Review of works combining GNSS and insar in Europe

The Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) can be combined to achieve different goals, owing to their main principles. Both enable the collection of information about ground deformation due to the differences of two consequent acquisitions. Their variable applications, even if strictly related to ground deformation and water vapor determination, have encouraged the scientific community to combine GNSS and InSAR data and their derivable products. In this work, more than 190 scientific contributions were collected spanning the whole European continent. The spatial and temporal distribution of such studies, as well as the distinction in different fields of application, were analyzed. Research in Italy, as the most represented nation, with 47 scientific contributions, has been dedicated to the spatial and temporal distribution of its studied phenomena. The state-of-the-art of the various applications of these two combined techniques can improve the knowledge of the scientific community and help in the further development of new approaches or additional applications in different fields. The demonstrated usefulness and versability of the combination of GNSS and InSAR remote sensing techniques for different purposes, as well as the availability of free data, EUREF and GMS (Ground Motion Service), and the possibility of overcoming some limitations of these techniques through their combination suggest an increasingly widespread approach

Elevation and Deformation Extraction from TomoSAR

3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings

Monitoring land surface deformation using persistent scatterers interferometric synthetic aperture radar technique

Land subsidence is one of the major hazards occurring globally due to several reasons including natural and human activities. The effect of land subsidence depends on the extent and severity. The consequences of this hazard can be seen in many forms including damaged of infrastructures and loss of human lives. Although land subsidence is a global problem, but it is very common in urban and sub urban areas especially in rapidly developing countries. This problem needs to be monitored effectively. Several techniques such as land surveying, aerial photogrammetry and Global Positioning System (GPS) can be used to monitor or detect the subsidence effectively but these techniques are mostly expensive and time consuming especially for large area. In recent decades, Interferometric Synthetic Aperture Radar (InSAR) technique has been used widely for the monitoring of land subsidence successfully although this technique has several limitations due to temporal decorrelation, atmospheric effects and so on. However, the uncertainties related to InSAR technique have been reduced significantly with the recent Persistent Scatterers Interferometric Synthetic Aperture Radar (PSInSAR) technique which utilized a stack of interferograms generated from several radar images to estimate deformation by finding a bunch of stable points. This study investigates the surface deformation focusing on Kuala Lumpur, a rapidly growing city and Selangor using PSInSAR technique with a set of ALOS PALSAR images from 2007 to 2011. The research methodology consists of several steps of image processing that incudes i) generation of Differential Interferometric Synthetic Aperture Radar (DInSAR), ii) selection of Persistent Scatterers (PS) points, iii) removal of noise, iv) optimization of PS point selection, and v) generation of time series deformation map. However, special consideration was given to optimize the PS selection process using two master images. Results indicate a complete variation of mean line-of-sight (LOS) velocities over the study area. Stable areas (mean LOS=1.1 mm/year) were mostly found in the urban center of Kuala Lumpur, while medium rate of LOS (from 20 mm/year to 30 mm/year) was observed in the south west area in Kuala Langat and Sepang districts. The infrastructures in Kuala Lumpur are mostly stable except in Kuala Lumpur International Airport (KLIA) where a significant subsidence was detected (28.7 mm/year). Meanwhile, other parts of the study area such as Hulu Langat, Petaling Jaya and Klang districts show a very low and non-continuous movement (LOS < 20 mm/year), although comparatively higher subsidence rate (28 mm/year) was detected in the mining area. As conclusion, PSInSAR technique has a potential to monitor subsidence in urban and sub urban areas, but optimization of PS selection processing is necessary in order to reduce the noise and get better estimation accuracy

Estimação do campo tridimensional do vapor de água troposférico através de técnicas de tomografia por GNSS e InSAR

