Burger
Journalpaper

Comparison of ESA climate change initiative land cover to CORINE land cover over Eastern Europe and the Baltic States from a regional climate modeling perspective

Abstract

High-quality land use and land cover (LULC) information is of crucial importance for the performance of regional climate models (RCMs), in particular at high spatial resolutions down to convection permitting scales below 4 km. Several satellite-based high-resolution products are currently available for implementation into RCMs. One of the most recent products is the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) dataset. While the ESA CCI LC has been assessed globally, an evaluation against regional, independent LULC datasets is necessary to identify LULC inaccuracies in the respective region of interest and to give regional climate modelers estimates for the uncertainty in the land use forcing. In the present work the ESA CCI LC dataset is compared to the COoRdination and INformation on the Environment (CORINE) Land Cover (CLC). Agreement between the datasets is assessed by proportional area comparison (PAC). The resulting agreement measures are compared to the results of a majority approach (MA) to explore possible differences between the methods. Three timesteps of ESA CCI LC matching the timesteps of CLC are assessed to take a change in agreement over time into account. In addition to the quantification of agreement, spatial patterns of possible issues with ESA CCI LC are identified through utilization of geospatial information systems (GIS). Using the PAC, the agreement of ESA CCI LC with CLC is found to be ∼76 % for the research area (RA). Although the agreement decreases slightly using the PAC, no substantial differences in agreement measures were found compared to the results of the MA. Dominant LULC categories agriculture and forest show an agreement of over 80 % with CLC. A few major issues were found for grassland, wetlands, settlements, and water bodies in the RA of which some might influence RCM performance if the dataset is implemented without adjustment. We highly recommend to apply the PAC to other regions in Europe and further globally to investigate if the found issues are also found elsewhere. The use of more independent regional and specified datasets for validation but also for possible improvement of the ESA CCI LC dataset is suggested.
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