Interferometric Synthetic Aperture Radar Products Research Articles

InSAR time series and LSTM model to support early warning detection tools of ground instabilities: mining site case studies

Early alarm systems can activate vital precautions for saving lives and the economy threatened by natural hazards and human activities. Interferometric synthetic aperture radar (InSAR) products generate valuable ground motion data with high spatial and temporal resolutions. Integrating the InSAR products and forecasting models make possible to set up early alarm systems to monitor vulnerable areas. This study proposes a technical support to early warning detection tools of ground instabilities using machine learning and InSAR time series that is capable of forecasting regions affected by potential collapses. A long short-term memory (LSTM) model is tailored to predict ground movements in three forecast ranges (i.e., SAR observations): 3, 4, and 5 multistep. A contribution of the proposed strategy is utilizing adjacent time series to decrease the possibility of falsely detecting safe regions as significant movements. The proposed tool offers ground motion-based outcomes that can be interpreted and utilized by experts to activate early alarms to reduce the consequences of possible failures in vulnerable infrastructures, such as mining areas. Three case studies in Spain, Brazil, and Australia, where fatal incidents happened, are analyzed by the proposed early alert detector to illustrate the impact of chosen temporal and spatial ranges. Since most early alarm systems are site dependent, we propose a general tool to be interpreted by experts for activating reliable alarms. The results show that the proposed tool can identify potential regions before collapse in all case studies. In addition, the tool can suggest an optimum selection of InSAR temporal (i.e., number of images) and spatial (i.e., adjacent measurement points) combinations based on the available SAR images and the characteristics of the study area.

Assessment of noise in InSAR timeseries using least squares variance component estimation

In recent decades, Interferometric Synthetic Aperture Radar (InSAR) has progressed as an effective and reliable tool for monitoring the surface deformations of the earth. Despite the potential of this method for deformation monitoring, the quality description of InSAR timeseries in terms of precision and noise structure and, consequently, the precision description of the InSAR-derived parameters (e.g., displacement and its velocity) are still somewhat ambiguous. In this paper, we propose a data-derived methodology that directly estimates the precision and noise structure of the final InSAR products, using Least Squares Variance Component Estimation (LS-VCE). Note that due to the spatial correlation among adjacent coherent pixels and adjacent acquisitions, a multivariate LS-VCE model should be applied. We used the proposed method on deformation timeseries derived from the Sentine-l data over city of Tehran, Iran. The results show that applying the multivariate LS-VCE method in our case study improves the results by about 50% compared with the case where the noise parameters are not considered. In addition, the results confirm that InSAR timeseries are highly correlated in time and space. Particularly, the spatial correlation between a series of neighbouring targets for the noise components is significant and gradually decreases with increasing arc length. It should be noted that the observed spatial correlation should be differentiated from the well-known spatial correlation imposed by atmospheric components. In fact, due to the atmosphere filtering step, the noise structure of the final results will be different from the statistical characteristics of a raw atmospheric signal. The proposed methodology is not case study dependent and can be used as an appropriate approach to provide the precision (as a quality descriptor) of the timeseries InSAR products.

Using InSAR Time Series to Monitor Surface Fractures and Fissures in the Al-Yutamah Valley, Western Arabia

Western Arabia routinely experiences geophysical phenomena that deform the surface of the earth in a variety of ways. These phenomena include earthquakes, volcanic eruptions, sinkholes, and earth fissuring and fracturing. We perform a time-series analysis of interferometric synthetic aperture radar (InSAR) observations derived from the ESA Sentinel-1 radar satellite constellation to map regional surface displacements in western Arabia as a function of time. We rely on InSAR products generated by the JPL-Caltech ARIA project to detect regions with short wavelength anomalies, and then manually reprocess InSAR products at a higher resolution for these regions to maximize spatial and temporal coverage. We post-process InSAR products using MintPy workflows to develop the InSAR time series. We report short wavelength anomalies localized within alluvial valleys across western Arabia and find a 5 cm/year line-of-sight surface displacement within the Al-Yutamah Valley. Part of the observed subsidence is correlated with surface fractures that developed in conjunction with severe rainfall events in regions characterized mainly by alluvial sediments at the surface. Regions of observed subsidence that are not associated with any surface fractures or fissures are correlated with the presence of basalt layers at the surface. Both regions are subject to groundwater exploitation. The observed subsidence is inferred to be driven by groundwater withdrawal perhaps modulated by the presence of a preexisting depositional environment (e.g., paleo-lake deposits) that promotes unconsolidated soil compaction.

