Sentinel-1 Imaging Performance Verification with TerraSAR-X

This paper presents dedicated analyses of TerraSAR-X data with respect to the Sentinel-1 TOPS imaging mode. First, the analysis of Doppler centroid behaviour for high azimuth steering angles, as occurs in TOPS imaging, is investigated followed by the analysis and compensation of residual scalloping. Finally, the Flexible-Dynamic BAQ (FD-BAQ) raw data compression algorithm is investigated for the first time with real TerraSAR-X data and its performance is compared to state-of-the-art BAQ algorithms. The presented analyses demonstrate the improvements of the new TOPS imaging mode as well as the new FD-BAQ data compression algorithm for SAR image quality in general and in particular for Sentinel-1

Analysis and evaluation of Terrain Observation by Progressive Scans (TOPSAR) mode in Synthetic Aperture Radar

Synthetic Aperture Radar (SAR) is a technique used in radar (RAdio Detection And Ranging) [1] systems to get high resolution images which are impossible to obtain with a conventional radar. This method allows us to acquire images from the surface of the Earth or other planets from large distances. In SAR [2], a single antenna is used to get information of the targets, and the platform movement, where the antenna is fixed, is used to spread the Doppler history of received echoes improving the resolution of processed images. Remote sensing is a wide area which studies different techniques to acquire information about targets situated at far distances. These techniques can be classified in two different areas according to their basic operation. The first group, called passive remote sensing [3] [4], uses passive sensors to acquire the energy radiated by the targets. This energy can come from an external source, such as Sun radiation, being reflected by the object or it can be emitted by the target itself. On the other hand, active remote sensing systems [4] emit pulses to illuminate the scanned area, providing their own energy. So, although it requires a more complex system, active sensing does not require an external source to operate which is an advantage when the conditions are not favourable. SAR and other radar techniques are examples of active sensors, working at frequencies between 0.3 GHz and 300GHz. These systems send pulses towards the scanned area, the interaction of each pulse with the surface originates an echo which arrives to the receiver. This echo is originated by the energy backscattered by the objects in the scene and it will be dependant of the backscattering profile of the targets (radar cross-section) [5] [6]. The time delay and strength of power received as well as frequency properties of the returns are processed to determine the target locations and characteristics. Synthetic aperture is similar to a conventional real aperture radar (RAR) antenna but it is achieved by signal processing. In a SAR, the antenna, installed in a moving platform, sends pulses to the scene and receives backscattered returns. The movement of the platform makes possible to illuminate the targets at different positions of the satellite trajectory, which is equivalent to have multiple antennas illuminating the scene at the same time. Thus, SAR is a fairly recent acquisition method that has some advantages in comparison with other remote sensing techniques. The most significant are: · Day/Night and all weather condition imaging since it does not depend on external power sources to detect the targets. · Geometric resolution independent of altitude or wavelength. · Signal data characteristic unique to the microwave region of EM spectrum which has suffers less deterioration in atmosphere propagation. The SAR systems started with aero-transported missions and later, first space missions were sent. The SAR beginning dates back to 1951 when C. Wiley postulated the Dopple

Implementing the European Space Agency’s SentiNel application platform’s open-source Python module for differential synthetic aperture radar interferometry coseismic ground deformation from Sentinel-1 data

Differential SAR Interferometry is a largely exploited technique to study ground deformations. A key application is the detection of the effects promoted by earthquakes, including detailed variations in ground deformations at different scales. In this work, an implemented Python script (Snap2DQuake) based on the “snappy” module by SNAP software 9.0.8 (ESA) for the processing of satellite imagery is proposed. Snap2DQuake is aimed at producing detailed coseismic deformation maps using Sentinel-1 C-band data by the DInSAR technique. With this alternative approach, the processing is simplified, and several issues that may occur using the software are solved. The proposed tool has been tested on two case studies: the Mw 6.4 Petrinja earthquake (Croatia, December 2020) and the Mw 5.7 to Mw 6.3 earthquakes, which occurred near Tyrnavós (Greece, March 2021). The earthquakes, which occurred in two different tectonic contexts, are used to test and verify the validity of Snap2DQuake. Snap2DQuake allows us to provide detailed deformation maps along the vertical and E-W directions in perfect agreement with observations reported in previous works. These maps offer new insights into the deformation pattern linked to earthquakes, demonstrating the reliability of Snap2DQuake as an alternative tool for users working on different applications, even with basic coding skills.Peer ReviewedPostprint (published version

Automatic generation of co-seismic displacement maps by using Sentinel-1 interferometric SAR data

