## 2 Development of Techniques for Local- to Regional-Scale Mapping  
The development of retrieval methods and applications has evolved with the increasing availability of satellite data. The vast majority of land cover mapping studies utilize optical satellite data. Of high importance was the launch of the Landsat series. A number of studies on habitat mapping in Alaska and the Canadian Arctic were undertaken in the 1970s and 1980s [9]. This was extended in the early 1990s with SPOT MSS (Satellite Pour l'Observation de la Terre Multispectral Scanner System) [10] and SPOT HRV (High Resolution Visible) [9; 11]. These early studies aimed specifically at habitat assessments, including water fowls, muskox and the impact of reindeer grazing. Synthetic Aperture Radar (SAR) data became available with the launch of the European ERS-1 (C-band, 5.6 cm) in 1991 and the Japanese JERS-1 (L-band, 23.5 cm) in 1992. The Russian Almaz SAR mission (S-band, 9.6 cm) operated for 17 months from 1991-1992. Shortly afterwards, studies emerged analyzing the relative information content of the data from classification algorithms applied to Almaz, ERS-1, JERS-1 and Landsat-TM (Thematic Mapper) [12] to discriminate wet tundra habitats. SAR properties alone have also been evaluated for tundra habitat classifications [13]. The efficiency of SAR data in discriminating traditional classes in geobotanical maps was shown to be lower compared to optical [12]. In 1995, [PERSON] et al. [13] demonstrated that multi-look processing of the SAR data was favorable for land cover studies. Such pre-processing steps are to date standard for SAR-based land cover applications. The additional bands available with Landsat TM compared to MSS were shown to be of high value for classifications in the context of wild live studies [14]. This spectral resolution is available from most optical high to medium resolution spaceborne sensors to date [15]. Many of these early studies focused on the Alaskan North Slope ([10; 12; 13; 14], 1994-1996). SPOT related studies were published for the Canadian High Arctic [9] and Scandinavia [11]. Beyond that, [PERSON] and [PERSON] [16] compared satellite imagery and infrared aerial photography for vegetation mapping methods in East Greenland using both SPOT and Landsat TM data. They highlighted the inadequacy of satellite data to capture the fine-scaled tundra Arctic vegetation mosaic. This problem has been partially overcome to date with high spatial resolution satellites, but remains a challenge when mapping larger areas. The heterogeneity in these landscapes has been also addressed for a study site on Svalbard by [PERSON] and [PERSON] [PERSON] and [PERSON] (2017). Landscape types have been retrieved from Landsat. Vegetation probabilities have been assigned to these classes rather than specific class names. This approach provides a better representation, but is until now not commonly applied. Although AVHRR images have been available since 1978, they were not studied until the late 1990s, when the data were first analyzed for Arctic land cover assessments. Due to its coarse spatial resolution (1 km), the sensor had not been of interest, as most research at that time had a local to regional focus only. A method for mapping land cover in Canada was however developed by [PERSON] et al. [PERSON] et al. (2018) and published in 1996. An AVHRR-based land cover map for the Alaskan North Slope was published in 1997 [PERSON] et al. (2019). This was soon followed by a prototype for a circumpolar map [PERSON] et al. (2019). The thematic application areas of satellite data widened at the same time. The retrieval of biogeophysical parameters, such as Net and Gross Primary Production (NPP/GPP) ([PERSON] and [PERSON], 2017; [PERSON] et al., 2018), came into focus. Vegetation productivity can be directly linked to the reflectance in the visible and near-infrared part of the electromagnetic spectrum. NPP and GPP are determined using multiple sources, not only from satellite-derived products alone. [PERSON] et al. [PERSON] et al.