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

Article Article scientifique Article Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube eng 2019 author Truckenbrodt John author Freemantle Terri author Williams Chris author Jones Tom author Small David author Dubois Clémence author Thiel Christian author Rossi Cristian author Syriou Asimina 857883 author Giuliani Gregory 0000-0002-1825-8865 94488 93 10.3390/data4030093 2306-5729 https://www.mdpi.com/2306-5729/4/3/93 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. Sentinel-1 SAR Analysis ready data ARD Interoperability Data cube Earth observation PyroSAR Dewey 333.7-333.9 18 Institut des sciences de l'environnement 0DB false 673 Envirospace Lab 859 Global Resource Information Database (GRID) 941 ISE Pôle Informatique environnementale 1057 TIGERS 970b71ee-b344-4440-bc52-c9af263975d0 Living Earth Lab Data 4 3 40023eec-050d-4ab8-94b0-797f9d14b9fd Article (Published version) Public data-04-00093.pdf 17174136 application/pdf CC BY openAccess Centres et instituts / Institut des sciences de l'environnement

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Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube	Data			2019	543	246

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