Towards Sentinel-2 Analysis Ready Data: a Swiss Data Cube Perspective

Earth Observations Data Cubes (EODC) are a new paradigm revolutionizing the way users can interact with Earth Observations (EO) data. They can provide access to large spatio-temporal data in analysis ready format. Systematic and regular provision of Analysis Ready Data (ARD) can significantly reduce the burden on EO data users by minimizing the time and scientific knowledge required to access and prepare remotely-sensed data having consistent and spatially aligned calibrated surface reflectance observations. Currently, Sentinel-2 ARD are not commonly generated by the Copernicus program and consequently getting uniform and consistent Sentinel-2 ARD remains a challenging task. This paper presents an approach to generate Sentinel-2 ARD using an automated processing services chain. The approach has been tested and validated to complete the Swiss Data Cube with Sentinel-2 data.

[1]  Denisa Rodila,et al.  Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD) , 2017 .

[2]  Stefano Nativi,et al.  A view-based model of data-cube to support big earth data systems interoperability , 2017 .

[3]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[4]  Curtis E. Woodcock,et al.  From imagery to ecology: leveraging time series of all available Landsat observations to map and monitor ecosystem state and dynamics , 2016 .

[5]  Denisa Rodila,et al.  Live Monitoring of Earth Surface (LiMES): A framework for monitoring environmental changes from Earth Observations , 2017 .

[6]  Adam Lewis,et al.  Rapid, high-resolution detection of environmental change over continental scales from satellite data – the Earth Observation Data Cube , 2016, Int. J. Digit. Earth.

[7]  David P. Roy,et al.  The global Landsat archive: Status, consolidation, and direction , 2016 .

[8]  Lewis Adam,et al.  The six faces of the data cube , 2017 .

[9]  Ben Evans,et al.  The Australian Geoscience Data Cube - foundations and lessons learned , 2017 .

[10]  Adam Lewis,et al.  Unlocking the Australian Landsat Archive – From dark data to High Performance Data infrastructures , 2015 .

[11]  S. Ullo,et al.  Contribution of Sentinel-2 data for applications in vegetation monitoring , 2016 .

[12]  Peter Baumann,et al.  Big Data Analytics for Earth Sciences: the EarthServer approach , 2016, Int. J. Digit. Earth.

[13]  Chao Yang,et al.  Cloud Computing Enabled Web Processing Service for Earth Observation Data Processing , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[14]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[15]  Michael A. Wulder,et al.  Landsat continuity: Issues and opportunities for land cover monitoring , 2008 .