InSAR Time Series Research Articles

Mitigation of time-series InSAR turbulent atmospheric phase noise: A review

Synthetic Aperture Radar (SAR) interferometry is one of the most powerful remote sensing tools for ground deformation detection. However, tropospheric delay greatly limits the measurement accuracy of the InSAR technique. While vertically stratified tropospheric delays have been extensively investigated and well tackled, turbulent tropospheric phase noise still remains an intractable issue. In recent years, great efforts have been made to reduce the influence of turbulent atmospheric delay. This contribution is intended to provide a systematic review of the progress achieved in this field. First, it introduces the physical characteristics of atmospheric signals in interferograms. Then, a review of the main mitigation algorithms proposed in the literature is provided. In addition, the strengths and weaknesses of each approach are analyzed to provide guidance for choosing a suitable method accordingly. Finally, suggestions for resolving the challenging issues and an outlook for future research are given.

A Deep Learning Approach for Change Points Detection in InSAR Time Series

Interferometric SAR (InSAR) algorithms exploit synthetic aperture radar (SAR) images to estimate ground displacements, which are updated at each new satellite acquisition, over wide areas. The analysis of the resulting time series finds its application, among others, in monitoring tasks regarding seismic faults, subsidence, landslides, and urban structures, for which an accurate and timely response is required. Typical analyses consist of identifying among the numerous time series the ones that exhibit an anomalous displacement, thus deserving to be further investigated. In practice, this is realized by selecting the time series that is characterized by trend changes w.r.t. the historical behavior. In this work, we propose a deep learning approach for change point detection in the InSAR time series. The designed architecture combines long short-term memory (LSTM) cells, to model the temporal correlation among samples in the input time series, and time-gated LSTM (TGLSTM) cells, to consider the sampling rate as additional information during learning. We further propose a solution to the lack of ground truth by developing a suitable pipeline for realistic data simulation. The method has been developed and validated through a large suite of experiments. Both quantitative and qualitative analyses have been conducted to demonstrate the detection capabilities of the learned model and how it is a valid alternative to the statistical reference algorithm. We further applied the developed method in a real continuous monitoring project to analyze the InSAR time series over the Tuscany region in Italy, proving its effectiveness in the real domain.

On Closure Phase and Systematic Bias in Multilooked SAR Interferometry

In this article, we investigate the link between the closure phase and the observed systematic bias in deformation modeling with multilooked SAR interferometry. Multilooking or spatial averaging is commonly used to reduce stochastic noise over a neighborhood of distributed scatterers in interferometric synthetic aperture radar (InSAR) measurements. However, multilooking may break consistency among a triplet of interferometric phases formed from three acquisitions leading to a residual phase error called closure phase. Understanding the cause of closure phase in multilooked InSAR measurements and the impact of closure phase errors on the performance of InSAR time-series algorithms is crucial for quantifying the uncertainty of ground displacement time series derived from InSAR measurements. We develop a model that consistently explains both closure phase and systematic bias in multilooked interferometric measurements. We show that nonzero closure phase can be an indicator of temporally inconsistent physical processes that alter both phase and amplitude of interferometric measurements. We propose a method to estimate the systematic bias in the InSAR time series with generalized closure phase measurements. We validate our model with a case study in Barstow-Bristol Trough, CA, USA. We find systematic differences on the order of cm/year between InSAR time-series results using subsets of varying maximum temporal baselines. We show that these biases can be identified and accounted for.

Combined Detection of Surface Changes and Deformation Anomalies Using Amplitude-Augmented Recursive InSAR Time Series

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.

Analysis of ground subsidence along Zhengzhou metro based on time series InSAR

Analysis of ground subsidence along Zhengzhou metro based on time series InSAR

Rock Glacier Characteristics Under Semiarid Climate Conditions in the Western Nyainqêntanglha Range, Tibetan Plateau

