Abstract

We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h.The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.

Highlights

  • D IFFERENTIAL synthetic aperture radar (SAR) interferometry (DInSAR) is a microwave remote sensing methodology playing nowadays a crucial role in the investigation of earth surface deformation phenomena with centimeter to millimeter level accuracy [1], [2]

  • Processing Chain Performance Assessment Within a cloud computing (CC) Environment. This paragraph is aimed at concisely describing the performance, in terms of elapsed computing times, of the CC implementation within the Amazon Web Services (AWS) environment of the S-1 interferometric wide (IW) Parallel Small BAseline Subset (P-Small BAseline Subset (SBAS)) processing chain presented in this paper

  • It is worth noting that the potentialities of the exploitation of CC environments for the massive processing of S-1 IW data, through the P-SBAS approach, were already thoroughly discussed in [76], where we focused on the capability to perform large scale interferometric analyses exploiting in parallel the AWS resources

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Summary

Introduction

D IFFERENTIAL synthetic aperture radar (SAR) interferometry (DInSAR) is a microwave remote sensing methodology playing nowadays a crucial role in the investigation of earth surface deformation phenomena with centimeter to millimeter level accuracy [1], [2]. The DInSAR technique permits to measure the deformation component along the radar line of sight (LOS) with a very large spatial coverage capability and with accuracy of a fraction of the wavelength relevant to the transmitted microwave signals [3]. This result is achieved by exploiting the phase difference (interferogram) between pairs of complex SAR images [4]–[7], usually referred to as single look complex (SLC). The SBAS approach relies on an appropriate combination of differential interferograms produced by data pairs characterized by small temporal and orbital separation

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