Regional Morpho-Kinematic Inventory of Slope Movements in Northern Norway

Mountain slopes in periglacial environments are affected by frost- and gravity-driven processes that shape the landscape. Both rock glaciers and rockslides have been intensively inventoried worldwide. Although most inventories are traditionally based on morphologic criteria, kinematic approaches based on satellite remote sensing have more recently been used to identify moving landforms at the regional scale. In this study, we developed simplified Interferometric Synthetic Aperture Radar (InSAR) products to inventory ground velocity in a region in Northern Norway covering approximately 7,500 km2. We used a multiple temporal baseline InSAR stacking procedure based on 2015–2019 ascending and descending Sentinel-1 images to take advantage of a large set of interferograms and exploit different detection capabilities. First, moving areas are classified according to six velocity brackets, and morphologically associated to six landform types (rock glaciers, rockslides, glaciers/moraines, talus/scree deposits, solifluction/cryoturbation and composite landforms). The kinematic inventory shows that the velocity ranges and spatial distribution of the different types of slope processes vary greatly within the study area. Second, we exploit InSAR to update pre-existing inventories of rock glaciers and rockslides in the region. Landform delineations and divisions are refined, and newly detected landforms (54 rock glaciers and 20 rockslides) are incorporated into the databases. The updated inventories consist of 414 rock glacier units within 340 single- or multi-unit(s) systems and 117 rockslides. A kinematic attribute assigned to each inventoried landform documents the order of magnitude of the creep rate. Finally, we show that topo-climatic variables influence the spatial distribution of the rock glaciers. Their mean elevation increases toward the continental interior with a dominance of relict landforms close to the land-sea margin and an increased occurrence of active landforms further inland. Both rock glaciers and rockslides are mostly located on west-facing slopes and in areas characterised by strongly foliated rocks, which suggests the influence of geological preconditioning factors. The study demonstrates the value of semi-quantitative InSAR products to characterise kinematic information at large scale and exploit the results for periglacial research. It highlights the complementarity of both kinematic and morphologic approaches for inventorying slope processes.

Statistical assessment metrics for InSAR atmospheric correction: Applications to generic atmospheric correction online service for InSAR (GACOS) in Eastern China

The atmospheric effect represents one of the major error sources in Interferometric Synthetic Aperture Radar (InSAR), and its mitigation is found crucial for high-precision InSAR applications. Numerous studies on InSAR atmospheric correction methods and applications covering a wide range of regions worldwide have been reported with varying degrees of success. However, more efforts on the performance assessment are needed, and the conclusions may lack statistical significance due to the limited interferograms involved in most of these analyses. To optimally utilise different InSAR atmospheric correction methods and avoid potential uncertainty caused, appropriate statistical metrics to assess the correction performance must be set up. In this work, we provide a general guideline for statistical assessment of InSAR atmospheric correction. Based on the physical properties of the atmosphere, three metrics are applied: (i) the phase standard deviation which assesses the overall performance of the correction; (ii) the spatial structure function which evaluates the reduction of the long-wavelength atmospheric effect; and (iii) the phase-elevation correlation coefficient which measures the reduction of the stratified component of the atmospheric delay. The performance of the Generic Atmospheric Correction Online Service for InSAR (GACOS) products for two typical terrains in Eastern China is evaluated. Statistical results of the 1250 Sentinel-1 interferograms covering the Yellow River Delta and Shandong hilly region show that (i) GACOS reduces the interferometric phase standard deviation in 84.6% of the interferograms by an average of 36.4%; (ii) the phase decorrelation distance decreases from 321 km to 225 km on average after correction; and (iii) the mean phase-elevation correlation declines by 33.3% for the areas with considerable topographic variations. The results verify the effectiveness of GACOS products in Eastern China for the first time, and the three proposed metrics further characterise the sources of improvement after correction.

