Figure 1 . Examples of heterogeneous vegetation and landforms in
tundra landscapes. (a) Close-up of ground lichens in upland tundra,
Izaviknek Hills, Alaska; (b) mosaic of shrublands, wetlands, and
waterbodies, Yukon Delta, Alaska; (c) mosaic of tall deciduous shrubs
and open tundra, Seward Peninsula, Alaska; (d) intermixed sedges and low
shrubs, Alaska North Slope; (e) polygonal ground, Alaska North Slope;
(f) High Arctic tundra dominated by mosses and cryptogamic crust, Franz
Josef Land, Russia; (g) thaw slump and exposed ground-ice, Yugorskiy
Peninsula, Russia; (h) frost boils in forest-tundra ecotone,
northwestern Siberia. The extent of the Arctic tundra biome is shown in
red in the central map based on the Circumpolar Arctic Vegetation Map
(CAVM Team, 2003).
The composition of tundra includes significant coverage by both
nonvascular and vascular vegetation. Nonvascular vegetation types pose
unique challenges, in that they have different spectral signals than
vascular plants (Hope & Stow, 1996; Stow et al., 1993) their spectra
are highly influenced by their moisture content (Bubier et al., 1997; A.
Harris et al., 2005; Vogelmann & Moss, 1993), and physiologically they
behave differently than vascular plants (Green & Lange, 1995; Tenhunen
et al., 1995). Relationships between remotely-sensed spectra and plant
function have not yet been developed at spatial scales adequate to
capture nonvascular plant physiological responses and the mixed
composition of vascular and nonvascular plants within spectral
footprints complicates interpretation of observations. Collectively,
these issues suggest a need for new methodologies for assessing the
composition of tundra systems. One approach is to collect colocated
ground vegetation composition data and remotely sensed spectral
observations at varying spatial scales, and utilize their relationships
to enable subpixel vegetation cover retrieval (Thomson et al., 2021).
Alternatively, spectral unmixing algorithms parameterized by fine-scale
observations can be used to disentangle the sub-pixel contributions to a
spatially integrated observation (Beamish et al., 2017; Bratsch et al.,
2016; Huemmrich et al., 2013). Such work will be critical to interpret
compositional effects on imaging spectroscopy observations from SBG -
but present a major opportunity for future work.
Meteorological conditions inherent to Arctic regions, such as high
frequency cloud occurrence, seasonal snow cover, and ephemeral surface
water often preclude high quality spatially contiguous or temporally
continuous observations (Walther et al., 2016, 2018). The limited snow-
and ice-free period (including episodic snowfall events in the middle of
the growing season) constrains the number of clear observations of
vegetation. Additionally, rapid transitions and highly variable shoulder
season weather restrict the utility of even high frequency spaceborne
observations to detect important phenological events (e.g.,
start-of-season and end-of-season) (Karlsen et al., 2021; Parazoo et
al., 2018; Vickers et al., 2020). Smoke from frequent and extensive
wildfires in the neighboring boreal forest biome can drift over the
tundra biome for substantial periods during the growing season of a
given year, making interannual comparisons challenging.
Illumination geometry at high latitudes also complicates remote sensing
of Arctic tundra (Buchhorn et al., 2016). High latitude regions
experience extremes in daylength, from continuous daylight in midsummer
to continuous darkness in midwinter, the latter of which limits the
capacity for reflectance-based observations on the winter edge of
shoulder seasons. The effects of the continuous daily photoperiod of
midsummer challenge assumptions established in the temperate regions
about the connections between spectral imaging observations and dynamic
physiological processes (e.g., accumulated stress). Overall, surface
radiation is lower due to high solar zenith angles and consequent
scattering due to atmospheric path length, and photon scattering at such
angles complicates radiative transfer.
Existing IS data over the Arctic is sporadic in space and time. For
example, since 2017 ABoVE (Miller et al., 2019) has collected a large
amount of airborne IS data over a broad Arctic region in North America
using NASA’s Next Generation Airborne Visible Infrared Imaging
Spectrometer (AVIRIS-NG). While these data are of high value for
characterizing vegetation function, stress, and mapping functional
traits (Gamon et al., 2019), the discontinuous coverage (non-overlapping
flight lines collected over a larger region) and the volume of data
(several gigabytes in size for an individual flight line) mean that, at
present, an individual researcher is often required to identify and
download a number of different scenes, and therefore a large data
volume, to carry out a study. Some of these challenges will be
exacerbated with upcoming satellite IS missions such as SBG
(Cawse-Nicholson et al., 2021) which will provide voluminous datasets.
More efficient usage of IS datasets for Arctic research will require new
data hosting and access methods to find, extract, and apply IS data
without large bandwidth or local storage requirements.
Here we present a technical perspective - informed by empirical
observations of spectral variability - of the numerous ecological,
geographic, and technical challenges associated with spectroscopic
observation of Arctic tundra ecosystems. We discuss how we may leverage
our understanding of spectral dynamics and characteristics to understand
tundra ecology. We delimit our region of interest based on the
Circumpolar Arctic Vegetation Map (CAVM Team, 2003) (see Fig. 1). First,
we provide context for the degree of spectral complexity of the tundra
biome by quantifying the intrinsic spectral dimensionality from a series
of observations from airborne IS (Section 2). Next, we describe how
attributes of the land surface in the tundra biome (e.g., plant
functional type and vegetation-substrate composition) impose particular
challenges for interpreting spectroscopy (Section 3). We then elaborate
on how IS enables an opportunity to achieve several common goals for
advancing our understanding of the Arctic tundra biome: long-term change
detection, land cover and vegetation classification, retrieval of
biophysical properties, and phenological and diurnal change (Section 4).
We conclude by providing recommendations for Arctic tundra spectroscopy
research (Section 5) by addressing the following key questions:
How can we use spectral observations at a variety of spatiotemporal
resolutions (e.g., from spaceborne, airborne, and surface-based
instruments) to address inherent challenges associated with IS and
better understand Arctic tundra ecosystems?
How can our understanding of Arctic tundra ecology advise further
research and the development of new instruments and sampling designs?