Accuracy assessments of the GLOBCOVER dataset using global statistical inventories and FLUXNET site data

The spatio-temporal distribution of land cover provides fundamental data for global climate and environmental change research. In recent decades, five global land cover maps have been produced based on remote sensing data sources and methodologies. Related research have shown that the availability and quality of the first four global land cover datasets are poor at the regional or the continental scale for a variety of reasons. There is still no consensus on the accuracy of the latest global land cover map. Based on comparison of the land cover dataset with the statistical cropland data from FAO and the FLUXNET site data, this paper discusses the accuracy of the fifth global land cover map, namely, the GLOBCOVER dataset, at different spatial scales. At the global scale, the cropland area obtained from the GLOBCOVER dataset is greater than that of the FAO statistical data by 47.06–84.49%, and the land cover types of the GLOBCOVER dataset have a 65.02% consistency with that of the FLUXNET site data. At the continental scale, the difference between cropland areas obtained from the GLOBCOVER dataset and the statistical cropland area vary from ?43.42% to 502.36%; continents that have a more accurate cropland area compared to the FAO statistical data tend to be less consistent with the FLUXNET site data. In general, North America has a higher accuracy and Oceania has a lower accuracy. At the country scale, the accuracy estimates vary sharply over a wide range: between ?100.00% and 190670.37%. It is recommended that future studies should pay careful attention to the data validation step before using the GLOBCOVER dataset for any particular problem. Future studies are also required for the development of a universal land cover classification system and advanced algorithms for remote sensing classification of global land cover maps.