small baseline subset (SBAS) technique. The results show that Hoh Xil was experiencing dynamic permafrost disturbances. Thawing permafrost with both the linear subsidence rate higher than 2 mm/yr and the periodic amplitude over 2 mm was primarily detected in areas of flat or gentle slopes. The InSAR cumulative deformation is highly correlated with permafrost thawing depth. Significant lag times were identified between seasonal oscillation of InSAR deformation and land surface temperature (LST). Thermokarst landforms of retrogressive thaw slumps and thermokarst lakes broadly formed and dynamically evolved as a consequence of permafrost degradation. Particularly, widespread thawing permafrost characterized by the spatial clustering of thermokarst lakes appeared to occur in areas adjacent to large lakes. The discovered dynamic permafrost disturbances in Hoh Xil manifested even the secluded Tibetan Plateau hinterland was facing the threat of climate change.

Investigation of Ground Deformation around The Segments of Kendeng Fault in East Java 2017-2020 using Multi-Temporal InSAR Analysis with GACOS Correction Product

Multi-Temporal Interferometric SAR (MT-InSAR) analysis technique can provide spatially continuous measurements of the ground deformation and monitoring caused by the movement of four segments in the Kendeng Fault, East Java. Most ground deformation in these segments is indicated by the dominance of local thrust movements, folds, and the seismic density of earthquakes. In this study, ground deformation detection was carried out using the Small Baseline Subset Algorithm in the MT-InSAR process with the tropospheric correction from GACOS to determine ground deformation during 2017-2020. The percentage of change in the deformation velocity value resulting from applying the GACOS correction is up to 33.8% compared to without the tropospheric correction. The most significant troposphere effect correction is the area around the Cepu segments, which are dominated by high elevation thrust and folds. The results of the mean LOS velocity around the southern segments of Cepu, Waru, and Surabaya based on the Sentinel 1A SAR represent the occurrence of land subsidence in the south part of the area around segments with values ranging from 3.0 mm/yr to 6.0 mm/yr. Thus, the deformation effect causes land subsidence in the area around the delineation of the northern fault segment up to -3.0 mm/yr.

Enhancing MTInSAR Phase Unwrapping in Decorrelating Environments by Spatiotemporal Observation Optimization

In multitemporal InSAR (MTInSAR) processing, phase unwrapping (PhU) is a vital procedure, which affects the accurate deformation retrieval, especially in environments suffering decorrelation. Although a great surge of work focuses on solving the integers of 2π. associated with the wrapped observations (i.e., the inputs of unwrapping algorithms), the quality of inputs deserves an equal attention, as it is a basis for a reliable unwrapping. In this letter, we seek to improve the PhU accuracy by enhancing the entire quality of differential phase observations. To select an optimal interferogram stack efficiently, we propose a quadtree-based coherence estimation method for fast evaluating the interferogram quality, and then develop a strategy that constructs redundant networks connecting SAR images and points, respectively. The proposed strategy utilizes the combination of minimum spanning tree (MST) and triangular closure to determine the pair of image/point included in the network. We validate the effectiveness of our method by Sentine-1 SAR data over Shenzhen airport, where decorrelated scatterers with weak backscattering energy in runways challenge a reliable subsidence extraction. The cross-comparison indicates the importance of the quality of inputs that affects the point connectivity in both temporal and spatial dimensions.

Space Geodetic Views on the 2021 Central Greece Earthquake Sequence: 2D Deformation Maps Decomposed From Multi-Track and Multi-Temporal Sentinel-1 InSAR Data

