Satellite interferometric SAR (InSAR) has demonstrated to be a powerful technology to perform millimeter-scale precision measurements of ground motions typically caused by landslides, subsidence, earthquakes or volcanic activity, and to monitor the stability of slopes, mining areas, buildings, infrastructures, etc. This work presents the European Ground Motion Service (EGMS), funded by the European Commission as an essential element of the Copernicus Land Monitoring Service (CLMS). EGMS constitutes the first application of the interferometric SAR (InSAR) technology to high-resolution monitoring of ground deformations over an entire continent, based on full-resolution processing of all Sentinel-1 (S1) satellite images over most of Europe. EGMS employs advanced persistent scatterer (PS) and distributed scatterer (DS) InSAR processing techniques. Moreover, a global navigation satellite system (GNSS) model is realized to calibrate the InSAR ground motion products. To foster as wide usage as possible, EGMS also provide tools for visualization, exploration, analysis and download of the ground deformation products, as well as elements to promote best practice applications and user uptake.

The European Ground Motion Service (EGMS) is the newest addition to the Copernicus Land Monitoring Service, managed by the European Environmental Agency (EEA). EGMS provides continental-scale, homogeneous maps of ground motion based on Copernicus Sentinel-1 mission data. The service delivers the InSAR based ground motion products with millimetre-per-year precision with full time-series included. Notably, part of the EGMS product portfolio is referenced with respect to a GNSS based model. This paper reports on the argumentation of how and why to use GNSS in the context of EGMS.

This paper introduces the extended timing annotation dataset (ETAD) product for Sentinel-1 (S-1) which was developed in a joint effort of German Aerospace Center (DLR) and the European Space Agency (ESA). It allows to correct range and azimuth timing of S-1 images for geophysical effects as well as for inaccuracies in synthetic aperture radar (SAR) image focusing. In combination with the precise orbit solution, these effects determine the absolute geolocation accuracy of S-1 SAR images and the relative collocation accuracy of repeat pass image stacks. ETAD contains the gridded timing corrections for the tropospheric and ionospheric path delays, the tidal-based surface displacements, and the SAR processing effects, all of which are computed for each data take using standard models from geodesy and auxiliary atmospheric data. The ETAD product helps S-1 users to significantly improve the geolocation accuracy of the S-1 SAR products to better than 0.2 m and offers a potential solution for correcting large scale interferometric phase variations. The product layout and the product generation are described schematically. The paper also reports first results for different SAR techniques: first, the improvement in geolocation accuracy down to a few centimeters by verification of accurately surveyed corner reflector positions in the range-azimuth plane; second, the well-established offset-tracking technique, that is used for systematic ice velocity monitoring of ice sheets and glaciers, where ETAD can reduce velocity biases down to sub-centimetric values; and third, the correction of atmospheric phase contributions in wide-area interferograms used for national and European ground motion services. These early results proof the added value of the ETAD corrections and that the product design is well suited to be integrated into the processing flows of established SAR applications such as absolute ranging of targets, speckle/feature tracking and interferometry.

Interferometric processing of a time series of acquisitions from synthetic aperture radar (SAR) satellites makes it possible to detect and measure ground motion phenomena, typically caused by landslides, subsidence, earthquakes or volcanic activity, with millimeter-scale precision. This enables, for example, monitoring of the stability of slopes, mining areas, buildings and infrastructures. This work presents the European Ground Motion Service (EGMS), funded by the European Commission as an essential element of the Copernicus Land Monitoring Service (CLMS). The EGMS constitutes the first application of the interferometric SAR (InSAR) technology to high-resolution monitoring of ground deformations over an entire continent, based on full-resolution processing of all Sentinel-1 (S1) satellite acquisitions over most of Europe (Copernicus Participating States). Upscaling from existing national precursor services to pan-European scale is challenging. The EGMS will employ the most advanced persistent scatterer (PS) and distributed scatterer (DS) InSAR processing techniques in combination with a high-quality Global Navigation Satellite System (GNSS) model to calibrate the ground motion products. To foster as wide usage as possible, the EGMS will also provide tools for visualization, exploration, analysis and download of the ground deformation products, as well as elements to promote best practice applications and user uptake.

