Topography dependent motion compensation for repeat-pass interferometric SAR systems

This letter presents a new motion compensation algorithm to process airborne interferometric repeat-pass synthetic aperture radar (SAR) data. It accommodates topography variations during SAR data processing, using an external digital elevation model. The proposed approach avoids phase artifacts, azimuth coregistration errors, and impulse response degradation, which usually appear due to the assumption of a constant reference height during motion compensation. It accurately modifies phase history of all targets before azimuth compression, resulting in an enhanced image quality. Airborne L-band repeat-pass interferometric data of the German Aerospace Center experimental airborne SAR (E-SAR) is used to validate the algorithm.Peer Reviewe

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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Ionospheric correction of interferometric SAR data with application to the cryospheric sciences

Thesis (Ph.D.) University of Alaska Fairbanks, 2018The ionosphere has been identified as an important error source for spaceborne Synthetic Aperture Radar (SAR) data and SAR Interferometry (InSAR), especially for low frequency SAR missions, operating, e.g., at L-band or P-band. Developing effective algorithms for the correction of ionospheric effects is still a developing and active topic of remote sensing research. The focus of this thesis is to develop robust and accurate techniques for ionospheric correction of SAR and InSAR data and evaluate the benefit of these techniques for cryospheric research fields such as glacier ice velocity tracking and permafrost deformation monitoring. As both topics are mostly concerned with high latitude areas where the ionosphere is often active and characterized by turbulence, ionospheric correction is particularly relevant for these applications. After an introduction to the research topic in Chapter 1, Chapter 2 will discuss open issues in ionospheric correction including processing issues related to baseline-induced spectrum shifts. The effect of large baseline on split spectrum InSAR technique has been thoroughly evaluated and effective solutions for compensating this effect are proposed. In addition, a multiple sub-band approach is proposed for increasing the algorithm robustness and accuracy. Selected case studies are shown with the purpose of demonstrating the performance of the developed algorithm. In Chapter 3, the developed ionospheric correction technology is applied to optimize InSAR-based ice velocity measurements over the big ice sheets in Greenland and the Antarctic. Selected case studies are presented to demonstrate and validate the effectiveness of the proposed correction algorithms for ice velocity applications. It is shown that the ionosphere signal can be larger than the actual glacier motion signal in the interior of Greenland and Antarctic, emphasizing the necessity for operational ionospheric correction. The case studies also show that the accuracy of ice velocity estimates was significantly improved once the developed ionospheric correction techniques were integrated into the data processing flow. We demonstrate that the proposed ionosphere correction outperforms the traditionally-used approaches such as the averaging of multi-temporal data and the removal of obviously affected data sets. For instance, it is shown that about one hundred multi-temporal ice velocity estimates would need to be averaged to achieve the estimation accuracy of a single ionosphere-corrected measurement. In Chapter 4, we evaluate the necessity and benefit of ionospheric-correction for L-band InSAR-based permafrost research. In permafrost zones, InSAR-based surface deformation measurements are used together with geophysical models to estimate permafrost parameters such as active layer thickness, soil ice content, and permafrost degradation. Accurate error correction is needed to avoid biases in the estimated parameters and their co-variance properties. Through statistical analyses of a large number of L-band InSAR data sets over Alaska, we show that ionospheric signal distortions, at different levels of magnitude, are present in almost every InSAR dataset acquired in permafrost-affected regions. We analyze the ionospheric correction performance that can be achieved in permafrost zones by statistically analyzing correction results for large number of InSAR data. We also investigate the impact of ionospheric correction on the performance of the two main InSAR approaches that are used in permafrost zones: (1) we show the importance of ionospheric correction for permafrost deformation estimation from discrete InSAR observations; (2) we demonstrate that ionospheric correction leads to significant improvements in the accuracy of time-series InSAR-based permafrost products. Chapter 5 summarizes the work conducted in this dissertation and proposes next steps in this field of research

Generic interferometric synthetic aperture radar atmospheric correction model and its application to co- and post-seismic motions

