Presently, Radar interferometry (InSAR) is the only measurement technique capable of observing ground motion on regional and even continental scale with sufficient point density for highly precise areal monitoring. It is predestined for performing wide area surveying tasks. In particular, first services in Italy and Austria that monitor foremost linear infrastructure (e.g. roads or railways) are proving to provide valuable information. In Germany, displacement data from the German Ground Motion Service (Bodenbewegungsdienst Deutschland, BBD) or the European Ground Motion Service (EGMS) that are available for the whole territory of the country, potentially could be the basis of such a service on regional scale. The usability is determined by the quality of data, the positioning accuracy, the frequency and timeliness of updates and the coverage with measurement points. Regarding coverage, neither BBD nor EGMS are using Distributed Scatterers (DS) for their InSAR analysis of Germany. In order to explore, if the additional effort of including DS in the processing is worthwhile, we evaluate displacement data from different sources to provide numbers for coverage of different categories of linear infrastructure (motorways, federal roads, state roads, county roads, train tracks). We compare EGMS data and data processed by the Geodetic Institute Karlsruhe (GIK) from the Oberrheingraben south of Freiburg. The data processed by GIK include DS and are based on essentially the same Sentinel-1-data as the EGMS data. We observe that a distinct increase of coverage results from the use of DS that may help to justify the additional effort to include DS in the processing of BBD or EGMS for Germany. In addition, the results from a stack of TerraSAR-X strip map data gives an impression of what can be achieved with higher data resolution.

Time series of interferometric SAR (InSAR) images offer the potential to detect and monitor surface deformation with high spatial resolution, even for slow deformation processes. However, many different sources contribute to phase changes which are used in InSAR to estimate displacements. Complex displacement mechanisms or strong atmospheric contributions can complicate the separation of these contributions and even cause problems when unwrapping the phase. A preliminary model of expected displacements can support this process but requires information about all involved deformation processes. However, as these processes are often the main subject of the investigation, they are not sufficiently understood in advance. In this contribution, we approach this issue by analyzing InSAR time series results of regions with complex deformation behavior with the established statistical methods of principal and independent component analysis to identify dominant displacement patterns. We study Sentinel-1 InSAR data from 2015 to 2022 above the storage cavern field Epe in North Rhine Westphalia, Germany. Epe displays a spatially and temporally complex surface deformation field, which was described in previous studies as consisting of a linear signal relating to the cavern convergence as well as of seasonal and cavern pressure-dependent contributions. Our resulting displacement components can be clearly separated and appointed to different sources. This is supported by ground truth data and supplemental measurements of cavern pressure levels and groundwater levels. We also find that the previously described linear parametrization of displacements related to cavern convergence is no longer sufficient for longer time series. Our results show that we can obtain source-dependent displacement models from long and complex InSAR time series when using ICA. These can then either be used to refine time series processing or to describe the physical processes causing to the surface displacements with a geophysical source model. Both will be the subject of future investigations.

Time series of interferometric SAR (InSAR) images offer the potential to detect and monitor surface displacements with high spatial and temporal resolution, even for small and slow deformation processes. Yet, due to the nature of InSAR, the interferometric signal can contain a multitude of contributions. Different displacement source mechanisms could superpose each other, signals that are residuals of atmospheric and topographic effects could not be completely removed during processing of the time series or non-coherent noise could exist. Therefore, the criteria for the selection of temporally stable pixels are often rather strict, leading to significant reduction of the spatial resolution density.However, to understand the underlying processes of a deformation field, it is important to extract the displacement signals from the data at the best resolution possible and differentiate signals from different source mechanisms. Furthermore, being able to describe the displacement field as superposition of several simple mechanisms is a possible answer to the general question how the information content from tens of thousands of points each coming with a time series over hundreds of acquisitions can be extracted and comprehended.We address these issues, by determining the dominant displacement signals of different sources in a subset of reliable pixels of InSAR time series datasets with data driven component analysis methods. Subsequently we use models of these signals to identify their displacement patterns in previously not regarded pixels. We utilize the statistical principal component analysis for removing uncorrelated signal contributions and compare different blind source separation methods, such as independent component analysis and independent vector analysis for differentiating between displacements of different origin.We apply our method to a dataset of multiple orbits of Sentinel-1 InSAR time series from 2015 to 2022 above the gas storage cavern field Epe in NRW, Germany. Epe displays a complex surface displacement field, consisting of trends caused by cavern convergence, cyclic gas pressure dependent contributions, as well as ground water dependent seasonal displacements. With our approach, we can successfully distinguish the signals of the different source mechanisms and obtain a dense spatial sampling of these signals. Our results show good agreement with geodetic measurements from GNSS and levelling and show a strong correlation to cavern filling levels and groundwater levels, suggesting causal relations.

