Uncertainty in land cover datasets for global land-surface models derived from 1-km global land cover datasets

The influence of the uncertainties or differences in 1-km global land cover datasets on a land cover dataset used in land-surface modelling is explored. The uncertainties in six 1-km global land cover datasets were found to be transferred to land cover datasets derived by either the dominant land cover type method (DLM) or the area ratio method (ARM). The agreement among the DLM-derived land cover datasets (the DLM agreement) was higher than the per-pixel agreement among the six 1-km global land cover datasets owing to the spatial aggregation effect. The agreement among the ARM-derived land cover datasets using the ARM (the ARM agreement) was higher than the DLM agreement because of the area ratio retention effect. The area ratios of all land cover types affect the ARM agreement, whereas only the dominant land cover type affects the DLM agreement. The DLM and ARM agreements were both strongly correlated with the per-pixel agreement among the 1-km global land cover datasets. Therefore, reducing the uncertainty in the 1-km global land cover datasets is the key to reducing the uncertainty in the land cover datasets used in land-surface models. Improving the land cover classification, especially in areas with small homogeneous regions or in transition zones between major land cover types, is also important for reducing the uncertainty in the datasets used for land-surface models. These sources of uncertainty should be taken into account when interpreting the land-surface model results. Copyright (C) 2011 John Wiley & Sons, Ltd.