Next Article in Journal / Special Issue
Displacements Monitoring over Czechia by IT4S1 System for Automatised Interferometric Measurements Using Sentinel-1 Data
Previous Article in Journal
Analysis of the Contribution Rate of the Influencing Factors to Land Subsidence in the Eastern Beijing Plain, China Based on Extremely Randomized Trees (ERT) Method
Previous Article in Special Issue
LiCSAR: An Automatic InSAR Tool for Measuring and Monitoring Tectonic and Volcanic Activity
Article

Automatic Generation of Sentinel-1 Continental Scale DInSAR Deformation Time Series through an Extended P-SBAS Processing Pipeline in a Cloud Computing Environment

Institute for Electromagnetic Sensing of the Environment, National Research Council (IREA-CNR), via Diocleziano 328, 80124 Naples, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(18), 2961; https://doi.org/10.3390/rs12182961
Received: 27 July 2020 / Revised: 7 September 2020 / Accepted: 9 September 2020 / Published: 11 September 2020
(This article belongs to the Special Issue Scaling-Up Deformation Monitoring and Analysis)
We present in this work an advanced processing pipeline for continental scale differential synthetic aperture radar (DInSAR) deformation time series generation, which is based on the parallel small baseline subset (P-SBAS) approach and on the joint exploitation of Sentinel-1 (S-1) interferometric wide swath (IWS) SAR data, continuous global navigation satellite system (GNSS) position time-series, and cloud computing (CC) resources. We first briefly describe the basic rationale of the adopted P-SBAS processing approach, tailored to deal with S-1 IWS SAR data and to be implemented in a CC environment, highlighting the innovative solutions that have been introduced in the processing chain we present. They mainly consist in a series of procedures that properly exploit the available GNSS time series with the aim of identifying and filtering out possible residual atmospheric artifacts that may affect the DInSAR measurements. Moreover, significant efforts have been carried out to improve the P-SBAS processing pipeline automation and robustness, which represent crucial issues for interferometric continental scale analysis. Then, a massive experimental analysis is presented. In this case, we exploit: (i) the whole archive of S-1 IWS SAR images acquired over a large portion of Europe, from descending orbits, (ii) the continuous GNSS position time series provided by the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL) available for the investigated area, and (iii) the ONDA platform, one of the Copernicus Data and Information Access Services (DIAS). The achieved results demonstrate the capability of the proposed solution to successfully retrieve the DInSAR time series relevant to such a huge area, opening new scenarios for the analysis and interpretation of these ground deformation measurements. View Full-Text
Keywords: Sentinel-1; DInSAR; P-SBAS; deformation time series; GNSS; DIAS Sentinel-1; DInSAR; P-SBAS; deformation time series; GNSS; DIAS
Show Figures

Graphical abstract

MDPI and ACS Style

Lanari, R.; Bonano, M.; Casu, F.; Luca, C.D.; Manunta, M.; Manzo, M.; Onorato, G.; Zinno, I. Automatic Generation of Sentinel-1 Continental Scale DInSAR Deformation Time Series through an Extended P-SBAS Processing Pipeline in a Cloud Computing Environment. Remote Sens. 2020, 12, 2961. https://doi.org/10.3390/rs12182961

AMA Style

Lanari R, Bonano M, Casu F, Luca CD, Manunta M, Manzo M, Onorato G, Zinno I. Automatic Generation of Sentinel-1 Continental Scale DInSAR Deformation Time Series through an Extended P-SBAS Processing Pipeline in a Cloud Computing Environment. Remote Sensing. 2020; 12(18):2961. https://doi.org/10.3390/rs12182961

Chicago/Turabian Style

Lanari, Riccardo, Manuela Bonano, Francesco Casu, Claudio D. Luca, Michele Manunta, Mariarosaria Manzo, Giovanni Onorato, and Ivana Zinno. 2020. "Automatic Generation of Sentinel-1 Continental Scale DInSAR Deformation Time Series through an Extended P-SBAS Processing Pipeline in a Cloud Computing Environment" Remote Sensing 12, no. 18: 2961. https://doi.org/10.3390/rs12182961

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop