Comparative Accuracy Assessment of Global Land Cover Datasets Using Existing Reference Data

Land cover is a key variable to monitor the impact of human and natural processes on the biosphere. As one of the Essential Climate Variables, land cover observations are used for climate models and several other applications. Remote sensing technologies have enabled the generation of several global land cover (GLC) products that are based on different data sources and methods (e.g. legends). Moreover, the reported map accuracies result from varying validation strategies. Such differences make the comparison of the GLC products challenging and create confusion on selecting suitable datasets for different applications.

This study aims to conduct comparative accuracy assessment of GLC datasets (LC-CCI 2005, MODIS 2005, and Globcover 2005) using the Globcover 2005 reference data which can represent the thematic differences of these GLC maps. This GLC reference dataset provides LCCS classifier information for 3 main land cover types for each sample plot. The LCCS classifier information was translated according to the legends of the GLC maps analysed. The preliminary analysis showed some challenges in LCCS classifier translation arising from missing important classifier information, differences in class definition between the legends and absence of class proportion of main land cover types. To overcome these issues, we consolidated the entire reference data (i.e. 3857 samples distributed at global scale). Then the GLC maps and the reference dataset were harmonized into 13 general classes to perform the comparative accuracy assessments.

To help users on selecting suitable GLC dataset(s) for their application, we conducted the map accuracy assessments considering different users’ perspectives: climate modelling, bio-diversity assessments, agriculture monitoring, and map producers. This communication will present the method and the results of this study and provide a set of recommendations to the GLC map producers and users with the aim to facilitate the use of GLC maps.