The Sentinel-1 Global Backscatter Model (S1GBM) - Polar Extension

This dataset was generated by the Remote Sensing Group of the TU Wien Department of Geodesy and Geoinformation (https://mrs.geo.tuwien.ac.at/), within a dedicated project by the European Space Agency (ESA). Rights are reserved with ESA. Open use is granted under the CC BY 4.0 license.

With the recently published Sentinel-1 Global Backscatter Model (S1GBM) Version 1.0, we provide a new perspective on Earth's land surface through normalised microwave backscatter maps from Sentinel-1's Synthetic Aperture Radar (SAR) observations. 

This first extension of the S1GBM, V1.1, providing an additional set of normalised mosaics covering the northern and southern polar zones and sea ice regions. V1.1 ingests Medium-resolution data (GRDM) from Sentinel 1's Extra Wide (EW) swath mode in HV- and HH-polarisation, at a pixel sampling of 40m. To reflect cold and warm conditions in the high latitudes, and in particular to capture the varying snow pack extents along Greenland's coastline, data collections are set to the months January and July of the period 2016-17, respectively. Processing, normalisation- and mosaicking methods, and publication terms follow with minor adaptions the existing V1.0 dataset publication.

We invite developers from the broader user community to exploit this novel data resource and to integrate S1GBM parameters in models for various variables of land cover, soil composition, or vegetation structure.

Please be referred to our peer-reviewed article at Nature Scientific Data for details, generation methods, and an in-depth dataset analysis. In this publication, we demonstrate – as an example of the S1GBM's potential use – the mapping of permanent water bodies and evaluate the results against the Global Surface Water (GSW) benchmark.

Dataset Record

The HH and HV mosaics are sampled at 40 m pixel spacing, georeferenced to the Equi7Grid and divided into six continental zones (Antarctica, Asia, Europe, North America, Oceania, South America), which are further divided into square tiles of 300 km extent ("T3"-tiles). With this setup, the S1GBM consists of about 2000 tiles over six continents, for HH and HV each, totaling to a compressed data volume of about 140 TB.

Please not that the collections for July over Antarctica suffer from a small number of tile-gaps.

The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given.

In this repository, we provide each mosaic as tiles that are organised in a folder structure per continent. With this, 24 zipped dataset-collections per continent per January and July are available for download.

Web-Based Data Viewer

In addition to this data provision here, there is a web-based data viewer set up at the facilities of the Earth Observation Data Centre (EODC) under http://s1map.eodc.eu/. It offers an intuitive pan-and-zoom exploration of the full S1GBM VV and VH mosaics. It has been designed to quickly browse the S1GBM, providing an easy and direct visual impression of the mosaics.

Code Availability

We encourage users to use the open-source Python package yeoda, a datacube storage access layer that offers functions to read, write, search, filter, split and load data from the S1GBM datacube. The yeoda package is openly accessible on GitHub at https://github.com/TUW-GEO/yeoda.

Furthermore, for the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629.

Acknowledgements

This study was partly funded by the project "Development of a Global Sentinel-1 Land Surface Backscatter Model", ESA Contract No. 4000122681/17/NL/MP for the European Union Copernicus Programme. The computational results presented have been achieved using the Vienna Scientific Cluster (VSC). We further would like to thank our colleagues at TU Wien and EODC for supporting us on technical tasks to cope with such a large and complex data set. Last but not least, we appreciate the kind assistance and swift support of the colleagues from the TU Wien Center for Research Data Management.