AbstractRock glaciers are receiving increased attention as a potential source of water and indicator of climate change in periglacial landscapes. They consist of an ice‐debris mixture, which creeps downslope. Although rock glaciers are a wide‐spread feature on the Tibetan Plateau, characteristics such as its ice fraction are unknown as a superficial debris layer inhibits remote assessments. We investigate one rock glacier in the semiarid western Nyainqêntanglha range (WNR) with a multi‐method approach, which combines geophysical, geological and geomorphological field investigations with remote sensing techniques. Long‐term kinematics of the rock glacier are detected by 4‐year InSAR time series analysis. The ice content and the active layer are examined by electrical resistivity tomography, ground penetrating radar, and environmental seismology. Short‐term activity (11‐days) is captured by a seismic network. Clast analysis shows a sorting of the rock glacier's debris. The rock glacier has three zones, which are defined by the following characteristics: (a) Two predominant lithology types are preserved separately in the superficial debris patterns, (b) heterogeneous kinematics and seismic activity, and (c) distinct ice fractions. Conceptually, the studied rock glacier is discussed as an endmember of the glacier—debris‐covered glacier—rock glacier continuum. This, in turn, can be linked to its location on the semiarid lee‐side of the mountain range against the Indian summer monsoon. Geologically preconditioned and glacially overprinted, the studied rock glacier is suggested to be a recurring example for similar rock glaciers in the WNR. This study highlights how geology, topography and climate influence rock glacier characteristics and development.

Estimating Single-Epoch Integrated Atmospheric Refractivity From InSAR for Assimilation in Numerical Weather Models

Numerical weather prediction (NWP) models are used to predict the weather based on current observations in combination with physical and mathematical models. Yet, they are limited by the spatial density and the accuracy of the available observations. Satellite radar interferometry (InSAR) is known to be extremely sensitive to the 3D atmospheric refractivity distribution, and has a high spatial resolution, providing information that can be used for assimilation in NWP models. However, due to the inherent superposition of two or more atmospheric states, only biased and temporally differenced signals can be retrieved, that can also be contaminated by deformation signals and decorrelation. Here we present a method to estimate single-epoch absolute atmospheric delays by combining InSAR time series with prior NWP model prediction time series, using a constrained least-squares estimation. We show that this leads to a solution that reliably extracts the single-epoch relative delays from InSAR data and uses prior NWP model data to find the absolute reference for these delays, while mitigating long-term deformation and decorrelation signal. This approach leads to repetitive delay updates with a spatial resolution of 500 m, that can be directly assimilated into numerical weather models.

Dual-polarization Sentinel-1 data polarization time series InSAR technology surface deformation monitoring—Taking shanghai pudong airport as an example

Dual-polarization Sentinel-1 data polarization time series InSAR technology surface deformation monitoring—Taking shanghai pudong airport as an example

Time-Series Analysis and Prediction of Surface Deformation in the Jinchuan Mining Area, Gansu Province, by Using InSAR and CNN–PhLSTM Network

Surface deformation poses a great threat to the safety of Jinchuan mining area production activities. At present, the spatio-temporal evolution law and mechanism of surface deformation in the Jinchuan mining area are unclear, and it is difficult to obtain reliable prediction results using the existing spatio-temporal prediction methods due to the lack of model parameters or relevant data. To solve these problems, this study proposes a new unified convolutional neural network with peephole long short-term memory (CNN-PhLSTM). Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used to obtain the spatio-temporal evolution characteristics of surface deformation in the period of 2014–2021. Time series InSAR deformation data are merged into a unified network model in series with a time-distributed CNN segmentation and stacked PhLSTM. The InSAR measurement results are shown to be reliable by comparison and verification with the benchmark and InSAR results of different orbits. The proposed CNN-PhLSTM model was evaluated by mean absolute error and structural similarity (SSIM) evaluation indexes, and was compared with support vector regression (SVR), multilayer perceptron (MLP) and CNN-LSTM models. The results show three continuous subsidence areas, namely the Longshou, second western and third eastern mining areas. The cumulative surface deformation continued to increase from 2014 to 2021. Faults and lithology control the spatial distribution of surface deformation in the Jinchuan mining area. The prediction results demonstrate that the surface deformation range will continue to expand and that time-series surface deformation will show a slow deceleration trend in the next two years.