Pioneering efforts well studied the deformation decomposition of single earthquake using a pair of ascending (ASC) and descending (DES) track InSAR data. However, deformation decomposition of sequent events is rarely discussed and hard to implement. That's because it's hard to ensure deformations related to each earthquake can be recorded by a pair of ASC and DES track data. Three sequent earthquakes (Mw>5.5) just hit Central Greece in March 2021, and this earthquake sequence provides us with a perfect case to study 2D (East-West and Up-Down) deformation decomposition when the mentioned premise cannot be satisfied. In this context, we proposed a Multi-track and Multi-temporal 2D (MTMT2D) method. Its novelty and behind rationale are to decompose 2D deformations of each event through fusing multi-track and multi-temporal interferograms. Based on the decomposed deformations, we invert the slip distribution of three earthquakes respectively. We found that the decomposed deformations can better constrain the fault geometry than the single InSAR interferogram. Furthermore, our geodetic inversion results also suggest a domino-like triggering rupture process for this earthquake sequence. It indicates that our MTMT2D method can potentially reveal more details about earthquake sequence.

Phase Estimation for Distributed Scatterers by Alternating Projection

Decorrelation is a major obstacle to the application of multitemporal interferometric synthetic aperture radar (InSAR) in areas with low coherence. Distributed scatterers (DSs) with similar backscattering in the spatial neighborhood are the key to improving the observational density and accuracy of deformation estimation in fast decorrelation regions. Phase series estimation from all possible interferograms is expected to improve the signal-to-noise ratio (SNR) and further enhance the sensitivity of deformation measurement. The coherence bias and the efficiency of the phase estimation raise concerns. In this article, we propose a computationally attractive algorithm for the interferometric phase estimation, namely alternating projection (AP), which is a combination of the alternating maximization and the projection matrix decomposition methods. The homogenous pixel selection and coherence estimation bias correction are conducted by the FaSHPS algorithm and DSIpro software toolbox. Results of simulations and real SAR data show that the proposed AP method could reconstruct credible phase series comparable to the quasi-Newton optimization algorithm [Broyden–Fletcher–Goldfarb–Shanno (BFGS)] while having three times the efficiency gain.

Extreme rainfall-related accelerations in landslides in Danba County, Sichuan Province, as detected by InSAR

Landslides are frequent mountainous geohazards induced by multiple factors, such as extreme rainfall, river erosion, and intense anthropogenic activities. On 17 June 2020, a rainstorm hit Danba County, Sichuan Province, and triggered the catastrophic Aniangzhai landslide. The impact of this rainstorm on landslide kinematics in this region has seldom been investigated. We used a time-series interferometric synthetic aperture radar (InSAR) analysis to map the active slopes in Danba County using one ascending (2015–2021) and one descending (2018–2021) Sentinel-1 dataset. A total of 36 landslides (63.1 km2) were detected along riverbanks. The time series of eastward and vertical displacements during 2018–2021 were retrieved by integrating the ascending and descending Sentinel-1 datasets. The maximum eastward and vertical displacement rates obtained are 229 and −75 mm/yr, respectively. Seasonal accelerations are correlated with concentrated rainfall. We suggest that the 2020 rainstorm and the abundant precipitation during the water year 2020 altered the long-term displacement trends of active landslides. We further constrained the volumes of the Niela and Gaoding landslides in the order 3.6–6.9 × 107 m3 and 5.4–6.0 × 108 m3, respectively, using 2D displacement rates and mass conservation equations. Our results demonstrate that multi-temporal and multi-orbit InSAR measurements can provide insights into the evolution and mechanism of landslides.

Correction of spatially varying stratified atmospheric delays in multitemporal InSAR

