Tropospheric phase delay in interferometric synthetic aperture radar estimated from meteorological model and multispectral imagery

ENVISAT Medium Resolution Imaging Spectrometer Instrument (MERIS) multispectral data and the mesoscale meteorological model MM5 are used to estimate the tropospheric phase delay in synthetic aperture radar (SAR) interferograms. MERIS images acquired simultaneously with ENVISAT Advanced Synthetic Aperture Radar data provide an estimate of the total water vapor content W limited to cloud-free areas based on spectral bands ratio (accuracy 0.17 g cm^(−2) and ground resolution 300 m). Maps of atmospheric delay, 2 km in ground resolution, are simulated from MM5. A priori pertinent cumulus parameterization and planetary boundary layer options of MM5 yield near-equal phase correction efficiency. Atmospheric delay derived from MM5 is merged with available MERIS W product. Estimates of W measured from MERIS and modeled from MM5 are shown to be consistent and unbiased and differ by ~0.2 g cm^(−2) (RMS). We test the approach on data over the Lebanese ranges where active tectonics might contribute to a measurable SAR signal that is obscured by atmospheric effects. Local low-amplitude (1 rad) atmospheric oscillations with a 2.25 km wavelength on the interferograms are recovered from MERIS with an accuracy of 0.44 rad or 0.03 g cm^(−2). MERIS water product overestimates W in the clouds shadow due to mismodeling of multiple scattering and underestimates W on pixels with undetected semitransparent clouds. The proposed atmospheric filter models dynamic atmospheric signal which cannot be recovered by previous filtering techniques which are based on a static atmospheric correction. Analysis of filter efficiency with spatial wavelength shows that ~43% of the atmospheric signal is removed at all wavelengths

Long-term monitoring of geodynamic surface deformation using SAR interferometry

Thesis (Ph.D.) University of Alaska Fairbanks, 2014Synthetic Aperture Radar Interferometry (InSAR) is a powerful tool to measure surface deformation and is well suited for surveying active volcanoes using historical and existing satellites. However, the value and applicability of InSAR for geodynamic monitoring problems is limited by the influence of temporal decorrelation and electromagnetic path delay variations in the atmosphere, both of which reduce the sensitivity and accuracy of the technique. The aim of this PhD thesis research is: how to optimize the quantity and quality of deformation signals extracted from InSAR stacks that contain only a low number of images in order to facilitate volcano monitoring and the study of their geophysical signatures. In particular, the focus is on methods of mitigating atmospheric artifacts in interferograms by combining time-series InSAR techniques and external atmospheric delay maps derived by Numerical Weather Prediction (NWP) models. In the first chapter of the thesis, the potential of the NWP Weather Research & Forecasting (WRF) model for InSAR data correction has been studied extensively. Forecasted atmospheric delays derived from operational High Resolution Rapid Refresh for the Alaska region (HRRRAK) products have been compared to radiosonding measurements in the first chapter. The result suggests that the HRRR-AK operational products are a good data source for correcting atmospheric delays in spaceborne geodetic radar observations, if the geophysical signal to be observed is larger than 20 mm. In the second chapter, an advanced method for integrating NWP products into the time series InSAR workflow is developed. The efficiency of the algorithm is tested via simulated data experiments, which demonstrate the method outperforms other more conventional methods. In Chapter 3, a geophysical case study is performed by applying the developed algorithm to the active volcanoes of Unimak Island Alaska (Westdahl, Fisher and Shishaldin) for long term volcano deformation monitoring. The volcano source location at Westdahl is determined to be approx. 7 km below sea level and approx. 3.5 km north of the Westdahl peak. This study demonstrates that Fisher caldera has had continuous subsidence over more than 10 years and there is no evident deformation signal around Shishaldin peak.Chapter 1. Performance of the High Resolution Atmospheric Model HRRR-AK for Correcting Geodetic Observations from Spaceborne Radars -- Chapter 2. Robust atmospheric filtering of InSAR data based on numerical weather prediction models -- Chapter 3. Subtle motion long term monitoring of Unimak Island from 2003 to 2010 by advanced time series SAR interferometry -- Chapter 4. Conclusion and future work