PhD ThesisThe tremendous development of Interferometric Synthetic Aperture Radar (InSAR) missions in recent years facilitates the study of smaller amplitude ground deformation over greater spatial scales using longer time series. However, this poses more challenges for correcting atmospheric effects due to the spatial-temporal variability of atmospheric delays. Previous attempts have used observations from Global Positioning System (GPS) and Numerical Weather Models (NWMs) to separate the atmospheric delays, but they are limited by (i) the availability (and distribution) of GPS stations; (ii) the time difference between NWM and radar observations; and (iii) the difficulties in quantifying their performance. To overcome the abovementioned limitations, we have developed the Iterative Tropospheric Decomposition (ITD) model to reduce the coupling effects of the troposphere turbulence and stratification and hence achieve similar performances over flat and mountainous terrains. Highresolution European Centre for Medium-Range Weather Forecasts (ECMWF) and GPS-derived tropospheric delays were properly integrated by investigating the GPS network geometry and topography variations. These led to a generic atmospheric correction model with a range of notable features: (i) global coverage, (ii) all-weather, all-time usability, (iii) available with a maximum of two-day latency, and (iv) indicators available to assess the model’s performance and feasibility. The generic atmospheric correction model enables the investigation of the small magnitude coseismic deformation of the 2017 Mw-6.4 Nyingchi earthquake from InSAR observations in spite of substantial atmospheric contamination. It can also minimize the temporal correlations of InSAR atmospheric delays so that reliable velocity maps over large spatial extents can be achieved. Its application to the post-seismic motion following the 2016 Kaikoura earthquake shows a success to recover the time-dependent afterslip distribution, which in turn evidences the deep inactive subduction slip mechanism. This procedure can be used to map surface deformation in other scenarios including volcanic eruptions, tectonic rifting, cracking, and city subsidence.This work was supported by a Chinese Scholarship Council studentship. Part of this work was also supported by the UK NERC through the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET)

3-D Satellite Interferometry for Interseismic Velocity Fields

The global interseismic strain rate map is being accomplished rapidly with measurements of the space-based geodetic technique of InSAR. High-resolution measurements of crustal deformation from InSAR can provide crucial constraints on a region's active tectonics, geodynamics, and seismic hazard. However, space-based InSAR usually only provides good constraints on horizontal displacement in the east-west direction, with the north-south component typically provided by low-resolution GNSS measurements. Sentinel-1, on the other hand, has the potential to provide measurements that are sensitive to north-south motion, through exploitation of the burst overlap areas produced by the TOPS acquisition mode. However, the significant noise contributions from decorrelation and propagation through the ionosphere make it challenging to detect surface displacements associated with interseismic deformation needing millimeters per year accuracy. The ionospheric phase advance is a significant nuisance term that can bias InSAR measurements. Although methods have been developed to mitigate the effect, they are not always routinely applied when processing C-band SAR images, for which the effect is generally expected to be small. Nevertheless, the effect can be significant, especially when analyzing low deformation gradients over large areas using time-series analysis. Here, the work in Chapter 3 presents a time-series approach to ionospheric noise mitigation, which improves on existing methods. Firstly, I estimate the ionospheric contribution for each individual acquisition from multiple interferograms, which reduces noise. Secondly, this work improved the identification of unwrapping errors, which can bias the estimation. Thirdly, I introduce a new filtering approach, which gives better results, particularly at image edges and areas with variable density of coherent measurements. Furthermore, the approach is applicable when estimating along-track motion in burst overlap areas. The results show that applying the correction improves velocity accuracy significantly for both conventional line-of-sight and burst overlap interferometry techniques. The application of measuring long-term tectonic signals that concentrate in the north-south component with millimeters per year accuracy is essential to constrain interseismic strain globally. In Chapter 4, I also demonstrate a time-series approach with the burst overlap interferometry appropriate for extracting subtle long-term displacements. The approach includes mitigation of ionospheric noise, and I investigate different filtering approaches to optimize the reduction of decorrelation noise. I present the mean ground velocity in the azimuth direction from data acquired between 2014 and 2019 along the West-Lut Fault, a north-south striking fault in eastern Iran. The chi-square statistic defines a good agreement between the results and independent GNSS measurements. Moreover, the denser coverage of the technique allows to detect the variation in strain accumulation between northern and southern segments of the fault, with our modeling indicating a variation of slip rate from 9.2±0.5 mm/yr in the south to 4.3±0.5 mm/yr in the north. With current efforts to use InSAR to constrain strain rates globally, along-track measurements can fill a crucial gap in north-south sensitivity. With the achievement of that the burst overlap InSAR technique can measure azimuth motions across a slowly deforming area where the surface displacements are concentrated in the north-south component, this results in that, in the TOPS burst overlap region, the number of observations for a ground displacement can reach 3-4 times with different observational components. Measurement redundancy allows for the decomposition of observed velocities into three-dimensional components. In Chapter 5, I apply InSAR observations to estimate a deformation across the Chaman fault in both line-of-sight and along-track components using images from ascending and descending passes. I demonstrate an inversion to estimate the decomposed velocities. The algorithm employs a sparse GNSS network across the region to transform InSAR velocities to the GNSS reference frame. The results show that constraining the long-wavelength signal across the InSAR observations using GNSS data can mitigate the long-wavelength ionospheric disturbance that remains in the observations. The variation in slip rates across the Chaman fault is depicted by two transect profiles. The mean velocity profile at latitude 31˚N, where the Chaman fault is the only tectonic structure to accommodate strain, is consistent with 10.4±0.4 mm/yr of slip rate derived from the interseismic modeling. The optimal fault slip rate to fit with the mean velocity of the southern profile at latitude 29˚N is 5.5±0.8 mm/yr across the Chaman fault and 15.5±0.9 mm/yr across the parallel fault (the Ghazaband fault). I also demonstrate the benefits of high temporal sampling of InSAR observations with TOPS acquisition mode to study time-dependent surface deformation. I present the evolution of fault creeps, including seismic and aseismic fault slip along the Chaman fault during 2014-2018