Toward Sustained Monitoring of Subsidence at the Coast Using InSAR and GPS: An Application in Hampton Roads, Virginia

AbstractHampton Roads is among the regions along the U.S. Atlantic Coast experiencing high rates of relative sea level rise. Partly to mitigate subsidence from aquifer compaction, Hampton Roads is injecting treated wastewater into the underlying aquifer. However, the GPS (Global Positioning System) station spacing (∼30 km) is too coarse to capture the spatial variability of subsidence and potential uplift from the injection. We present a cost‐effective workflow for generating an InSAR (interferometric synthetic aperture radar) and GPS combined displacement product. We leverage a live, open‐access archive of InSAR products generated from Sentinel‐1 data. We find an overall subsidence rate of −3.6 ± 2.3 mm/year with considerable spatial variability. The effects of groundwater injection are currently below detection. The workflow presented here is an asset for sustained monitoring of the injection effort and regional subsidence that is applicable along the U.S. coasts for assisting in mitigation and adaptation of relative sea level rise.

A multiscale approach for detection and mapping differential subsidence using multi-platform InSAR products

Abstract. Detecting and mapping subsidence is currently supported by interferometric synthetic aperture radar (InSAR) products. However, several factors, such as band-dependent processing, noise presence, and strong subsidence limit the use of InSAR for assessing differential subsidence, which can lead to ground instability and damage to infrastructure. In this work, we propose an approach for measuring and mapping differential subsidence using InSAR products. We consider synthetic aperture radar (SAR) data availability, data coverage over time and space, and the region's subsidence rates to evaluate the need of post-processing, and only then we interpret the results. We illustrate our approach with two case-examples in Central Mexico, where we process SAR data from the Japanese ALOS (L-band), the German TerraSAR-X (X-band), the Italian COSMO-SkyMed (X-band) and the European Sentinel-1 (C-band) satellites. We find good agreement between our results on differential subsidence and field data of existing faulting and find potential to map yet-to-develop faults.

Assessment of Mitigation Strategies for Tropospheric Phase Contributions to InSAR Time-Series Datasets over Two Nicaraguan Volcanoes

Interferometric Synthetic Aperture Radar (InSAR) studies of ground displacement are often plagued by tropospheric artifacts, which are phase delays resulting from spatiotemporal variations in the refractivity of air within the troposphere. In this study, we focus on COSMO-SkyMed (X-band) InSAR products obtained over two different types of volcanoes in Nicaragua: the Telica stratovolcano and the Masaya caldera. We examine the applicability of an empirical linear correction method and three Global Weather Models (GWMs) with different spatial and temporal resolutions for removing the tropospheric phase component. We linearly invert the tropospheric-corrected interferograms using the Small BAseline Subset (SBAS) time-series technique to produce time-series of ground displacement. Statistical assessments were performed on the corrected interferograms to examine the significance of the applied corrections on the individual interferograms and time-series results. We find that the applicability of the correction methods is highly case-dependent and that in general, the temporal resolution of GWMs influences their ability to capture turbulent tropospheric phase delays. At the two target volcanoes, our study shows that none of the GWMs are able to accurately capture the tropospheric phase delays. Our study provides a guide for researchers using InSAR data in tropical regions who wish to use tropospheric model corrections to carefully assess the applicability of the different types of tropospheric correction methods.

