Accurate monitoring of ground deformation is important for understanding the processes that lead to natural disasters, and the health and safety of society. The discovery of a fading signal has put the accuracy of methods that utilise multilooking and short-temporal interferograms into question. A symptom of this signal is that multilooked interferograms may exhibit a non-zero phase loop closure. We compare the phase loop closure in C-band and L-band InSAR data over different land cover types for an area centered on Milan, Italy. Our findings suggest that changes in volume scattering are the leading cause of phase loop misclosure and the fading signal in this area. We also investigate the effects of multilooking on the magnitude of phase loop misclosure with the goal of developing a model of the fading signal for both C-band and L-band InSAR.

Information extraction from polarimetric synthetic radar data has been limited by target orientation-induced scattering mechanism ambiguity. The existing methods are not able to fully compensate the data for the orientation of polarimetric scattering targets. In this paper, a new de-orientation processing methodology is presented that makes novel use of radar targets phenomenological theory to split off and effectively de-orient orientationally perturbed components of the target. In particular, a physical procedure is described to compensate orientation of elementary components of a distributed target using their independent orientation angles. Validation against fully polarimetric radar data shows significant agreement with the expected results based on the theoretical description.

This article contains description of an improved tool for phase unwrapping, as developed and to be applied routinely within COMET LiCSAR system producing Sentinel-1 interferograms in 0.001° resolution (WGS-84). We apply existing standard methods on routinely available data in a way to improve reliability of the standard phase unwrapping procedure and increase coverage of unwrapped pixels. The primary idea is to reduce high phase gradients, causing both decorrelation and zonal unwrapping errors, by extracting and unwrapping long-wave spatial signal (extracted by a combination of multilooking and Gaussian filtering) and signal related to atmosphere that can be modeled (e.g. using COMET GACOS data or by correlation of phase with a DEM). We show the effect of the improved approach is significant in standard time series processing (here using LiCSBAS).

High precision monitoring of infrastructure using artificial reflectors is possible with freely available Sentinel-1 data, but large reflectors are needed. We find that a triangular trihedral corner reflector should typically have at least 1 m inner leg length. As such large reflectors are often not feasible for use in urban areas for essential infrastructure monitoring, we designed a multiple corner-reflector array to replace a single corner reflector with an inner leg length of 1 m. In this case, we use four reflectors where each of them is a truncated triangular trihedral with an inner leg length of 0.33 m. We measured InSAR amplitude, phase and coherence of this reflector array with various configurations of alignments of the array. We find that as long as great care is taken in the relative positioning of the four corner reflectors, so that they constructively interfere, each horizontal or vertical configuration provides the expected amplitude, coherence and phase stability. Applications of multiple small corner reflectors in urban areas range from essential infrastructure monitoring (e.g bridges, overpasses, tunnel constructions), through assessment of structural health of buildings, to monitoring highway and railway embankments. We show that the multiple corner array works when placed in a single InSAR resolution cell, but depending on the application, the number and projection of corner reflectors can be varied, as long as sufficient signal-to-clutter ratio is achieved in the area of interest.

There are ~ 1400 subaerial volcanoes with the potential to erupt, but less than 10% are instrumentally monitored. Routine acquisition by the European Sentinel-1 radar mission now offers the potential to monitor most of them with at least two acquisitions every twelve days. We have developed a system to routinely apply radar interferometry (InSAR) whenever a new image is acquired by Sentinel-1 over a volcano. Displacement of the ground between images shows up in the resulting interferograms, but there are too many of these to inspect individually. We have therefore developed an automated approach to identify signs of both new deformation patterns and changes in rate for existing deformation patterns, using independent component analysis. We first use a set of training interfero-grams to identify components associated with background deformation and common atmospheric patterns. We then analyse new interferograms in the context of those components, and flag changes in rate above the background variation, and significant unexplained new signals. We have found this approach to be successful even without having to first explicitly estimate the atmospheric signal in individual interferograms. We have also developed an alternative algorithm using a deep-learning approach, with the aim of extending this approach to forecasting of volcanic activity. This is implemented with a “double-headed” convolutional neural network, which is able to both locate deformation and classify the type of deformation, in single interferograms. We achieve this through creating a large dataset of synthetic interfero-grams, which features labels of both the type and location of any deformation, and is used for initial training of the network. We then fine-tune the network's performance through the inclusion of a smaller amount of real data.

