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.