GRID product detail

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)


TypeReport

Duration: January 1984 - December 2017

Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability. Some of these environmental issues can be monitored using remotely sensed Earth Observations (EO) data that are increasingly available from a number of freely and openly accessible repositories. However, the full information potential of EO data has not been yet realized. They remain still underutilized mainly because of their complexity, increasing volume, and the lack of efficient processing capabilities. EO Data Cubes (DC) are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready form. Systematic and regular provision of Analysis Ready Data (ARD) will significantly reduce the burden on EO data users. Nevertheless, ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging task. This paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services chains. The approach has been tested and validated generating Landsat ARD while building the Swiss Data Cube.

Data credit(s): Gregory Giuliani (UNEP/DEWA/GRID-Geneva), Hy Dao (UNEP/DEWA/GRID-Geneva), Denisa Rodila (Technical University of Cluj - Napoca), Bruno Chatenoux (UNEP/DEWA/GRID-Geneva), Andrea De Bono (UNEP/DEWA/GRID-Geneva), Pascal Peduzzi (UNEP/DEWA/GRID-Geneva), Karin Allenbach (UNEP/DEWA/GRID-Geneva), Jean-Philippe Richard (UNEP/DEWA/GRID-Geneva)

GRID unit: Global Change and Vulnerability

UNEP region: Europe/Western Europe

UNEP priorities:  Climate Change, Ecosystems Management, Resource Efficiency, Environmental Governance

 
english version (2017, Size:2.33Mb)