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

InSAR bias and uncertainty due to the systematic and stochastic tropospheric delay

We quantify the bias and uncertainty of interferometric synthetic aperture radar (InSAR) displacement time series and their derivatives, the displacement velocities, by analyzing the systematic and stochastic components of the temporal variation of the tropospheric delay. The biases due to the systematic seasonal delay depend on the SAR acquisition times, whereas the uncertainties depend on the standard deviation of the random delay, the number of acquisitions, the total time span covered, and the covariance of the time series of the stochastic delay between a pixel and the reference. We study the contribution of the wet delay to the InSAR observations along the western India plate boundary using (i) Moderate Resolution Imaging Spectroradiometer precipitable water vapor, (ii) stratified tropospheric delay estimated from the ERA-I global atmospheric model, and (iii) seven Envisat InSAR swaths. Our analysis indicates that the amplitudes of the annual delay vary by up to ~10 cm in this region equivalent to a maximum displacement bias of ~24 cm in InSAR line of sight direction between two epochs (assuming Envisat IS6 beam mode). The stratified tropospheric delay correction mitigates this bias and reduces the scatter due to the stochastic delay. For ~7 years of Envisat acquisitions along the western India plate boundary, the uncertainty of the InSAR velocity field due to the residual stochastic wet delay after stratified tropospheric delay correction using the ERA-I model is in the order of ~2 mm/yr over 100 km and ~4 mm/yr over 400 km. We discuss the implication of the derived uncertainties on the full variance-covariance matrix of the InSAR data

Source parameters of the 2017 M_w 6.2 Yukon earthquake doublet inferred from coseismic GPS and ALOS-2 deformation measurements

We investigated an M_w ∼ 6.2 earthquake doublet on the border of the USA and Canada using ALOS2 Light-of-Sight displacements and GPS measurements. We selected three L-band ALOS-2 interfergorams with temporal baselines of one yr to extract coseismic deformation maps, in which master and slave images were both acquired in July. A subpixel-based alignment and another range spectral splitting techniques under the GAMMA InSAR software framework were applied to improve the interferometric coherence and reduce the effects of phase anomalies in two of the three interferometric pairs due to either ionospheric delay or a potential focusing issues in the generation of the ALOS2 SLC data. The updated interferograms convincingly reveal deformation fringe patterns produced by the two earthquakes. We conducted a nonlinear geophysical inversion to estimate the geometric parameters of the earthquakes with the InSAR and GPS measurements. The best-fitting model shows that a thrust faulting on a reverse fault and left-lateral strike-slip faulting on a nearly vertical fault with the centroid depths of 9.3±0.6 and 8.4±0.7 km, respectively, are most likely responsible for the earthquake doublet. The eastern Denali fault (EDF) and Duke River fault are major active faults in the region and the earthquake doublet could be due to reactivation of the part of the two faults system

Monitoring land subsidence of airport using InSAR time-series techniques with atmospheric and orbital error corrections

Land subsidence is one of the common geological hazards worldwide and mostly caused by human activities including the construction of massive infrastructures. Large infrastructure such as airport is susceptible to land subsidence due to several factors. Therefore, monitoring of the land subsidence at airport is crucial in order to prevent undesirable loss of property and life. Remote sensing technique, especially Interferometric Synthetic Aperture Radar (InSAR) has been successfully applied to measure the surface deformation over the past few decades although atmospheric artefact and orbital errors are still a concerning issue in this measurement technique. Multi-temporal InSAR, an extension of InSAR technique, uses large sets of SAR scenes to investigate the temporal evolution of surface deformation and mitigate errors found in a single interferogram. This study investigates the long-term land subsidence of the Kuala Lumpur International Airport (KLIA), Malaysia and Singapore Changi Airport (SCA), Singapore by using two multi-temporal InSAR techniques like Small Baseline Subset (SBAS) and Multiscale InSAR Time Series (MInTS). General InSAR processing was conducted to generate interferogram using ALOS PALSAR data from 2007 until 2011. Atmospheric and orbital corrections were carried out for all interferograms using weather model, namely European Centre for Medium Range Weather Forecasting (ECMWF) and Network De-Ramping technique respectively before estimating the time series land subsidence. The results show variation of subsidence with respect to corrections (atmospheric and orbital) as well as difference between multi-temporal InSAR techniques (SBAS and MInTS) used. After applying both corrections, a subsidence ranging from 2 to 17 mm/yr was found at all the selected areas at the KLIA. Meanwhile, for SCA, a subsidence of about less than 10 mm/yr was found. Furthermore, a comparison between two techniques (SBAS and MInTS) show a difference rate of subsidence of about less than 1 mm/yr for both study area. SBAS technique shows more linear result as compared to the MInTS technique which shows slightly scattering pattern but both techniques show a similar trend of surface deformation in both study sites. No drastic deformation was observed in these two study sites and slight deformation was detected which about less than 20mm/yr for both study areas probably occurred due to several reasons including conversion of the land use from agricultural land, land reclamation process and also poor construction. This study proved that InSAR time series surface deformation measurement techniques are useful as well as capable to monitor deformation of large infrastructure such as airport and as an alternative to costly conventional ground measurement for infrastructure monitoring