An important application of synthetic aperture radar interferometry (InSAR) is displacement analysis (DA), which allows to observe movements of the earth's surface from space. As usually a large number of the points in a SAR data stack lack long term phase stability and hence cannot be used to extract displacements, it is a crucial task of InSAR DA to identify those points that carry information. Points suitable for DA can be grouped into persistent scatterers (PS) or distributed scatterers (DS). After a DS pre-processing step, DS can be used like PS in PS based DA algorithms. When PS and DS are jointly processed, it has to be decided for each point if the PS signal or the DS signal will be used. This work presents a new approach to make this decision, which is based on point coherence estimation (PCE).

Since the end of 2022, two ground motion services that cover the complete area of Germany are available as web services: the German Ground Motion Service ( Bodenbewegungsdienst Deutschland , BBD) provided by the Federal Institute for Geosciences and Natural Resources (BGR), and the first release of the European Ground Motion Service (EGMS) as part of the Copernicus Land Monitoring Service. Both services are based on InSAR displacement estimations generated from Sentinel‑1 data. It would seem relevant to compare the products of the two services against one another, assess the data coverage they provide, and investigate how well they perform compared to other geodetic techniques. For a study commissioned by the surveying authority of the state of Baden-Württemberg ( Landesamt für Geoinformation und Landentwicklung Baden-Württemberg , LGL), BBD and EGMS data from different locations in Baden-Württemberg, Saarland, and North Rhine-Westphalia (NRW) were investigated and validated against levelling and GNSS data. We found that both services provide good data quality. BBD shows slightly better calibration precision than EGMS. The coverage provided by EGMS is better than that of BBD on motorways, federal roads, and train tracks of the Deutsche Bahn . As an example, where both services have difficulties in determining the correct displacements, as they cannot be described well by the displacement models used for processing, we present the test case of the cavern field at Epe (NRW). Finally, we discuss the implications of our findings for the use of the products of BBD and EGMS for monitoring tasks.

Radar interferometry (InSAR) has experienced an enormous development during the past decades. Presently, it is the only measurement technique capable of observing ground motion on regional and even continental scale with sufficient point density for highly precise areal monitoring. Thus, it is, besides observation of single objects, predestined for performing wide area surveying tasks. In particular, observation of linear infrastructure (e.g. roads or railways) with InSAR has proven to provide valuable information. First services that monitor foremost linear infrastructure are operative in Italy and Austria. In Germany, no such devoted service on regional scale is currently available. As a starting point for such a service, displacement data from the German Ground Motion Service (Bodenbewegungsdienst Deutschland, BBD) or the European Ground Motion Service (EGMS) that are available for the whole territory of the country, potentially could be used. Factors that determine the usability are the quality of data, the positioning accuracy, the frequency and timeliness of updates and the coverage with measurement points. Regarding coverage, neither BBD nor EGMS are using Distributed Scatterers (DS) for their InSAR analysis of Germany. In order to explore if DS could improve the coverage on roads or train tracks and to provide numbers for coverage of different categories of linear infrastructure (motorways, federal roads, state roads, county roads, train tracks), we evaluate displacement data from different sources. Our investigation is performed for data from the Oberrheingraben south of Freiburg that were processed on the one hand by EGMS and on the other hand by Geodetic Institute Karlsruhe (GIK). The data processed by GIK include DS and are based on essentially the same Sentinel-1 data as the EGMS data. The observed distinct increase of coverage that results from the use of DS may help to justify the additional effort to include DS in the processing of BBD or EGMS for Germany. Furthermore, the obtained numbers will allow stakeholders to better assess the usability of the data. In addition, the results from a stack of TerraSAR-X strip map data gives an impression of what can be achieved with higher data resolution.