Abstract We present a tool for the automatic generation of co-seismic Differential Synthetic Aperture Radar Interferometry (DInSAR) products by using space-borne SAR data. In particular, the implemented tool relies on the large availability of Sentinel-1 SAR data and on-line earthquake catalogues (e.g. USGS, INGV) to generate co-seismic Line Of Sight (LOS) interferograms and displacement maps. The processing is triggered by the occurrence of a main seismic event, according to the accessible earthquake catalogues. The tool automatically retrieves all the needed SAR acquisitions that cover a defined area across the epicentre and generates the DInSAR products that will be then openly available through the European Plate Observing System (EPOS) portal. Moreover, the possibility to implement the presented tool into the upcoming Copernicus Data and Information Access Services (DIAS) will significantly reduce the product processing time, thus implying a faster product generation and delivery. Accordingly, such a tool not only will contribute to expand the use of DInSAR products in the geoscience field, but also will play a key role on the support of the Civil Protection authorities during the management of seismic crisis

A Network-Based Enhanced Spectral Diversity Approach for TOPS Time-Series Analysis

For multitemporal analysis of synthetic aperture radar (SAR) images acquired with a terrain observation by progressive scan (TOPS) mode, all acquisitions from a given satellite track must be coregistered to a reference coordinate system with accuracies better than 0.001 of a pixel (assuming full SAR resolution) in the azimuth direction. Such a high accuracy can be achieved through geometric coregistration, using precise satellite orbits and a digital elevation model, followed by a refinement step using a time-series analysis of coregistration errors. These errors represent the misregistration between all TOPS acquisitions relative to the reference coordinate system. We develop a workflow to estimate the time series of azimuth misregistration using a network-based enhanced spectral diversity (NESD) approach, in order to reduce the impact of temporal decorrelation on coregistration. Example time series of misregistration inferred for five tracks of Sentinel-1 TOPS acquisitions indicates a maximum relative azimuth misregistration of less than 0.01 of the full azimuth resolution between the TOPS acquisitions in the studied areas. Standard deviation of the estimated misregistration time series for different stacks varies from 1.1e-3 to 2e-3 of the azimuth resolution, equivalent to 1.6-2.8 cm orbital uncertainty in the azimuth direction. These values fall within the 1-sigma orbital uncertainty of the Sentinel-1 orbits and imply that orbital uncertainty is most likely the main source of the constant azimuth misregistration between different TOPS acquisitions. We propagate the uncertainty of individual misregistration estimated with ESD to the misregistration time series estimated with NESD and investigate the different challenges for operationalizing NESD

Assessing the feasibility of a National InSAR Ground Deformation Map of Great Britain with Sentinel-1

This work assesses the feasibility of national ground deformation monitoring of Great Britain using synthetic aperture radar (SAR) imagery acquired by Copernicus’ Sentinel-1 constellation and interferometric SAR (InSAR) analyses. As of December 2016, the assessment reveals that, since May 2015, more than 250 interferometric wide (IW) swath products have been acquired on average every month by the constellation at regular revisit cycles for the entirety of Great Britain. A simulation of radar distortions (layover, foreshortening, and shadow) confirms that topographic constraints have a limited effect on SAR visibility of the landmass and, despite the predominance of rural land cover types, there is potential for over 22,000,000 intermittent small baseline subset (ISBAS) monitoring targets for each acquisition geometry (ascending and descending) using a set of IW image frames covering the entire landmass. Finally, InSAR results derived through ISBAS processing of the Doncaster area with an increasing amount of Sentinel-1 IW scenes reveal a consistent decrease of standard deviation of InSAR velocities from 6 mm/year to ≤2 mm/year. Such results can be integrated with geological and geohazard susceptibility data and provide key information to inform the government, other institutions and the public on the stability of the landmas

