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Many investigators use global land cover (GLC) maps for different purposes, such as an input for global climate models. The currentGLC maps used for such purposes are based on different remote sensing data, methodologies and legends. Consequently,comparison of GLC maps is difficult and information about their relative utility is limited. The objective of this study is to analyseand compare the thematic accuracies of GLC maps (i.e., IGBP-DISCover, UMD, MODIS, GLC2000 and SYNMAP) at 1 kmresolutions by (a) re-analysing the GLC2000 reference dataset, (b) applying a generalized GLC legend and (c) comparing theirthematic accuracies at different homogeneity levels. The accuracy assessment was based on the GLC2000 reference dataset with1253 samples that were visually interpreted. The legends of the GLC maps and the reference datasets were harmonized into 11general land cover classes. There results show that the map accuracy estimates vary up to 10-16% depending on the homogeneity ofthe reference point (HRP) for all the GLC maps. An increase of the HRP resulted in higher overall accuracies but reduced accuracyconfidence for the GLC maps due to less number of accountable samples. The overall accuracy of the SYNMAP was the highest atany HRP level followed by the GLC2000. The overall accuracies of the maps also varied by up to 10% depending on the definitionof agreement between the reference and map categories in heterogeneous landscape. A careful consideration of heterogeneouslandscape is therefore recommended for future accuracy assessments of land cover maps.* Corresponding author1. INTRODUCTIONThe consistent and continuous observation of land cover is oneof the most important foundations for understanding the Earth’senvironment and ecosystems (Verburg et al., 2011). Currently,several global land cover datasets (GLC) have been developedand these datasets are evolving towards higher spatial resolution(Gong et al., 2013; Mora et al., 2014) . Most GLC maps weredeveloped by individual groups as one-time efforts and thesubsequent mapping standards reflect the varied interests,requirements and methodologies of the originating programs(Herold et al., 2006). These differences of GLC maps and theeffects of their quality on the model outcome are not alwaysconsidered when selecting a map as an input for specificmodeling applications (Verburg et al., 2011). Uncertainties ofGLC maps can result in considerable differences in modelingoutcomes (Hibbard et al., 2010; Nakaegawa, 2011; Verburg etal., 2011).The accuracies of GLC maps are assessed using independentvalidation datasets and regional maps or cross validated againsttraining datasets. The results of accuracy assessments ofprevious maps indicate that overall area-weighted accuracy isaround 70% for the existing GLC maps (Defourny et al., 2012).However, the use of different approaches in the GLC mapproduction (e.g., classification scheme, data sources andalgorithms) as well as in validation data collection (e.g.,sampling scheme, data source and method of referenceclassification) raise inconsistency issues and make mapcomparisons difficult. Several comparative analyses of landcover maps were conducted at regional levels