Tropospheric delay is a major limiting factor in ground displacement retrieval using interferometric synthetic aperture radar (InSAR). Such delays are caused by the variations of pressure, temperature, and humidity of the troposphere, potentially leading to elevation-dependent phases in interferograms. The atmospheric heterogeneity makes the phase-elevation dependence vary in both space and time, posing challenges on empirical models that estimated the elevation-topography relationship in empirically predefined windows interferogram by interferogram. Moreover, the portion of the deformation that is correlated with topography can be mistaken as tropospheric delays and therefore degrade the performance of linear regression between the tropospheric phase and the elevation. The effectiveness of these methods is further limited by the requisite knowledge presumed of the user, particularly with regard to how to divide the scene and which observation model is suitable. These limitations make reliable isolation of tropospheric delay challenging. We present here a novel approach that combines tropospheric delay, deformation and possible topographic error to a joint model, enabling a precise separation of tropospheric delay. The rationale is rooted in the distinct temporal dependencies of these parameters. To avoid empirical setting of segmentation windows, we employ quadtree to adaptively control the spatial variability of tropospheric properties. We validate the proposed method at two volcanic areas, Bali and Hawaii, where tropospheric delay presents notable variability coupled with elevation-correlated deformation signals. Both ascending and descending Sentinel-1 data over the island of Bali show that the proposed method outperforms the conventional ones. The misfit standard deviation (STD) is reduced by ∼50% at the island of Bali, where elevation-correlated deformation is simulated according to a predefined model. More impressively, at the island of Hawaii, the misfit STD between InSAR and ground truth (i.e., GPS displacements) decreases from 25.1 to 4.5 mm. The experimental results demonstrate that the proposed method is effective in reducing the spatially variable tropospheric delay while preserving the elevation-correlated deformation signal.

Building risk monitoring and prediction using integrated multi-temporal InSAR and numerical modeling techniques

• Large-scale ground subsidence was measured using an improved MTInSAR method. • Building risk level was assessed at the district scale using InSAR measurements. • The simulated subsidence due to underground construction agreed with InSAR data. • The integrated method achieves building risk monitoring and prediction. Frequent anthropogenic activities associated with rapid urbanization may cause ground subsidence and endanger the adjacent buildings. Interferometric synthetic aperture radar (InSAR) has generated dense measurement points for large-scale deformation monitoring and risk assessment. However, the applications of InSAR for building deformation and risk level prediction are still rare, especially under complex loading scenarios. In this study, we present an integrated method to monitor and predict building deformation and risk levels using multi-temporal InSAR and numerical modeling. Persistent and distributed scatterers were jointly detected to monitor deformation using TerraSAR-X, COSMO-SkyMed, and Sentinel-1 images over the Kowloon district of Hong Kong and the Futian district of Shenzhen. First, the building risk level was assessed at the district scale using ground subsidence and angular distortion to identify potentially damaged buildings. 10 out of 807 buildings in the Kowloon and 2 out of 145 buildings in the Futian were classified as medium risk. Back-analysis was used to retrieve the geological and hydrological conditions of buildings at the medium risk level. Then, time-series subsidence of the Lucky and BMW buildings during 2012 to 2016 and the Jingli building during 2010 to 2014 was simulated by numerical modeling, which agreed well with the InSAR measurements. By defining the groundwater level changes and upper loading, the predicted subsidence of the Lucky, BMW, and Jingli buildings reached 47.9, 62.8, and 39.9 mm by 2021, respectively, with a decelerated trend. Time-series results showed that the Lucky and BMW buildings were still at the negligible-to-medium risk level in 2021, and should be constantly inspected; the risk level of the Jingli building became negligible from 2020. This method can be transferred to other rapidly urbanized areas prone to ground subsidence for geohazard assessment and improve the understanding of the dynamic behaviors of anthropogenic activity-induced ground subsidence.

Distributed Scatterer Processing Based on Binary Partition Trees with Multi-Baseline PolInSAR Data

