Copernicus Global Land Cover Layers—Collection 2

Published on
March 24, 2020

An article of Marcel Buchhorn, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Martin Herold, Luc Bertels and Bruno Smets: Copernicus Global Land Cover Layers—Collection 2, has been published in Remote Sensing, 2020, 12(6), 1044.


In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel. This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme. Overall, 20 layers are provided which allow customization of land cover maps to specific user needs or applications (e.g., forest monitoring, crop monitoring, biodiversity and conservation, climate modeling, etc.). However, Collection 2 was not just a global up-scaling, but also includes major improvements in the map quality, reaching around 80% or more overall accuracy. The processing system went into operational status allowing annual updates on a global scale with an additional implemented training and validation data collection system. In this paper, we provide an overview of the major changes in the production of the land cover maps, that have led to this increased accuracy, including aligning with the Sentinel 2 satellite system in the grid and coordinate system, improving the metric extraction, adding better auxiliary data, improving the biome delineations, as well as enhancing the expert rules. An independent validation exercise confirmed the improved classification results. In addition to the methodological improvements, this paper also provides an overview of where the different resources can be found, including access channels to the product layer as well as the detailed peer-review product documentation. View Full-Text

Keywords: Copernicus; land use/cover classification; cover fractions; remote sensing; global land cover mapping; random forest; time series analysis