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A global cloud free pixel- based image composite from Sentinel-2 data.

C Corbane1, P Politis2, P Kempeneers1

  • 1European Commission, Joint Research Centre.

Data in Brief
|June 4, 2020
PubMed
Summary
This summary is machine-generated.

This study presents a global, cloud-free Sentinel-2 satellite imagery composite dataset for land cover mapping. The 15 TB dataset, available via Google Earth Engine, overcomes challenges in processing large volumes of remote sensing data.

Keywords:
Pixel based compositeSentinel-2 satelliteland cover classificationlarge area mappingremote sensing

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Area of Science:

  • Earth Observation
  • Remote Sensing
  • Geospatial Science

Background:

  • Large-scale land cover classification is hindered by data volume, cloud cover, and seasonal variations.
  • Sentinel-2 satellite data offers high spatial and temporal resolution for detailed land cover mapping.
  • Processing Sentinel-2 data presents challenges in scene selection, download, storage, and computational resources.

Purpose of the Study:

  • To create a global, cloud-free Sentinel-2 pixel-based composite dataset.
  • To facilitate large-scale land cover classification by addressing data accessibility and processing limitations.
  • To provide a valuable resource for researchers and practitioners in Earth observation.

Main Methods:

  • Utilized Sentinel-2 Level L1C data from the Copernicus program.
  • Leveraged Google Earth Engine for processing the extensive satellite data archive.
  • Developed a methodology for generating a global cloud-free composite for January 2017 - December 2018.

Main Results:

  • A 15 TB global composite dataset of cloud-free Sentinel-2 imagery was generated.
  • The dataset features decametric resolution (10 m) and is stored on a Big Data platform.
  • Metadata and download options per UTM grid zone are provided for easy integration into GIS clients.

Conclusions:

  • The presented dataset significantly reduces barriers to large-scale land cover classification using Sentinel-2 data.
  • This resource enables more efficient and accurate mapping of global land cover.
  • The availability of pre-computed overviews enhances usability for diverse applications.