Artificially created and oil or gas filled caverns in salt layers are an important component for energy storage. Due to the pressure difference between the interior of the caverns and the surrounding rock, caverns experience constant convergence, that causes significant surface displacements. Monitoring these displacements is not only important for infrastructure health, but also to study the way the volume loss of the cavern translates to the surface. Traditionally, surface displacements are monitored with GNSS and levelling campaigns. However, both techniques usually only offer either good temporal resolution or good spatial coverage. Multitemporal SAR interferometry (InSAR) can provide both and complement traditional geodetic methods but is only slowly becoming popular due to the more complex data processing and interpretation. We want to show that utilizing InSAR can help to understand a complex surface displacement field such as Epe storage cavern field. Epe is Germanys second largest storage cavern field and not only subject to simple linear cavern convergence dependent subsidence. Prior studies found varying cyclic signals that have been connected to injection and extraction cycles of the gas caverns, as well as seasonal displacements induced by groundwater level changes. With InSAR time series of more than eight years of data, we can now observe displacement patterns and gain new insights into the dynamics of the cavern field, that cannot be detected without the dense spatial and high temporal coverage of multitemporal InSAR. We show that even for a long time span, the InSAR estimated surface displacements fit quite well to levelling and GNSS measurements and prove InSAR of equal quality and an excellent addition to traditional surface displacement monitoring methods. Analyzing our results, we find a clear relation of cavern filling levels and shape and amount of subsidence in the cavern field.

Since the end of 2022, two ground motion services that cover the complete area of Germany are available as web services: the latest release of Bodenbewegungsdienst Deutschland (BBD) [1] provided September 2022 by Federal Institute for Geosciences and Natural Resources (BGR) and the first release of the European Ground Motion Service (EGMS) [2] as part of the Copernicus Land Monitoring Service. Both services are based on InSAR displacement estimations generated from Sentinel-1 data that were processed by GAF AG with software developed by Earth Observation Center (EOC), which is part of German Aerospace Center (DLR). It suggests itself to ask, if there is some added value of BBD over EGMS and how well do the two new releases perform compared to other geodetic techniques. For a study commissioned by the surveying authorities of the state of Baden-Württemberg (Landesamt für Geoinformation und Landentwicklung Baden-Württemberg (LGL)), we investigated the performance of BBD and EGMS and validated them against levelling and GNSS data.

Summary In 2018 the last active German hard coal mines have been abandoned and transitioned into the postmining phase. In the postmining phase, mine water pumping, necessary during active mining, becomes technically and economically unnecessary and unprofitable. As a consequence, the controlled process of mine water rebound to predefined levels increases the pore fluid pressure of subsurface rocks and changes the local stress field. As a result, faults may be (re-)activated inducing ground movements and microseismicity. This study presents an interdisciplinary approach consisting of geology, geomechanics, gas technology, geodesy, and geophysics in order to provide a process understanding how subsurface and surface are geomechanically coupled in the eastern Ruhr hard coal mining area, western Germany. Thus, mine workings and favourably oriented fractures represent the major pathways for mine water. This in accordance with CO2, 222Rn, and O2 anomalies detected along a fault. Rock matrices, however, are tight (mean porosity <1 %, mean permeability <1 mD). Furthermore, ground subsidence and microseismic events (–0.8 to 2.6 MLV) are spatially and temporally correlated with the mine water rebound and mine workings, but unrelated to tectonic faults. The applicability of these findings to other hard coal mining areas (e.g. Saarland, Ibbenbüren) will be tested.

Distributed scatter (DS) interferometric synthetic aperture radar is a powerful technology for analyzing displacements of the earth's surface. Unfortunately, the preparatory step of DS pre-processing is enormously time consuming. The present research puts forward a deep learning-based approach called Distributed Scatterers Prediction Net (DSPN), that can reduce the computational load considerably. DSPN is a convolutional neural network, which generates DS candidate masks based on nine input layers. Masked pixels with low prospect of being DS are omitted during DS pre-processing. Tests on 6 different terrains in North Rhine-Westphalia and Sicily with Sentinel-1 data show that DSPN saves 11% to 87% computation time depending on the scene without significantly reducing coverage with information. Our experiments show that the proposed approach can effectively predict DS candidates and speeds up processing, indicating its potential for analyzing the big data of remote sensing. To the best of our knowledge, this is the first attempt to do a classification in DS candidates and non-DS candidates as a preparatory step to DS pre-processing.

The past two decades have seen continuing progress in the field of deformation analysis based on Interferometric Synthetic Aperture Radar (InSAR) data. In particular, refined methods for processing distributed scatterers (DS) have been developed. A general idea is that in a pre-processing step DS can be transformed to quasi PS and that the quasi PS together with PS can be analyzed with an arbitrary PS algorithm. This can be achieved by grouping statistically similar pixels, applying some outlier rejection, estimating the coherence matrix based on the group of pixels, performing some bias correction and extracting the signal of the DS from the coherence matrix. In the first part of this study, presented at IGARSS 2021, different alternatives for grouping, estimation of the coherence matrix and signal extraction have been investigated with help of comprehensive simulations. Meanwhile, these investigations have been complemented with tests on Sentinel 1 data and of outlier rejection and bias correction, which are the focus of this second part of the study.