InSAR Norway, a public national ground motion service, based on Copernicus Sentinel-1 data, was launched in November 2018. The service provides regularly updated ground motion time series on over two billion locations in Norway. The service provides this data freely and openly to all interested users, through a web browser interface. Within the government, this data in instrumental for landslide hazard and risk assessment as well as monitoring. Other applications include evaluation of urban subsidence and infrastructure monitoring. Many scientific applications also benefit, such as in the fields of geomorphology and geodesy. InSAR Norway is operated by the Geological Survey of Norway, with cofunding from the Norwegian Space Centre and the Norwegian Water and Energy Directorate. Processing is done on a high-performance computing cluster (HPCC) using software developed by the KSAT-GMS partnership (NORCE - formerly NORUT, PPO.labs and Kongsberg Satellite Services).

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.

The Next ESA SAR Toolbox (NEST) is a free and open source toolbox suite for reading, processing, analysis and visualization of SAR data. The toolbox is developed by the European Space Agency (ESA) under the GNU GPL license. The toolbox supports the exploitation of SAR data from ESA's space-borne SAR missions (ERS-1&2, ENVISAT), and also of other space-borne sensors (e.g., TerraSAR-X, RADARSAT 1&2, COSMO-SkyMed, JERS-1, ALOS PALSAR). The input data is SAR data processed to Level-1 or higher. NEST provides basic and advanced tools for SAR user community, such as an absolute calibration, automatic coregistration of detected and complex products, multilooking, speckle filtering, external precise obit ingestion, geocoding, mosaicking and a Product Library Metadata Database, etc. In addition to this basic processing toolset, NEST offers a collection of routines for oil spill and ship detection, and wind field estimation. Finally, a fully integrated and featured InSAR processor is being implemented into NEST.

Radar interferometry has proven to be a very suitable, low-cost and accurate tool to measure surface displacements. We investigate several data fusion or time-series analysis strategies which aim to mitigate C-band InSAR restrictions for volcano deformation monitoring applications. The focus is on active African volcanic areas. Firstly, data fusion of C-band ENVISAT/ASAR and L-band ALOS/PALSAR sensors helps the determination of a rifting event sequence that took place in summer 2007 in Lake Natron area. The second strategy investigated is a new Wavelet Based InSAR time series applied on ERS-2 data covering the Nyiragongo-Nyamulagira area. It allows new ground displacements identifications outside the local rift valley. Lastly, PALSAR Quad-Pol POLInSAR applicability is explored for La Palma Island.

InSAR data acquired from independent overlapping tracks can be exploited for a reliability assessment of the Persistent Scatterer InSAR (PS-InSAR) technique. This is obtained by means of the datum connection of multiple tracks, simultaneously evaluating the misclosures between multi-track PS-InSAR estimates. Due to a different viewing geometry, many of the detected PS will physically not be the same. However, their estimates may still refer to the same deformation signal. The existence of independent observations of the same deformation signal provides a powerful tool to increase the redundancy and evaluate the reliability. The datum connection can be subdivided in two steps. The first step consists of the conversion of PS locations to a common datum. Secondly, the PS-InSAR parameter estimates (velocities, displacements, heights) are connected. In stead of the conventional approach of separately geocoding each track, we propose the use of a common radar datum defined by the acquisition geometry of the 'master track'. Multi-track datum connection has been applied in the Groningen region, the Netherlands, which is affected by subsidence due to gas extraction with displacement rates up to 7 mm/year. The main reservoir is (partly) visible in 6 independent overlapping ERS tracks from 1992 (ascending and descending). Datum connection resulted in a consistent set of PS-InSAR deformation estimates. Additionally, the deformation signal was decomposed in horizontal and vertical movements, utilizing the different viewing geometries of the tracks.

This paper presents the concept of Dynamic Persistent Scatterers Interferometry (PSI) processing, which enables the sequential estimation of parameters. The method is based on the Integer Least Squares (ILSQ) PSI concept and makes use of the estimation vector and corresponding variance-covariance matrix of the initial estimation epoch. In addition, the concept of multi-modal adaptive estimation and testing is applied. The algorithm systematically adds a new acquisition or set of acquisitions to an existing stack, updates the solution of the previous run, and analyzes whether the behavior of the (pre-) selected points fits the expected one.

SAR amplitude calibration is performed prior to the selection of potential Persistent Scatterers (PS) to avoid amplitude variations due to sensor characteristics and viewing geometry. As only the interferometric phases of a small percentage of the radar pixels in an image is used in the PS-InSAR analysis, it is investigated if this time and storage space consuming step can be omitted. We present an integrated method which does not perform amplitude calibration explicitly, but integrates it into the PS point selection procedure for validation purposes by evaluating the hypothesis that a point would have been selected if all images were calibrated beforehand. Its performance assessment is based on coherent phase behavior of the selected potential PS and indicates that empirical calibration validation is an alternative for calibrating full images based on physical sensor parameters.