Biomass Representation in Synthetic Aperture Radar Interferometry Data Sets

This work makes an attempt to explain the origin, features and potential applications of the elevation bias of the synthetic aperture radar interferometry (InSAR) datasets over areas covered by vegetation. The rapid development of radar-based remote sensing methods, such as synthetic aperture radar (SAR) and InSAR, has provided an alternative to the photogrammetry and LiDAR for determining the third dimension of topographic surfaces. The InSAR method has proved to be so effective and productive that it allowed, within eleven days of the space shuttle mission, for acquisition of data to develop a three-dimensional model of almost the entire land surface of our planet. This mission is known as the Shuttle Radar Topography Mission (SRTM). Scientists across the geosciences were able to access the great benefits of uniformity, high resolution and the most precise digital elevation model (DEM) of the Earth like never before for their a wide variety of scientific and practical inquiries. Unfortunately, InSAR elevations misrepresent the surface of the Earth in places where there is substantial vegetation cover. This is a systematic error of unknown, yet limited (by the vertical extension of vegetation) magnitude. Up to now, only a limited number of attempts to model this error source have been made. However, none offer a robust remedy, but rather partial or case-based solutions. More work in this area of research is needed as the number of airborne and space-based InSAR elevation models has been steadily increasing over the last few years, despite strong competition from LiDAR and optical methods. From another perspective, however, this elevation bias, termed here as the “biomass impenetrability”, creates a great opportunity to learn about the biomass. This may be achieved due to the fact that the impenetrability can be considered a collective response to a few factors originating in 3D space that encompass the outermost boundaries of vegetation. The biomass, presence in InSAR datasets or simply the biomass impenetrability, is the focus of this research. The report, presented in a sequence of sections, gradually introduces terminology, physical and mathematical fundamentals commonly used in describing the propagation of electromagnetic waves, including the Maxwell equations. The synthetic aperture radar (SAR) and InSAR as active remote sensing methods are summarised. In subsequent steps, the major InSAR data sources and data acquisition systems, past and present, are outlined. Various examples of the InSAR datasets, including the SRTM C- and X-band elevation products and INTERMAP Inc. IFSAR digital terrain/surface models (DTM/DSM), representing diverse test sites in the world are used to demonstrate the presence and/or magnitude of the biomass impenetrability in the context of different types of vegetation – usually forest. Also, results of investigations carried out by selected researchers on the elevation bias in InSAR datasets and their attempts at mathematical modelling are reviewed. In recent years, a few researchers have suggested that the magnitude of the biomass impenetrability is linked to gaps in the vegetation cover. Based on these hints, a mathematical model of the tree and the forest has been developed. Three types of gaps were identified; gaps in the landscape-scale forest areas (Type 1), e.g. forest fire scares and logging areas; a gap between three trees forming a triangle (Type 2), e.g. depending on the shape of tree crowns; and gaps within a tree itself (Type 3). Experiments have demonstrated that Type 1 gaps follow the power-law density distribution function. One of the most useful features of the power-law distributed phenomena is their scale-independent property. This property was also used to model Type 3 gaps (within the tree crown) by assuming that these gaps follow the same distribution as the Type 1 gaps. A hypothesis was formulated regarding the penetration depth of the radar waves within the canopy. It claims that the depth of penetration is simply related to the quantisation level of the radar backscattered signal. A higher level of bits per pixels allows for capturing weaker signals arriving from the lower levels of the tree crown. Assuming certain generic and simplified shapes of tree crowns including cone, paraboloid, sphere and spherical cap, it was possible to model analytically Type 2 gaps. The Monte Carlo simulation method was used to investigate relationships between the impenetrability and various configurations of a modelled forest. One of the most important findings is that impenetrability is largely explainable by the gaps between trees. A much less important role is played by the penetrability into the crown cover. Another important finding is that the impenetrability strongly correlates with the vegetation density. Using this feature, a method for vegetation density mapping called the mean maximum impenetrability (MMI) method is proposed. Unlike the traditional methods of forest inventories, the MMI method allows for a much more realistic inventory of vegetation cover, because it is able to capture an in situ or current situation on the ground, but not for areas that are nominally classified as a “forest-to-be”. The MMI method also allows for the mapping of landscape variation in the forest or vegetation density, which is a novel and exciting feature of the new 3D remote sensing (3DRS) technique. Besides the inventory-type applications, the MMI method can be used as a forest change detection method. For maximum effectiveness of the MMI method, an object-based change detection approach is preferred. A minimum requirement for the MMI method is a time-lapsed reference dataset in the form, for example, of an existing forest map of the area of interest, or a vegetation density map prepared using InSAR datasets. Preliminary tests aimed at finding a degree of correlation between the impenetrability and other types of passive and active remote sensing data sources, including TerraSAR-X, NDVI and PALSAR, proved that the method most sensitive to vegetation density was the Japanese PALSAR - L-band SAR system. Unfortunately, PALSAR backscattered signals become very noisy for impenetrability below 15 m. This means that PALSAR has severe limitations for low loadings of the biomass per unit area. The proposed applications of the InSAR data will remain indispensable wherever cloud cover obscures the sky in a persistent manner, which makes suitable optical data acquisition extremely time-consuming or nearly impossible. A limitation of the MMI method is due to the fact that the impenetrability is calculated using a reference DTM, which must be available beforehand. In many countries around the world, appropriate quality DTMs are still unavailable. A possible solution to this obstacle is to use a DEM that was derived using P-band InSAR elevations or LiDAR. It must be noted, however, that in many cases, two InSAR datasets separated by time of the same area are sufficient for forest change detection or similar applications

