Search

Ground movement is a critical concern for urban safety, and understanding its causes and consequences is essential. Mining of hard coal in the Ruhr area of Germany has caused long-term subsidence, leading to building damage and sinkholes in affected cities. The multi-temporal interferometric synthetic aperture radar (MTInSAR) technique has been widely adopted for estimating ground movement at millimetre-level accuracy using spaceborne SAR data. Although many European government institutes have implemented this technique for monitoring purposes, the European Ground Motion Service (EGMS) provides up-to-date ground movement products (until the end of 2020) across 31 countries. However, these products may not always meet the requirements for local use due to being untimely or missing important features. This paper demonstrates how we estimated ground movement in Ahlen, around the Ruhr area of Germany, using EGMS data as a priori and reference source. We implemented MTInSAR using Sentinel-1 data from 2018 to 2021 and compared our results with those of EGMS. Our results reveal the movement one year ahead, contain more measurement points, and cover natural areas such as cropland and bare soil. We interpreted several movement scenarios in detail, and our work highlights the benefits of EGMS as an open-source overview for local monitoring applications. We also explored the influence of soil conditions on ground movement. Our findings suggest that the overall uplift trend observed during the post-mining phase has been mitigated or even reversed, likely due to soil shrinkage resulting from drought conditions, particularly in areas with high organic soil content. [ABSTRACT FROM AUTHOR]

A timely and spatially high-resolution detection of drought-affected forest stands is important to assess and deal with the increasing risk of forest fires. In this paper, we present how multitemporal Sentinel-1 synthetic aperture radar (SAR) data can be used to detect drought-affected and fire-endangered forest stands in a spatially and temporally high resolution. Existing approaches for Sentinel-1 based drought detection currently do not allow to deal simultaneously with all disturbing influences of signal noise, topography and visibility geometry on the radar signal or do not produce pixel-based high-resolution drought detection maps of forest stands. Using a novel Sentinel-1 Radar Drought Index (RDI) based on temporal and spatial averaging strategies for speckle noise reduction, we present an efficient methodology to create a spatially explicit detection map of drought-affected forest stands for the year 2020 at the Donnersberg study area in Rhineland-Palatinate, Germany, keeping the Sentinel-1 maximum spatial resolution of 10 m × 10 m. The RDI showed significant (p < 0.05) drought influence for south, south-west and west-oriented slopes. Comparable spatial patterns of drought-affected forest stands are shown for the years 2018, 2019 and with a weaker intensity for 2021. In addition, the assessment for summer 2020 could also be reproduced with weekly repetition, but spatially coarser resolution and some limitations in the quality of the resulting maps. Nevertheless, the mean RDI values of temporally high-resolution drought detection maps are highly correlated (R2 = 0.9678) with the increasing monthly mean temperatures in 2020. In summary, this study demonstrates that Sentinel-1 data can play an important role for the timely detection of drought-affected and fire-prone forest areas, since availability of observations does not depend on cloud cover or time of day. [ABSTRACT FROM AUTHOR]

Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5% was achieved, with class-specific F1-scores above 80% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6% and 9%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7%. In comparison to census data, the crop areas could be approximated well with, on average, only 1% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping. [ABSTRACT FROM AUTHOR]

Tomographic synthetic aperture radar (TomoSAR) inversion of urban areas is an inherently sparse reconstruction problem and, hence, can be solved using compressive sensing (CS) algorithms. This paper proposes solutions for two notorious problems in this field. First, TomoSAR requires a high number of data sets, which makes the technique expensive. However, it can be shown that the number of acquisitions and the signal-to-noise ratio (SNR) can be traded off against each other, because it is asymptotically only the product of the number of acquisitions and SNR that determines the reconstruction quality. We propose to increase SNR by integrating nonlocal (NL) estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible. Second, CS-based inversion is computationally expensive and therefore, barely suitable for large-scale applications. We introduce a new fast and accurate algorithm for solving the NL L1-L2-minimization problem, central to CS-based reconstruction algorithms. The applicability of the algorithm is demonstrated using simulated data and TerraSAR-X high-resolution spotlight images over an area in Munich, Germany. [ABSTRACT FROM AUTHOR]

