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      The normalised Sentinel-1 Global Backscatter Model, mapping Earth’s land surface with C-band microwaves

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          Abstract

          We present a new perspective on Earth’s land surface, providing a normalised microwave backscatter map from spaceborne Synthetic Aperture Radar (SAR) observations. The Sentinel-1 Global Backscatter Model (S1GBM) describes Earth for the period 2016–17 by the mean C-band radar cross section in VV- and VH-polarisation at a 10 m sampling. We processed 0.5 million Sentinel-1 scenes totalling 1.1 PB and performed semi-automatic quality curation and backscatter harmonisation related to orbit geometry effects. The overall mosaic quality excels (the few) existing datasets, with minimised imprinting from orbit discontinuities and successful angle normalisation in large parts of the world. Regions covered by only one or two Sentinel-1 orbits remain challenging, owing to insufficient angular variation and not yet perfect sub-swath thermal noise correction. Supporting the design and verification of upcoming radar sensors, the obtained S1GBM data potentially also serve land cover classification and determination of vegetation and soil states. Here, we demonstrate, as an example of its potential use, the mapping of permanent water bodies and evaluate against the Global Surface Water benchmark.

          Abstract

          Measurement(s) C-band radar backscatter • extent of permanent water bodies
          Technology Type(s) Synthetic Aperture Radar (SAR) • Synthetic Aperture Radar (SAR) normalised mosaic
          Factor Type(s) radar backscatter
          Sample Characteristic - Environment land
          Sample Characteristic - Location global

          Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16432983

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          Array programming with NumPy

          Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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            The FAIR Guiding Principles for scientific data management and stewardship

            There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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              Google Earth Engine: Planetary-scale geospatial analysis for everyone

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                Author and article information

                Contributors
                bbm@geo.tuwien.ac.at
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                28 October 2021
                28 October 2021
                2021
                : 8
                : 277
                Affiliations
                [1 ]GRID grid.5329.d, ISNI 0000 0001 2348 4034, Technische Universität Wien, Department of Geodesy and Geoinformation, ; 1040 Vienna, Austria
                [2 ]Earth Observation Data Centre for Water Resources Monitoring (EODC), 1030 Vienna, Austria
                [3 ]Spire Global, Space Program, 2763 Sainte-Zithe, Luxembourg
                [4 ]GRID grid.424669.b, ISNI 0000 0004 1797 969X, European Space Agency, European Space Research and Technology Centre, ; 2201 AZ Noordwijk, The Netherlands
                [5 ]Airbus Defense and Space, 2333 CS Leiden, The Netherlands
                [6 ]GRID grid.424669.b, ISNI 0000 0004 1797 969X, European Space Agency, ; 2200 AG Noordwijk, The Netherlands
                Author information
                http://orcid.org/0000-0001-7356-7516
                http://orcid.org/0000-0001-7704-6857
                Article
                1059
                10.1038/s41597-021-01059-7
                8553891
                34711850
                27874991-9ead-4333-a30a-88b7452ba695
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.

                History
                : 18 March 2021
                : 9 September 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000844, European Space Agency (ESA);
                Award ID: 4000122681/17/NL/MP
                Award Recipient :
                Categories
                Data Descriptor
                Custom metadata
                © The Author(s) 2021

                aerospace engineering,environmental sciences,hydrology,biogeography,geomorphology

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