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

Author

Listed:
  • John Truckenbrodt

    (Department for Earth Observation, Friedrich-Schiller-University Jena, 07743 Jena, Germany
    Institute for Data Science, German Aerospace Center DLR, 07745 Jena, Germany)

  • Terri Freemantle

    (Satellite Applications Catapult, Harwell Campus, Didcot OX11 0QR, UK)

  • Chris Williams

    (Satellite Applications Catapult, Harwell Campus, Didcot OX11 0QR, UK)

  • Tom Jones

    (Satellite Applications Catapult, Harwell Campus, Didcot OX11 0QR, UK)

  • David Small

    (Remote Sensing Laboratories, Dept. of Geography, University of Zurich, 8057 Zurich, Switzerland)

  • Clémence Dubois

    (Department for Earth Observation, Friedrich-Schiller-University Jena, 07743 Jena, Germany)

  • Christian Thiel

    (Institute for Data Science, German Aerospace Center DLR, 07745 Jena, Germany)

  • Cristian Rossi

    (Satellite Applications Catapult, Harwell Campus, Didcot OX11 0QR, UK)

  • Asimina Syriou

    (Satellite Applications Catapult, Harwell Campus, Didcot OX11 0QR, UK)

  • Gregory Giuliani

    (Institute for Environmental Sciences, University of Geneva, 1205 Geneva, Switzerland)

Abstract

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.

Suggested Citation

  • John Truckenbrodt & Terri Freemantle & Chris Williams & Tom Jones & David Small & Clémence Dubois & Christian Thiel & Cristian Rossi & Asimina Syriou & Gregory Giuliani, 2019. "Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube," Data, MDPI, vol. 4(3), pages 1-37, July.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:3:p:93-:d:246039
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    References listed on IDEAS

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    1. Michael A. Wulder & Nicholas C. Coops, 2014. "Satellites: Make Earth observations open access," Nature, Nature, vol. 513(7516), pages 30-31, September.
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    Cited by:

    1. Gregory Giuliani & Gilberto Camara & Brian Killough & Stuart Minchin, 2019. "Earth Observation Open Science: Enhancing Reproducible Science Using Data Cubes," Data, MDPI, vol. 4(4), pages 1-6, November.
    2. Gregory Giuliani & Elvire Egger & Julie Italiano & Charlotte Poussin & Jean-Philippe Richard & Bruno Chatenoux, 2020. "Essential Variables for Environmental Monitoring: What Are the Possible Contributions of Earth Observation Data Cubes?," Data, MDPI, vol. 5(4), pages 1-25, October.

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