화산 활동 관측을 위한 SAR 간섭 기법에서의 대기 보정 기법

학위논문 (석사)-- 서울대학교 대학원 : 지구환경과학부, 2013. 2. 김덕진.Ground deformation in volcano is a consequence of changes in magma chambers volume. Magma storage, migration and volume change is closely associated phenomena with the ground deformation. Therefore, measuring ground deformation provides important information to understand the volcanic activities. For some specific volcanoes, such as Shinmoedake volcano, ground deformation of even a few centimeters can occur before eruption. Thus, measuring ground deformation needs to be fairly accurate. SAR interferometry is a potential technique to measure the ground deformation accurately. One of the limitations in SAR interferometry, however, is atmospheric phase delay effects, which are induced when microwave propagates into the atmosphere. In this aspect, various methods for mitigating atmospheric phase delay effects have been developed. This study aims to mitigate the atmospheric phase delay especially in volcano because the stratified and turbulent atmospheric phase delay effects could severely contaminate the deformation patterns. First method used in this study is the atmospheric correction technique using MODIS data. Multispectral observation can measure the integrated water vapor in the atmosphere by analyzing ratios of water vapor absorbing channel and atmospheric window channel. It can be directly used for calculating the tropospheric phase delay effect caused by water vapor. Recent researches using multispectral datasets are restricted to approach using ENVISAT. Therefore, new approach is necessary in application using ALOS PALSAR. This study evaluates the applicability and possibility. In adequate temporal difference and cloud coverage, available datasets of MODIS successfully converted to the atmospheric phase delay corresponding to SAR acquisition time. However, there are some limitations in application into all dataset because of the cloud cover and temporal difference between the SAR acquisition time and MODIS acquisition time. In spite of limitations, the use of MODIS data in atmospheric correction yield better results and minimize misinterpreted errors. The WRF model complements the limitations of MODIS data. In this respect, an application of the WRF model in atmospheric correction of differential interferogram was carried out in the second methods. The estimated APS from the WRF model can explain the stratified APS involved in differential interferograms. However, the accuracy of model prediction should be evaluated. The direct use of the WRF model predictions for atmospheric correction yield errors for mitigating the turbulent APS and the small-scaled APS. Final approach is a time-series analysis. In model experiments, several properties of atmospheric phase screen (APS) are found out. The first is that APS could remain in a time-series analysis and mainly comes from the stratified APS. The second is that it is possible to estimate and minimize the stratified APS by using sufficient WRF models. In the case of the turbulent APS, time-weighting low pass filtering is capable to reduce it. Therefore, the main idea of the atmosphere corrected time-series analysis adopt the stratified APS and turbulent APS correction method using WRF model and time-weighting methods. In comparison with observational dataset such as GPS and MODIS dataset, the estimated ground deformation and APS from the atmosphere corrected method have low rms errors, and high correlation. Therefore, this method can be believed as an accurate approach for measuring the ground deformation in volcanic region.1. INTRODUCTION 15 1.1. SAR INTERFEROMETRY AND VOLCANO MONITORING 15 1.2. ATMOSPHERIC PHASE DELAY IN INSAR 17 1.3. OBJECTIVES OF THIS RESEARCH 20 2. THE THEORETICAL BASIC OF SAR INTERFEROMETRY AND TIME-SERIES ANALYSIS 22 2.1. SAR INTERFEOMETRY 22 2.2. DIFFERENTIAL SAR INTERFEROMETRY 28 2.3. TIME-SERIES ANALYSIS 35 3. STUDY AREA AND DATASET 43 3.1. STUDY AREA 43 3.2. DATA 45 4. ATMOSPHERIC CORRECTION IN INDIVIDUAL DIFFERENTIAL INTERFEROGRAMS 50 4.1. DIFFERENTIAL SAR INTERFEROMETRY 50 4.2. ATMOSPHERIC PHASE DELAY EFFECTS SIMULATION 50 4.3. RESULTS 63 5. ATMOSPHERIC CORRECTION USING TIME-SERIES ANALYSIS 70 5.1. APS ESTIMATION ERRORS IN TIME-SERIES INSAR 71 5.2. PROPERTIES OF APS IN TIME AND SPACE 76 5.3. APPLICATION TO AVAILABLE DATASET AND DATA PROCESSING 88 5.4. COMPARISON BETWEEN CONVENTIONAL AND ATMOSPHERE CORRECTED TIME SERIES ANALYSIS 95 5.5. VALIDATION 101 6. CONCLUSION 108Maste