Measuring Subsidence Over Soft Clay Highways Using a Novel Time-Series InSAR Deformation Model With an Emphasis on Rheological Properties and Environmental Factors (NREM)

Long-term monitoring of highways in soft soil areas, especially during the postconstruction period, is of great significance to ensure transportation safety and the quality of highway construction. Multitemporal interferometric synthetic aperture radar (MTInSAR) provides an effective tool for soft clay highway monitoring. However, most time-series models used in MTInSAR modeling are empirical mathematical functions, which ignores the physical properties of the observed objects and may limit the accuracy of the retrieved deformation and the understanding of the underground settlement dynamics. We propose a novel InSAR time-series deformation model (namely, NREM) with an emphasis on the rheological mechanisms for soft soil highways and environmental factors (temperature, humidity, and precipitation) to improve the accuracy of the traditional InSAR model and assist in the analyzing the rheological properties of soft soil. The NREM is constructed based on a combination of the seasonal model and the Burgers model introduced from the field of rheology. The primary parameters (i.e., viscosity and elastic modulus) are introduced in the NREM and estimated with the generation of time-series surface deformation. In the real data experiments, two highways are selected as the test areas. The results show that the standard deviations (STDs) of the high-pass deformation, which can reflect the modeling accuracy, derived by the NREM are lower than those of the three traditional models, yielding an improvement of 45% for the Lungui Highway (LH) and 50% for the G1508 Highway (GH). The root mean square errors (RMSEs) for deformation results derived from NREM are estimated to be ±5.1 mm compared with the leveling measurements, which outperforms the traditional models. The obtained rheological parameters can broaden the application of InSAR technology and provide a reference for highway engineering.

Rapid and Automatic Detection of New Potential Landslide Based on Phase-Gradient DInSAR

Although the widely used time-series InSAR technology makes up for the shortcomings of traditional geological investigation, such as small coverage and low efficiency, it cannot achieve rapid and dynamic detection of new potential landslide due to its long data processing time and insensitivity to short-term new displacement. In this paper, a rapid method for automatically detection new potential landslides in wide area is proposed. Phase-gradient processing is performed based on the DInSAR results to automatically detect the potential landslide, in which the influence from geometric distortion, water, noise from low coherence area, and other errors are analyzed and removed. This method was performed in the Maoergai Reservoir Area where many potential landslides newly emerged during the impoundment period as the great water level fluctuations. As a result, 7 potential landslides with continuous deformation and relatively large deformation were detected. The error source was analyzed and removed. In the validation, an overall accuracy of up to 81% was achieved by comparing the results with the manual detection. This method provides a new way for rapid and automatic detection of new displacements in wide-area, especially for the area (e.g. reservoir area) with dynamic and rapid detection needs.

Time-Series InSAR Dynamic Analysis With Robust Sequential Adjustment

Nowadays, SAR instruments such as ESA’s Sentinel-1 constellation, ICEYE’s constellation of small and agile radar satellites, and the upcoming ALOS-4 and NASA/ISRO SAR missions provide new opportunities for near-real-time monitoring of geohazards with enhanced spatiotemporal resolution. Sequential dynamic adjustment model is regarded as an effective way to rapidly update time-series InSAR measurements. However, the accuracy of geophysical parameters of interest estimated from the conventional sequential least squares is greatly sensitive to the anomalous observations and/or anomalous prior parameter information. This letter aims to introduce the robust sequential adjustment method based on the M-estimation principle into near-real-time InSAR deformation monitoring to mitigate the effect of anomalous errors. Using both synthetic and real Sentinel-1 SAR datasets over Echigo plain in Japan, we fully evaluate the performance of the robust sequential estimation approach with respect to unwrapping errors in the SAR data stack. Measurements at 9 GPS stations located in the study area are used to validate the results. We find that the averaged RMSE of robust sequential adjustment is reduced by 15% in comparison with that of the conventional sequential least-squares method.

