Petar Marinkovic

The 2014–2015 eruption of Fogo volcano: Geodetic modeling of Sentinel‐1 TOPS interferometry

After 20 years of quiescence, Fogo volcano erupted in November 2014. The eruption produced fast-moving lava flows that traveled for several kilometers and destroyed two villages. This event represents the first episode of significant surface deformation imaged by the new European Space Agencys Sentinel-1 satellite in its standard acquisition mode, Terrain Observation by Progressive Scans (TOPS), which differs from that of previous synthetic aperture radar (SAR) missions. We perform a Bayesian inversion of Sentinel-1 TOPS SAR interferograms spanning the eruption and accurately account for variations in the TOPS line-of-sight vector when modeling displacements. Our results show that magma ascended beneath the Pico do Fogo cone and then moved laterally toward its southwestern flank, where the eruptive fissure opened. This study provides important insights into the inner workings of Fogo volcano and shows the potential of Sentinel-1 TOPS interferometry for geophysical (e.g., volcano monitoring) applications.

Spatial variations in fault friction related to lithology from rupture and afterslip of the 2014 South Napa, California, earthquake

Following earthquakes, faults are often observed to continue slipping aseismically. It has been proposed that this afterslip occurs on parts of the fault with rate-strengthening friction that are stressed by the main shock, but our understanding has been limited by a lack of immediate, high-resolution observations. Here we show that the behavior of afterslip following the 2014 South Napa earthquake in California varied over distances of only a few kilometers. This variability cannot be explained by coseismic stress changes alone. We present daily positions from continuous and survey GPS sites that we remeasured within 12 h of the main shock and surface displacements from the new Sentinel-1 radar mission. This unique geodetic data set constrains the distribution and evolution of coseismic and postseismic fault slip with exceptional resolution in space and time. We suggest that the observed heterogeneity in behavior is caused by lithological controls on the frictional properties of the fault plane.

NEST: An esa open source Toolbox for scientific exploitation of SAR data

The Next ESA SAR Toolbox (NEST) is a free and open source toolbox suite for reading, processing, analysis and visualization of SAR data. The toolbox is developed by the European Space Agency (ESA) under the GNU GPL license. The toolbox supports the exploitation of SAR data from ESAs space-borne SAR missions (ERS-1&2, ENVISAT), and also of other space-borne sensors (e.g., TerraSAR-X, RADARSAT 1&2, COSMO-SkyMed, JERS-1, ALOS PALSAR). The input data is SAR data processed to Level-1 or higher. NEST provides basic and advanced tools for SAR user community, such as an absolute calibration, automatic coregistration of detected and complex products, multilooking, speckle filtering, external precise obit ingestion, geocoding, mosaicking and a Product Library Metadata Database, etc. In addition to this basic processing toolset, NEST offers a collection of routines for oil spill and ship detection, and wind field estimation. Finally, a fully integrated and featured InSAR processor is being implemented into NEST.

Systematic InSAR monitoring of African active volcanic zones: What we have learned in three years, or an harvest beyond our expectations

We present here a brief overview of some findings and preliminary results obtained after almost three years of systematic monitoring of active volcanic areas in Africa by means of differential synthetic aperture radar interferometry (InSAR). With a database rich of more than 400 SAR scenes of Fogo (Cape Verde), Ol Doinyo Lengai (Tanzania), Nyiragongo-Nyamulagira (DR of Congo) and Mount Cameroon volcanoes, we processed more than 2000 interferograms among which we could detect significant and major geophysical processes: the first dyking event ever captured geodetically in a continental rift (Lake Natron; Northern Tanzania), the co-eruptive deformations of the Lengai, Nyiragongo and Nyamulagira volcanoes, the co-seismic displacements associated to the mb 6.1 February 3rd 2008 Bukavu earthquake as well as the identification of atmospheric induced phase delays over Fogo and Mount Cameroon volcanoes to be attributed to the seasonal oscillations of the inter-tropical convergence zone (ITCZ). These results have been reached given the abundance of data that increases the chances to capture unpredictable events, and capture them with the most favorable interferometric conditions as possible (e.g. in terms of geometrical and temporal baselines that minimized the vegetation-induced decorrelation). They provided strong scientific material as well as tools for hazard assessment.

