## 2 Development of Techniques for Local- to Regional-Scale Mapping
(2018) demonstrated the application potential for such studies using the AVHRR land cover map of [PERSON] et al. [PERSON] et al. (2018) for a study site in Canada in 1999. [PERSON] et al. [PERSON] et al. (2003) investigated in 2003 the potential of AVHRR for up-scaling of methane emissions in the West Siberian Lowlands, which are mostly located in the boreal biome, but of interest to high latitude studies, as the boreal biome is partially underlain by permafrost. SPOT HRV data from a case study area were used to develop an approach for the retrieval of land cover fractions within the AVHRR pixels, including the classes birch forest, conifer forest, forested bog and open bog. AVHRR land cover fraction maps were subsequently used to upscale methane emissions over the entire West Siberian Lowland area. Landsat-based classifications and their applications are well established, and methods that combine them with high spatial resolution satellite data have been frequently applied (e.g., [PERSON] et al. (2010); [PERSON] et al. (2010); [PERSON] et al. (2010)). Specific methods are required to deal with the information content in the high spatial resolution images. [PERSON] et al. [PERSON] et al. (2010) presented a technical solution for unsupervised classification of land cover in multispectral satellite imagery from WorldView-2, using so-called sparse representations in learned dictionaries for Barrow on the Alaskan North Slope. This approach also considers texture information. The resulting classes seem to detect real variability, but re-grouping based on expert knowledge is suggested (e.g., vegetative versus hydrologic studies). An approach for a hybrid map (pixel-, as well as object-based classification) for up-scaling soil organic carbon using Landsat-5 TM and WorldView-2 has recently been suggested by [PERSON] et al. [PERSON] et al. (2010). [PERSON] et al. [PERSON] et al. (2010) demonstrate that predictive ecosystem mapping can be improved with a supervised classification of SPOT HRV that uses a 28-class ecosystem map based on air-photos as training data. A number of studies have recently been published that make use of SAR data only, but utilize the different polarizations, which are acquired by modern SAR systems. [PERSON] et al. [PERSON] et al. (2010) investigated the impact of radiometric correction on the accuracy of polarimetric land cover classification with ALOS PALSAR data in 2012. This led to significant qualitative and quantitative improvements. [PERSON] et al. [PERSON] et al. (2013) assessed the potential of dual polarimetric TerraSAR-X and quad-pol Radarsat-2 imagery for land cover mapping using backscatter coefficients and Kennaugh matrix elements. A combination of both sensors is suggested for the best results. The impact of the incidence angle on the scattering behavior of quad-pol Radarsat-2 data has in addition been evaluated and assessed for its potential for unsupervised classification using three polarimetric SAR classifiers [PERSON] et al. (2018). Shallow incidence angle images provided better results than steep angle images. A combination of polarimetric SAR data with optical data also led to better results than using SAR data alone [PERSON] et al. (2010). confirm the potential of polarimetric SAR for shrub mapping using the support vector machine method. The land cover class 'water' has received comparably great attention at high latitudes [34] as it supports the identification of thaw lakes, which are associated with permafrost features. Both, optical and SAR data are commonly used for this purpose. Spatial resolution has been identified as crucial for their mapping [6; 35], but their extent is highly variable in time. Mapping Arctic water bodies therefore demands SAR methods, as the sensor's cloud independence ensures better sampling [36; 37]. This issue is discussed in more detail in Section 4.