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Engenharia Geográfica), Universidade de Lisboa, Faculdade de Ciências, 2016A avaliação do conteúdo do vapor de água atmosférico é uma tarefa crucial para o estudo da meteorologia. Presentemente o vapor de água não é observado pelos sensores meteorológicos com uma resolução espacial e temporal suficiente, revelando-se como uma das maiores fontes de erro na previsão dos modelos numéricos, particularmente em situações de ocorrência de fenómenos meteorológicos severos. O objetivo principal deste trabalho consistiu em desenvolver um sistema tomográfico baseado em dados GNSS (Global Navigation Satellite System), que permitisse estimar o campo 3D do vapor de água troposférico numa região, de forma a avaliar a sua elevada variabilidade espácio-temporal num referencial 4D (3D espaciais mais o tempo). As observações oblíquas que rastreiam continuamente a atmosfera a partir de um conjunto de estações GNSS no terreno permitem, através da discretização do espaço da troposfera numa grelha 3D, a formulação de um sistema de equações que relaciona a medida do atraso troposférico do sinal GNSS e o vapor de água em cada espaço da grelha. O problema inverso, proposto pela tomografia GNSS, é resolvido aplicando técnicas de mínimos quadrados com incorporação de constrangimentos espácio-temporais na estabilização do sistema, que são necessários devido à cobertura insuficiente da grelha 3D por parte das observações GNSS. É investigada a inclusão de diversas medições meteorológicas externas no sistema, como perfis de radiossondas, imagens de satélite processadas com a técnica da interferometria SAR e a inclusão de produtos provenientes de sensores multiespectrais como o MODIS (Moderate-resolution imaging spectroradiometer) ou o AIRS (Atmospheric Infrared Sounder). Os lançamentos de radiossondas fornecem informação sobre a distribuição vertical da humidade na troposfera, que é fundamental para resolver o sistema tomográfico, enquanto a aquisição de imagens de satélite introduz uma alta densidade espacial de informação, devido à elevada quantidade de píxeis fornecida numa só imagem. A metodologia para realizar a tomografia GNSS foi desenvolvida de raiz e aplicada a um conjunto de estações existentes na região da Grande Lisboa, sendo um estudo pioneiro ao nível do país. Foram reunidas imagens InSAR, MODIS e AIRS localizadas sobre esta área, para realizar os constrangimentos espácio-temporais no sistema de equações. A aferição da qualidade dos resultados obtidos pela tomografia é realizada através de perfis radiossondagens e simulações da atmosfera determinadas através de simulações de modelos WRF (Weather Research and Forecast). É observado que a introdução de medições externas de humidade, para além de permitir preencher melhor o espaço da grelha tomográfica facilitando o processo de inversão do sistema de equações, permite obter uma solução 3D do vapor de água mais próxima da realidade. Neste trabalho foram também efetuados alguns estudos não relacionados diretamente com a tomografia. Foi analisada uma série contínua de dados para avaliar a relação entre o comportamento do sinal meteorológico GNSS e a ocorrência de precipitação nas estações meteorológicas locais. É observada uma correlação positiva entre eventos de precipitação intensa e o aumento rápido do PWV medido em estações GNSS. Ficou demonstrando que a combinação de dados meteorológicos com dados GNSS numa estação pode fornecer informação adicional para a previsão local e em tempo quase real de precipitação intensa. Outro estudo importante consistiu na simulação de dados GNSS, a partir dos sistemas GPS (Global Positioning System) e Galileo, avaliando o benefício para a solução tomográfica quando o sistema europeu estiver operacional. É avaliada uma série temporal contínua de soluções de um dia através da introdução de perturbações numa solução de atmosfera padrão, tendo-se verificado que o aumento do número de observações com a introdução do sistema Galileo aumenta a capacidade da tomografia GNSS na reconstrução das perturbações. A comparação visual e estatística das soluções da tomografia GNSS, avaliando a sua precisão através de perfis de radiossonda ou de simulações do modelo atmosférico WRF, indica em geral uma boa concordância com estas técnicas para todas as experiências e testes de sensibilidade realizados neste trabalho. A possibilidade de no