Radar Backscatter Modeling Based on Global TanDEM-X Mission Data

Radarrückstreuung bezeichnet den Teil eines ausgesendeten elektromagnetischen Signals, der von einem Ziel am Boden wieder zurück zur Antenne gerichtet ist. Die Eigenschaften des zurückgestreuten Signals ändern sich in Abhängigkeit von Frequenz und Polarisation des Radarsignals, der Aufnahmegeometrie, sowie vom Zustand des Erdbodens und der Art der Bodenbedeckung. Informationen über das Radarrückstreuverhalten sind von höchster Wichtigkeit für die Auslegung von SAR-Missionen und werden verbreitet zur Entwicklung wissenschaftlicher Modelle genutzt, beispielsweise bei der Erforschung der Biosphäre und Kryosphäre. Hauptziel dieser Arbeit ist die Auswertung und Nutzung des globalen TanDEM-X-Datensatzes zur Modellierung der Radarrückstreuung im X-Band unter Berücksichtigung unterschiedlicher Aufnahmeparameter und Landnutzungsarten, sowie die Bereitstellung einer Reihe von globalen Rückstreumodellen, die auf aktuellen Daten basieren, für die wissenschaftliche Gemeinschaft. Es wurde ein neuer Ansatz zur statistischen Modellierung der Rückstreuinformation entwickelt, der die Qualität der zugrunde liegenden Messungen berücksichtigt. Daraus ergeben sich gewichtete polynomiale Modelle für die verschiedenen Landnutzungsarten, wie sie in der GlobCover-Karte der ESA definiert sind. Darüber hinaus wird ein eigener Validierungsansatz vorgestellt, mit zusätzlicher Betrachtung der saisonalen Variation der Rückstreuung und einer separaten Analyse des Rückstreuverhaltens des Tropischen Regenwaldes. Der nächste Schwerpunkt ist die Betrachtung des Grönländischen Eisschildes, das gekennzeichnet ist durch das Vorhandensein verschiedener Arten von Schneebedeckung, die von trockenem bis hin zu sehr feuchtem Schnee variiert. Der begrenzte Detailgrad, den die GlobCover Karte in Grönland aufweist (nur eine Klasse für das gesamte Eisschild), erlaubt dort keine verlässliche Modellierung der Rückstreuung. Diese Schwierigkeit lieferte die Motivation für die Entwicklung eines neuen Ansatzes zur Analyse des Informationsgehalts der interferometrischen TanDEM-X-Daten mit dem Ziel, unterschiedliche Schnee-Fazien mit Hilfe des sog. C-Means Fuzzy Clustering Algorithmus zu lokalisieren. Aus dieser Untersuchung konnte die Existenz von vier unterschiedlichen Klassen von Schnee-Fazien abgeleitet werden, deren Eigenschaften anschließend mit Hilfe externer Referenzdaten interpretiert wurden. Die daraus entstandene Karte wurde zur Erstellung eines einfallswinkelabhängigen Rückstreumodells genutzt, separat für jede der vier Klassen, wobei eine modifizierte Version des entwickelten Algorithmus zur Generierung globaler Rückstreumodelle eingesetzt wurde. Darüber hinaus wurde als Nebenprodukt zusätzlich die Eindringtiefe von TanDEM-X in die Eisschicht geschätzt, durch Inversion des von Weber Hoen und Zebker vorgeschlagenen "Ein-chicht Volumendekorrelationsmodells". Die Ergebnisse wurden mit dem Höhenunterschied zwischen dem globalen TanDEM-X-DEM und ICESat-Messungen verglichen. Abschließend wird ein neu entwickelter Algorithmus zur Generierung von Rückstreukarten großer Gebiete vorgestellt. Dieser erlaubt unter Verwendung von Rückstreumodellen das Angleichen der erstellten Karten anhand eines Referenzeinfallswinkels, was dann das Füllen verbleibender Lücken ermöglicht, die aufgrund fehlender Eingangsdaten vorhanden sind

The 2-Look TOPS Mode: Design and Demonstration with TerraSAR-X

Burst-mode acquisition schemes achieve wide coverage at the expense of a degraded azimuth resolution, reducing therefore the performance on the retrieval of ground displacements in the azimuth direction, when interferometric acquisitions are combined. Moreover the azimuth varying line-of-sight can induce discontinuities in the interferometric phase when local azimuth displacements are present, e.g., due to ground deformation. In this contribution we propose the interferometric 2-look TOPS mode, a sustaining innovation, which records bursts of radar echoes of two separated slices of the Doppler spectrum. The spectral separation allows to exploit spectral diversity techniques, achieving sensitivities to azimuth displacements better than with StripMap, and eliminating discontinuities in the interferometric phase. Moreover some limitations of the TOPS mode to compensate ionospheric perturbations, in terms of data gaps or restricted sensitivity to azimuth shifts, are overcome. The design of 2-look TOPS acquisitions will be provided, taking the TerraSAR-X system as reference to derive achievable performances. The methodology for the retrieval of the azimuth displacement is exposed for the case of using pairs of images, as well as for the calculation of mean azimuth velocities when working with stacks. We include results with experimental TerraSAR-X acquisitions demonstrating its applicability for both scenarios