Constructing water vapor maps by fusing InSAR, GNSS and WRF data

The work aims at constructing maps of the total water vapor in the atmosphere by fusing InSAR, GNSS, and WRF data

Atmospheric artifacts correction for InSAR using empirical model and numerical weather prediction models

lnSAR has been proved its unprecedented ability and merits of monitoring ground deformation on large scale with centimeter to millimeter scale accuracy. However, several factors affect the reliability and accuracy of its applications. Among them, atmospheric artifacts due to spatial and temporal variations of atmosphere state often pose noise to interferograms. Therefore, atmospheric artifacts m itigalion remains one of the biggest challenges to be addressed in the In SAR community. State-of-the-art research works have revealed atmospheric artifacts can be partially compensated with empirical models, temporal-spatial filtering approach in lnSAR time series, pointwise GPS zenith path delay and numerical weather prediction models. In this thesis, firstly, we further develop a covariance weighted linear empirical model correction method. Secondly, a realistic LOS direction integration approach based on global reanalysis data is employed and comprehensively compared with the conventional method that integrates along zenith direction. Finally, the realistic integration method is applied to local WRF numerical forecast model data. l'vbreover, detailed comparisons between different global reanalysis data and local WRF model are assessed. In terms of empirical models correcting methods, many publications have studied correcting stratified tropospheric phase delay by assuming a linear model between them and topography. However, most of these studies ha\19 not considered the effect of turbulent atmospheric artefacts when adjusting the linear model to data. In this thesis, an improved technique that minimizes the influence of turbulent atmosphere in the model adjustment has been presented. In the proposed algorithm, the model is adjusted to the phase differences of pixels instead of using the unwrapped phase of each pixel. In addition, the different phase differences are weighted as a function of its APS covariance estimated from an empirical variogram to reduce in the model adjustment the impact of pixel pairs with significant turbulent atmosphere. The performance of the proposed method has been validated with both simulated and real Sentinel-1 SAR data in Tenerife island, Spain. Considering methods using meteorological observations to mitigate APS, an accurate realistic com puling strategy utilizing global atmospheric reanalysis data has been implemented. With the approach, the realistic LOS path along satellite and the monitored points is considered, rather than converting from zenith path delay. Com pared with zenith delay based method, the biggest advantage is that it can avoid errors caused by anisotropic atmospheric behaviour. The accurate integration method is validated with Sentinel-1 data in three test sites: Tenerife island, Spain, Almeria, Spain and Crete island, Greece. Compared to conventional zenith method, the realistic integration method shows great improvement. A variety of global reanalysis data are available from different weather forecasting organizations, such as ERA-Interim, ERAS, MERRA2. In this study, the realistic integration mitigation method is assessed on these different reanalysis data. The results show that these data are feasible to mitigate APS to some extent in most cases. The assessment also demonstrates that the ERAS performs the best statistically, compared to other global reanalysis data. l'vbreover, as local numerical weather forecast models have the ability to predict high