LiDAR and InSAR analysis of deformation in the Krafla rift zone, NE Iceland

Current models of fault growth examine the relationship of fault length (L) to vertical displacement (D) where the faults exhibit the classic fault shape of gradually increasing vertical displacement from zero at the fault tips to a maximum displacement (Dmax) at the middle of the fault. These models cannot adequately explain displacement length observations at the Krafla fissure swarm, in Iceland's northern volcanic zone, where I observe that many of the faults with significant vertical displacements still retain fissure-like features, with no vertical displacement, along portions of their lengths. I have created a high resolution digital elevation model (DEM) of the Krafla region using airborne LiDAR and measured the displacement/length profiles of 775 faults, with lengths ranging from 10s to 1000s of metres. I have categorised the faults based on the proportion of the profile that was still fissure-like. Fully-developed faults (no fissure-like regions) were further grouped into those with profiles that had a flattened appearance (large regions of constant vertical displacment), those with a classical fault shape and those that show regions of fault linkage. I measured the Dmax/L ratio of each identifiable original fault within the linked fault profiles, evidencing that the majority of the original faults had reached the maximum D/L prior to linkage. I suggest that a fault can most easily accommodate stress by displacing regions that are still fissure-like, and that a fault would be more likely to accommodate stress by linkage once it has reached the maximum displacement for its fault length. My results demonstrate that there is a pattern of growth from fissure to fault in the Dmax/L ratio of the categorised faults and propose a model for this growth. I suggest it is possible to better understand how faults grow in their earliest stages of development and that the proposed model can be incorporated as an early stage of fault growth for current models which only model behaviour of a fault once it has acquired the classical D/L profile. The range in the distribution of the published Dmax/L data is mainly attributed to tectonic setting, rock type and resolution limiting the choice of sample rate and fault length range. Using the LiDAR data I have examined the effect that data resolution has on the interpretation of the D/L relationship. I have resampled the LiDAR point data to produce two additional DEMs of 10 m and 30 m resolution, from which I have measured 90 and 40 fault profiles respectively. I have compared (Dmax)/L for all of these fault profiles with those of the published data. I have shown that by varying resolution the interpretation of the (Dmax)/L relationship gives trends for each resolution that together account for the spread in results of the combined published data for the length of faults measured. I have proposed that it may be possible to identify whether a measured fault is a single structure or if it is actually a segmented structure, when measured at a higher resolution, based on its location in the Dmax/L published distribution. The currently available surface displacement data in post-rifting Krafla and interrifting Askja are limited either to single point time series of displacement or regional displacement maps that are averaged over time and do not provide details of changes in rate through time. I have created a 24-epoch InSAR time series from ERS-1 and ERS-2 satellite SAR images over the 16-year period between 1992-2008. Using this I have extracted time series at 39 locations, both along- and across-axis at Krafla and Askja, and have identified trends in displacement rates over time. I have produced cumulative displacement profiles, based on the trends in displacement rate, both along- and acrossaxis and identifed key periods of displacement behaviour in the NVZ. I suggest that Krafla has three possible major sources of surface displacement: the shallow magma chamber under the Krafla caldera provides a decaying surface deflation between 1992 and 1999 and two possible deeper sources further north, the first between the Krafla and Fremrinamar fissure swarms creating uplift between 1992 and 1999 and the second beneath the Theistareykir volcanic centre between 2004 and 2008. In Askja I observe that the displacement rate in the caldera, previously thought to be a slowly decaying inflation, incurred a significant increase in rate to ~30 mm/yr in 1996-2004 followed by a decrease in rate to ~10 mm/yr