futuro se integrar simultaneamente as observações dos diversos sistemas na tomografia GNSS poderá permitir a obtenção de soluções mais realistas.Evaluation of the atmospheric water vapor content is a crucial task for meteorology. Water vapor is not currently observed by the meteorological sensors with sufficient spatial resolution, becoming an important error source in numerical weather forecast models, particularly in situations related to severe weather phenomena. The main goal of this work consisted in the development of a tomographic system based on GNSS (Global Navigation Satellite System) data, which allowed estimating a 3D tropospheric water vapor field in a region, in order to evaluate its high spatial-temporal variability in a 4D referential (spatial 3D plus time). A set of slant observations that traverse the atmosphere continuously from a GNSS network on the terrain, with the signal properties sensible to water vapor content, allows thorough the discretization of the tropospheric space into a 3D grid, to setup a system of equations which relate the tropospheric delay of the GNSS signal with the water vapor content inside each grid space. The inverse problem, which is introduced by the GNSS tomography formulation, is usually solved by applying least square techniques, together with spatial and temporal constraints to stabilize the system inversion, which are needed to overcome the insufficient grid coverage provided by the GNSS observations. The inclusion of several external meteorological measurements into the system is investigated, like radiosonde profiles, satellite images processed using SAR interferometry techniques and the inclusion of products derived from multispectral sensors like MODIS (Moderate-resolution imaging spectroradiometer) or AIRS (Atmospheric Infrared Sounder). Radiosonde launches provide information about the vertical distribution of humidity along the troposphere, which is crucial to solve the tomographic system, while satellite data acquisition introduces high spatial density information due to the high pixel amount of information stored in one image. A GNSS tomographic methodology was developed from scratch and applied to a network of stations located in the Greater Lisbon region (Portugal), being a groundbreaking study at country-level. InSAR, MODIS and AIRS data located in this area were gathered to perform the spatial-temporal constraints into the system of equations. Quality of the results obtained from the tomography was assessed throughout radiosonde profiles and atmospheric simulations produced by the WRF (Weather Research and Forecast) model. The inclusion of external humidity measurements allows a better fulfilling of the tomographic grid, facilitating the systems inversion process and allowing to obtain a 3D water vapor solution closer to the real atmospheric state. Studies not directly related to the tomography technique were also performed in this work. A continuous data series was analyzed in order to evaluate the relationship between the GNSS meteorological signal and the occurrence of precipitation in local meteorological stations. Positive correlation was verified between the rapid PWV growth measured in a GNSS station and the occurrence of intense precipitation. It was demonstrated that the combination of meteorological data with GNSS data from a station can provide additional information to nowcast locally strong precipitation. Another important study was based in GNSS data simulation with GPS (Global Positioning System) and Galileo systems, evaluating the benefit to the tomography solution when the European system becomes operational. A continuous temporal series of solutions for one day was assessed through the introduction of perturbations in an atmospheric standard solution, being verified that the increment in the observation number throughout the Galileo observations improves the GNSS tomography capacity to reconstruct the induced perturbations. Statistical and visual comparison of the GNSS tomography solutions, evaluating its precision with radiosonde profile or numerical weather WRF simulations, shows in general a good agreement between the techniques throughout all of the experiments performed in this thesis. The future possibility of integrating multiple observations from several GNSS systems into the tomography could generate solutions closer to the real atmospheric state

