An accurate method to correct atmospheric phase delay for InSAR with the ERA5 global atmospheric model

Differential SAR Interferometry (DInSAR) has proven its unprecedented ability and merits of monitoring ground deformation on a large scale with centimeter to millimeter accuracy. However, atmospheric artifacts due to spatial and temporal variations of the atmospheric state often affect the reliability and accuracy of its results. The commonly-known Atmospheric Phase Screen (APS) appears in the interferograms as ghost fringes not related to either topography or deformation. Atmospheric artifact mitigation remains one of the biggest challenges to be addressed within the DInSAR community. State-of-the-art research works have revealed that atmospheric artifacts can be partially compensated with empirical models, point-wise GPS zenith path delay, and numerical weather prediction models. In this study, we implement an accurate and realistic computing strategy using atmospheric reanalysis ERA5 data to estimate atmospheric artifacts. With this approach, the Line-of-Sight (LOS) path along the satellite trajectory and the monitored points is considered, rather than estimating it from the zenith path delay. Compared with the zenith delay-based method, the key advantage is that it can avoid errors caused by any anisotropic atmospheric phenomena. The accurate method is validated with Sentinel-1 data in three different test sites: Tenerife island (Spain), Almería (Spain), and Crete island (Greece). The effectiveness and performance of the method to remove APS from interferograms is evaluated in the three test sites showing a great improvement with respect to the zenith-based approach.Peer ReviewedPostprint (published version

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

State-of-the-art in studies of glacial isostatic adjustment for the British Isles: a literature review

Understanding the effects of glacial isostatic adjustment (GIA) of the British Isles is essential for the assessment of past and future sea-level trends. GIA has been extensively examined in the literature, employing different research methods and observational data types. Geological evidence from palaeo-shorelines and undisturbed sedimentary deposits has been used to reconstruct long-term relative sea-level change since the Last Glacial Maximum. This information derived from sea-level index points has been employed to inform empirical isobase models of the uplift in Scotland using trend surface and Gaussian trend surface analysis, as well as to calibrate more theory-driven GIA models that rely on Earth mantle rheology and ice sheet history. Furthermore, current short-term rates of GIA-induced crustal motion during the past few decades have been measured using different geodetic techniques, mainly continuous GPS (CGPS) and absolute gravimetry (AG). AG-measurements are generally employed to increase the accuracy of the CGPS estimates. Synthetic aperture radar interferometry (InSAR) looks promising as a relatively new technique to measure crustal uplift in the northern parts of Great Britain, where the GIA-induced vertical land deformation has its highest rate. This literature review provides an in-depth comparison and discussion of the development of these different research approaches

Basin scale assessment of landslides geomorphological setting by advanced InSAR analysis

An extensive investigation of more than 90 landslides affecting a small river basin in Central Italy was performed by combining field surveys and remote sensing techniques. We thus defined the geomorphological setting of slope instability processes. Basic information, such as landslides mapping and landslides type definition, have been acquired thanks to geomorphological field investigations and multi-temporal aerial photos interpretation, while satellite SAR archive data (acquired by ERS and Envisat from 1992 to 2010) have been analyzed by means of A-DInSAR (Advanced Differential Interferometric Synthetic Aperture Radar) techniques to evaluate landslides past displacements patterns. Multi-temporal assessment of landslides state of activity has been performed basing on geomorphological evidence criteria and past ground displacement measurements obtained by A-DInSAR. This step has been performed by means of an activity matrix derived from information achieved thanks to double orbital geometry. Thanks to this approach we also achieved more detailed knowledge about the landslides kinematics in time and space

Coherency Matrix Decomposition-Based Polarimetric Persistent Scatterer Interferometry