GNSS transpolar earth reflectometry exploriNg system (G-TERN): mission concept

The global navigation satellite system (GNSS) Transpolar Earth Reflectometry exploriNg system (G-TERN) was proposed in response to ESA's Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists. The primary objective of the mission is to quantify at high spatio-temporal resolution crucial characteristics, processes and interactions between sea ice, and other Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The objective is articulated through three key questions. 1) In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere, and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation, and melt)? 2) What are the impacts of extreme events and feedback mechanisms on sea ice evolution? 3) What are the effects of the cryosphere behaviors, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events? To contribute answering these questions, G-TERN will measure key parameters of the sea ice, the oceans, and the atmosphere with frequent and dense coverage over polar areas, becoming a “dynamic mapper”of the ice conditions, the ice production, and the loss in multiple time and space scales, and surrounding environment. Over polar areas, the G-TERN will measure sea ice surface elevation (<;10 cm precision), roughness, and polarimetry aspects at 30-km resolution and 3-days full coverage. G-TERN will implement the interferometric GNSS reflectometry concept, from a single satellite in near-polar orbit with capability for 12 simultaneous observations. Unlike currently orbiting GNSS reflectometry missions, the G-TERN uses the full GNSS available bandwidth to improve its ranging measurements. The lifetime would be 2025-2030 or optimally 2025-2035, covering key stages of the transition toward a nearly ice-free Arctic Ocean in summer. This paper describes the mission objectives, it reviews its measurement techniques, summarizes the suggested implementation, and finally, it estimates the expected performance.Peer ReviewedPostprint (published version

Radar satellite imagery for humanitarian response. Bridging the gap between technology and application

