S1 - TOPS InSAR

This contribution will present the investigations carried out with stacked time-series of TerraSAR-X acquisitions made in the experimental 2-looks TOPS mode. The characteristics and benefits of this interferometric mode have been already presented in [1]. One of the advantages of the use of 2-looks TOPS mode images is the ability to estimate the scene motion in a repeat-pass configuration in the azimuth direction over non-stationary areas with a similar accuracy to the one given by the stripmap mode but providing wide coverage.

The focus of this contribution is the application to time-series, where slow azimuthal motion is expected. Several aspects will be analyzed. In first place, the impact of the tropospheric delay on the azimuthal measurements is reviewed. The achievable accuracy employing a time series is presented for the along-track direction and compared to the accuracy achievable in the across-track direction. Moreover the achievable 3D performance when combining ascending and descending acquisitions will be exposed.

A time-series over Mexico City with experimental TerraSAR-X acquistions has been started in March 2016 and we planned to make a validation of the achievable along-track accuracy and present results.

[1] P. Prats-Iraola, N. Yague-Martinez, S. Wollstadt, T. Kraus, R. Scheiber. Demonstration of the Applicability of 2-Look Burst Modes in Non-Stationary Scenarios with TerraSAR-X. EUSAR 2016, Hamburg, Germany.

The Geohazards Exploitation Platform (GEP) [1] is one of the six Thematic Exploitation Platforms (TEP) initiated by ESA to bring together scientists, system developers and data providers onto a single platform in order to ease access to and accelerate use of the massive amounts of satellite data provided by the Sentinel missions. The GEP has a major focus on the use of Sentinel-1 (SAR) data coupled with InSAR techniques for the geohazards thematic application area but considers Sentinel-2 optical/infrared data as well.

In support of the goal of providing geohazard users working in the fields of tectonics, volcanoes, floods and landslides with useful information, the DLR has adapted its Integrated Wide Area Processor (IWAP), an operational multi-mode multi-sensor InSAR/PSI processor, for use on the GEP platform developed by the consortium leader Terradue Srl. The so-called Sentinel-1 InSAR Browse Service produces 6 product layers from IWS mode InSAR pairs with short temporal baseline interferograms of 6-24 days which are publicly viewable on the GEP GeoBrowser [2].

The visualisation and search capabilities of the GeoBrowser are extensive and leverage the Open Geospatial Consortium (OGC®) OpenSearch geo and time extensions [3], allowing one to constrain searches by product layer, spatial or temporal windows, orbit direction or relative number, among others.

The Sentinel-1 InSAR Browse Service provides a 50m high and a 100m medium resolution service. The former is intended for application areas such as volcanoes and landslides which benefit from a high resolution. This service is currently running over 22 active volcanoes worldwide as part of the ESA GSP Disaster Risk Reduction (DDR) Volcano Trial Case and is only visible to the trial case partners. The later medium resolution service is intended for the tectonics theme and automatically produces interferograms over a subset of the global strain rate model based CEOS world seismic mask where earthquakes are most likely to impact society. This service is currently in a ramp-up phase that began in September 2016 covering 20% of the world seismic mask and is planned to reach its peak of 50% by the 2^nd quarter of 2017. The results are publicly visible on the GeoBrowser and accessible by registered users.

Both services run systematically over their respective processing masks and all steps – the InSAR pair search, ingestion of Sentinel-1 IW and auxiliary SAFE products, InSAR processor triggering, InSAR processing and publishing on the GeoBrowser – are fully automated. In addition, each service is manually triggered by a controller when the need arises, for instance, when a major event occurs outside the processing mask.

This contribution will provide an overview of the Sentinel-1 InSAR Browse service including its capabilities, the workflow, ramp-up phase status and results to date including processing examples for the geohazard user case scenarios such as earthquakes and volcanos.

Furthermore, this contribution will provide an update from LPS16 [4] on the investigation into the long-term azimuth coregistration accuracy achievable with TOPS, including suggestions on how this can improved, which is being carried out as part of the GEP project. The coregistration workflow and azimuth coregistration quality assessment proposed in [4] will be presented and their use in GEP including experiences to date will be discussed.

References

[1]    Geohazards TEP: https://geohazards-tep.eo.esa.int.

[2]    GEP GeoBrowser: https://geohazards-tep.eo.esa.int/geobrowser.

[3]    OGC® OpenSearch Geo and Time Extensions, Reference Number: OGC 10-032r8, 2014-04-14, http://www.opengis.net/doc/IS/opensearchgeo/1.0.

[4]    Ramon Brcic and Fernando Rodriguez Gonzalez, “InSAR Stacking of Sentinel-1 IW TOPS Mode Acquisitions,” ESA Living Planet Symposium 2016, Prague.

With an operational Sentinel-1 (S1) constellation, the 6-day revisit is a new reality. Coupled with the large spatial coverage of the IW mode, and the long time horizon of the mission, it is obvious that the S1 spatiotemporal data graph is rapidly growing. Thus, designing an optimal strategy for “coherence mining” in the data graph is not an optional theoretical exercise anymore, but a must-do challenge to resolve.

We dissect the workflows considered conventional by the InSAR community, and investigate opportunities for algorithmical scaling in temporal and spatial domain. Based on this, we design and prototype a new workflow to handle large-scale problems over long time scales. The key objective is to allow for dynamic processing of InSAR data in space and time.

