## 7 The Arctic in Global Land Cover Maps  
The Arctic plays an important role for the global climate. Sustained increases in air temperatures are warming the Arctic more than twice as fast compared to low/mid-latitudes [116]. Rapid changes in the Arctic system feed into the global climate. The Arctic therefore needs to be addressed in its entirety rather than simply regionally. The impact of climate warming on permafrost and associated carbon fluxes is one of the issues addressed in current climate (land surface) modeling studies [3]. Land cover is required for benchmarking and initializing Earth system models. Such datasets are also required for large-scale numerical permafrost modeling [1]. A further potential application at this scale is up-scaling of carbon pools in permafrost areas. It has been shown in a range of studies that high resolution data can be used at the regional scale [25]. Land cover classes need to reflect certain land cover types that are associated with a certain Soil Organic Carbon Content (SOCC) range. The thematic detail for this application is missing in global maps to date. An alternative approach that does not require land cover classification has been developed by [PERSON] et al. [76]. SOCC has been directly derived from C-band SAR backscatter. C-band SAR may thus provide complementary information to optical data on soil properties. Organic carbon content has a significant impact on the thermal properties of the soil, information required for permafrost modeling. Global land cover maps are based on AVHRR ([117; 118] IGBP DISCover and UMD maps), MODIS [119; 120], SPOT 4 VEGETATION ([121] GLC2000), ENVISAT MERIS ([122; 123] GlobCover and subsequent CCI maps) and recently also Landsat (e.g., Global30 by the National Geomatics Center of China [124]). Existing land cover and seasonal datasets (from AVHRR) have been used to produce the ECOCLIMAP [125; 126], a dataset developed for climate modeling purposes. The GlobCover and CCI maps are based on bi-monthly, seasonal and annual mosaics [122; 123]; ECOCLIMAP on eight years of NDVI time series (see also Table 2). Classes representing Plant Functional Types (PFTs) are required by Earth system models to define the vegetation characteristics in terms of, e.g., photosynthesis capacity, phenology and roughness [3]. The spatial resolution has been shown to be a clear advantage of GlobCover compared to GLC2000 by [PERSON] et al. [3]. GlobCover tends to have better distributions of tree cover, but may lead to overestimation of forest PFTs. [PERSON] et al. [3] suggested the use of additional information to address this issue. The opposite seems to be the case for the other maps. An assessment of the IGBP DisCover and MODIS land cover map over the West Siberian Lowlands demonstrated that at high latitudes, both underestimate deciduous needle-leaf forest in favor of woody savannas or open shrublands, respectively [127]. The transformation to PFTs by [PERSON] et al. [3] using GlobCover led to an increase of bare soil in vegetated tundra areas in Siberia (except for Taymir) compared to a previous definition for the model ORCHIDEE. The spatial resolution poses a major constraint for the applicability of coarse resolution data, such as AVHRR, MODIS and MERIS, in the Arctic. This is apparent and has been demonstrated for the water class [6; 127]. Insufficient thematic detail for reindeer pasture mapping has also been demonstrated for the GLC2000 by [PERSON] and [PERSON] [110]. An option is the retrieval of the water fraction, which can be obtained by the fusion of datasets of different temporal and spatial resolution (e.g., [128] 5 km, AMSR-E and MODIS). Such approaches do, however, not provide information on lake objects as required for, e.g., thermokarst lake studies. Global land cover maps are mostly produced by variants of unsupervised classification approaches. Table 2 provides details on the input data, method and validation of ECOCLIMAP-, GLC2000- and the MERIS-based land cover maps. They are shown in Figure 2 over the extent of the CAVM.