This work deals with radar satellite imagery and its potential to assist of humanitarian operations. As the number of displaced people annually increases, both hosting countries and relief organizations face new challenges which are often related to unclear situations and lack of information on the number and location of people in need, as well as their environments. It was demonstrated in numerous studies that methods of earth observation can deliver this important information for the management of crises, the organization of refugee camps, and the mapping of environmental resources and natural hazards. However, most of these studies make use of -high-resolution optical imagery, while the role of radar satellites is widely neglected. At the same time, radar sensors have characteristics which make them highly suitable for humanitarian response, their potential to capture images through cloud cover and at night in the first place. Consequently, they potentially allow quicker response in cases of emergencies than optical imagery. This work demonstrates the currently unused potential of radar imagery for the assistance of humanitarian operations by case studies which cover the information needs of specific emergency situations. They are thematically grouped into topics related to population, natural hazards and the environment. Furthermore, the case studies address different levels of scientific objectives: The main intention is the development of innovative techniques of digital image processing and geospatial analysis as an answer on the identified existing research gaps. For this reason, novel approaches are presented on the mapping of refugee camps and urban areas, the allocation of biomass and environmental impact assessment. Secondly, existing methods developed for radar imagery are applied, refined, or adapted to specifically demonstrate their benefit in a humanitarian context. This is done for the monitoring of camp growth, the assessment of damages in cities affected by civil war, and the derivation of areas vulnerable to flooding or sea-surface changes. Lastly, to foster the integration of radar images into existing operational workflows of humanitarian data analysis, technically simple and easily-adaptable approaches are suggested for the mapping of rural areas for vaccination campaigns, the identification of changes within and around refugee camps, and the assessment of suitable locations for groundwater drillings. While the studies provide different levels of technical complexity and novelty, they all show that radar imagery can largely contribute to the provision of a variety of information which is required to make solid decisions and to effectively provide help in humanitarian operations. This work furthermore demonstrates that radar images are more than just an alternative image source for areas heavily affected by cloud cover. In fact, what makes them valuable is their information content regarding the characteristics of surfaces, such as shape, orientation, roughness, size, height, moisture, or conductivity. All these give decisive insights about man-made and natural environments in emergency situations and cannot be provided by optical images Finally, the findings of the case studies are put into a larger context, discussing the observed potential and limitations of the presented approaches. The major challenges are summarized which need be addressed to make radar imagery more useful in humanitarian operations in the context of upcoming technical developments. New radar satellites and technological progress in the fields of machine learning and cloud computing will bring new opportunities. At the same time, this work demonstrated the large need for further research, as well as for the collaboration and transfer of knowledge and experiences between scientists, users and relief workers in the field. It is the first extensive scientific compilation of this topic and the first step for a sustainable integration of radar imagery into operational frameworks to assist humanitarian work and to contribute to a more efficient provision of help to those in need.Die vorliegende Arbeit beschäftigt sich mit bildgebenden Radarsatelliten und ihrem potenziellen Beitrag zur Unterstützung humanitärer Einsätze. Die jährlich zunehmende Zahl an vertriebenen oder geflüchteten Menschen stellt sowohl Aufnahmeländer als auch humanitäre Organisationen vor große Herausforderungen, da sie oft mit unübersichtlichen Verhältnissen konfrontiert sind. Effektives Krisenmanagement, die Planung und Versorgung von Flüchtlingslagern, sowie der Schutz der betroffenen Menschen erfordern jedoch verlässliche Angaben über Anzahl und Aufenthaltsort der Geflüchteten und ihrer natürlichen Umwelt. Die Bereitstellung dieser Informationen durch Satellitenbilder wurde bereits in zahlreichen Studien aufgezeigt. Sie beruhen in der Regel auf hochaufgelösten optischen Aufnahmen, während bildgebende Radarsatelliten bisher kaum Anwendung finden. Dabei verfügen gerade Radarsatelliten über Eigenschaften, die hilfreich für humanitäre Einsätze sein können, allen voran ihre Unabhängigkeit von Bewölkung oder Tageslicht. Dadurch ermöglichen sie in Krisenfällen verglichen mit optischen Satelliten eine schnellere Reaktion. Diese Arbeit zeigt das derzeit noch ungenutzte Potenzial von Radardaten zur Unterstützung humanitärer Arbeit anhand von Fallstudien auf, in denen konkrete Informationen für ausgewählte Krisensituationen bereitgestellt werden. Sie sind in die Themenbereiche Bevölkerung, Naturgefahren und Ressourcen aufgeteilt, adressieren jedoch unterschiedliche wissenschaftliche Ansprüche: Der Hauptfokus der Arbeit liegt auf der Entwicklung von innovativen Methoden zur Verarbeitung von Radarbildern und räumlichen Daten als Antwort auf den identifizierten Forschungsbedarf in diesem Gebiet. Dies wird anhand der Kartierung von Flüchtlingslagern zur Abschätzung ihrer Bevölkerung, zur Bestimmung von Biomasse, sowie zur Ermittlung des Umwelteinflusses von Flüchtlingslagern aufgezeigt. Darüber hinaus werden existierende oder erprobte Ansätze für die Anwendung im humanitären Kontext angepasst oder weiterentwickelt. Dies erfolgt im Rahmen von Fallstudien zur Dynamik von Flüchtlingslagern, zur Ermittlung von Schäden an Gebäuden in Kriegsgebieten, sowie zur Erkennung von Risiken durch Überflutung. Zuletzt soll die Integration von Radardaten in bereits existierende Abläufe oder Arbeitsroutinen in der humanitären Hilfe anhand technisch vergleichsweise einfacher Ansätze vorgestellt und angeregt werden. Als Beispiele dienen hier die radargestützte Kartierung von entlegenen Gebieten zur Unterstützung von Impfkampagnen, die Identifizierung von Veränderungen in Flüchtlingslagern, sowie die Auswahl geeigneter Standorte zur Grundwasserentnahme. Obwohl sich die Fallstudien hinsichtlich ihres Innovations- und Komplexitätsgrads unterscheiden, zeigen sie alle den Mehrwert von Radardaten für die Bereitstellung von Informationen, um schnelle und fundierte Planungsentscheidungen zu unterstützen. Darüber hinaus wird in dieser Arbeit deutlich, dass Radardaten für humanitäre Zwecke mehr als nur eine Alternative in stark bewölkten Gebieten sind. Durch ihren Informationsgehalt zur Beschaffenheit von Oberflächen, beispielsweise hinsichtlich ihrer Rauigkeit, Feuchte, Form, Größe oder Höhe, sind sie optischen Daten überlegen und daher für viele Anwendungsbereiche im Kontext humanitärer Arbeit besonders. Die in den Fallstudien gewonnenen Erkenntnisse werden abschließend vor dem Hintergrund von Vor- und Nachteilen von Radardaten, sowie hinsichtlich zukünftiger Entwicklungen und Herausforderungen diskutiert. So versprechen neue Radarsatelliten und technologische Fortschritte im Bereich der Datenverarbeitung großes Potenzial. Gleichzeitig unterstreicht die Arbeit einen großen Bedarf an weiterer Forschung, sowie an Austausch und Zusammenarbeit zwischen Wissenschaftlern, Anwendern und Einsatzkräften vor Ort. Die vorliegende Arbeit ist die erste umfassende Darstellung und wissenschaftliche Aufarbeitung dieses Themenkomplexes. Sie soll als Grundstein für eine langfristige Integration von Radardaten in operationelle Abläufe dienen, um humanitäre Arbeit zu unterstützen und eine wirksame Hilfe für Menschen in Not ermöglichen