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability and environmental carrying capacity. This research proposed a method to integrate time series Sentinel-1 (S1) and Sentinel-2 (S2) data for crop type mapping over oasis agricultural areas through a case study in Northwest China. Previous studies using synthetic aperture radar (SAR) data alone often yield quite limited accuracy in crop type identification due to speckles. To improve the quality of SAR features, we adopted a statistically homogeneous pixel (SHP) distributed scatterer interferometry (DSI) algorithm, originally proposed in the interferometric SAR (InSAR) community for distributed scatters (DSs) extraction, to identify statistically homogeneous pixel subsets (SHPs). On the basis of this algorithm, the SAR backscatter intensity was de-speckled, and the bias of coherence was mitigated. In addition to backscatter intensity, several InSAR products were extracted for crop type classification, including the interferometric coherence, master versus slave intensity ratio, and amplitude dispersion derived from SAR data. To explore the role of red-edge wavelengths in oasis crop type discrimination, we derived 11 red-edge indices and three red-edge bands from Sentinel-2 images, together with the conventional optical features, to serve as input features for classification. To deal with the high dimension of combined SAR and optical features, an automated feature selection method, i.e., recursive feature increment, was developed to obtain the optimal combination of S1 and S2 features to achieve the highest mapping accuracy. Using a random forest classifier, a distribution map of five major crop types was produced with an overall accuracy of 83.22% and kappa coefficient of 0.77. The contribution of SAR and optical features were investigated. SAR intensity in VH polarization was proved to be most important for crop type identification among all the microwave and optical features employed in this study. Some of the InSAR products, i.e., the amplitude dispersion, master versus slave intensity ratio, and coherence, were found to be beneficial for oasis crop type mapping. It was proved the inclusion of red-edge wavelengths improved the overall accuracy (OA) of crop type mapping by 1.84% compared with only using conventional optical features. In comparison, it was demonstrated that the synergistic use of time series Sentinel-1 and Sentinel-2 data achieved the best performance in the oasis crop type discrimination.

From ERS 1/2 to Sentinel-1: Subsidence Monitoring in Italy in the Last Two Decades

The use of InSAR (Interferometric Synthetic Aperture Radar) products has greatly increased in the last years because of the technological advances in terms of both acquisition sensors and processing algorithms. The development of multi-interferogram techniques and the availability of free SAR analysis tools has significantly increased the number of worldwide applications of satellite measurements for mapping and monitoring geohazards. InSAR techniques excel in determining ground deformation in urban areas, where the coherence of the radar images is high, and the obtainable results are particularly reliable. Thus, measuring urban subsidence has always been one of the main targets of the InSAR analysis. In this paper, we present a brief review on the applications, in the last decades, of both single and multi-interferogram techniques to monitor ground lowering in urban areas along the Italian Peninsula. Because of its geological context, Italy is prone to slow natural subsidence phenomena sometimes aggravated and accelerated, especially along the coasts and in urbanized areas, by anthropogenic factors (i.e. groundwater overexploitation, consolidation in recent urban expansion, geothermal activities). The review will show how the interferometric data allowed the scientific community to increase the knowledge of the phenomena, map their spatial distribution and reconstruct their temporal evolution. The final goal of the review is to demonstrate the added value of InSAR data in supporting groundwater management and urban development in Italy.

The InSAeS4 Airborne X-Band Interferometric SAR System: A First Assessment on Its Imaging and Topographic Mapping Capabilities

We present in this work a first assessment of the imaging and topographic mapping capabilities of the InSAeS4 system, which is a single-pass interferometric airborne X-Band Synthetic Aperture Radar (SAR). In particular, we first provide a brief description of the InSAeS4 sensor. Then, we discuss the results of our analysis on the SAR and interferometric SAR products relevant to the first flight-test campaign. More specifically, we have exploited as reference the GPS measurements relevant to nine Corner Reflectors (CRs) deployed over the illuminated area during the campaign and a laser scanner Digital Elevation Model (DEM). From the analysis carried out on the CRs we achieved a mean geometric resolution, for the SAR products, of about 0.14 m in azimuth and 0.49 m in range, a positioning misalignment with standard deviation of 0.07 m in range and 0.08 m in azimuth, and a height error with standard deviation of 0.51 m. From the comparison with the laser scanner DEM we estimated a height error with standard deviation of 1.57 m.

Nonlocal Adaptive Multilooking in SAR Multipass Differential Interferometry

Filtering of interferometric synthetic aperture radar (InSAR) data has been the object of a growing number of papers, in recent years, aimed at improving the quality of differential InSAR (DInSAR) and tomography products based on multibaseline/multitemporal data. In this work, we investigate the potential of nonlocal adaptive multilooking (AML) techniques for the filtering of interferometric stacks. Nonlocal filtering has been already successfully applied to SAR data in many contexts, including the restoration of single interferograms. We propose several workflows, with different solutions for the data preprocessing and for the estimation of similarity, at the core of all nonlocal methods. Experiments on simulated and real data, the latter both at low and high spatial resolution, prove the proposed methods to provide significant improvements over nonadaptive multilooking in terms of both resolution and detail preservation, allowing for the generation of better interferometric products.