spatial resolution atmospheric parameters, by using which, it has the potential to achieve APS mitigation. In this thesis, the realistic integration method is also employed on the local WRF model data in Tenerife and Almeria test s ites. However, it turns out that the WRF model performs worse than the original global reanalysis data.Las técnicas lnSAR han demostrado su capacidad sin precedentes y méritos para el monitoreo de la deformaci6n del suelo a gran escala con una precisión centimétrica o incluso milimétrica. Sin embargo, varios factores afectan la fiabilidad y precisión de sus aplicaciones. Entre ellos, los artefactos atmosféricos debidos a variaciones espaciales y temporales del estado de la atm6sfera a menudo añaden ruido a los interferogramas. Por lo tanto, la mitigación de los artefactos atmosféricos sigue siendo uno de los mayores desafíos a abordar en la comunidad lnSAR. Los trabajos de investigaci6n de vanguardia han revelado que los artefactos atmosféricos se pueden compensar parcialmente con modelos empíricos, enfoque de filtrado temporal-espacial en series temporales lnSAR, retardo puntual del camino cenital con GPS y modelos numéricos de predicción meteorológica. En esta tesis, en primer lugar, desarrollamos un método de corrección de modelo empírico lineal ponderado por covarianza. En segundo lugar, se emplea un enfoque realista de integracion de dirección LOS basado en datos de reanálisis global y se compara exhaustivamente con el método convencional que se integra a lo largo de la dirección cenital. Finalmente, el método de integraci6n realista se aplica a los datos del modelo de pronóstico numérico WRF local. Ademas, se evalúan las comparaciones detalladas entre diferentes datos de reanálisis global y el modelo WRF local. En términos de métodos de corrección con modelos empíricos, muchas publicaciones han estudiado la corrección del retraso estratificado de la fase troposférica asumiendo un modelo lineal entre ellos y la topografía. Sin embargo, la mayoría de estos estudios no han considerado el efecto de los artefactos atmosféricos turbulentos al ajustar el modelo lineal a los datos. En esta tesis, se ha presentado una técnica mejorada que minimiza la influencia de la atm6sfera turbulenta en el ajuste del modelo. En el algoritmo propuesto, el modelo se ajusta a las diferencias de fase de los pixeles en lugar de utilizar la fase sin desenrollar de cada pixel. Además, las diferentes diferencias de fase se ponderan en función de su covarianza APS estimada a partir de un variograma empírico para reducir en el ajuste del modelo el impacto de los pares de pixeles con una atm6sfera turbulenta significativa. El rendimiento del método propuesto ha sido validado con datos SAR Sentinel-1 simulados y reales en la isla de Tenerife, España. Teniendo en cuenta los métodos que utilizan observaciones meteorológicas para mitigar APS, se ha implementado una estrategia de computación realista y precisa que utiliza datos de reanálisis atmosférico global. Con el enfoque, se considera el camino realista de LOS a lo largo del satélite y los puntos monitoreados, en lugar de convertirlos desde el retardo de la ruta cenital. En comparación con el método basado en la demora cenital, la mayor ventaja es que puede evitar errores causados por el comportamiento atmosférico anisotrópico. El método de integración preciso se valida con los datos de Sentinel-1 en tres sitios de prueba: la isla de Tenerife, España, Almería, España y la isla de Creta, Grecia. En comparación con el método cenital convencional, el método de integración realista muestra una gran mejora.Postprint (published version

Atmospheric artifacts correction for InSAR using empirical model and numerical weather prediction models