This article contains brief overview on current key improvements in the interferograms generator part of LiCSAR, a system to generate open access moderate resolution Sentinel-1 interferograms aiming towards monitoring tectonic and volcanic deformation. We were focusing on improvements in performance and quality of unwrapping, and quality control of LiCSAR data.

Using a combination of multi-directional SAR and TIR measurements, the Harmony Earth Explorer 10 mission candidate will provide high resolution simultaneous measurements of surface stress, surface currents SST and wave spectra over oceans, 3-D deformation vectors over solid Earth, and time-series of surface elevation changes over volcanic areas and land ice masses. This will serve a series of science objectives aimed at better understating multi-scale processed and feedbacks in the Earth System.

Interferometric Synthetic Aperture Radar (InSAR) is a powerful tool for monitoring ground deformation associated with earthquakes, volcanoes, landslides, and different anthropogenic activities. The accuracy of the estimated deformation depends on a number of parameters including tropospheric and ionospheric delays, unwrapping errors, phase decorrelation due to changes in scattering behavior and system noise. However, recently an additional source of phase noise has been identified [1], which is strongest in short-interval multi-looked interferograms and, unlike other sources of noise, leads to biased, non-zero loop closure phases. This is problematic for time-series analysis because short-interval interferograms may be the only ones that maintain coherence for some areas. In this study, we explore the characteristics of this phenomenon and propose a mitigation strategy.

Bridge owners are faced with the challenge of maintaining an ageing and deteriorating asset portfolio. There is an increasing amount of InSAR data becoming available for built environment monitoring, with the opportunity to leverage free ESA Sentinel-1 data for regular monitoring. By understanding the potential to augment existing modelling tools used in common bridge engineering practice, InSAR can provide valuable additional insights and spot potential problems. In this paper we study Hammersmith Flyover, a concrete bridge in London, United Kingdom. We combine geometric models, structural models and InSAR data within a GIS environment and demonstrate how these systems can be used to regularly monitor specific behaviors including thermal expansion.

We present a new procedure and program system to integrate Sentinel-1 synthetic aperture radar (SAR) interferometry and Global Navigation Satellite System (GNSS) network observations to estimate 3-D surface changes caused by environmental processes. The procedure is based on the integrated geodetic/geodynamic benchmarks, which are equipped with both ascending (ASC) and descending (DSC) corner reflectors. The results of sparse GNSS observations are interpolated using the ASC and DSC line-of-sight changes provided by the Sentinel-1 mission every six days. The data integration is carried out using a Kalman filter where north, east, and up (vertical) coordinates and their instantaneous velocities are estimated and updated by the GNSS-derived data. The north components are essentially provided by the GNSS observations alone. The procedure was developed and successfully tested on landslide areas in Hungary.

Geodetic measurements of crustal deformation rates provide important information on earthquake hazard, indicating that strain is accumulating either faster or slower than the rate suggested by known earthquakes. With the COMET-LiCSAR InSAR processing system, which performs large-scale automated processing and timeseries analysis of Sentinel-1 data, we aim to generate strain rate maps for the entire Alpine-Himalayan Belt, and use these to investigate seismic hazard. In this paper we will present results for Anatolia. Deformation is also a key indicator of volcanic unrest, and is often associated with the flow of magma to shallower depths. The operational nature of Sentinel-1, with frequent revisits and rapid data delivery, makes it suitable for monitoring subaerial volcanoes globally. In order to deal with the large quantity of new data that is continuously being generated, we have developed machine learning approaches to flag when either a new deformation pattern emerges, or an existing deformation pattern changes rate.

There are potentially dangerous areas where InSAR technology cannot be applied routinely in the absence of proper persistent or distributed scatterers. Here, we planned and investigated the use of truncated trihedral triangle corner reflectors (CRs) oriented to ascending and descending directions for Sentinel-1 orbit, which were mounted on the optimal concrete basement including an additional global navigation satellite system (GNSS) adapter. These integrated benchmarks were designed to produce a signal-to-clutter ratio of about 100 (i.e., 20 dB). The mechanical design allows optimal orientation of the reflectors and resistance against dynamic effects. We investigated 1:5 models of the CRs and integrated benchmarks in an anechoic chamber to estimate the effects of truncation and the interference of the twin reflectors. The main effect of the interference is the asymmetric monostatic radar cross section, which can be neglected. The integrated benchmarks were also investigated in two recent landslide areas in Hungary using Sentinel-1 single look complex (SLC) scenes, which confirmed that the preliminary requirements can be met.