The European COPERNICUS program provides an unprecedented breakthrough in the broad use and application of satellite remote sensing data. Maintained on a sustainable basis, the COPERNICUS system is operated on a free-and-open data policy. Its guaranteed availability in the long term attracts a broader community to remote sensing applications. In general, the increasing amount of satellite remote sensing data opens the door to the diverse and advanced analysis of this data for earth system science.However, the preparation of the data for dedicated processing is still inefficient as it requires time-consuming operator interaction based on advanced technical skills. Thus, the involved scientists have to spend significant parts of the available project budget rather on data preparation than on science. In addition, the analysis of the rich content of the remote sensing data requires new concepts for better extraction of promising structures and signals as an effective basis for further analysis.In this paper we propose approaches to improve the preparation of satellite remote sensing data by a geo-database. Thus the time needed and the errors possibly introduced by human interaction are minimized. In addition, it is recommended to improve data quality and the analysis of the data by incorporating Artificial Intelligence methods. A use case for data preparation and analysis is presented for earth surface deformation analysis in the Upper Rhine Valley, Germany, based on Persistent Scatterer Interferometric Synthetic Aperture Radar data. Finally, we give an outlook on our future research. [ABSTRACT FROM AUTHOR]

We present the first analysis of surface current fields derived from TerraSAR-X along-track interferometric synthetic aperture radar (along-track InSAR, ATI) data. The images were acquired over the mouth of the Elbe river (Germany) during six satellite overpasses in spring and summer 2008, using the experimental "aperture switching" mode of TerraSAR-X. In this mode, the phased-array synthetic aperture radar (SAR) antenna is split into two halves for receiving, but in contrast to the "dual receive antenna" mode, which uses two independent receivers in parallel, a single receiver is multiplexed to process signals from the two antenna halves in an alternating manner at a doubled pulse repetition frequency. The effective ATI baseline is on the order of 0.8 m. The SAR/ATI raw data processing is described in another paper in this issue. This paper focuses on the conversion of the basic interferograms into line-of-sight surface current fields, which includes an elimination of ship signatures, identification, and correction (as far as possible) of imaging artifacts, additional filtering and smoothing, and a subtraction of contributions of wave motions to detected velocities according to a theoretical model. We evaluate the quality of the results by comparison with current fields from a numerical flow model and with available in situ data. The ATI performance of TerraSAR-X is found to be basically consistent with theoretical expectations. After applying the same data processing algorithms to all six images, mean differences between TerraSAR-X-derived currents and reference currents in our main test area range from -0.11 to +0.08 m/s in five of the six cases with one outlier at +0.42 m/s. The spatial current variations within the TerraSAR-X-derived current fields are consistent with the model in three cases, but unrealistically strong variations across the images are found in the other three cases. We attribute this to shortcomings of our preliminary raw data processing algorithms, which can probably be fixed after some more detailed analysis and testing. The results obtained so far encourage us to believe that our internal performance goal of a typical current measuring accuracy of 0.1 m/s at an effective spatial resolution better than 1 km can be met. [ABSTRACT FROM AUTHOR]

Experimental airborne synthetic aperture radar (SAR) systems achieve spatial resolutions of approximately 10 cm, whereas the new spaceborne very high spatial resolution (VHR) SAR sensors onboard the TerraSAR-X and COSMO-SkyMed satellites achieve spatial resolutions down to 1 m. In VHR SAR data, features fromindividual urban structures (i.e., buildings) can be identified by their characteristic settings in urban settlement patterns. In this paper, we present a novel concept for the height estimation of generic man-made structures from single detected SAR data. The proposed approach is based on the definition of a hypothesis on the height of the building and on the simulation of a SAR image for testing that hypothesis. A matching procedure is applied between the estimated and the actual SAR image in order to test the height hypothesis. The process is iterated for different height assumptions until the matching function is optimized, and thus, the building height is estimated. The efficiency of the proposed method is demonstrated on a set of 40 flat- and gable-roof buildings using two submeter VHR airborne and two 1-m resolution TerraSAR-X SAR scenes all acquired from the same residential area in Dorsten, Germany. The results show that, in the absence of string disturbing effects, the method is able to estimate the height of flat- and gable-roof buildings in the submeter data to the order of a meter, while the accuracy for the meter resolution spaceborne data is lower but still sufficient to estimate the number of floors of a building. [ABSTRACT FROM AUTHOR]

TerraSAR-X, the first national German remote-sensing satellite, was launched on-June 15, 2007. It-carries an X-band high-resolution synthetic aperture radar (SAR) instrument featuring imaging modes like StripMap, ScanSAR, and, particularly, SpotLight in a variety of different polarization modes. Primary mission goal is the provision of both science and commercial users with a variety of products from advanced SAR modes. The TerraSAR-X Ground Segment, which is provided by the German Aerospace Center (DLR), constitutes the central element for controlling and operating the TerraSAR-X satellite, for calibrating its SAR instrument, and for archiving the SAR data, as well as generating and distributing the basic data products. This paper depicts the ground-segment layout and describes its major elements. The ordering and product-generation workflow is presented. It introduces the applied prelaunch integration, testing, verification, and validation approach, a major key to the completion not only of the SAR technical-verification program but also the operational qualification of the ground segment itself within the commissioning phase. [ABSTRACT FROM AUTHOR]