GNSS and InSAR based water vapor tomography: A Compressive Sensing solution

Unvollständig oder ungenau erstellte Modelle atmosphärischer Effekte schränken die Qualität geodätischer Weltraumverfahren wie GNSS (Globale Satelliten-Navigationssysteme) und InSAR (Interferometrisches Radar mit synthetischer Apertur) ein. Gleichzeitig enthalten Zustandsgrößen der Erdatmosphäre, allen voran die dreidimensionale (3D) Wasserdampf-Verteilung, wertvolle Informationen für Klimaforschung und Wettervorhersage, welche aus GNSS- oder InSAR-Beobachtungen abgeleitet werden können. Es gibt etliche Verfahren zur 3DWasserdampf-Rekonstruktion aus GNSS-basierten feuchten Laufzeitverzögerungen. Aufgrund der meist spärlich verteilten GNSS-Stationen und durch die begrenzte Anzahl sichtbarer GNSS-Satelliten, treten in tomographischen Anwendungen in der Regel jedoch schlecht gestellte Probleme auf, die z.B. über geometrische Zusatzbedingungen regularisiert werden, welche oft glättend auf die Wasserdampf-Schätzungen wirken. Diese Arbeit entwickelt und analysiert daher einen Ansatz, der auf einer Compressive Sensing (CS) Lösung des tomographischen Modells beruht. Dieser Ansatz nutzt die Spärlichkeit der Wasserdampf-Verteilung in einem geeigneten Transformationsbereich zur Regularisierung des schlecht gestellten tomographischen Problems und kommt somit ohne glättende geometrische Zusatzbedingungen aus. Eine weitere Motivation für die Nutzung einer spärlichen Compressive Sensing Lösung besteht darin, dass die Anzahl an zu bestimmenden von Null verschiedenen Koeffizienten bei gleichbleibender Anzahl an Beobachtungen in Compressive Sensing geringer sein kann als die Anzahl an zu schätzenden Parametern in üblichen Kleinste Quadrate (LSQ) Ansätzen. Zur Erhöhung der räumlichen Auflösung der Beobachtungen führt diese Arbeit zudem sowohl feuchte Laufzeitverzögerungen aus GNSS als auch aus InSAR in das tomographische Gleichungssystem ein. Die Neuheiten des vorgestellten Ansatzes sind 1) die Nutzung von sowohl GNSS als auch absoluten InSAR Laufzeitverzögerungen für die tomographische Wasserdampf-Rekonstruktion und 2) die Lösung des tomographischen Systems mittels Compressive Sensing. Zudem wird 3) die Qualität der CS-Rekonstruktion mit der Qualität üblicher LSQ-Schätzungen verglichen. Die tomographische Rekonstruktion der durch feuchte Refraktivitäten beschriebenen atmosphärischen Wasserdampf-Verteilung beruht auf der einen Seite auf realen feuchten Laufzeitverzögerungen aus GNSS und InSAR und auf der anderen Seite auf drei verschiedenen synthetischen Datensätzen feuchter Laufzeitverzögerungen, die aus Wasserdampf-Simulationen des Weather Research and Forecasting (WRF) Modells abgeleitet wurden. Die Validierung der geschätzten Wasserdampf-Verteilung stützt sich somit zum einen auf Radiosonden Profile und zum anderen auf einen Vergleich der geschätzten Refraktivitäten mit den WRF Refraktivitäten, die zugleich als Eingangsdaten zur Generierung der synthetischen Laufzeitverzögerungen genutzt werden. Der reale bzw. der erste synthetische Datensatz vergleicht die Rekonstruktionsqualität des entwickelten CS-Ansatzes mit üblichen Kleinste Quadrate Wasserdampf-Schätzungen und untersucht, inwieweit die Nutzung von InSAR Laufzeitverzögerungen bzw. von synthetischen InSAR Laufzeitverzögerungen die Genauigkeit und die Präzision der Wasserdampf-Rekonstruktion erhöht. Der zweite synthetische Datensatz wurde dafür ausgelegt, den allgemeinen Einfluss der Beobachtungsgeometrie auf die Refraktivitätsschätzungen zu analysieren. Der dritte synthetische Datensatz untersucht insbesondere die Empfindlichkeit der tomographischen Rekonstruktion gegenüber variierenden GNSS-Stationszahlen, unterschiedlichen Voxel-Diskretisierungen und verschiedenen Orbit-Konstellationen. Im realen Datensatz verhalten sich die Kleinste Quadrate Schätzung und der Compressive Sensing Ansatz sowohl für die reine GNSS-Lösung als auch für die kombinierte GNSS- und InSAR-Lösung konsistent. Die synthetischen Datensätze zeigen, dass Compressive Sensing in geeigneten Szenarien sehr genaue und präzise Ergebnisse liefern kann. Die Qualität der Wasserdampf-Schätzungen hängt in erster Linie ab i) von der Genauigkeit des funktionalen Modells, das die feuchten Laufzeitverzögerungen, die zu schätzenden Refraktivitäten und die von den Strahlen in den Voxeln zurückgelegten Distanzen in Beziehung zueinander setzt, ii) von der Anzahl verfügbarer GNSS Stationen, iii) von der Voxel-Diskretisierung, und iv) von der Vielseitigkeit der in das tomographische System eingebauten Strahlrichtungen. Die mittels des realen Datensatzes bzw. mittels der synthetischen Datensätze untersuchten Regionen sind etwa 120 × 120 km2 bzw. 100 × 100 km2 groß. Im realen Datensatz stehen acht GNSS-Stationen zur Verfügung und es werden feuchte Laufzeitverzögerungen von GPS InSAR genutzt. In den synthetischen Datensätzen werden unterschiedliche Stationsanzahlen definiert und vielseitige Strahlrichtungen getestet

