2.2.3. Land Cover

The MCD12Q1 product provides global land cover type data at a spatial resolution of 500 m at an annual time step from 2001 to 2019. It is based on the supervised classification of MODIS reflectance data with six different classification schemes, including the IGBP (Annual International Geosphere-Biosphere Programmer), which was widely utilized due to its high accuracy and widespread acceptance [33]. Thus, the IGBP classification method was utilized in this study. The land cover data from 2001 to 2019 were chosen to produce a spatially continuous dataset via mosaic. The filling data of MCD12Q1 was removed to reduce their effect on the results. Evergreen needleleaf forests, evergreen broadleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, and mixed forests were grouped into forests. Closed shrublands and open shrublands were grouped together as shrublands. Woody savannas and savannas were combined into savannas.

#### 2.2.4. DEM (Digital Elevation Model)

Multi-Error-Removed Improved-Terrain (MERIT) DEM is an improvement of SRTM3 (Shuttle Radar Topography Mission v.3) DEM, with a spatial resolution of 3 arc-second (~90 m). It removes multiple error components from the SRTM3 DEM, including stripe noise caused by the sensor error, speckle noise of surface reflectance, absolute bias derived from the limited control points of the ground, and tree height bias where the canopies were incorrectly classified as the land surface [34,35]. MERIT was chosen because its accuracy is higher than that of SRTM and NASADEM (NASA Digital Elevation Model) [36] and because of the data availability in the Arctic. In order to match the resolution of MOD17A2H, the DEM dataset was resampled to 500 m using the bilinear method.