TerraSAR-X, the first civil German synthetic aperture radar (SAR) satellite, was successfully launched on June 15, 2007. After 4.5 days, the first processed image was obtained. The overall quality of the image was outstanding; however, suspicious features could be identified which showed precipitation-related signatures. These rain-cell signatures are thoroughly investigated, and the physical background of the related propagation effects is provided. In addition, rain-cell signatures from former missions like SIR-C/X and the Shuttle Radar Topography Mission are provided for comparison. During the commissioning phase of TerraSAR-X, a total of 12000 scenes were investigated for potential propagation effects, and about 100 scenes revealed atmospheric effects to a visible extent. Some of the particularly interesting events were selected and are discussed in greater detail. An interesting case of data acquisition over New York will be presented, which shows typical rain-cell signatures, and the SAR image will be compared with weather-radar data acquired nearly simultaneously (within the same minute). By comparing the images, it can be clearly seen that reflectivities in the weather-radar image of 50 dBZ may cause visible artifacts in SAR images. Furthermore, in this paper, we discuss the influence of the atmosphere (troposphere) on the external calibration of TerraSAR-X. By acquiring simultaneous weather-radar data over the test site and the SAR acquisition, it was possible to flag affected SAR images and to exclude them from the procedure to derive the absolute calibration constant. Thus, it was possible to decrease the 1 σ uncertainty of the absolute calibration factor by 0.15 dB. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) measurements are unique for mapping forest 3D structure and its changes in time. Tomographic SAR (TomoSAR) configurations exploit this potential by reconstructing the 3D radar reflectivity. The frequency of the SAR measurements is one of the main parameters determining the information content of the reconstructed reflectivity in terms of penetration and sensitivity to the individual vegetation elements. This paper attempts to review and characterize the structural information content of L-band TomoSAR reflectivity reconstructions, and their potential to forest structure mapping. First, the challenges in the accurate TomoSAR reflectivity reconstruction of volume scatterers (which are expected to dominate at L-band) and to extract physical structure information from the reconstructed reflectivity is addressed. Then, the L-band penetration capability is directly evaluated by means of the estimation performance of the sub-canopy ground topography. The information content of the reconstructed reflectivity is then evaluated in terms of complementary structure indices. Finally, the dependency of the TomoSAR reconstruction and of its structural information to both the TomoSAR acquisition geometry and the temporal change of the reflectivity that may occur in the time between the TomoSAR measurements in repeat-pass or bistatic configurations is evaluated. The analysis is supported by experimental results obtained by processing airborne acquisitions performed over temperate forest sites close to the city of Traunstein in the south of Germany. [ABSTRACT FROM AUTHOR]

40 percent of the world's population is presently living in coastal areas. Taking into account that the vulnerability of these areas is increasing due to sea-level rise and coastal hazards, the importance of safety structures is increasing as well. Hence, a spatial distributed dike monitoring should be part of a sustainable adaptation strategy. Persistent Scattering Interferometry combined with the Small Baseline Subset technique will be a very suitable monitoring technique for dike structures to identify dike movement with the accuracy of few millimeters. In opposition to ground-measurements, the spatial coverage of this technique provides comparable results for different parts of the dike. This could prevent future dike crevasses and help to reduce risks in high-populated areas. This paper describes the potential of the combined StaMPS MTI technique for a spatial distributed dike monitoring at the North Sea coast in Germany. 21 ERS-2 scenes and 16 Envisat ASAR scenes were processed for two different types of dikes. On the first an adequate density of PS pixels was found to perform spatial distributed monitoring. Due to decorrelation effects of vegetation covered surfaces, no reliable statements about the dike body could be made on the second dike, still deformation processes could be detected. [ABSTRACT FROM AUTHOR]

The retrieval of soil surface parameters, in particular soil moisture and roughness, based on Synthetic Aperture Radar (SAR) data, has been the subject of a large number of studies, of which results are available in the scientific literature. However, although refined methods based on theoretical/analytical scattering models have been proposed and successfully applied in experimental studies, at the operative level very simple, empirical models with a number of adjustable parameters are usually employed. One of the reasons for this situation is that retrieval methods based on analytical scattering models are not easy to implement and to be employed by non-expert users. Related to this, commercially and freely available software tools for the processing of SAR data, although including routines for basic manipulation of polarimetric SAR data (e.g., coherency and covariance matrix calculation, Pauli decomposition, etc.), do not implement easy-to-use methods for surface parameter retrieval. In order to try to fill this gap, in this paper we present a user-friendly computer program for the retrieval of soil surface parameters from Polarimetric Synthetic Aperture Radar (PolSAR) imageries. The program evaluates soil permittivity, soil moisture and soil roughness based on the theoretical predictions of the electromagnetic scattering provided by the Polarimetric Two-Scale Model (PTSM) and the Polarimetric Two-Scale Two-Component Model (PTSTCM). In particular, nine different retrieval methodologies, whose applicability depends on both the used polarimetric data (dual- or full-pol) and the characteristics of the observed scene (e.g., on its topography and on its vegetation cover), as well as their implementation in the Interactive Data Language (IDL) platform, are discussed. One specific example from Germany's Demmin test-site is presented in detail, in order to provide a first guide to the use of the tool. Obtained retrieval results are in agreement with what was expected according to the available literature. [ABSTRACT FROM AUTHOR]