lnSAR has been proved its unprecedented ability and merits of monitoring ground deformation on large scale with centimeter to millimeter scale accuracy. However, several factors affect the reliability and accuracy of its applications. Among them, atmospheric artifacts due to spatial and temporal variations of atmosphere state often pose noise to interferograms. Therefore, atmospheric artifacts m itigalion remains one of the biggest challenges to be addressed in the In SAR community. State-of-the-art research works have revealed atmospheric artifacts can be partially compensated with empirical models, temporal-spatial filtering approach in lnSAR time series, pointwise GPS zenith path delay and numerical weather prediction models. In this thesis, firstly, we further develop a covariance weighted linear empirical model correction method. Secondly, a realistic LOS direction integration approach based on global reanalysis data is employed and comprehensively compared with the conventional method that integrates along zenith direction. Finally, the realistic integration method is applied to local WRF numerical forecast model data. l'vbreover, detailed comparisons between different global reanalysis data and local WRF model are assessed. In terms of empirical models correcting methods, many publications have studied correcting stratified tropospheric phase delay by assuming a linear model between them and topography. However, most of these studies ha\19 not considered the effect of turbulent atmospheric artefacts when adjusting the linear model to data. In this thesis, an improved technique that minimizes the influence of turbulent atmosphere in the model adjustment has been presented. In the proposed algorithm, the model is adjusted to the phase differences of pixels instead of using the unwrapped phase of each pixel. In addition, the different phase differences are weighted as a function of its APS covariance estimated from an empirical variogram to reduce in the model adjustment the impact of pixel pairs with significant turbulent atmosphere. The performance of the proposed method has been validated with both simulated and real Sentinel-1 SAR data in Tenerife island, Spain. Considering methods using meteorological observations to mitigate APS, an accurate realistic com puling strategy utilizing global atmospheric reanalysis data has been implemented. With the approach, the realistic LOS path along satellite and the monitored points is considered, rather than converting from zenith path delay. Com pared with zenith delay based method, the biggest advantage is that it can avoid errors caused by anisotropic atmospheric behaviour. The accurate integration method is validated with Sentinel-1 data in three test sites: Tenerife island, Spain, Almeria, Spain and Crete island, Greece. Compared to conventional zenith method, the realistic integration method shows great improvement. A variety of global reanalysis data are available from different weather forecasting organizations, such as ERA-Interim, ERAS, MERRA2. In this study, the realistic integration mitigation method is assessed on these different reanalysis data. The results show that these data are feasible to mitigate APS to some extent in most cases. The assessment also demonstrates that the ERAS performs the best statistically, compared to other global reanalysis data. l'vbreover, as local numerical weather forecast models have the ability to predict high spatial resolution atmospheric parameters, by using which, it has the potential to achieve APS mitigation. In this thesis, the realistic integration method is also employed on the local WRF model data in Tenerife and Almeria test s ites. However, it turns out that the WRF model performs worse than the original global reanalysis data.Las técnicas lnSAR han demostrado su capacidad sin precedentes y méritos para el monitoreo de la deformaci6n del suelo a gran escala con una precisión centimétrica o incluso milimétrica. Sin embargo, varios factores afectan la fiabilidad y precisión de sus aplicaciones. Entre ellos, los artefactos atmosféricos debidos a variaciones espaciales y temporales del estado de la atm6sfera a menudo añaden ruido a los interferogramas. Por lo tanto, la mitigación de los artefactos atmosféricos sigue siendo uno de los mayores desafíos a abordar en la comunidad lnSAR. Los trabajos de investigaci6n de vanguardia han revelado que los artefactos atmosféricos se pueden compensar parcialmente con modelos empíricos, enfoque de filtrado temporal-espacial en series temporales lnSAR, retardo puntual del camino cenital con GPS y modelos numéricos de predicción meteorológica. En esta tesis, en primer lugar, desarrollamos un método de corrección de modelo empírico lineal ponderado por covarianza. En segundo lugar, se emplea un enfoque realista de integracion de dirección LOS basado en datos de reanálisis global y se compara exhaustivamente con el método convencional que se integra a lo largo de la dirección cenital. Finalmente, el método de integraci6n realista se aplica a los datos del modelo de pronóstico numérico WRF local. Ademas, se evalúan las comparaciones detalladas entre diferentes datos de reanálisis global y el modelo WRF local. En términos de métodos de corrección con modelos empíricos, muchas publicaciones han estudiado la corrección del retraso estratificado de la fase troposférica asumiendo un modelo lineal entre ellos y la topografía. Sin embargo, la mayoría de estos estudios no han considerado el efecto de los artefactos atmosféricos turbulentos al ajustar el modelo lineal a los datos. En esta tesis, se ha presentado una técnica mejorada que minimiza la influencia de la atm6sfera turbulenta en el ajuste del modelo. En el algoritmo propuesto, el modelo se ajusta a las diferencias de fase de los pixeles en lugar de utilizar la fase sin desenrollar de cada pixel. Además, las diferentes diferencias de fase se ponderan en función de su covarianza APS estimada a partir de un variograma empírico para reducir en el ajuste del modelo el impacto de los pares de pixeles con una atm6sfera turbulenta significativa. El rendimiento del método propuesto ha sido validado con datos SAR Sentinel-1 simulados y reales en la isla de Tenerife, España. Teniendo en cuenta los métodos que utilizan observaciones meteorológicas para mitigar APS, se ha implementado una estrategia de computación realista y precisa que utiliza datos de reanálisis atmosférico global. Con el enfoque, se considera el camino realista de LOS a lo largo del satélite y los puntos monitoreados, en lugar de convertirlos desde el retardo de la ruta cenital. En comparación con el método basado en la demora cenital, la mayor ventaja es que puede evitar errores causados por el comportamiento atmosférico anisotrópico. El método de integración preciso se valida con los datos de Sentinel-1 en tres sitios de prueba: la isla de Tenerife, España, Almería, España y la isla de Creta, Grecia. En comparación con el método cenital convencional, el método de integración realista muestra una gran mejora