© 2019 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.The rationale of polarimetric optimization techniques is to enhance the phase quality of the interferograms by combining adequately the different polarization channels available to produce an improved one. Different approaches have been proposed for polarimetric persistent scatterer interferometry (PolPSI). They range from the simple and computationally efficient BEST, where, for each pixel, the polarimetric channel with the best response in terms of phase quality is selected, to those with high-computational burden like the equal scattering mechanism (ESM) and the suboptimum scattering mechanism (SOM). BEST is fast and simple, but it does not fully exploit the potentials of polarimetry. On the other side, ESM explores all the space of solutions and finds the optimal one but with a very high-computational burden. A new PolPSI algorithm, named coherency matrix decomposition-based PolPSI (CMD-PolPSI), is proposed to achieve a compromise between phase optimization and computational cost. Its core idea is utilizing the polarimetric synthetic aperture radar (PolSAR) coherency matrix decomposition to determine the optimal polarization channel for each pixel. Three different PolSAR image sets of both full- (Barcelona) and dual-polarization (Murcia and Mexico City) are used to evaluate the performance of CMD-PolPSI. The results show that CMD-PolPSI presents better optimization results than the BEST method by using either $D_{\mathrm{ A}}$ or temporal mean coherence as phase quality metrics. Compared with the ESM algorithm, CMD-PolPSI is 255 times faster but its performance is not optimal. The influence of the number of available polarization channels and pixel's resolutions on the CMD-PolPSI performance is also discussed.Peer ReviewedPostprint (author's final draft

Advanced InSAR atmospheric correction: MERIS/MODIS combination and stacked water vapour models

A major source of error for repeat-pass Interferometric Synthetic Aperture Radar (InSAR) is the phase delay in radio signal propagation through the atmosphere (especially the part due to tropospheric water vapour). Based on experience with the Global Positioning System (GPS)/Moderate Resolution Imaging Spectroradiometer (MODIS) integrated model and the Medium Resolution Imaging Spectrometer (MERIS) correction model, two new advanced InSAR water vapour correction models are demonstrated using both MERIS and MODIS data: (1) the MERIS/MODIS combination correction model (MMCC); and (2) the MERIS/MODIS stacked correction model (MMSC). The applications of both the MMCC and MMSC models to ENVISAT Advanced Synthetic Aperture Radar (ASAR) data over the Southern California Integrated GPS Network (SCIGN) region showed a significant reduction in water vapour effects on ASAR interferograms, with the root mean square (RMS) differences between GPS- and InSAR-derived range changes in the line-of-sight (LOS) direction decreasing from ,10mm before correction to ,5mm after correction, which is similar to the GPS/MODIS integrated and MERIS correction models. It is expected that these two advanced water vapour correction models can expand the application of MERIS and MODIS data for InSAR atmospheric correction. A simple but effective approach has been developed to destripe Terra MODIS images contaminated by radiometric calibration errors. Another two limiting factors on the MMCC and MMSC models have also been investigated in this paper: (1) the impact of the time difference between MODIS and SAR data; and (2) the frequency of cloud-free conditions at the global scale

Imaging multi-age construction settlement behaviour by advanced SAR interferometry

This paper focuses on the application of Advanced Satellite Synthetic Aperture Radar Interferometry (A-DInSAR) to subsidence-related issues, with particular reference to ground settlements due to external loads. Beyond the stratigraphic setting and the geotechnical properties of the subsoil, other relevant boundary conditions strongly influence the reliability of remotely sensed data for quantitative analyses and risk mitigation purposes. Because most of the Persistent Scatterer Interferometry (PSI) measurement points (Persistent Scatterers, PSs) lie on structures and infrastructures, the foundation type and the age of a construction are key factors for a proper interpretation of the time series of ground displacements. To exemplify a methodological approach to evaluate these issues, this paper refers to an analysis carried out in the coastal/deltaic plain west of Rome (Rome and Fiumicino municipalities) affected by subsidence and related damages to structures. This region is characterized by a complex geological setting (alternation of recent deposits with low and high compressibilities) and has been subjected to different urbanisation phases starting in the late 1800s, with a strong acceleration in the last few decades. The results of A-DInSAR analyses conducted from 1992 to 2015 have been interpreted in light of high-resolution geological/geotechnical models, the age of the construction, and the types of foundations of the buildings on which the PSs are located. Collection, interpretation, and processing of geo-thematic data were fundamental to obtain high-resolution models; change detection analyses of the land cover allowed us to classify structures/infrastructures in terms of the construction period. Additional information was collected to define the types of foundations, i.e., shallow versus deep foundations. As a result, we found that only by filtering and partitioning the A-DInSAR datasets on the basis of the above-mentioned boundary conditions can the related time series be considered a proxy of the consolidation process governing the subsidence related to external loads as confirmed by a comparison with results from a physically based back analysis based on Terzaghi's theory. Therefore, if properly managed, the A-DInSAR data represents a powerful tool for capturing the evolutionary stage of the process for a single building and has potential for forecasting the behaviour of the terrain-foundation-structure combination