We present the overall design, with special attention to the following topics:

“When to stitch and how to stitch” - stitching the bursts into an overall solution in a scalable way.

Strategies for dynamic processing and updating of results,

Scalable approach on the system level,

“Coherence mining” - optimal selection of interferometric combinations to maximize the coherence while limiting error propagation and computational complexity.

Our solution is deployed in InSAR.no, a public Norwegian nationwide deformation mapping service. To demonstrate the algorithmic scalability of the workflow, we present a number of large-scale examples.

The Sentinel-1 (S1) constellation is a family of satellites designed to collect C-band SAR data in continuity with the previous ERS-1/2 and ENVISAT missions, within the framework of the Copernicus Programme of the European Union, with the aim to detect and analyze Earth’s surface displacements. S1 is characterized by significant enhancements in terms of spatial coverage, revisit time, timeliness and service reliability. In particular, S1 Interferometric Wide Swath (IWS) scenes are collected through the innovative acquisition mode referred to as Terrain Observation by Progressive Scans (TOPS) [1], which allows a considerable improvement of the range coverage (of about 250 km) with respect to the conventional Stripmap mode, and, at the same time, a significant increase of the acquired SAR data size (around 10 times greater than ERS and ENVISAT scenes). Moreover, the constellation is nowadays made up of two twin sensors (Sentinel-1A and Sentinel-1B) that acquire images all around the world with a repeat pass of 6 days in most areas, thus leading to the creation of large SAR data archives, which can be exploited according to a “free and open” data distribution policy.

Such characteristics require the development of innovative and appropriate solutions aimed at handling these huge SAR data archives, more and more increasing in terms of both temporal and spatial coverage, to effectively and routinely exploit the advanced DInSAR methodologies.

In this work we present a strategy to perform massive, systematic and automatic analysis of S1 SAR data via the generation of deformation time series. In particular, we propose a solution, based on the Parallel version of the well-known SBAS algorithm (P-SBAS) [2][3], properly designed for processing S1 SAR data. Our solution takes advantage of the P-SBAS characteristics to run on distributed computing infrastructures (i.e., cluster, grid, cloud) by making use of both multi-core and multi-node programming techniques and exploiting an “ad - hoc” designed distributed storage [4]. Moreover, it strongly takes into account the data characteristics of the TOPS mode. Indeed, IWS scenes consist of series of bursts that can be considered as independent, separate acquisitions. This makes a large part of the processing inherently parallel at a burst granularity level; such a condition implies that the processing time can be significantly reduced when large computing resources are available.

The developed S1 P-SBAS processing chain is exploited to generate mean deformation velocity maps and corresponding deformation time series of South Italy. In particular, we processed six S1 interferometric SAR data stacks, acquired along descending orbits (tracks 22, 124, 51) and spanning the time interval October 2014 – September 2016. Starting from these data (almost 300 slices), we generated 850 differential interferograms with 5 looks in azimuth and 20 in range directions, thus resulting in a pixel size of approximately 60 x 60 m. In Figure 1 we display the retrieved LoS mean deformation velocity map, geocoded and superimposed on an optical image of the area. The map reveals the presence of localized displacements associated, for instance, to the volcanic activity of the caldera of Campi Flegrei and Mt. Etna volcano.

The achieved results demonstrate the capability of the presented processing chain to effectively deal with massive amount of data to generate advanced DInSAR products aimed at detecting displacements at very large spatial scale. Moreover, they show that S1 P-SBAS can be exploited to build up operational services for the easy and rapid generation of advanced interferometric products that can be very useful within risk management and natural hazard monitoring scenarios.

Acknowledgments

This work has been supported by the Ministry of Economic Development - DGS-UNMIG (Directorate-General for Safety of Mining and Energy Activities - National Mining Office for Hydrocarbons and Georesources), the Italian Department of Civil Protection, the European Union Horizon 2020 research and innovation programme under the EPOS-IP project (grant agreement No 676564), the ESA GEP (Geohazards Exploitation Platform) and I-AMICA (Infrastructure of High Technology for Environmental and Climate Monitoring - PONa3_00363) projects. Sentinel-1 data are copyright of Copernicus (2016). The DEMs of the investigated zone were acquired through the SRTM archive.

References

[1]       F. De Zan and A. M. Monti Guarnieri, “TOPSAR: Terrain Observation by Progressive Scans,” IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 9, pp. 2352-2360, Sept. 2006.

[2]       Berardino, P., Fornaro, G., Lanari, R., Sansosti, E., “A new Algorithm for Surface Deformation Monitoring based on Small Baseline Differential SAR Interferograms”, IEEE Trans. Geo. Rem. Sens., 40, 11, pp. 2375-2383, 2002.

[3]       F. Casu, S. Elefante, P. Imperatore, I. Zinno, M. Manunta, C. D. Luca, and R. Lanari, “SBAS-DInSAR Parallel Processing for Deformation Time-Series Computation,” Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, 2014.

[4]       Zinno, S. Elefante, L. Mossucca, C. De Luca, M. Manunta, O. Terzo, R. Lanari, and F. Casu, “A First Assessment of the P-SBAS DInSAR Algorithm Performances Within a Cloud Computing Environment,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., pp. 1–12, 2015.

S1 - TOPS InSAR