Development of an Operational Platform for the Provision and Processing of Sentinel Data in Support of GMES Services

OPUS is a cooperation between the German Aerospace Center (DLR) and CloudEO, to test how the advanced analytics developed by DLR can be made available to a larger community, which will be particularly useful to the value-adding industry.

The objective of the research and development project OPUS-GMES is to support activities for the preparation and establishment of a Copernicus Center in Germany. This center shall provide national access to satellite data of the European Sentinel program, provide processing and archiving functions as well as national and European functions to supply users with data, products, and services.

Against this background, key concepts and technologies are being developed and tested in OPUS-GMES that support the creation of a powerful link between the Sentinel data streams of the Copernicus space component and satellite-based geoinformation services. The development work is carried out in close cooperation between science and industry to ensure the implementation of innovative and at the same time demand-oriented and thus viable approaches. The focus here is on the areas of:

  • Data access and distribution

  • Multimission and multidata-capable processing systems

  • Innovative processing modules and chains

  • Certification, standardization and quality assessment of products and services.

The knowledge gained and techniques developed in OPUS-GMES will be directly incorporated into the effective and efficient design and implementation of a national Copernicus center, ultimately making an important contribution to increasing the competitiveness of the service sector and science in Germany.

An example of such an advanced analytic is "TimeScan". Here, spectral indices are derived not only from one single data set, but a detailed statistical analysis is performed over many data sets describing the characteristic of the land cover in a detailed and more meaningful way. For a given spot on Earth, you not only can see a normalized vegetation index but also statistical parameters like mean and standard deviation. The results form an ideal basis for further analysis with new and higher-resolution imagery or other measurements.

For this project, the entire datacubes of Landsat 8 imagery over Africa and Germany from 2013 and 2014 were analyzed. The results are available on demand from the cloudeo marketplace.

cloudeo AG, Dimitris Bellos 24 October, 2016
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