Sentinel-1 data exploitation for terrain deformation monitoring

Persistent Scatterer interferometry (PSI) is a group of advanced differential interferometric Synthetic Aperture Radar (SAR) techniques used to measure and monitor terrain deformation. Sentinel-1 has improved the data acquisition throughout and, compared to previous sensors, increased considerably the Differential Interferometric SAR (DInSAR) and PSI deformation monitoring potential. The effect of the refractive atmosphere on the interferometric phase and phase unwrapping ambiguity are two critical issues of InSAR. The low density of Persistent Scatterer (PS) in non-urban areas, another critical issue, has inspired the development of alternative approaches and refinement of the PS chains. Along with the efforts to develop methods to mitigate the three above-mentioned problems, the work presented in this thesis also deals with the presence of a new signal in multilooked interferograms which cannot be explained by noise, atmospheric or earth surface topography changes. This paper describes a method for atmospheric phase screen estimation using rain station weather data and three different data driven procedures to obtain terrain deformation maps. These approaches aim to exploit Sentinel-1 highly coherent interferograms and their short revisit time. The first method called the splitting makes uses of the power spectrum of the interferograms to split the signals into high and low frequency, and following a mutually exclusive consecutive processing chain for the two sets. This approach has resulted in greater density of PSs with decreased phase unwrapping errors. The second approach, called Direct Integration (DI), aims at providing a very fast and straightforward approach to screen wide areas and easily detect active areas. This approach fully exploits the coherent interferograms from the consecutive images provided by Sentinel-1 resulting in a very high sampling density. However, it lacks robustness and its usability lays on the operator experience. The third method, called PSIG (Persistent Scatterer Interferometry Geomatics) short temporal baseline, provides a constrained application of the PSIG chain, the CTTC approach to the PSI. It uses short temporal baseline interferograms and do not assume any deformation model for point selection. It is also quite a straightforward approach and a perfect complement to the direct integration approach. It improves the performances of the standard PSIG approach, increasing the PS density and providing robust measurements. The effectiveness of the approaches is illustrated through analyses performed on different test sites.La técnica Persistent Scatterer Interferometry (PSI) es un grupo de técnicas avanzadas de radar de apertura sintética interferométrica diferencial (SAR) que se utiliza para medir y monitorear losmovimientos del terreno. Sentinel-1 ha mejorado sensiblemente la adquisición de datos y, en comparación con los sensores SAR anteriores, ha aumentado considerablemente el potencial uso de la interferometría diferencial SAR y del PSI para medir y monitorizar desplazamientos del terreno. El efecto de la atmósfera sobre la fase interferométrica y la naturaleza ambigua de esta son dos cuestiones críticas de InSAR. Además, la baja densidad de Persistent Scatterer (PSs) en áreas no urbanas, es otro tema crítico que ha inspirado el desarrollo de enfoques alternativos y el refinamiento de las cadenas PS existentes. Junto con los esfuerzos por desarrollar métodos para mitigar los tres problemas antes mencionados, el trabajo presentado en esta tesis también aborda la presencia de una nueva señal en interferogramas multilooked que no puede explicarse por cambios de ruido, atmosféricos o topográficos de la superficie terrestre. Esta tesis describe un método para la estimación de la fase atmosférica utilizando datos meteorológicos adquiridos in-situ y tres aproximaciones diferentes basadas en datos Sentinel-1 para obtener mapas de deformación del terreno. Estos enfoques tienen como objetivo explotar los interferogramas altamente coherentes proporcionados por Sentinel-1 gracias a su corto tiempo de revisita. El primer método llamado división hace uso de filtros en el dominico frecuencial de los interferogramas para dividir las señales en alta y baja frecuencia, y siguiendo una cadena de procesamiento consecutiva independiente para cada clase. Este enfoque ha dado como resultado una mejora substancial de PS minimizando los errores debidos al desenrollado de fase. El segundo enfoque, llamado Integración Directa (DI), tiene como objetivo proporcionar un enfoque muy rápido y sencillo para examinar áreas amplias y detectar fácilmente áreas activas. Este enfoque aprovecha al máximo los interferogramas coherentes de las imágenes consecutivas proporcionadas por Sentinel-1, lo que da como resultado una densidad de muestreo muy alta. Sin embargo, carece de robustez y su usabilidad depende de la experiencia del operador. El tercer método, llamado PSIG (Persistent Scatterer Interferometry Geomatics) de línea de base temporal corta, proporciona una aplicación restringida de la cadena PSIG, el enfoque CTTC para el PSI. Utiliza interferogramas de línea base temporales cortos y no asume ningún modelo de deformación para la selección de puntos. Su uso es complementario al enfoque de integración directa proporcionando robustez en las zonas. Mejora el rendimiento del enfoque estándar de PSIG, aumentando la densidad de PS y proporcionando mediciones robustas. La efectividad de los enfoques se ilustra a través de análisis realizados en diferentes sitios de prueba.Postprint (published version

