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

Autor:	Corbane C; European Commission, Joint Research Centre., Politis P; Arhs Developments S.A., 4370, Belvaux, Luxembourg., Kempeneers P; European Commission, Joint Research Centre., Simonetti D; European Commission, Joint Research Centre., Soille P; European Commission, Joint Research Centre., Burger A; European Commission, Joint Research Centre., Pesaresi M; European Commission, Joint Research Centre., Sabo F; Arhs Developments S.A., 4370, Belvaux, Luxembourg., Syrris V; European Commission, Joint Research Centre., Kemper T; European Commission, Joint Research Centre.
Jazyk:	angličtina
Zdroj:	Data in brief [Data Brief] 2020 May 21; Vol. 31, pp. 105737. Date of Electronic Publication: 2020 May 21 (Print Publication: 2020).
DOI:	10.1016/j.dib.2020.105737
Abstrakt:	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. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2020 The Author(s).)
Databáze:	MEDLINE
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