The storage cavern field at Epe has been brined out of a salt deposit belonging to the lower Rhine salt flat, which extends under the surface of the North German lowlands and part of the Netherlands. Cavern convergence and operational pressure changes cause surface displacements that have been studied for this work with the help of SAR interferometry (InSAR) using distributed and persistent scatterers. Vertical and East-West movements have been determined based on Sentinel-1 data from ascending and descending orbit. Simple geophysical modeling is used to support InSAR processing and helps to interpret the observations. In particular, an approach is presented that allows to relate the deposit pressures with the observed surface displacements. Seasonal movements occurring over a fen situated over the western part of the storage site further complicate the analysis. Findings are validated with ground truth from levelling and groundwater level measurements.

The last ten years have seen continuing progress in the field of deformation analysis based on Interferometric Synthetic Aperture Radar (InSAR) data. In particular, refined methods for processing distributed scatterers and integrated approaches that jointly use distributed scatterers (DS) and persistent scatterers (PS) have been developed. A general idea is that in a pre-processing step DS can be transformed to quasi PS and that the quasi PS together with PS can be analyzed with an arbitrary PS algorithm. The present paper reports on first results achieved with a modified version of the Stanford Method for Persistent Scatterers (StaMPS) that is adapted following this idea. Furthermore, an experiment is described that has been conceived to better understand how to do the initial selection of DS and PS. An interesting observation is that the difference of amplitude dispersion of a pixel and of amplitude dispersion of the DS-pre-processed pixel proves to be a useful characteristic for the investigated test case.

Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technique able to measure deformation of the earth's surface over large areas. InSAR deformation analysis uses two main categories of backscatter: Persistent Scatterers (PS) and Distributed Scatterers (DS). While PS are characterized by a high signal-to-noise ratio and predominantly occur as single pixels, DS possess a medium or low signal-to-noise ratio and can only be exploited if they form homogeneous groups of pixels that are large enough to allow for statistical analysis. Although DS have been used by InSAR since its beginnings for different purposes, new methods developed during the last decade have advanced the field significantly. Preprocessing of DS with spatio-temporal filtering allows today the use of DS in PS algorithms as if they were PS, thereby enlarging spatial coverage and stabilizing algorithms. This review explores the relations between different lines of research and discusses open questions regarding DS preprocessing for deformation analysis. The review is complemented with an experiment that demonstrates that significantly improved results can be achieved for preprocessed DS during parameter estimation if their statistical properties are used.

Unwrapping is a crucial step when analyzing ground displacements with InSAR techniques. Formulations of unwrapping as integer linear programming (ILP) problem suitable for PSInSAR were given by Costantini et al. under the name redundant integration of finite differences and by Shanker et al. under the name edge list algorithm. They allow the treatment of "4D"-graphs, in the sense of making use of the redundancy both in the PS-net and temporal graph, in a consistent way. This important advantage is paid for by a high computational load, which for large graphs even might prevent the algorithm from finishing in an acceptable time. The approach presented in this paper suggests subdividing the graph into spatio-temporal regions, thereby maintaining the advantage of redundancy, and solving the corresponding ILPs. This results in a considerable acceleration. Besides explaining our approach we report on results obtained for simulated and real data.

Several years ago an advanced approach for processing InSAR time series was introduced under the name SqueeSAR. At its core is an estimation procedure for the phase history of distributed scatterers (DS), which is based on the covariance matrix of the complex pixel values over time. As in practice we often encounter the situation that data do not behave perfectly Gaussian, we started to investigate an augmentation of the under-lying stochastic model. In this paper we present new results from tests of a large number of parameter settings and give recommendations for their choice.

Several years ago a promising approach for processing InSAR time series was introduced under the name SqueeSAR [1]. The successful application of this framework poses some delicate questions. This paper focuses on the problem that real data do rarely behave perfectly Gaussian. An augmentation of the stochastic model underlying the phase linking step is presented and the applicability under the assumption of complex elliptically symmetric distribution is discussed. Results from tests with two time series of TerraSAR-X HRS data are presented and preliminary conclusions drawn.

Unwrapping is a crucial step when analyzing ground displacements with InSAR techniques. A new formulation of unwrapping as linear integer programming problem was given by Costantini et al. under the name redundant integration of finite differences, which can be thought of as generalizing the Minimum Cost Flow (MCF) approach. An important parameter regarding quality of outcomes for both types of algorithm is the choice of cost function. The results of our tests with simulated and real data for different cost functions and both types of algorithm are presented in this paper. Solvers are based on open source code (LEMON, SYMPHONY).