The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment

Differential synthetic aperture radar interferometry (DInSAR) has been widely applied since the pioneering space-borne experiment in 1989, and subsequently with the launch of the ERS-1 program in 1992. The DInSAR technique is well assessed in the case of space-borne SAR data, whereas in the case of data acquired from aerial platforms, such as airplanes, helicopters, and drones, the effective application of this technique is still a challenging task, mainly due to the limited accuracy of the information provided by the navigation systems mounted onboard the platforms. The first airborne DInSAR results for measuring ground displacement appeared in 2003 using L-and X-bands. DInSAR displacement results with long correlation time in P-band were published in 2011. This letter presents a SAR system and, to the best of our knowledge, the first accuracy assessment of the DInSAR technique using a drone-borne SAR in L-band. A deformation map is shown, and the accuracy and resolution of the methodology are presented and discussed. In particular, we have obtained an accuracy better than 1 cm for the measurement of the observed ground displacement. It is in the same order as that achieved with space-borne systems in C-and X-bands and the airborne systems in X-band. However, compared to these systems, we use here a much longer wavelength. Moreover, compared to the satellite experiments available in the literature and aimed at assessing the accuracy of the DInSAR technique, we use only two flight tracks with low time decorrelation effects and not a big data stack, which helps in reducing the atmospheric effects.

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“…C-and X-band satellite-borne systems reported in [14][15][16] and the airborne system published in [10] have similar accuracy.…”

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confidence: 69%

Drone-borne Differential SAR Interferometry

Luebeck

Wimmer²,

Moreira

et al. 2020

Remote Sensing

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“…In particular, we focus on the DInSAR time-series relevant to the acquisitions of the Sentinel-1 constellation, collected during the 2015-2018 time interval from descending orbits. Note that this dataset has been generated through the SBAS-DInSAR processing approach and is composed by 17 frames [77,78]. They have a maximum spatial extension of 56,000 km 2 and are relevant to a temporal sequence of Sentinel-1 images ranging from a minimum number of 152 to a maximum one of 181.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, where both the acquisition of the S1-A and S1-B satellite are available (the entire Europe, for instance) the revisit time is 6 days, thus allowing us to follow the temporal evolution of significantly rapid phenomena. Accordingly, innovative and appropriate solutions are needed to effectively and routinely exploit the huge amount of surface deformation measurements produced by these new generation SAR sensors through the available advanced DInSAR methodologies [77,78].…”

Section: Dinsar Techniquesmentioning

confidence: 99%

A GeoNode-Based Platform for an Effective Exploitation of Advanced DInSAR Measurements

et al. 2019

Self Cite

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This work presents the development of an efficient tool for managing, visualizing, analysing, and integrating with other data sources, the deformation time-series obtained by applying the advanced differential interferometric synthetic aperture radar (DInSAR) techniques. To implement such a tool we extend the functionalities of GeoNode, which is a web-based platform providing an open source framework based on the Open Geospatial Consortium (OGC) standards, that allows development of Geospatial Information Systems (GIS) and Spatial Data Infrastructures (SDI). In particular, our efforts have been dedicated to enable the GeoNode platform to effectively analyze and visualize the spatio/temporal characteristics of the DInSAR deformation time-series and their related products. Moreover, the implemented multi-thread based new functionalities allow us to efficiently upload and update large data volumes of the available DInSAR results into a dedicated geodatabase. The examples we present, based on Sentinel-1 DInSAR results relevant to Italy, demonstrate the effectiveness of the extended version of the GeoNode platform.which properly combine the information available from a set of multi-temporal SAR acquisitions relevant to an area of interest, in order to compute the deformation time-series [26][27][28][29][30][31][32][33][34].Currently, the DInSAR scenario is characterized by a huge availability of SAR data acquired during the last 25 years, comprising the long-term C-band European Space Agency (ESA) archives (e.g., ERS-1, ERS-2, and ENVISAT), the RADARSAT-1 and RADARSAT-2 C-band data sequences, those provided by the L-band ALOS and ALOS-2 sensors and by the X-band generation of SAR sensors, such as the COSMO-SkyMed (CSK) and TerraSAR-X constellations. Moreover, a massive and ever increasing data flow is currently supplied by the C-band Sentinel-1 (S1) constellation of the European Copernicus program [35,36] that is composed of twin SAR satellites, Sentinel-1A and Sentinel-1B, which were launched on April 2014 and April 2016, respectively, and are specifically oriented to DInSAR applications, for the imaging of land surfaces [36].This Big Data DInSAR scenario needs the development of advanced methodologies and techniques to manage, visualize and analyze these data, and to integrate them with other sources. In this context the Spatial Data Infrastructures (SDI) may play a key role because they implement a framework of geographic data, metadata, users and tools that are interactively connected [37]. Moreover, a SDI is relevant because it represents a collection of technologies, policies, standards, human resources, and related activities permitting the acquisition, processing, distribution, use, maintenance, and preservation of spatial data [38]. We remark that in the SDI framework the technologies used have significantly changed over time and will certainly continue to evolve, thus implying a significant effort to integrate distributed data among the geospatial data producers, to fully benefit from new technologies. The O...

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“…The land cover is heterogeneous to observe the behavior of different DS types. The area is investigated by different studies, such as [18], [19], which potentially allows independent performance comparison.…”

Section: Analysis Of Biasesmentioning

confidence: 99%

Study of Systematic Bias in Measuring Surface Deformation with SAR Interferometry

Ansari¹,

Zan²,

Parizzi³

2020

Preprint

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<div>This paper investigates the presence of a new interferometric signal in multilooked Synthetic Aperture Radar (SAR) interferograms which cannot be attributed to atmospheric or earth surface topography changes. The observed signal is short-lived and decays with temporal baseline; however, it is distinct from the stochastic noise usually attributed to temporal decorrelation. The presence of such fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. <br></div><div>The contribution of the mentioned phase component is quantitatively assessed. For short temporal baseline interferograms, we quantify the phase contribution to be in the regime of 5 rad at C-band. The biasing impact on deformation signal retrieval is further evaluated. As an example, exploiting a subset of short temporal baseline interferograms which connects each acquisition with the successive 5 in the time series, a significant bias of -6.5 mm/yr is observed in the estimation of deformation velocity from a four-year Sentinel-1 data stack. A practical solution for mitigation of this physical fading signal is further discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease to -0.24 mm/yr for the Sentinel-1 time series.</div>Based on these analyses, we put forward our recommendations for efficient and accurate deformation signal retrieval from large stacks of multilooked interferograms.

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The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment

Cited by 148 publications

References 85 publications

Drone-borne Differential SAR Interferometry

Drone-borne Differential SAR Interferometry

A GeoNode-Based Platform for an Effective Exploitation of Advanced DInSAR Measurements

Study of Systematic Bias in Measuring Surface Deformation with SAR Interferometry

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