Sentinel-1 represents a major step forward in enabling us to monitor the Earth's hazardous tectonic and volcanic zones. Here, we present the latest progress from the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET), where we provide deformation results to the community for volcanoes and the tectonic belts. For the estimation of seismic hazard, we require relative accuracy on the order of 1 mm/yr between points 100 km apart. This requires mass processing of long time series of radar acquisitions. As of January 2018, we are producing in-terferograms systematically for the entire Alpine-Himalayan belt (~9000 × 2000 km) and the majority of subaerial volcanoes. Currently we make interferograms and coherence products available to the community, but we plan to also provide average deformation rates and displacement time series, in the future. The results are made available through a dedicated COMET portal, and we are in the process of linking them to the ESA G-TEP and EPOS. COMET also responds routinely to significant continental earthquakes, larger than ~Mw 6.0. The short repeat interval of Sentinel-1, together with the rapid availability of the data, allows us to do this within a few days for most earthquakes. For example, after the Mw 7.8 Kaikoura earthquake we supplied a processed interferogram to the community just 5 hours and 37 minutes after the Sentinel-1 acquisition. In this paper we provide an overview of some of the latest results for tectonics and volcanism and discuss how the accuracy of these products will improve as the number of data products acquired by Sentinel-1 increases.

The interferometric coherence is a measure of the correlation between two SAR images and constitutes a commonly used estimator of the phase quality. Its estimation requires a spatial average within a 2-D window, usually named as multilook. The multilook processing allows reducing noise at the expenses of a resolution loss. In this letter, we analyze the influence of the multilook size while applying a polarimetric optimization of the coherence. The same optimization algorithm has been carried out with different multilook sizes and also with the nonlocal SAR filter filter, which has the advantage of preserving the original resolution of the interferogram. Our experiments have been carried out with a single pair of quad-polarimetric RADARSAT-2 images mapping the Mount Etna's volcanic eruption of May 2008. Results obtained with this particular data set show that the coherence is increased notably with respect to conventional channels when small multilook sizes are employed, especially over low-vegetated areas. Conversely, very decorrelated areas benefit from larger multilook sizes but do not exhibit an additional improvement with the polarimetric optimization.

While polarimetric persistent scatterer InSAR (PSI) is an effective technique for increasing the number and quality of selected PS pixels, existing methods are suboptimal; a polarimetric channel combination is selected for each pixel based either on amplitude, which works well only for high-amplitude scatterers such as man-made structures, or on the assumption that pixels in a surrounding window all have the same scattering mechanism. In this paper, we present a new polarimetric PSI method in which we use a phase-based criterion to select the optimal channel for each pixel, which can work well even in nonurban environments. This algorithm is based on polarimetric optimization of temporal coherence, as defined in the Stanford Method for PS (StaMPS), to identify the scatterers with stable phase characteristics. We form all possible copolar and cross-polar interferograms from the available polarimetric channels and find the optimum coefficients for each pixel using defined search spaces to optimize the temporal coherence. We apply our algorithm, PolStaMPS, to an area in the Tehran basin that is covered primarily by vegetation. Our results confirm that the algorithm substantially improves on StaMPS performance, increasing the number of PS pixels by 48%, 80%, and 82% with respect to HH+VV, VV, and HH channels, respectively, and increasing the signal-to-noise ratio of selected pixels.

The two-satellite Copernicus Sentinel-1 (S1) constellation became operational in Sep 2016, with the successful in-orbit commissioning of the S1B unit. During, the commissioning phase and early operational phase it has been confirmed that the interferometric performance of the constellation is excellent, with no observed phase anomalies. In this work, we show an analysis of selected performance parameters for the S1 constellation, as well as initial results based on the available data from the first months of operations.

Multitemporal polarimetric synthetic aperture radar (SAR) data can be used to estimate the dominant scattering mechanism of targets in a stack of SAR data and to improve the performance of SAR interferometric methods for deformation studies. In this letter, we developed a polarimetric form of amplitude difference dispersion (ADD) criterion for time-series analysis of pixels in which interferometric noise shows negligible decorrelation in time and space in small baseline algorithm. The polarimetric form of ADD is then optimized in order to find the optimum scattering mechanism of the pixels, which in turn is used to produce new interferograms with better quality than single-pol SAR interferograms. The selected candidates are then combined with temporal coherency criterion for final phase stability analysis in full-resolution interferograms. Our experimental results derived from a data set of 17 dual polarizations X-band SAR images (HH/VV) acquired by TerraSAR-X shows that using optimum scattering mechanism in the small baseline method improves the number of pixel candidates for deformation analysis by about 2.5 times in comparison with the results obtained from single-channel SAR data. The number of final pixels increases by about 1.5 times in comparison with HH and VV in small baseline analysis. Comparison between persistent scatterer (PS) and small baseline methods shows that with regards to the number of pixels with optimum scattering mechanism, the small baseline algorithm detects 10% more pixels than PS in agricultural regions. In urban regions, however, the PS method identifies nearly 8% more coherent pixels than small baseline approach.