The Synthetic Aperture Radar (SAR) satellite Sentinel-1 provides excellent traffic infrastructure monitoring capabilities due to its short revisit time, wide-scale coverage and free of charge data policy. However, pixels from Sentinel-1 have a medium spatial resolution so that traffic infrastructure is only covered by a few pixels. Moreover, Interferometric Synthetic Aperture Radar (InSAR) yields deformation time series for coherent pixels only, thus limiting the number of pixels reporting the ground motion at traffic infrastructure. Although various InSAR time series methods have been successfully applied for traffic infrastructure monitoring, the selection of appropriate methods achieving a high pixel density has not yet been evaluated. In this study, we test whether we can improve the monitoring capabilities by combining different InSAR time series methods. On the one hand, we applied widely used Stanford Method for Persistent Scatterers (StaMPS) as a baseline method to retrieve the deformation time series. On the other hand, we enhanced the phase quality in a pre-processing step using the Phase-Linking (PL) approach and afterwards estimated the deformation time series also with StaMPS based on pixels selected by PL. We compared and evaluated the achieved pixel densities from both methods at main roads, highways and railways in a study area in Germany. We found that the InSAR time series methods selected complementary sets of pixels at all traffic infrastructure types. Moreover, the results indicate a great potential for railway monitoring using Sentinel-1 InSAR due to both high pixel density and homogeneous spatial distribution of pixels. Interestingly, coherent scatterers selected by StaMPS were observed to coincide with large traffic signs at highways which show a double-bounce scattering in the amplitude images. This study shows the benefits of combining PL and StaMPS for increased pixel density at traffic infrastructure and confirms Sentinel-1 data as a suitable data source for traffic infrastructure monitoring. [ABSTRACT FROM AUTHOR]

Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying the critical diameter of particles for ice-to-snow conversion by aggregation (Dice) and the threshold temperature responsible for graupel production by riming (Tgr), was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias (lower magnitude than observation) in simulated polarimetric moments at lower levels above the melting layer (- 3 to - 13 ∘ C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g. fragmentation due to ice–ice collisions) and use of more reliable snow-scattering models to draw valid conclusions. [ABSTRACT FROM AUTHOR]

Model-based incoherent polarimetric decomposition is a frequently used technique to analyze multilook data of polarimetric synthetic aperture radars (POLSARs). The purpose of this study is to analyze and compare different model-based incoherent polarimetric decomposition algorithms from the polarimetric information change aspect. For the input of a model-based incoherent polarimetric decomposition algorithm, polarimetric entropy was used to represent the polarimetric information of a coherency matrix. For the output of a model-based incoherent polarimetric decomposition algorithm, there are usually several decomposed components. To quantitatively represent their total polarimetric information, a new concept, generalized polarimetric entropy, was proposed which generalized the concept of polarimetric entropy based on the information entropy additivity of information theory. Generalized polarimetric entropy consists of two parts named as polarimetric power entropy and polarimetric residual entropy, respectively. Polarimetric power entropy describes the distribution status of the Span values of all decomposed components. Polarimetric residual entropy represents the residual randomness of all decomposed components. With the three new concepts, eight model-based incoherent polarimetric decomposition algorithms were compared and analyzed. Two real POLSAR images, respectively, derived by the E-SAR airborne system of Germany and the GF-3 satellite of China were used for the experiments. Experimental results had illustrated several useful conclusions. [ABSTRACT FROM AUTHOR]

In this study, an analysis of multi-temporal and multi-frequency Synthetic Aperture Radar data is performed to investigate the backscatter behavior of various semantic classes in the context of flood mapping in central Europe. The focus is mainly on partially submerged vegetation such as forests and agricultural fields. The test area is located at River Saale, Saxony-Anhalt, Germany, which is covered by a time series of 39 TerraSAR-X data acquired within the time interval December 2009 to June 2013. The data set is supplemented by ALOS PALSAR L-band and RADARSAT-2 C-band data. The time series covers two inundations in January 2011 and June 2013 which allows evaluating backscatter variations between flood periods and normal water level conditions using different radar wavelengths. According to the results, there is potential in detecting flooding beneath vegetation in all microwave wavelengths, even in X-band for sparse vegetation or leaf-off forests. [ABSTRACT FROM AUTHOR]