Interferometric Synthetic Aperture Radar for slow slip applications

Over the last two decades, Slow Slip Events (SSEs) have been observed across many subduction zones, primarily through continuous GNSS networks. SSEs represent shearing of two tectonic plates, at much slower rates than earthquakes but more rapidly than plate motion. They are not dangerous in themselves, but change the stress field and can potentially trigger devastating earthquakes. While highly valuable, GNSS networks at most locations lack the spatial-resolution required to describe the spatial extent of the slow slip at depth. A better constraint of slow slip at depth in combination with other observations from seismology could be essential in addressing key research questions. These include: “Why do slow slip events occurs in some regions and not others?”, “What drives slow slip events?”, “Do slow slip events delay the occurrence of devastating earthquakes?”, and “Can slow slip events trigger devastating earthquakes?”. Interferometric Synthetic Aperture Radar (InSAR) is an established and attractive technique to study surface displacements at high-spatial resolution. Until now, InSAR has not been fully exploited for the study of SSEs. Here, I provide the necessary InSAR methodology, and further demonstrate the use of InSAR for static and time-dependant slow slip modelling. My developments have a direct benefit for various other applications such as earthquake cycle processes. I Specifically address the following two challenges which limit the wide uptake of InSAR: (1) Decorrelation noise introduced by changing backscattering properties of the surface and a change in satellite acquisition geometry, making it difficult to correctly unwrap meaningful signal. I address this problem by applying existing advanced time-series InSAR processing methods. (2) Atmospheric delays masking the smaller slow slip signal. These are mainly due to spatial and temporal variations in pressure, temperature, and relative humidity in the lower part of the troposphere, which result in an apparent signal in the InSAR data. Different tropospheric correction methods exist, all with their own limitations. Auxiliary data methods often lack the spatial and temporal resolution, while the phase-based methods cannot account for a spatially-varying troposphere. In response, I develop a phase-based power-law representation of tropospheric delay that can be applied in the presence of deformation and which accounts for spatial variation of tropospheric properties. I demonstrate its application over Mexico, where it reduces tropospheric signals both locally (on average by ~0.45 cm for each kilometer of elevation) and the long wavelength components. Moreover, I provide to the research community a Toolbox for Reducing Atmospheric InSAR Noise (TRAIN), which includes all the state-of-the-art correction methods, implemented as opensource matlab routines. When comparing these methods, I find spectrometers give the largest reduction in tropospheric noise, but are limited to cloud-free and daylight acquisitions. I also find that all correction methods perform ~10-20% worse when there is cloud cover. As all methods have their own limitations, future efforts should aim at combining the different correction methods in an optimal manner. Additionally, I apply my InSAR methodology and power-law correction method to the study of the 2006 Guerrero SSE, where I jointly invert cumulative GNSS and InSAR SSE surface displacements. In Guerrero, SSEs have been observed in a “seismic gap”, where no earthquakes have occurred since 1911, accumulating a seismic potential of Mw 8.0-8.4. I find slow slip enters the seismogenic zone and the Guerrero Gap, with ~5 cm slip reaching depths as shallow as 12 km, and where the spatial extent of the slow slip collocates on the interface with a highly coupled inter-SSE region as found from an GNSS study. In addition, slow slip decreased the total accumulated moment since the previous SSE (4.7 years earlier) by ~50% Over time and while accounting for SSEs, the moment deficit in the Guerrero Gap increases each year by Mw ~6.8. Therefore I find that the Guerrero Gap still has the potential for a large earthquake, with a seismic potential of Mw ~8.15 accumulated over the last century. Finally, I show the application to use InSAR for time-dependant slow slip modelling. From a simulation of the 2006 SSE, I demonstrate that InSAR is able to provide valuable information to constrain the spatial extent of the slow slip signal. With a future perspective of continued high repeat acquisitions of various SAR platforms, my expansion of the Network Inversion Filter with InSAR will become a powerful tool for investigating the spatio-temporal correlation between slow slip and other phenomena such as non volcanic tremor. Moreover, this approach can apply to earthquake cycle processes. Studying the broader earthquake cycle will further our knowledge of seismic hazard and increase our resilience to such events