Distributed scatterers (DSs) are necessary to increase point density in multi-temporal InSAR (MT-InSAR) monitoring. The identification of homogeneous pixels (HPs) is the first and key step for DS processing to overcome the low signal-to-noise ratio condition. Since multi-polarization data are good at describing geometrical structures and dielectric properties of ground objects, they have been applied for HP identification. However, polarimetric information is not enough for identifying areas with similar ground objects but different deformation. We propose a novel DS preprocessing algorithm based on polarimetric interferometric homogeneous pixel (PIHP) identification. Firstly, a novel Polarimetric InSAR (PolInSAR) similarity that combines polarimetric intensity, interferometric coherence, and phase is proposed, which is readily available in multi-baseline and multi-polarization data and flexible by controlling weighting factors. Secondly, based on the binary partition tree (BPT) framework, object-orientated multi-scale PIHP identification is achieved, which is suitable for complex deformation scenes. Tested with simulated quad-polarization data, our method shows improvement in phase quality and point density, especially in the deformed areas, compared with the traditional HP identification method based on the polarimetric homogeneity (PolHom) test and the method with ground object type map. Tested with 30 quad-polarization Radarsat-2 images over Kilauea Volcano, the point density of our method is three times higher than that of the PolHom test in vegetation areas. Our method is proven to be more sensitive and mechanically more advanced to homogeneous pixels identification than the traditional ones, which is helpful for phase optimization, spatial enlargement of monitoring points, and stability of the MT-InSAR algorithm.

Deformation monitoring and influence factor analysis of expressway over strong saline soil based on an advanced multi-temporal InSAR technique

Saline soil harbors crystallized salt in the solid phase and salt solution in the liquid phase. By the natural environmental and human activity factors, migration and accumulation of the salt and crystallization process alternated with dissolving, which makes saline soil with significant seasonal differences appear in the deformation and regional scattering characteristic. These phenomena raise the limitation of conventional multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique and consequently high-precision deformation monitoring of strong saline soil subgrade and pavement. To overcome the limitations, this study aimed to propose an advanced MT-InSAR method, which considers the seasonal interferometric coherences caused by precipitation and the temporal physical deformation evolution of the subgrade and pavement over strong saline soil. To present the better performance of the advanced method, a segment of the Qarhan–Golmud Expressway (QGE), which is the first expressway built in the strong saline soil area in China, was selected for this study. Two sets of SAR images acquired from January 2018 to January 2022 from Sentinel-1A ascending and descending orbits over the QGE were utilized, 774 and 928 seasonal filtered interferograms are optimized for deformation monitoring based on the deformation Poisson curve (PC) model. Compared with the method in the previous studies, the accuracy, efficiency, and reliability of the monitoring results increased dramatically. Subsequently, further discussions are conducted in detail on the regularity of expressway deformation over strong saline soil, especially from physical and chemical perspectives. Findings show that the ratio between Cl− and SO42- determines the deformation distribution and morphology. Also, the precipitation and temperature affect the seasonal characteristics. The contributions of this investigation might provide technical references for related expressway management and policy-making departments to ensure the long-term safe operation and stability of the QGE.

Multi-temporal InSAR tropospheric delay modelling using Tikhonov regularization for Sentinel-1 C-band data

The increased availability of satellite SAR data coupled with improved InSAR processing algorithms has led to higher accuracy of InSAR-derived displacements. However, obtaining millimeter-level accuracy still remains a challenge due to the inability to accurately model the tropospheric delay, which is sometimes larger than the actual deformation. We propose to estimate the relative daily SAR tropospheric delay by double differencing interferograms that share a common image by leveraging Sentinel-1’s short revisit time and the interchanging role of the common image. The resulting double-differenced interferograms consist of tropospheric delay and processing noise. This combination leads to an inverse problem as a result of the differential nature of the interferograms. The unknown SAR tropopsheric delay is solved by Tikhonov regularization and the processing errors are accounted for by covariance modelling. In cases of rapid localized displacement, the deforming region is masked and the tropospheric delay for the deforming region is determined through kriging. Validation is performed using simulated SAR data and GNSS tropospheric delays. We find a good correlation between estimated SAR tropospheric delays and GNSS tropospheric delays, with an average root mean square error (RMSE) of 1.93 radians across six GNSS locations. In addition, we examine the performance of the correction on interferograms through variogram modelling, which indicates improved correction both for short-wavelength and long-wavelength tropospheric noise. SAR tropospheric delay estimates are integrated into a multitemporal InSAR workflow through either interferometric subtraction or stochastic modelling. Validation using GNSS measurements indicates that SAR tropospheric delay estimates significantly improve the accuracy of the InSAR-derived time-series displacement.