Within this study we tested a combination of processing tools in order to overcome certain limitations of Advanced Differential Interferometrie SAR (A-DInSAR) for the detection of intraplate surface motion. The study area is located over the Hyblean Plateau in south-east Sicily, Italy. Instead of usually applied spatio-temporal smoothing of the stacked dataset, we calculated the atmospheric contribution within the interferometrie signal by external data from the global meteorological model ERA-INTERIM. Furthermore we enhanced the coverage of the mean line-of-sight velocity by gap filling and applied a multi-directional filter. For this we used ENVISAT ASAR imagery (49 scenes descending and 58 scenes ascending orbit). The output was then integrated with local GPS data into the SISTEM framework to decompose the measurements into a 3-dimensional velocity field. While in areas of no GPS stations, detected deformation is not precise enough, the results in areas of good GPS coverage look promising with accuracies down to 1 mm/y and various zones of surface deformation could be detected successfully.

The Arctic environment offers the biggest challenge for In-SAR; huge seasonal change, ice and snow cover for much of the year, and large variations in soil moisture. Yet the Arctic is often seen as a barometer for climate change and therefore being able to exploit technologies such as InSAR provides invaluable information not only for the communities involved, but also for climate change science as a whole. Furthermore, InSAR's unmatched spatial coverage make it an obvious choice for covering vast landmasses such as the Canadian Archipelago.

FUTUREVOLC is a collaborative project funded through the FP7 Environment Supersite Concept call encompassing 26 partners in 10 countries. The main objectives of FUTUREVOLC are to establish an integrated volcanological monitoring procedure through European collaboration, develop new methods to evaluate volcanic crises, increase scientific understanding of magmatic processes and improve delivery of relevant information to civil protection and authorities. FUTUREVOLC is in the first of its 3.5 year duration, therefore this paper presents aims and expectations rather than results.

Terrestrial Radar Interferometry (TRI) has several advantages for measuring glacier velocity. These ground-based systems alleviate problems associated with the long revisit times of satellites, and provide higher spatial sampling compared to GPS-based approaches. TRI is the technique of choice for rapidly moving glaciers, especially their terminal zones, which tend to exhibit high spatial and temporal variability. In this study, we use the Gamma Portable Radar Interferometer (GPRI) to measure the velocity of Breidamerkurjokull, a marine-terminating outlet glacier on the southeastern coast of Iceland, and compare it to TerraSAR-X data taken shortly after. We document significant temporal and spatial variability of ice velocity within 800 meters of the calving front.

Ash, tephra, and gas ejected during explosive eruptions pose a major far-reaching threat to population, health, and air traffic. Lava flows, lahars, and floods from ice-capped volcanos, as well as landslides that have a potential for tsunami generation if they reach into sea or lakes, can also have a major influence. Remote sensing contributes to the mitigation of these hazards through the use of synthetic aperture radar interferometry (InSAR) and spectroradiometry. In the case of InSAR, displacements of a volcano's surface can be interpreted in terms of magma movement beneath the ground. Thus, the technique can be used to identify precursors to eruptions and to track the evolution of eruptions. Recent advances in algorithm development enable relative displacements over many kilometers to be measured with an accuracy of only a few millimeters. Spectroradiometry on the other hand allows monitoring of a volcanic eruption through the detection of hot spots, and monitoring and quantification of the ash and SO2emitted by volcanos into the atmosphere. The tracking of ash plumes during eruptions assists in the identification of areas that should be avoided by aircraft. Here we present a review of these two remote sensing techniques, and their application to volcanic hazards.

The analysis of radar time series with persistent scatterer techniques usually relies on temporal unwrapping, because phase behavior can be often described by simple models. However, one of the major limitations of temporal algorithms is that they do not take advantage of spatially correlated information. Here, we focus on two types of information that can be spatially estimated, namely, observation precision and the probability density function of the model parameters. We introduce them in phase unwrapping using Bayesian theory. We test the proposed method using simulated data. We also apply them to a small area in the southern Netherlands and compare with conventional temporal unwrapping methods.