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Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube

Truckenbrodt, John and Freemantle, Terri and Williams, Chris and Jones, Tom and Small, David and Dubois, Clémence and Thiel, Christian and Rossi, Cristian and Syriou, Asimina and Giuliani, Gregory (2019) Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube. Data, 4 (3). Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/data4030093. ISSN 2306-5729.

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Official URL: http://dx.doi.org/10.3390/data4030093


This study aims at assessing the feasibility of automatically producing analysis-ready radiometrically terrain-corrected (RTC) Synthetic Aperture Radar (SAR) gamma nought backscatter data for ingestion into a data cube for use in a large spatio-temporal data environment. As such, this study investigates the analysis readiness of different openly available digital elevation models (DEMs) and the capability of the software solutions SNAP and GAMMA in terms of overall usability as well as backscatter data quality. To achieve this, the study builds on the Python library pyroSAR for providing the workflow implementation test bed and provides a Jupyter notebook for transparency and future reproducibility of performed analyses. Two test sites were selected, over the Alps and Fiji, to be able to assess regional differences and support the establishment of the Swiss and Common Sensing Open Data cubes respectively.

Item URL in elib:https://elib.dlr.de/129404/
Document Type:Article
Title:Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Truckenbrodt, Johnjohn.truckenbrodt (at) dlr.dehttps://orcid.org/0000-0002-7259-101X
Freemantle, TerriTerri.Freemantle (at) sa.catapult.org.ukhttps://orcid.org/0000-0002-3716-5891
Williams, ChrisChris.Williams (at) sa.catapult.org.ukUNSPECIFIED
Jones, TomTom.Jones (at) sa.catapult.org.ukUNSPECIFIED
Small, Daviddavid.small (at) geo.uzh.chhttps://orcid.org/0000-0002-1440-364X
Dubois, Clémenceclemence.dubois (at) uni-jena.deUNSPECIFIED
Thiel, Christianchristian.thiel (at) dlr.deUNSPECIFIED
Rossi, CristianCristian.Rossi (at) sa.catapult.org.ukUNSPECIFIED
Syriou, Asiminaasimina.syriou (at) esa.intUNSPECIFIED
Giuliani, Gregorygregory.giuliani (at) unige.chhttps://orcid.org/0000-0002-1825-8865
Date:5 July 2019
Journal or Publication Title:Data
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3390/data4030093
EditorsEmailEditor's ORCID iD
Giuliani, Gregorygreory.giuliani@unige.chUNSPECIFIED
Camara, Gilbertogcamara@geosec.orgUNSPECIFIED
Killough, BrianBrian.D.Killough@nasa.govUNSPECIFIED
Minchin, StuartStuart.Minchin@ga.gov.auUNSPECIFIED
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:Earth Observation Data Cubes
Keywords:Sentinel-1; SAR; analysis ready data; ARD; interoperability; data cube; Earth observation; pyroSAR
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Citizen Science
Deposited By: Truckenbrodt, John
Deposited On:01 Oct 2019 15:20
Last Modified:14 Oct 2020 04:17

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