A low-cost active reflector and a passive corner reflector network for assisting landslide monitoring using multi-temporal InSAR

ABSTRACT A C-band Low-cost Active Reflector (AR) has been tested in a real experimental campaign aimed at monitoring through multi-temporal InSAR an area threatened by a landslide that occurred in 2019. To monitor and characterize the movement, a network of eight Passive Corner Reflectors and one Active Reflector were installed along a forested slope. A set of 285 interferograms obtained combining 60 Sentinel-1 SAR images were processed to evaluate the stability of the area. The AR, installed in a stable location close to the landslide, was used to provide a reference point in this low coherence area. Despite the high sensitivity of the phase response of such devices to temperature changes, the device operates with a stability of ±2 mm in deformation retrieval, a value acceptable for monitoring purposes, with a moderate range of temperature values.

An MT-InSAR Data Partition Strategy for Sentinel-1A/B TOPS Data

The Sentinel-1A/B satellite launched by European Space Agency (ESA) in 2014 provides a huge amount of free Terrain Observation by Progressive Scans (TOPS) data with global coverage to the public. The TOPS data have a frame width of 250 km and have been widely used in surface deformation monitoring. However, traditional Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) methods require large computer memory and time when processing full resolution data with large width and long strips. In addition, they hardly correct atmospheric delays and orbital errors accurately over a large area. In order to solve these problems, this study proposes a data partition strategy based on MT-InSAR methods. We first process the partitioned images over a large area by traditional MT-InSAR method, then stitch the deformation results into a complete deformation result by correcting the offsets of adjacent partitioned images. This strategy is validated in a flat urban area (Changzhou City in Jiangsu province, China), and a mountainous region (Qijiang in Chongqing City, China). Compared with traditional MT-InSAR methods, the precision of the results obtained by the new strategy is improved by about 5% for Changzhou city and about 15% for Qijiang because of its advantage in atmospheric delay correction. Furthermore, the proposed strategy needs much less memory and time than traditional methods. The total time needed by the traditional method is about 20 h, and by the proposed method, is about 8.7 h, when the number of parallel processing is 5 in the Changzhou city case. The time will be further reduced when the number of parallel processes increases.

Time series monitoring and prediction of coal mining subsidence based on multitemporal InSAR technology and GSM-HW model

The surface subsidence caused by coal mining will cause serious environmental problems, and an effective monitoring and prediction system is indispensable. Aiming at this phenomenon, a mining subsidence monitoring and dynamic prediction model combining time series stacking differential interferometric synthetic aperture radar (TSS-DInSAR) technology, small baseline subset InSAR technology (SBAS-InSAR), and golden section method-Holt–Winters (GSM-HW) model was proposed. First, based on the nine scene images of Sentinel-1A satellite in the study area, the time series subsidence of 811 work-face in Guobei coal mine from April 5 to July 10, 2021, was obtained using TSS-DInSAR and SBAS-InSAR monitoring technologies, respectively. Then, the original training samples of the GSM-HW model were generated by combining the cumulative surface subsidence results monitored by TSS-DInSAR and SBAS-InSAR, and the monitoring and prediction of surface subsidence were realized using this model. The experimental results show that the application of TSS-DInSAR and SBAS-InSAR monitoring technology to the impact of surface mining in mining areas has certain reliability, and the GSM-HW prediction model can make up for the deficiency of a single HW model in parameter optimization. The maximum fitting accuracy and prediction accuracy of the model for the 10 monitoring pilot sites are 96.9% and 98.4%, respectively, which can provide a reference for the design of surface monitoring and prediction system in the mining area.