Advanced InSAR coregistration using point clusters

In this study, we introduce a refined algorithm for the fine InSAR image coregistration which could be used in highly decorrelated scenes. The refinement is introduced at the point of selection of points necessary for the estimation of the offset vectors between master and slave image. A new approach for point selection based on the Harris corner detector algorithm is presented. The new point selection algorithm results with the clusters of point candidates for the offset vectors over a scene. Consequently, the number of points and their spatial distribution are improved, which results in a better global quality of the coregistration model

L-band and C-band InSAR studies of African volcanic areas

Radar interferometry has proven to be a very suitable, low-cost and accurate tool to measure surface displacements. We investigate several data fusion or time-series analysis strategies which aim to mitigate C-band InSAR restrictions for volcano deformation monitoring applications. The focus is on active African volcanic areas. Firstly, data fusion of C-band ENVISAT/ASAR and L-band ALOS/PALSAR sensors helps the determination of a rifting event sequence that took place in summer 2007 in Lake Natron area. The second strategy investigated is a new Wavelet Based InSAR time series applied on ERS-2 data covering the Nyiragongo-Nyamulagira area. It allows new ground displacements identifications outside the local rift valley. Lastly, PALSAR Quad-Pol POLInSAR applicability is explored for La Palma Island.

Initial point selection and validation in PS-InSAR using integrated amplitude calibration

SAR amplitude calibration is performed prior to the selection of potential Persistent Scatterers (PS) to avoid amplitude variations due to sensor characteristics and viewing geometry. As only the interferometric phases of a small percentage of the radar pixels in an image is used in the PS-InSAR analysis, it is investigated if this time and storage space consuming step can be omitted. We present an integrated method which does not perform amplitude calibration explicitly, but integrates it into the PS point selection procedure for validation purposes by evaluating the hypothesis that a point would have been selected if all images were calibrated beforehand. Its performance assessment is based on coherent phase behavior of the selected potential PS and indicates that empirical calibration validation is an alternative for calibrating full images based on physical sensor parameters. I. INTRODUCTION Persistent Scatterer (PS) (1) InSAR is based on a network of point scatterers with a non-random phase behavior in time. As their interferometric phase contributions due to deformation are obscured by other effects, their identification is based on the amplitude behavior in time. For an unbiased selection of potential PS, a preceding SAR calibration is performed to isolate the amplitude observations corresponding with phys- ical PS properties from amplitude variations due to viewing geometry and sensor characteristics. Since the amount of PS is generally a small percentage of the full image and only their interferometric phase observations are used in the PS- InSAR analysis, the necessity of calibrating full images can be questioned. This study investigates the integration of amplitude calibration in the selection procedure of potential PS as a validation tool to determine if the potential PS would have been selected if the SAR images were calibrated beforehand.

