A global cloud free pixel- based image composite from Sentinel-2 data
Large-scale land cover classification from satellite imagery is still a challenge due to the big volume of data to be processed, to persistent cloud-cover in cloud-prone areas as well as seasonal artefacts that affect spatial homogeneity. Sentinel-2 times series from Copernicus Earth Observation pro...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-08-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340920306314 |
| _version_ | 1828861109826748416 |
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| author | C. Corbane P. Politis P. Kempeneers D. Simonetti P. Soille A. Burger M. Pesaresi F. Sabo V. Syrris T. Kemper |
| author_facet | C. Corbane P. Politis P. Kempeneers D. Simonetti P. Soille A. Burger M. Pesaresi F. Sabo V. Syrris T. Kemper |
| author_sort | C. Corbane |
| collection | DOAJ |
| description | Large-scale land cover classification from satellite imagery is still a challenge due to the big volume of data to be processed, to persistent cloud-cover in cloud-prone areas as well as seasonal artefacts that affect spatial homogeneity. Sentinel-2 times series from Copernicus Earth Observation program offer a great potential for fine scale land cover mapping thanks to high spatial and temporal resolutions, with a decametric resolution and five-day repeat time. However, the selection of best available scenes, their download together with the requirements in terms of storage and computing resources pose restrictions for large-scale land cover mapping. The dataset presented in this paper corresponds to global cloud-free pixel based composite created from the Sentinel-2 data archive (Level L1C) available in Google Earth Engine for the period January 2017- December 2018. The methodology used for generating the image composite is described and the metadata associated with the 10 m resolution dataset is presented. The data with a total volume of 15 TB is stored on the Big Data platform of the Joint Research Centre. It can be downloaded per UTM grid zone, loaded into GIS clients and displayed easily thanks to pre-computed overviews. |
| first_indexed | 2024-12-13T02:54:06Z |
| format | Article |
| id | doaj.art-ab9fce0c38294896a6f4823be3cd0e75 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-13T02:54:06Z |
| publishDate | 2020-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-ab9fce0c38294896a6f4823be3cd0e752022-12-22T00:01:59ZengElsevierData in Brief2352-34092020-08-0131105737A global cloud free pixel- based image composite from Sentinel-2 dataC. Corbane0P. Politis1P. Kempeneers2D. Simonetti3P. Soille4A. Burger5M. Pesaresi6F. Sabo7V. Syrris8T. Kemper9European Commission, Joint Research Centre; Corresponding authorArhs Developments S.A., 4370, Belvaux, LuxembourgEuropean Commission, Joint Research CentreEuropean Commission, Joint Research CentreEuropean Commission, Joint Research CentreEuropean Commission, Joint Research CentreEuropean Commission, Joint Research CentreArhs Developments S.A., 4370, Belvaux, LuxembourgEuropean Commission, Joint Research CentreEuropean Commission, Joint Research CentreLarge-scale land cover classification from satellite imagery is still a challenge due to the big volume of data to be processed, to persistent cloud-cover in cloud-prone areas as well as seasonal artefacts that affect spatial homogeneity. Sentinel-2 times series from Copernicus Earth Observation program offer a great potential for fine scale land cover mapping thanks to high spatial and temporal resolutions, with a decametric resolution and five-day repeat time. However, the selection of best available scenes, their download together with the requirements in terms of storage and computing resources pose restrictions for large-scale land cover mapping. The dataset presented in this paper corresponds to global cloud-free pixel based composite created from the Sentinel-2 data archive (Level L1C) available in Google Earth Engine for the period January 2017- December 2018. The methodology used for generating the image composite is described and the metadata associated with the 10 m resolution dataset is presented. The data with a total volume of 15 TB is stored on the Big Data platform of the Joint Research Centre. It can be downloaded per UTM grid zone, loaded into GIS clients and displayed easily thanks to pre-computed overviews.http://www.sciencedirect.com/science/article/pii/S2352340920306314Pixel based compositeSentinel-2 satelliteland cover classificationlarge area mappingremote sensing |
| spellingShingle | C. Corbane P. Politis P. Kempeneers D. Simonetti P. Soille A. Burger M. Pesaresi F. Sabo V. Syrris T. Kemper A global cloud free pixel- based image composite from Sentinel-2 data Data in Brief Pixel based composite Sentinel-2 satellite land cover classification large area mapping remote sensing |
| title | A global cloud free pixel- based image composite from Sentinel-2 data |
| title_full | A global cloud free pixel- based image composite from Sentinel-2 data |
| title_fullStr | A global cloud free pixel- based image composite from Sentinel-2 data |
| title_full_unstemmed | A global cloud free pixel- based image composite from Sentinel-2 data |
| title_short | A global cloud free pixel- based image composite from Sentinel-2 data |
| title_sort | global cloud free pixel based image composite from sentinel 2 data |
| topic | Pixel based composite Sentinel-2 satellite land cover classification large area mapping remote sensing |
| url | http://www.sciencedirect.com/science/article/pii/S2352340920306314 |
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