Monitoring Subsidence over the Planned Jakarta–Bandung (Indonesia) High-Speed Railway Using Sentinel-1 Multi-Temporal InSAR Data

Investigation of subsidence situations is important for the design of railway lines. Traditional geological investigations are expensive. Multi-temporal InSAR (MT-InSAR) analysis has shown great potential for detecting subsidence areas along large-scale manmade linear features (LMLFs). The disadvantage of applying high-resolution SAR data such as that from TerraSAR-X and COSMO-SkyMed data is the high cost. By contrast, the free and open data from Sentinel-1 A/B offer an unprecedented opportunity for InSAR applications. Despite these advantages, research investigating the potential for monitoring subsidence along LMSFs using multi-temporal Sentinel-1 InSAR data at the design stage of LMSFs is limited. This paper explores the monitoring ability of MT-InSAR analysis with Sentinel-1 A/B multi-temporal data in an experiment along the planned Jakarta-Bandung high-speed railway in Bandung, Indonesia. Subsidence patterns along the route of the planned high-speed railway were analyzed in detail. By means of validation using 12 leveling points, the root mean square error (RMSE) of the average displacement velocity that was obtained from the MT-InSAR results was found to be 10.31 mm/yr. This paper demonstrates the potential of Sentinel-1 multi-temporal INSAR data to detect potential subsidence areas along planned LMLFs (e.g., the planned high-speed railway that is the focus of this study), serving as a reference for further planning of monitoring subsidence over planned LMLFs.

A hybrid method for MT-InSAR phase unwrapping for deformation monitoring in urban areas

• A hybrid method is proposed to enhance the phase unwrapping of MT-InSAR in urban. • Consideration of the impact of building height and spatial network structure. • Improvements in phase unwrapping allow for better geocoding of coherent points. The accuracy of phase unwrapping in multi-temporal interferometric synthetic aperture radar (MT-InSAR) technology affect closely on the accuracy of ground deformation monitoring. In the urban area, urban infrastructures such as high-rise buildings present additional challenges to phase unwrapping due to the sudden elevation changes. The failure rate of phase unwrapping is often much higher in such areas, bringing more biases to the estimated deformation and height values. We propose a new method for MT-InSAR phase unwrapping especially for deformation monitoring in urban areas. The method isolates the propagation path of information by considering the building height information of a study area. Two quality controllers are also developed to refine the arc observation network structure for network adjustment and to correct the phase unwrapping errors. The proposed method is tested using both simulated and real datasets. The results show that the building height and the geolocation of the coherence points were estimated much more accurately, demonstrating the effectiveness of the proposed method in monitoring deformation of urban area.

Time Series Multi-Sensors of Interferometry Synthetic Aperture Radar for Monitoring Ground Deformation

A stack of images is a prerequisite for the multi-temporal interferometric synthetic aperture radar (MT-InSAR) due to the wrapped nature of the interferometric phase. Although the SBAS technique can relieve the requirement of the amount of SAR data, dozens of SAR acquisitions could be regarded as the minimum requirement. However, due to the limitation of the imaging capability of the spaceborne SAR system, the amount of available SAR data acquired from only one SAR sensor is often not enough to satisfy the requirement for phase unwrapping based on the Nyquist sampling assumption. Fortunately, there sometimes may be more than one SAR stack, that is, stacks of SAR data acquired from different SAR systems. In this study, we propose a methodology to detect ground deformation by combining multiple SAR images acquired from different satellite systems for MT-InSAR analysis. First, the low-pass deformation is estimated based on time series SAR acquisitions with low spatial resolution and long wavelengths such as ENVISAT ASAR (ASAR). This information is then incorporated into the processing of time series of SAR acquisitions with high spatial resolution and short wavelength, such as TerraSAR-X (TSX). Specifically, the low-pass deformation will be subtracted from each differential interferogram generated from short-wavelength SAR images, and the rest of the MT-InSAR analysis will be based on the double-differentiation interferograms. Then, the residual deformation will be calculated from these double-differentiation interferograms and together with the low-pass deformation forms the full deformation. As the principal component of deformation has already been subtracted, the phase gradient of those double-differentiated interferograms will be smooth enough to facilitate the phase unwrapping. Between January 2009 and September 2010, 14 ASAR images and 11 TSX images acquired from Tianjin, China are selected as the test data. A root means square error (RMSE) of 9.1 mm/year is achieved from 11 TSX images, while a root means square error of 3.7 mm/year is achieved from 14 ASAR images. However, an RMSE of 1.6 mm/year is achieved when integrating 11 TSX images and 14 ASAR images for MT-InSAR analysis. The experiments show that the proposed method can effectively detect ground deformation.