Ground deformation monitoring over Xinjiang coal fire area by an adaptive ERA5-corrected stacking-InSAR method

Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas.This research was supported in part by the National Natural Science Foundation of China (Grant No.41874044 and Grant No. 42004011), in part by project G2HOTSPOTS (PID2021-122142OB- I00) from the MCIN /AEI /10.13039 /501100011033 /FEDER, UE and in part by China Postdoctoral Science Foundation (Grant No. 2020M671646). At the same time, the research was also funded by the Construction Program of Space-Air-Ground-Well Cooperative Awareness Spatial Information Project (B20046) and National Key R&D Program of China (Grant No. 2022YFE0102600).Peer ReviewedPostprint (published version

Advances on the investigation of landslides by space-borne synthetic aperture radar interferometry

Landslides are destructive geohazards to people and infrastructure, resulting in hundreds of deaths and billions of dollars of damage every year. Therefore, mapping the rate of deformation of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated risks. In this paper, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59,339 “Earth observation for seismic hazard assessment and landslide early warning system” are reported. The primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks, detect potential landslides in wide regions, and demonstrate EO-based landslide early warning system over selected landslides. This work only focuses on the landslide hazard content of the project, and thus, in order to achieve these objectives, the following tasks were developed up to now: a) a procedure for phase unwrapping errors and tropospheric delay correction; b) an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements; c) the application of polarimetric SAR interferometry (PolInSAR) to increase the number and quality of monitoring points in landslide-prone areas; d) the semiautomatic mapping and preliminary classification of active displacement areas on wide regions; e) the modeling and identification of landslides in order to identify triggering factors or predict future displacements; and f) the application of an InSAR-based landslide early warning system on a selected site. The achieved results, which mainly focus on specific sensitive regions, provide essential assets for planning present and future scientific activities devoted to identifying, mapping, characterizing, monitoring and predicting landslides, as well as for the implementation of early warning systems.This work was supported by the ESA-MOST China DRAGON-5 project with ref. 59339, by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI), and the European Funds for Regional Development under grant [grant number PID2020-117303GB-C22], by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital in the framework of the project CIAICO/2021/335, by the Natural Science Foundation of China [grant numbers 41874005 and 41929001], the Fundamental Research Funds for the Central University [grant numbers 300102269712 and 300102269303], and China Geological Survey Project [grant numbers DD20190637 and DD20190647]. Xiaojie Liu and Liuru Hu have been funded by Chinese Scholarship Council Grants Ref. [grant number 202006560031] and [grant number 202004180062], respectively