Introduction

The Arctic tundra biome is of urgent and enduring scientific interest due to the rapid climatic and environmental changes occurring in this domain (IPCC, 2021) and the broad implications for ecosystems, Arctic people, and feedbacks to the global carbon cycle and climate system (Zhang et al., 2018). Because they are vast, remote, and have relatively little infrastructure, it has been challenging to study and characterize Arctic tundra ecosystems across large spatial scales and through time. Recent advances in imaging spectroscopy (IS)—remote acquisition of spatially coregistered images in narrow, spectrally contiguous bands (Schaepman et al., 2009)—have enabled unprecedented characterization of terrestrial vegetation across a range of biomes, and anticipated missions will soon enable regular and comprehensive spectral monitoring (Ustin & Middleton, 2021). The Arctic environment poses unique challenges and opportunities for the use of spectroscopy to help resolve uncertainties about the ecological sensitivity of the tundra biome and its response to a changing climate.
Recent years have seen the dramatic growth of spectral imaging studies in the Earth science and global ecology communities. The rapid technical progress of these methodologies has led to their designation as an integral part of the US National Aeronautics and Space Administration (NASA) new Earth System Observatory (ESO) set to launch in the 2027–28 timeframe. The Surface Biology and Geology (SBG) component of this observatory will include an imaging spectrometer in the solar-reflected range (400 - 2500 nm), with coverage at biweekly intervals and pixel size as fine as 30 m over the terrestrial and coastal aquatic areas of the globe. Combining these data with similar missions launching around the same timeframe, such as the European Space Agency (ESA) Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) instrument (Nieke & Rast, 2018), will enable even denser spatial and temporal coverage. A key objective of the SBG mission is to use the solar-reflected spectrum to measure global ecosystem traits and diversity at high spatial resolution (Ustin & Middleton, 2021). Specific properties to be estimated from these data include plant traits, such as canopy nitrogen, leaf mass per area, liquid water content, and the fractional coverage of photosynthetically active (i.e., green) vegetation. By leveraging these data, specific plant functional types and canopy structures can be identified and mapped at the regional scale (European Space Agency 2021). With these new measurements, the forthcoming missions will provide the capacity to map ecosystem properties across the entire Arctic with unprecedented fidelity and temporal frequency - thereby serving as an important input to understanding Arctic ecosystem responses to a changing climate.
SBG measurements will complement a long history of prior airborne andin situ investigations of Arctic spectroscopy (e.g., Boreal Ecosystem-Atmosphere Study, BOREAS, and Arctic Boreal Vulnerability Experiment, ABoVE). These spectral measurements are often paired with ground-based measurements of ecosystem characteristics, including flux towers with eddy covariance estimates of carbon dynamics. These local measurements and highly temporally resolved flux datasets are spatially sparse, which introduces uncertainties when upscaling to estimate Arctic productivity as a whole. Airborne observations, such as those from ABoVE, have mapped spectral surface reflectance over broad spatial extents, enabling trait maps for representative locales (Miller et al., 2019). These airborne data provide some capacity to fill the spatial gaps between study sites and flux towers but represent snapshots for a single point in time and therefore fall short of comprehensive temporal coverage (i.e., high frequency and long durations). Traditional multispectral broad-band satellite remote sensing (e.g., Landsat, MODIS) covers a broad spatial extent and multi-decadal period; however, these data cannot fully measure the broad suite of ecosystem parameters at the spectral resolution required for robust analyses of ecosystem structure, function, and responses (A. Beamish et al., 2020; Liu et al., 2017; Myers-Smith et al., 2020; Ustin & Middleton, 2021). SBG will rely on a long history of precursor investigations, but by combining imaging spectroscopy with spatiotemporal resolution akin to Landsat, the acquired data promise a unique and substantial advance in our capacity to understand Arctic ecosystems.
To realize this promise, SBG must overcome the unique challenges of spectroscopy in the Arctic environment, primary among them spatiotemporal scaling. Tundra ecosystems exhibit a high degree of sub-pixel heterogeneity in composition, structure, traits, and function that is consistent across high-altitude spectral imaging platforms with spatial resolutions typically > 5 m (Lantz et al., 2010; Niittynen et al., 2020). Underlying this heterogeneity is the small stature of most tundra vegetation, with individual plant canopies occupying centimeters to a few meters of space and characterized by compressed vertical structure. Vegetation cover in certain Arctic regions is discontinuous with extensive exposed rock and soil. The widespread presence of permafrost and periglacial geomorphic features that produce fine-scale variation in microtopography, soil moisture, and surface water exposure (e.g., ice-wedge polygons, frost circles, thermokarst features) contribute to this spatial heterogeneity of vegetation (Figure 1) (Li et al., 2021; D. A. Walker et al., 2003). Strong gradients in microclimate and topography yield a high degree of variance in physiological traits and function, even within individual species in close spatial proximity (John A. Gamon et al., 2013; Kade et al., 2005). Thus, remote observations of tundra ecosystems usually integrate across a complex mixture of plant functional types, non-vegetated surfaces, and physiological traits.