Abstract

The Sentinel-1 satellite system continuously observes European countries at a relatively high revisit frequency of six days per orbital track. Given the Sentinel-1 configuration, most areas in Czechia are observed every 1–2 days by different tracks in a moderate resolution. This is attractive for various types of analyses by various research groups. The starting point for interferometric (InSAR) processing is an original data provided in a Single Look Complex (SLC) level. This work represents advantages of storing data augmented to a specifically corrected level of data, SLC-C. The presented database contains Czech nationwide Sentinel-1 data stored in burst units that have been pre-processed to the state of a consistent well-coregistered dataset of SLC-C. These are resampled SLC data with their phase values reduced by a topographic phase signature, ready for fast interferometric analyses (an interferogram is generated by a complex conjugate between two stored SLC-C files). The data can be used directly into multitemporal interferometry techniques, e.g., Persistent Scatterers (PS) or Small Baseline (SB) techniques applied here. A further development of the nationwide system utilising SLC-C data would lead into a dynamic state where every new pre-processed burst triggers a processing update to detect unexpected changes from InSAR time series and therefore provides a signal for early warning against a potential dangerous displacement, e.g., a landslide, instability of an engineering structure or a formation of a sinkhole. An update of the processing chain would also allow use of cross-polarised Sentinel-1 data, needed for polarimetric analyses. The current system is running at a national supercomputing centre IT4Innovations in interconnection to the Czech Copernicus Collaborative Ground Segment (CESNET), providing fast on-demand InSAR results over Czech territories. A full nationwide PS processing using data over Czechia was performed in 2017, discovering several areas of land deformation. Its downsampled version and basic findings are demonstrated within the article.

Highlights

  • Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) satellite constellation offers radar imagery of the European continent every 12 days since October 2014 and every 6 days since autumn 2016

  • Whereas common High-Performance Computing (HPC) approaches of utilising Sentinel-1 images for InSAR start their processing chain from either original Single Look Complex (SLC) data or raw non-coregistered data [46], the IT4S1 system allowed a faster and more flexible multitemporal processing thanks to availability of pre-processed SLCC analysis ready data (ARD) images, based on ISCE/ISCE2 [7,8]. As these ARD already do not contain most of the phase induced by topography, Earth curvature, etc., there is no need to simulate and remove the topography and orbital ramp components per each generated interferogram, as typically done in other InSAR-oriented systems, e.g., LiCSAR [17]

  • The SLCC ARD proved its quality and effectivity to be used for a typical InSAR processing toward deformation monitoring

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Summary

Introduction

Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) satellite constellation offers radar imagery of the European continent every 12 days since October 2014 and every 6 days since autumn 2016. A motion of a slope can be identified making the techniques and the satellite itself useful for distinguishing between active and non-active landslides [14] These works and findings were used as a proof of a unique applicability of Sentinel-1 InSAR analyses and were the base for establishing this nation-wide Sentinel-1 InSAR monitoring system first as Sentineloshka [15], with an improved version as IT4S1 [16] at the Czech national supercomputing center, IT4Innovations. This article provides an overview on the implemented InSAR processing approaches and presents a nation-wide processing output using PS InSAR method from data covering up to 10/2014-10/2017 It evaluates quality of monitoring by comparison of results over an undermined CSM Mine area (using a dataset updated till 09/2019) to levelling

Goals and Expected Benefits for Society
Computational and Storage Resources
Data Coverage
InSAR Functionality Implemented within IT4S1
Generation of SLC-C Data
Establishing Base Dataset
Coregistration Process
Multitemporal InSAR Processing
Primary Processing by PS InSAR
STAMPS SB and Quasi-SB Processing
LiCSBAS Processing
Visualization of Results
Processing over Czech Bursts
Nationwide Processing Output of Czechia
Small Area On-Demand Processing—CSM Mine Example
Discussion
Computational Load
Current and Future Storage Needs for Czechia
Conclusions
Full Text
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