A Novel Multitemporal InSAR Model for Joint Estimation of Deformation Rates and Orbital Errors

Orbital errors, characterized typically as longwavelength artifacts, commonly exist in interferometric synthetic aperture radar (InSAR) imagery as a result of inaccurate determination of the sensor state vector. Orbital errors degrade the precision of multitemporal InSAR products (i.e., ground deformation). Although research on orbital error reduction has been ongoing for nearly two decades and several algorithms for reducing the effect of the errors are already in existence, the errors cannot always be corrected efficiently and reliably. We propose a novel model that is able to jointly estimate deformation rates and orbital errors based on the different spatial-temporal characteristics of the two types of signals. The proposed model is able to isolate a long-wavelength ground motion signal from the orbital error even when the two types of signals exhibit similar spatial patterns. The proposed algorithm is efficient and requires no ground control points. In addition, the method is built upon wrapped phases of interferograms, eliminating the need of phase unwrapping. The performance of the proposed model is validated using both simulated and real data sets. The demo codes of the proposed model are also provided for reference.

Improvement of the Accuracy of InSAR Image Co-Registration Based On Tie Points – A Review

Interferometric Synthetic Aperture Radar (InSAR) is a new measurement technology, making use of the phase information contained in the Synthetic Aperture Radar (SAR) images. InSAR has been recognized as a potential tool for the generation of digital elevation models (DEMs) and the measurement of ground surface deformations. However, many critical factors affect the quality of InSAR data and limit its applications. One of the factors is InSAR data processing, which consists of image co-registration, interferogram generation, phase unwrapping and geocoding. The co-registration of InSAR images is the first step and dramatically influences the accuracy of InSAR products. In this paper, the principle and processing procedures of InSAR techniques are reviewed. One of important factors, tie points, to be considered in the improvement of the accuracy of InSAR image co-registration are emphatically reviewed, such as interval of tie points, extraction of feature points, window size for tie point matching and the measurement for the quality of an interferogram.

Integrated satellite interferometry: Tropospheric noise, GPS estimates and implications for interferometric synthetic aperture radar products

Interferometric synthetic aperture radar (INSAR), like other astronomic and space geodetic techniques, is limited by the spatially and temporally variable delay of electromagnetic waves propagating through the neutral atmosphere. Statistical analysis of these variations, from a wide variety of instruments, reveals a power law dependence on frequency that is characteristic of elementary (Kolmogorov) turbulence. A statistical model for a major component of the delay fluctuations, the “wet” component, has previously been developed by Treuhaft and Lanyi [1987] for very long baseline interferometry. A continuous Global Positioning System (GPS) network is now in place in southern California that allows estimation of, along with geodetic parameters, the total delay due to the atmosphere above each site on a subhourly basis. These measurements are shown to conform to the Treuhaft and Lanyi (TL) statistical model both temporally and spatially. The TL statistical model is applied to the problem of INSAR and used to produce the covariance between two points separated in time and/or space. The error, due to the atmospheric variations, for SAR products such as topography and surface deformation is calculated via propagation of errors. There are two methods commonly cited to reduce the effect of atmospheric distortion in products from SAR interferometry, stacking and calibration. Stacking involves averaging independent interferograms to reduce the noise. Calibration involves removing part (or all) of the delay using data from an independent source such as total zenith delay estimates from continuous GPS networks. Despite the relatively poor spatial density of surface measurements, calibration can be used to reduce noise if the measurements are sufficiently accurate. Reduction in tropospheric noise increases with increasing number of measurement points and increasing accuracy up to a maximum of √N, where N is the number of points. Stacking and calibration are shown to be complementary and can be used simultaneously to reduce the noise to below that achievable by either method alone.