Uncertainty in land cover datasets for global land-surface models derived from 1-km global land cover datasets (pages 2703–2714)

摘要

The influence of the uncertainties or differences in 1-km global land cover datasets on a land cover dataset used in land-surfacenmodelling is explored. The uncertainties in six 1-km global land cover datasets were found to be transferred to land coverndatasets derived by either the dominant land cover type method (DLM) or the area ratio method (ARM). The agreementnamong the DLM-derived land cover datasets (the DLM agreement) was higher than the per-pixel agreement among the sixn1-km global land cover datasets owing to the spatial aggregation effect. The agreement among the ARM-derived land coverndatasets using the ARM (the ARM agreement) was higher than the DLM agreement because of the area ratio retention effect.nThe area ratios of all land cover types affect the ARM agreement, whereas only the dominant land cover type affects thenDLM agreement. The DLM and ARM agreements were both strongly correlated with the per-pixel agreement among the 1-kmnglobal land cover datasets. Therefore, reducing the uncertainty in the 1-km global land cover datasets is the key to reducingnthe uncertainty in the land cover datasets used in land-surface models. Improving the land cover classification, especially innareas with small homogeneous regions or in transition zones between major land cover types, is also important for reducingnthe uncertainty in the datasets used for land-surface models. These sources of uncertainty should be taken into account whenninterpreting the land-surface model results. Copyright  2011 John Wiley & Sons, Ltd.