Recursive data processing and data volume minimization for PS-InSAR

PS-InSAR has proven to be an accurate and ef- ficient technique for the joint estimation of topographic and displacement signal from stacked interferometric combinations. In this contribution a new method for PS-Insert processing is introduced, which enables the recursive estimation of parameters of interest. The method is based on the ILSQ PS-InSAR concept and makes use of the estimation vector and corresponding variance-covariance matrix of the initial estimation epoch. The presented methodology systematically adds a new acquisition (or set of acquisitions) to the existing stack, updates the solution of the previous run, and analyzes whether the behaviour of the (pre-) selected points fits the expected one. This contribution focuses on a mathematical framework, rather then on specific applicational problems. Nevertheless, the performed numerical analysis on simulated data sets is analyzed and discussed, which shows that the preset aims of the recursive PS-InSAR estimation technique is achieved. I. INTRODUCTION Time series InSAR analysis using persistent scatterer (PS) techniques aims at the joint estimation of topographic and displacement signal from a number of interferometric com- binations, (1), (2). Since the estimates of both parameters are correlated and error signal due to, e.g., atmospheric signal can significantly affect the adjustment, an accurate estimation depends on the availability of a large data stack, i.e., more than 20-30 images. A smaller number of images usually results in problems like detecting the potential PS, reducing the atmospheric signal, separating topography and displacement, and phase ambiguity estimation. An additional problem for all current multi-image pro- cessing concepts is that the parameter estimation is usually performed in batches, i.e., by using all available acquisitions at once. Hence, in order to incorporate a newly available acquisition into the processing chain, and consequently update the estimates, the whole processing (at least the PS part) has to be performed again. Such an approach consequently leads to an increase of processing time, limits the application to the areas where only a sufficient number of images is available, and reduces the potential application of the method to a semi- real-time deformation monitoring. The two main processing concepts of PS-InSAR are the concept of the ambiguity function, (1), and Integer Least Squares (ILSQ) method, (2). The main drawback of the first one is that the propagation concept of observations to the unknown parameters is suboptimal. Moreover, the method strongly depends on the discretization of the solution space and it treats unknown ambiguities as deterministic parameters instead of stochastic ones. The ILSQ approach is based on the principles of Best Linear Unbiased Estimation (BLUE) - it is based on the minimization of the mean squared error and it is formulated as a constrained minimization problem on the integer nature of the unknowns, (6). By means of the ILSQ method, the quality description of estimated parameters is the one of the end products of the analysis, which can conse- quently be used to determine the significance and reliability of the estimated parameters. The ILSQ PS-InSAR processing framework sets the basis for a recursive data processing strategy, where new acquisi- tions can be easily added to an existing data stack, significantly reducing the computational requirements. This implies that the presented methodology systematically adds a new acquisition to the existing stack, updates the solution of the previous run, and analyzes whether the behaviour of the (pre-)selected points fits to the expected behaviour of parameters of interest. If not, an alternative hypothesis is tested against the prior solution, leading to the rejection of the point, adaptation of the model, or manual intervention. For the conditions on the practical application of recursive PS-InSAR processing, it can be referred to the block-diagonal structure of the variance-covariance matrix of the introduced recursive model (the estimates from the initialization run and phase observations of the additional acquisition are assumed to be uncorrelated). Secondly, the atmospheric and non-modelled displacement contributions to the interferometric phase have to be modelled and incorporated into the variance matrix by means of covariance functions, (4), (5) - in the presented study the covariance functions are not further elaborated on. Moreover, in numerical experiments, phase contributions are isolated by low-pass filtering in the spatial domain and high- pass filtering in the temporal domain. Furtheron, in order to correctly perform the initialization run (candidate selection and unwrapping), a sufficient number of images (15-20) is needed. In the following sections the concept of the recursive PS- InSAR is presented. Examples on simulated data are used

Land subsidence monitoring in city area by time series interferometric SAR data

In this paper, the time series multi-image stack processing technique is implemented based on the ERS-1, ERS-2 SAR data set of cities of Las Vegas in America. A single master approach is used in the stack data processing based on the permanent scatterers processing technique invented by Ferretti et al. (1,2). After the differential phase model establishment and stable points selection, linear subsidence velocity and digital elevation model errors are estimated, non-linear subsidence velocity and atmospheric artifacts related to each SAR acquisition are separated, so a land subsidence history covering all the SAR data acquisitions in each city can be achieved. In our research, more test data in cities of China will be implemented in the next step.

The Sentinel-1 constellation for InSAR applications: Experiences from the InSARAP project

The two-satellite Copernicus Sentinel-1 (S1) constellation became operational in Sep 2016, with the successful in-orbit commissioning of the S1B unit. During, the commissioning phase and early operational phase it has been confirmed that the interferometric performance of the constellation is excellent, with no observed phase anomalies. In this work, we show an analysis of selected performance parameters for the S1 constellation, as well as initial results based on the available data from the first months of operations.