Detection of landslide surface deformation in Barpak, Nepal using time-series InSAR images

Detection of landslide surface deformation in Barpak, Nepal using time-series InSAR images

High-Precision Monitoring Method for Airport Deformation Based on Time-Series Insar Technology

High-Precision Monitoring Method for Airport Deformation Based on Time-Series Insar Technology

Applicability Analysis of Potential Landslide Identification by InSAR in Alpine-Canyon Terrain—Case Study on Yalong River

Landslides occur frequently in the western mountainous areas of China, causing huge losses every year. InSAR technology can efficiently and accurately identify potential landslides and is a powerful tool for landslide hazards mitigation. However, the successful application of InSAR technology is limited by several factors, such as geometric distortion and dense vegetation, especially in area with alpine-canyon terrain. This study investigates the applicability of InSAR observations in identifying potential landslide of the middle section of Yalong River, which is a typical alpine-canyon terrain area. Using time-series InSAR Sentinel-1 datasets, we detect six potential landslides, which are verified and analyzed by using optical remote sensing images. Then the applicability analysis is performed considering geometric distortion and band suitability. The results reveal that combining ascending and descending data can increase the detectable area (not in the geometric distortion) from 70% to 92.9%. The comparison of the performance of C-band and L-band data in identifying potential landslide shows that the latter is able to detect potential landslides with high vegetation coverage, but it may miss the area with slight displacement. This study demonstrates the use of InSAR for potential landslide identification in alpine-canyon terrain area and reveals its applicability, which provides a deep understanding on SAR data selection and would play an important role for the InSAR-based landslides geohazard mitigation application.

Displacement Characterization and Spatial-Temporal Evolution of the 2020 Aniangzhai Landslide in Danba County Using Time-Series InSAR and Multi-Temporal Optical Dataset

On 17 June 2020, a large ancient landslide over the Aniangzhai (ANZ) slope, Danba County, Sichuan Province, China, was reactivated by a series of multiple phenomena, including debris flow triggered by heavy rainfall and flooding. In this study, Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1A/B satellite and optical images captured by the PlanetScope satellites were jointly used to analyze and explore the deformation characteristics and the Spatial-Temporal evolution of the ANZ landslide before and after the multi-hazard chain. Several areas of pre-failure movements were found from the multi-temporal optical images analysis before the reactivation of the ANZ landslide. The large post-failure surface deformation over the ANZ slope was also retrieved by the optical pixel offset tracking (POT) technique. A major northwest movement with the maximum horizontal deformation of up to 14.4 m was found. A time-series InSAR technique was applied to analyze the descending and ascending Sentinel-1A/B datasets spanning from March 2018 to July 2020, showing that the maximum magnitudes of the Line of Sight (LoS) displacement velocities were −70 mm/year and 45 mm/year, respectively. The Spatial-Temporal evolution over the ANZ landslide was analyzed based on the time-series results. No obvious change in acceleration (precursory deformation) was detected before the multi-hazard chain, while clear accelerated deformation can be observed over the slope after the event. This suggested that heavy rainfall was the most significant triggering factor for the generation and reactivation of the ANZ landslide. Other preparatory factors, including the deformation behavior, the undercutting and erosion of the river and the outburst flood, the local terrain conditions, and earthquakes, might also have played an important role in the generation and reactivation of the landslide.

Sentinel-1-derived coherence time-series for crop monitoring in Indian agriculture region

Synthetic aperture radar (SAR) remote sensing has been widely used for crop monitoring due to its high-resolution imaging and all-weather data acquisition capabilities. In this study, time-series interferometric SAR (InSAR) coherence products generated from Sentinel-1 data were employed to monitor phenological stages, determine total cropping duration and estimate sowing as well as harvest dates for the Bengal-gram crop. The Bengal-gram crop was cultivated in the study region lies in Chhattisgarh, India, during the Rabi 2018–2019 season. Upon analysis, the temporal fluctuations in InSAR coherence were observed as a function of crop phenological stages as well as sowing and harvesting events. The lowest coherence value (i.e. 0.29) represents the peak vegetation. Sowing and harvesting dates were determined by observing the sudden shifts in coherence trend i.e. from high to low and low to high, respectively. The temporal analysis performed using InSAR coherence was also cross-verified with the time-series normalized difference vegetation index (NDVI) index derived using Sentinel-2 bands. Based on detailed analysis, we found that the time-series analysis of InSAR coherence provides reliable information on the crop growth stages and this approach can be used to monitor other crops as well.