PS-InSAR has proven to be an accurate and ef- ficient technique for the joint estimation of topographic and displacement signal from stacked interferometric combinations. In this contribution a new method for PS-Insert processing is introduced, which enables the recursive estimation of parameters of interest. The method is based on the ILSQ PS-InSAR concept and makes use of the estimation vector and corresponding variance-covariance matrix of the initial estimation epoch. The presented methodology systematically adds a new acquisition (or set of acquisitions) to the existing stack, updates the solution of the previous run, and analyzes whether the behaviour of the (pre-) selected points fits the expected one. This contribution focuses on a mathematical framework, rather then on specific applicational problems. Nevertheless, the performed numerical analysis on simulated data sets is analyzed and discussed, which shows that the preset aims of the recursive PS-InSAR estimation technique is achieved. I. INTRODUCTION Time series InSAR analysis using persistent scatterer (PS) techniques aims at the joint estimation of topographic and displacement signal from a number of interferometric com- binations, (1), (2). Since the estimates of both parameters are correlated and error signal due to, e.g., atmospheric signal can significantly affect the adjustment, an accurate estimation depends on the availability of a large data stack, i.e., more than 20-30 images. A smaller number of images usually results in problems like detecting the potential PS, reducing the atmospheric signal, separating topography and displacement, and phase ambiguity estimation. An additional problem for all current multi-image pro- cessing concepts is that the parameter estimation is usually performed in batches, i.e., by using all available acquisitions at once. Hence, in order to incorporate a newly available acquisition into the processing chain, and consequently update the estimates, the whole processing (at least the PS part) has to be performed again. Such an approach consequently leads to an increase of processing time, limits the application to the areas where only a sufficient number of images is available, and reduces the potential application of the method to a semi- real-time deformation monitoring. The two main processing concepts of PS-InSAR are the concept of the ambiguity function, (1), and Integer Least Squares (ILSQ) method, (2). The main drawback of the first one is that the propagation concept of observations to the unknown parameters is suboptimal. Moreover, the method strongly depends on the discretization of the solution space and it treats unknown ambiguities as deterministic parameters instead of stochastic ones. The ILSQ approach is based on the principles of Best Linear Unbiased Estimation (BLUE) - it is based on the minimization of the mean squared error and it is formulated as a constrained minimization problem on the integer nature of the unknowns, (6). By means of the ILSQ method, the quality description of estimated parameters is the one of the end products of the analysis, which can conse- quently be used to determine the significance and reliability of the estimated parameters. The ILSQ PS-InSAR processing framework sets the basis for a recursive data processing strategy, where new acquisi- tions can be easily added to an existing data stack, significantly reducing the computational requirements. This implies that the presented methodology systematically adds a new acquisition to the existing stack, updates the solution of the previous run, and analyzes whether the behaviour of the (pre-)selected points fits to the expected behaviour of parameters of interest. If not, an alternative hypothesis is tested against the prior solution, leading to the rejection of the point, adaptation of the model, or manual intervention. For the conditions on the practical application of recursive PS-InSAR processing, it can be referred to the block-diagonal structure of the variance-covariance matrix of the introduced recursive model (the estimates from the initialization run and phase observations of the additional acquisition are assumed to be uncorrelated). Secondly, the atmospheric and non-modelled displacement contributions to the interferometric phase have to be modelled and incorporated into the variance matrix by means of covariance functions, (4), (5) - in the presented study the covariance functions are not further elaborated on. Moreover, in numerical experiments, phase contributions are isolated by low-pass filtering in the spatial domain and high- pass filtering in the temporal domain. Furtheron, in order to correctly perform the initialization run (candidate selection and unwrapping), a sufficient number of images (15-20) is needed. In the following sections the concept of the recursive PS- InSAR is presented. Examples on simulated data are used

In this study, we introduce a refined algorithm for the fine InSAR image coregistration which could be used in highly decorrelated scenes. The refinement is introduced at the point of selection of points necessary for the estimation of the offset vectors between master and slave image. A new approach for point selection based on the Harris corner detector algorithm is presented. The new point selection algorithm results with the clusters of point candidates for the offset vectors over a scene. Consequently, the number of points and their spatial distribution are improved, which results in a better global quality of the coregistration model