Atmospheric artifacts correction with a covariance-weighted linear model over mountainous regions

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Mitigating the atmospheric phase delay is one of the largest challenges faced by the differential synthetic aperture radar (SAR) interferometry community. Recently, many publications have studied correcting the stratified tropospheric phase delay by assuming a linear model between them and the topography. However, most of these studies have not considered the effect of turbulent atmospheric artifacts when adjusting the linear model to data. In this paper, we present an improved technique that minimizes the influence of the turbulent atmosphere in the model adjustment. In the proposed algorithm, the model is adjusted to the phase differences of pixels instead of using the unwrapped phase of each pixel. In addition, the different phase differences are weighted as a function of its atmospheric phase screen covariance estimated from an empirical variogram to reduce, in the model adjustment, the impact of pixel pairs with a significant turbulent atmosphere. The good performance of the proposed method has been validated with both the simulated and real Sentinel-1A SAR data in the mountainous area of Tenerife island, Spain.Peer ReviewedPostprint (author's final draft

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

On the accuracy of integrated water vapor observations and the potential for mitigating electromagnetic path delay error in InSAR

Abstract. A field campaign was carried out in the framework of the Mitigation of Electromagnetic Transmission errors induced by Atmospheric Water Vapour Effects (METAWAVE) project sponsored by the European Space Agency (ESA) to investigate the accuracy of currently available sources of atmospheric columnar integrated water vapor measurements. The METAWAVE campaign took place in Rome, Italy, for the 2-week period from 19 September to 4 October 2008. The collected dataset includes observations from ground-based microwave radiometers and Global Positioning System (GPS) receivers, from meteorological numerical model analysis and predictions, from balloon-borne in-situ radiosoundings, as well as from spaceborne infrared radiometers. These different sources of integrated water vapor (IWV) observations have been analyzed and compared to quantify the accuracy and investigate the potential for mitigating IWV-related electromagnetic path delay errors in Interferometric Synthetic Aperture Radar (InSAR) imaging. The results, which include a triple collocation analysis accounting for errors inherently present in every IWV measurements, are valid not only to InSAR but also to any other application involving water vapor sensing. The present analysis concludes that the requirements for mitigating the effects of turbulent water vapor component into InSAR are significantly higher than the accuracy of the instruments analyzed here. Nonetheless, information on the IWV vertical stratification from satellite observations, numerical models, and GPS receivers may provide valuable aid to suppress the long spatial wavelength (>20 km) component of the atmospheric delay, and thus significantly improve the performances of InSAR phase unwrapping techniques