Articles | Volume 13, issue 6
Earth Syst. Sci. Data, 13, 2753–2776, 2021
https://doi.org/10.5194/essd-13-2753-2021
Earth Syst. Sci. Data, 13, 2753–2776, 2021
https://doi.org/10.5194/essd-13-2753-2021

Data description paper 15 Jun 2021

Data description paper | 15 Jun 2021

GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery

Xiao Zhang et al.

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Cited articles

Azzari, G. and Lobell, D. B.: Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring, Remote Sens. Environ., 202, 64–74, https://doi.org/10.1016/j.rse.2017.05.025, 2017. 
Ban, Y., Gong, P., and Giri, C.: Global land cover mapping using Earth observation satellite data: Recent progresses and challenges, ISPRS J. Photogramm., 103, 1–6, https://doi.org/10.1016/j.isprsjprs.2015.01.001, 2015. 
Belgiu, M. and Drăguţ, L.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016. 
Bontemps, S., Defourny, P., Bogaert, E. V., Arino, O., Kalogirou, V., and Perez, J. R.: GLOBCOVER 2009 Products Description and Validation Report, available at: http://due.esrin.esa.int/files/GLOBCOVER2009_Validation_Report_2.2.pdf (last access: 15 August 2020), 2010. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001. 
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Short summary
Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m landcover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib on the Google Earth Engine computing platform.