Trait-based mapping of functionally diverse tundra types using hyperspectral EnMap data

Abstract. The Arctic is undergoing rapid environmental change, with vegetation responses that feed back into surface energy balance, permafrost dynamics, and biogeochemical cycling at global scale. Yet existing satellite-based monitoring products, including broadband vegetation indices and categorical land cover maps, lack the sensitivity to detect early, process-driven shifts in vegetation function that precede visible compositional change. Plant functional traits retrieved from space-borne hyperspectral imagery offer a more physiologically based alternative, enabling spatially continuous characterisation of ecosystem function independently of species identity or land cover class. Here, we use EnMAP hyperspectral imagery and a convolutional neural network to retrieve 20 plant functional traits across Arctic tundra spotlight acquisitions spanning the North American Arctic and Greenland from 2022 to 2024. We characterise the structure of the resulting trait space, introduce pixel originality as a spatially continuous measure of functional uniqueness derived from multivariate trait distributions, and assess the capacity of trait-based predictors to differentiate vegetation types from the Circumpolar Arctic Land Use map using Random Forest models. Two principal components captured ≈ 86 % of total trait variability, organised along axes corresponding to the leaf economics spectrum and pigment composition. Pixel originality was almost entirely predictable from the selected traits (R² ≈ 0.97), with LAI, LMA, and carbon content as the dominant drivers, and showed smooth spatial gradients consistent with microtopographically structured tundra landscapes rather than discrete functional hotspots. Trait-based Random Forest classification of vegetation types achieved a mean accuracy of ≈ 73 %, demonstrating that functional traits alone carry substantial discriminatory power for broad vegetation class differentiation. These findings establish EnMAP-derived functional traits as a robust basis for landscape-scale monitoring of Arctic tundra diversity and function, with pixel originality offering a promising tool for tracking functional change across rapidly transforming ecosystems.