## Maps for Upscaling Studies
### Super-Sites
\begin{table}
\begin{tabular}{l l l l} \hline
**Site Name** & **Usa Basin (Non-Forest Part)** & **Kuparuk Basin** & **Lena Delta** \\ \hline
**Pixel size** & 30 m &
\begin{tabular}{l} resampled from 80 down \\ to 50 m \\ \end{tabular} & 30 m \\ \hline
**Year of input data** & 1988/1995 (six images) & 1976–1985 & 2000/2001 (two images) \\ \hline
**Total area** & 49,370 km\({}^{2}\) & 25,300 km\({}^{2}\) & 66,470 km\({}^{2}\) \\ \hline
**Classification Method** & \begin{tabular}{l} semi-supervised \\ classification of the \\ spectrally-matched image \\ mosaic (TM Bands 2–5, 7) \\ (parallelepiped decision rule \\ and maximum likelihood) \\ \end{tabular} & \
\begin{tabular}{l} unsupervised ISODATA \\ classification (MSS green, \\ red and infrared bands) \\ \end{tabular} &
\begin{tabular}{l} unsupervised ISODATA \\ classification (cloud masking) \\ and supervised minimum \\ distance classification (TM \\ Bands 2–5, 7) \\ \end{tabular} \\ \hline
**Sources** & [PERSON] et al. [38] & [PERSON] et al. [39] & [PERSON] et al. [40] \\ \hline
**Non-vegetated/** \\
**Barrens** & Mainly bare land \\
**Barrens** & Human infrastructures \\ \hline
**Shrub/Trees** & \begin{tabular}{l} Willow stands \\ Dwarf birch heath \\ Dwarf shrub moss \\ \end{tabular} & \
\begin{tabular}{l} Moist dwarf shrub, \\ Tussock-graminoid tundra \\ \end{tabular} &
\begin{tabular}{l} Moist to dry dwarf shrub- \\ dominated tundra \\ \end{tabular} \\ \cline{2-3}  & tundra heath \\  & Dry dwarf shrub, \\ lichen tundra & & shrub and other shrublands \\ \hline
**Tundra** & \begin{tabular}{l} Tundra with some \\ bare peat \\ Sparse alpine tundra \\ \end{tabular} & \begin{tabular}{l} Dry prostrate shrub tundra \\ Moist graminoid, prostrate \\ \end{tabular} & \begin{tabular}{l} Moist grass- and moss- \\ dominated tundra \\ \end{tabular} \\ \cline{2-3}  & \begin{tabular}{l} Human-impacted \\ tundra \\ \end{tabular} & \
\begin{tabular}{l} Wet graminoid tundra \\ \end{tabular} &
\begin{tabular}{l} Dry grass-dominated \\ tundra \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Non-water classes of selected multi-purpose land cover classifications based on Landsat. The dominating land cover class is underlined. For the location, see Figure 1.  
areas already in 1998. [PERSON] et al. [40] used Landsat 7 ETM+ for up-scaling of methane emissions over the Lena Delta in 2009 (see Table 1). Classification was carried out in two steps using the unsupervised ISODATA method and supervised maximum-likelihood methods. Only the land cover class wet sedge- and moss-dominated tundra was eventually used due to its abundance in the area and importance regarding emissions. To estimate soil organic carbon and nitrogen stocks in the Lena Delta, [PERSON] et al. [47] grouped the land cover classes to differentiate between the Holocene river terrace and the active floodplains. A Landsat-based map was also recently developed for the West Siberian Lowlands, suggesting its potential use for up-scaling of methane emissions [48]. A multi-purpose land cover classification based on Landsat TM5 has also been derived for the Usa basin in northeastern European Russia [38] using a semi-supervised approach and a similar classification scheme as for the Kuparuk basin in Alaska. The Usa basin does however also extend into the boreal biome. In total, 21 classes were therefore considered, including nine non-water classes outside forested areas (see Table 1). Applications included carbon flux up-scaling [49], as well as up-scaling of soil carbon estimates [50]. [PERSON] et al. [49] grouped the original set of classes into seven new classes for one of the sub-basins: wet peat pauty tundra, dry peaty tundra, peat plateau, tundra health, willow dominated stand, lake and pond margins and thermokarst lakes and profundal. Landsat TM classes were eventually combined with a high spatial resolution QuickBird image for a subset of the area [25]. The same thematic vegetation classes have been used in a nested approach. The original classes by [PERSON] et al.