GNSS and InSAR based water vapor tomography: A Compressive Sensing solution

An accurate knowledge of the three-dimensional (3D) distribution of water vapor in the atmosphere is a key element for weather forecasting and climate research. In addition, a precise determination of water vapor is also required for accurate positioning and deformation monitoring using Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). Several approaches for 3D tomographic water vapor reconstruction from GNSS-based Slant Wet Delay (SWD) estimates exist. Yet, due to the usually sparsely distributed GNSS sites and due to the limited number of visible GNSS satellites, the tomographic system usually is ill-posed and needs to be regularized, e.g. by means of geometric constraints that risk to over-smooth the tomographic refractivity estimates. Therefore, this work develops and analyzes a Compressive Sensing (CS) approach for neutrospheric water vapor tomographies benefiting of the sparsity of the refractivity estimates in an appropriate transform domain as a prior for regularization. The CS solution is developed because it does not include any geometric smoothing constraints as applied in common Least Squares (LSQ) approaches and because the sparse CS solution containing only a few non-zero coefficients may be determined, at a constant number of observations, based on less parameters than the corresponding LSQ solution. In addition to the developed CS solution, this work introduces SWDs obtained from both GNSS and InSAR into the tomographic system in order to dispose of a better spatial distribution of the observations. The novelties of this approach are 1) the use of both absolute GNSS and absolute InSAR SWDs for tomography and 2) the solution of the tomographic system by means of Compressive Sensing. In addition, 3) the quality of the CS reconstruction is compared with the quality of common LSQ approaches to water vapor tomography. The tomographic reconstruction is performed, on the one hand, based on a real data set using GNSS and InSAR SWDs and, on the other hand, based on three different synthetic SWD data sets generated using wet refractivity information from the Weather Research and Forecasting (WRF) model. Thus, the validation of the achieved results focuses, on the one hand, on radiosonde profiles and, on the other hand, on a comparison of the refractivity estimates with the input WRF refractivities. The real data set resp. the first synthetic data set compares the reconstruction quality of the developed CS approach with LSQ approaches to water vapor tomography and investigates in how far the inclusion of InSAR resp. synthetic InSAR SWDs increases the accuracy and precision of the refractivity estimates. The second synthetic data set is designed in order to analyze the general effect of the observing geometry on the quality of the refractivity estimates. The third synthetic data set places a special focus on the sensibility of the tomographic reconstruction to different numbers of GNSS sites, varying voxel discretization, and different orbit constellations. In case of the real data set, for both the GNSS only solution and a combined GNSS and InSAR solution, the refractivities estimated by means of the LSQ and CS methodologies show a consistent behavior, although the two solution strategies differ. The synthetic data sets show that CS can yield very precise and accurate results, if an appropriate tomographic setting is chosen. The reconstruction quality mainly depends on i) the accuracy of the functional model relating the SWD estimates to the refractivity parameters and to the distances passed by the rays within the voxels, ii) the number of available GNSS sites, iii) the voxel discretization, and iv) the variety of ray directions introduced into the tomographic system. The sizes of the study areas associated to the real resp. to the synthetic data sets are about 120 × 120 km2 and about 100 × 100 km2, respectively. In the real data set, a total of eight GNSS sites is available and SWD estimates of GPS and InSAR are introduced. In the synthetic data sets, different numbers of sites are defined and a variety of ray directions is tested