| Summary: | Fractional cover of green vegetation (FCover) is a key variable when observing Arctic vegetation under a changing climate. Vegetation changes over large areas are traditionally monitored by linking plot-scale measurements to satellite data. However, integrating field and satellite data is not straightforward. Typically, the satellite data are at a much coarser scale in comparison to field measurements. Here, we studied how Unmanned Aerial Systems (UASs) can be used to bridge this gap. We covered three 250 m x 250 m sites in Fennoscandian tundra with varying productivity ana FCover, ranging from barren vegetation to shrub tundra. The UAS sites were then used to train satellite data-based FCover models. First, we created a binary vegetation classification (absent, present) by using UAS-derived RGB-orthomosaics and logistic regression. Secondly, we used the classification to calculate FCover to Planet CubeSat (3 m), Sentinel-2A MSI (10 m, 20 m), and Landsat 8 OLI (30 m) grids, and examined how well FCover is explained by various spectral vegetation indices (VI) derived from satellite data. The overall classification accuracies for the UAS sites were >= 90%. The UAS-FCover were strongly related to the tested VIs (D-2 89% at best). The explained deviance was generally higher for coarser resolution data, indicating that the effect of data resolution should be taken into account when comparing results from different sensors. VIs based on red-edge (at 740 nm, 783 nm), or near-infrared and shortwave infrared (SWIR) had the highest performance. We recommend wider inspection of red-edge and SWIR bands for future Arctic vegetation research. Our results demonstrate that UASs can be used for observing FCover at multiple scales. Individual UAS sites can serve as focus areas, which provide information at the finest resolution (e.g. individual plants), whereas a sample of several UAS sites can be used to train satellite data and examine vegetation over larger extents. Peer reviewed
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