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

Radar interferometry techniques for the study of ground subsidence phenomena: a review of practical issues through cases in Spain

Subsidence related to multiple natural and human-induced processes affects an increasing number of areas worldwide. Although this phenomenon may involve surface deformation with 3D displacement components, negative vertical movement, either progressive or episodic, tends to dominate. Over the last decades, differential SAR interferometry (DInSAR) has become a very useful remote sensing tool for accurately measuring the spatial and temporal evolution of surface displacements over broad areas. This work discusses the main advantages and limitations of addressing active subsidence phenomena by means of DInSAR techniques from an end-user point of view. Special attention is paid to the spatial and temporal resolution, the precision of the measurements, and the usefulness of the data. The presented analysis is focused on DInSAR results exploitation of various ground subsidence phenomena (groundwater withdrawal, soil compaction, mining subsidence, evaporite dissolution subsidence, and volcanic deformation) with different displacement patterns in a selection of subsidence areas in Spain. Finally, a cost comparative study is performed for the different techniques applied.The different research areas included in this paper has been supported by the projects: CGL2005-05500-C02, CGL2008-06426-C01-01/BTE, AYA2 010-17448, IPT-2011-1234-310000, TEC-2008-06764, ACOMP/2010/082, AGL2009-08931/AGR, 2012GA-LC-036, 2003-03-4.3-I-014, CGL2006-05415, BEST-2011/225, CGL2010-16775, TEC2011-28201, 2012GA-LC-021 and the Banting Postdoctoral Fellowship to PJG

MONITORING THE 2018 ERUPTION OF KĪLAUEA VOLCANO USING VARIOUS REMOTE SENSING TECHNIQUES

Monitoring the regions that are prone to natural hazards is essential in disaster management to provide early warnings. Airborne and space-borne remote sensing techniques are cost-effective in accomplishing this task. Interferometric Synthetic Aperture Radar (InSAR) is an advanced remote sensing technique used to detect and measure the changes in the Earth’s topography over time. Spaceborne InSAR is a precise (~mm accuracy) way to measure the land surface altitudinal changes. Persistent Scatterer Interferometry (PSI) is a powerful method of differential SAR interferometry that processes the InSAR data by automatically selecting the persistent scatterers in the region. In this thesis, I developed a new algorithm to estimate the areal coverage and volume of newly erupted lava by integrating the space-borne InSAR, thermal infrared, Light Detection and Ranging (LiDAR), and Normalized Difference Vegetation Index (NDVI) techniques. I applied this algorithm to the eruption of the East Rift Zone (ERZ) of the Kīlauea volcano that took place between May and August 2018 as a case study, and estimated the areal coverage and volume of lava erupted. I compared the results of InSAR to those derived from airborne LiDAR. I found that although air-borne LiDAR provides data with higher resolution and accuracy, InSAR is almost as good as LiDAR in monitoring deformed areas and has larger spatial and temporal coverage. I also performed the PSI analysis using the Stanford Method for Persistent Scatterers (StaMPS) algorithm, and determined the Line-of-Sight (LOS) deformations prior, during, and after the 2018 eruption of the Kīlauea volcano. Results from the PSI processing show regional subsidence on the Big Island, indicating the deflation of the southern and western part of the Big Island during the eruption at the East Rift Zone. Keywords: Kilauea

Spatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images

A recent development in Interferometric Synthetic Aperture Radar (InSAR) technology is integrating multiple SAR satellite data to dynamically extract ground features. This paper addresses two relevant challenges: identification of common ground targets from different SAR datasets in space, and concatenation of time series when dealing with temporal dynamics. To address the first challenge, we describe the geolocation uncertainty of InSAR measurements as a three-dimensional error ellipsoid. The points, among InSAR measurements, which have error ellipsoids with a positive cross volume are identified as tie-point pairs representing common ground objects from multiple SAR datasets. The cross volumes are calculated using Monte Carlo methods and serve as weights to achieve the equivalent deformation time series. To address the second challenge, the deformation time series model for each tie-point pair is estimated using probabilistic methods, where potential deformation models are efficiently tested and evaluated. As an application, we integrated two Radarsat-2 datasets in Standard and Extra-Fine modes to map the subsidence of the west of the Netherlands between 2010 and 2017. We identified 18128 tie-point pairs, 5 intersection types of error ellipsoids, 5 deformation models, and constructed their long-term deformation time series. The detected maximum mean subsidence velocity in Line-Of-Sight direction is up to 15 mmyr-1. We conclude that our method removes limitations that exist in single-viewing-geometry SAR when integrating multiple SAR data. In particular, the proposed time-series modeling method is useful to achieve a long-term deformation time series of multiple datasets