Review of the SBAS InSAR Time-series algorithms, applications, and challenges

In the past 30 years, the small baseline subset (SBAS) InSAR time-series technique has emerged as an essential tool for measuring slow surface displacement and estimating geophysical parameters. Because of its ability to monitor large-scale deformation with millimeter accuracy, the SBAS method has been widely used in various geodetic fields, such as ground subsidence, landslides, and seismic activity. The obtained long-term time-series cumulative deformation is vital for studying the deformation mechanism. This article reviews the algorithms, applications, and challenges of the SBAS method. First, we recall the fundamental principle and analyze the shortcomings of the traditional SBAS algorithm, which provides a basic framework for the following improved time series methods. Second, we classify the current improved SBAS techniques from different perspectives: solving the ill-posed equation, increasing the density of high-coherence points, improving the accuracy of monitoring deformation and measuring the multi-dimensional deformation. Third, we summarize the application of the SBAS method in monitoring ground subsidence, permafrost degradation, glacier movement, volcanic activity, landslides, and seismic activity. Finally, we discuss the difficulties faced by the SBAS method and explore its future development direction.

Permafrost Dynamics and Degradation in Polar Arctic From Satellite Radar Observations, Yamal Peninsula

We investigate permafrost surface features revealed from satellite radar data in the Siberian arctic at the Yamal peninsula. Surface dynamics analysis based on SRTM and TanDEM-X DEMs shows up to 2 m net loss of surface relief between 2000 and 2014 indicating a highly dynamic landscape. Surface features for the past 14 years reflect an increase in small stream channels and a number of new lakes that developed, likely caused by permafrost thaw. We used Sentinel-1 SAR imagery to measure permafrost surface changes. Owing to limited observation data we analyzed only 2 years. The InSAR time-series has detected surface displacements in three distinct spatial locations during 2017 and 2018. At these three locations, 60–120 mm/yr rates of seasonal surface permafrost changes are observed. Spatial location of seasonal ground displacements aligns well with lithology. One of them is located on marine sediments and is linked to anthropogenic impact on permafrost stability. Two other areas are located within alluvial sediments and are at the top of topographic elevated zones. We discuss the influence of the geologic environment and the potential effect of local upwelling of gas. These combined analyses of InSAR time-series with analysis of geomorphic features from DEMs present an important tool for continuous process monitoring of surface dynamics as part of a global warming risk assessment.

Insights on fault reactivation during the 2019 November 11, Mw 4.9 Le Teil earthquake in southeastern France, from a joint 3-D geological model and InSAR time-series analysis

SUMMARY The 2019, Mw 4.9 Le Teil earthquake occurred in southeastern France, causing substantial damage in this slow deforming region. Field observations, remote sensing and seismological studies following the event revealed that coseismic slip concentrates at shallow depth along a ∼5 km long rupture associated with surface breaks and a thrusting mechanism. We further investigate this earthquake by combining geological field mapping, 3-D geology, InSAR time-series analysis and a coseismic slip inversion. From structural, stratigraphic and geological data collected around the epicentre, we first produce a 3-D geological model of the region surrounding the rupture using the GeoModeller software. Our model includes the geometry of the geological layers and the main faults, including the La Rouvière Fault, (LRF) the Oligocene normal fault that ruptured during the earthquake. We generate a time-series of surface displacement from Sentinel-1 SAR data ranging from early 2019 January to late 2020 January using the NSBAS processing chain. The spatio-temporal patterns of surface displacement for this time span show neither a clear pre-seismic signal nor significant post-seismic transient deformation. We extract the coseismic displacement pattern from the InSAR time-series, highlighting along-strike variations of coseismic surface slip. The maximum relative displacement along the line of sight is up to ∼16 cm and is located in the southwestern part of the rupture. We invert for the slip distribution on the fault from the InSAR coseismic surface displacement field. Constraining our fault geometry from the geological model, acceptable fault dip ranges between 55° and 60°. Our model confirms the reactivation of LRF, with reverse slip at very shallow depth and two main slip patches reaching, respectively, 30 and 24 cm of slip, both around 500 m depth. We finally discuss how the 3-D fault geometry and geological structure may have impacted the slip distribution and propagation during the earthquake. This study is a step to reassess the seismic hazard of the many faults similar to the La Rouvière one along the Cévennes fault system, in a densely populated area hosting several sensitive nuclear sites.