Characteristics of Freeze–Thaw Cycles in an Endorheic Basin on the Qinghai-Tibet Plateau Based on SBAS-InSAR Technology

The freeze–thaw (F-T) cycle of the active layer (AL) causes the “frost heave and thaw settlement” deformation of the terrain surface. Accurately identifying its amplitude and time characteristics is important for climate, hydrology, and ecology research in permafrost regions. We used Sentinel-1 SAR data and small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain the characteristics of F-T cycles in the Zonag Lake-Yanhu Lake permafrost-affected endorheic basin on the Qinghai-Tibet Plateau from 2017 to 2019. The results show that the seasonal deformation amplitude (SDA) in the study area mainly ranges from 0 to 60 mm, with an average value of 19 mm. The date of maximum frost heave (MFH) occurred between November 27th and March 21st of the following year, averaged in date of the year (DOY) 37. The maximum thaw settlement (MTS) occurred between July 25th and September 21st, averaged in DOY 225. The thawing duration is the thawing process lasting about 193 days. The spatial distribution differences in SDA, the date of MFH, and the date of MTS are relatively significant, but there is no apparent spatial difference in thawing duration. Although the SDA in the study area is mainly affected by the thermal state of permafrost, it still has the most apparent relationship with vegetation cover, the soil water content in AL, and active layer thickness. SDA has an apparent negative and positive correlation with the date of MFH and the date of MTS. In addition, due to the influence of soil texture and seasonal rivers, the seasonal deformation characteristics of the alluvial-diluvial area are different from those of the surrounding areas. This study provides a method for analyzing the F-T cycle of the AL using multi-temporal InSAR technology.

A model-backfeed deformation estimation method for revealing 20-year surface dynamics of the Groningen gas field using multi-platform SAR imagery

• The model-backfeed deformation estimation method offers more reliable InSAR deformation parameter estimations. • Multiple Hypothesis Testing is employed to determine the best deformation models. • Geolocation error ellipsoids and Monte Carlo methods are used for spatial integration. • Three SAR missions are used to map 20-year Groningen gas field subsidence. The Groningen gas field, the largest natural gas field in Europe, was discovered in 1959 and started its production in 1963. The Earth surface above it experienced subsidence over the past six decades because of gas extraction activities. To accurately reveal this surface movement with satellite SAR data, our study first proposes and demonstrates a model-backfeed (MBF) InSAR deformation estimation method to improve InSAR deformation time series modeling. This method allowed us to include a priori knowledge and to iteratively optimize functional and stochastic models. Using this method and employing a spatio-temporal SAR data integration method based upon Monte Carlo and Multiple Hypothesis Testing methods, we retrieved the 20-year subsidence history of the Groningen gas field by integrating 32 ERS-1/2, 68 Envisat and 82 Radarsat-2 SAR images. The results show that the maximum cumulative surface subsidence in this gas field has been as much as 15.5 cm between 1995 and 2015. In terms of precision and accuracy, our MBF method offered a better result than the standard Multi-Temporal InSAR analysis method: the Ensemble Coherence increased by 10%–19% and Spatio-Temporal Consistency decreased by 2%–20%. In terms of accuracy, our results better concur with the external GNSS reference observations. We further show that the spatio-temporal SAR data integration method has better links with multi-platform SAR data if the uncertainties of the InSAR geolocation and temporal deformation are included. The study demonstrates that the MBF method optimized the estimation of deformation parameters and mitigated unwrapping errors.