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\begin{document}

\title{Remote Sensing of Tundra Ecosystems using High Spectral Resolution
Reflectance: Opportunities and Challenges}



\author[1]{Peter Ross Nelson}%
\author[2]{Andrew J Maguire}%
\author[3]{Zoe Pierrat}%
\author[4]{Erica L Orcutt}%
\author[5]{Dedi (Daryl) Yang}%
\author[6]{Shawn Paul Serbin}%
\author[7]{Gerald V. Frost}%
\author[8]{Matthew J Macander}%
\author[9]{Troy Sehlin Magney}%
\author[2]{David Ray Thompson}%
\author[10]{Jonathan Wang}%
\author[11]{Steven F. Oberbauer}%
\author[12]{Sergio A Vargas Zesati}%
\author[13]{Scott J Davidson}%
\author[14]{Howard Epstein}%
\author[11]{Steven Unger}%
\author[15]{Petya K.E. Campbell}%
\author[16]{Nimrod Carmon}%
\author[12]{Miguel Velez-Reyes}%
\author[15]{K Fred Huemmrich}%
\affil[1]{Schoodic Institute at Acadia National Park}%
\affil[2]{Jet Propulsion Laboratory, California Institute of Technology}%
\affil[3]{University of California Los Angeles}%
\affil[4]{University of California, Davis}%
\affil[5]{Brookhaven National Laboratory}%
\affil[6]{Brookhaven National Laboratory (DOE)}%
\affil[7]{ABR, Inc.-Environmental Research \& Services}%
\affil[8]{Alaska Biological Research, Inc.}%
\affil[9]{California Institute of Technology}%
\affil[10]{UC Irvine}%
\affil[11]{Florida International University}%
\affil[12]{University of Texas at El Paso}%
\affil[13]{University of Plymouth}%
\affil[14]{University of Virginia}%
\affil[15]{Univeristy of Maryland, Baltimore County}%
\affil[16]{Jet PRopulsion Laboratory, California Institute of Technology}%


\vspace{-1em}



  
  \date{June 10, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Observing the environment in the vast inaccessible regions of Earth
through remote sensing platforms provides the tools to measure
ecological dynamics. The Arctic tundra biome, one of the largest
inaccessible terrestrial biomes on Earth, requires remote sensing across
multiple spatial and temporal scales, from towers to satellites,
particularly those equipped for imaging spectroscopy (IS). We describe a
rationale for using IS derived from advances in our understanding of
Arctic tundra vegetation communities and their interaction with the
environment. To best leverage ongoing and forthcoming IS resources,
including NASA's Surface Biology and Geology mission, we identify a
series of opportunities and challenges based on intrinsic spectral
dimensionality analysis and a review of current data and literature that
illustrates the unique attributes of the Arctic tundra biome. These
opportunities and challenges include thematic vegetation mapping,
complicated by low-stature plants and very fine-scale surface
composition heterogeneity; development of scalable algorithms for
retrieval of canopy and leaf traits; nuanced variation in vegetation
growth and composition that complicates detection of long-term trends;
and rapid phenological changes across brief growing seasons that may go
undetected due to low revisit frequency or be obscured by snow cover and
clouds. We recommend improvements to future field campaigns and
satellite missions, advocating for research that combines multi-scale
spectroscopy, from lab studies to satellites that enable frequent and
continuous long term monitoring, to inform statistical and biophysical
approaches to model vegetation dynamics.%
\end{abstract}%



\sloppy


Remote Sensing of Tundra Ecosystems using High Spectral Resolution
Reflectance: Opportunities and Challenges

Peter R. Nelson\textsuperscript{1,*,++}, Andrew J.
Maguire\textsuperscript{2,*,++}, Zoe Pierrat\textsuperscript{3}, Erica L.
Orcutt\textsuperscript{4}, Dedi Yang\textsuperscript{5}, Shawn
Serbin\textsuperscript{5}, Gerald V. Frost\textsuperscript{6}, Matthew
J. Macander\textsuperscript{6}, Troy S. Magney\textsuperscript{4}, David
R. Thompson\textsuperscript{2}, Jonathan A. Wang\textsuperscript{7},
Steven F. Oberbauer\textsuperscript{8}, Sergio Vargas
Zesati\textsuperscript{9}, Scott J. Davidson\textsuperscript{10,11},
Howard E. Epstein\textsuperscript{12}, Steven Unger\textsuperscript{8},
Petya K.E. Campbell\textsuperscript{13}, Nimrod
Carmon\textsuperscript{2}, Miguel Velez-Reyes\textsuperscript{11}, K.
Fred Huemmrich\textsuperscript{13}

\textsuperscript{1} Schoodic Institute at Acadia National Park, Winter
Harbor, ME

\textsuperscript{2} Jet Propulsion Laboratory, California Institute of
Technology, Pasadena, CA

\textsuperscript{3} University of California, Los Angeles

\textsuperscript{4} University of California, Davis

\textsuperscript{5} Brookhaven National Laboratory, Upton, NY

\textsuperscript{6} Alaska Biological Research, Inc. Fairbanks, AK

\textsuperscript{7} University of California, Irvine

\textsuperscript{8} Florida International University, Miami, FL

\textsuperscript{9} University of Texas at El Paso

\textsuperscript{10} University of Plymouth, UK

\textsuperscript{11} University of Waterloo, Canada

\textsuperscript{12} University of Virginia, Charlottesville, VA

\textsuperscript{6} University of Maryland Baltimore County, Baltimore,
MD

++ PRN and AJM made equal contributions

* Correspondence to:

Peter R. Nelson,\emph{pnelson@schoodicinstitute.org}

Andrew J. Maguire, \emph{andrew.j.maguire@jpl.nasa.gov}

\section*{Abstract}

{\label{abstract}}

Observing the environment in the vast inaccessible regions of Earth
through remote sensing platforms provides the tools to measure
ecological dynamics. The Arctic tundra biome, one of the largest
inaccessible terrestrial biomes on Earth, requires remote sensing across
multiple spatial and temporal scales, from towers to satellites,
particularly those equipped for imaging spectroscopy (IS). We describe a
rationale for using IS derived from advances in our understanding of
Arctic tundra vegetation communities and their interaction with the
environment. To best leverage ongoing and forthcoming IS resources,
including NASA's Surface Biology and Geology mission, we identify a
series of opportunities and challenges based on intrinsic spectral
dimensionality analysis and a review of current data and literature that
illustrates the unique attributes of the Arctic tundra biome. These
opportunities and challenges include thematic vegetation mapping,
complicated by low-stature plants and very fine-scale surface
composition heterogeneity; development of scalable algorithms for
retrieval of canopy and leaf traits; nuanced variation in vegetation
growth and composition that complicates detection of long-term trends;
and rapid phenological changes across brief growing seasons that may go
undetected due to low revisit frequency or be obscured by snow cover and
clouds. We recommend improvements to future field campaigns and
satellite missions, advocating for research that combines multi-scale
spectroscopy, from lab studies to satellites that enable frequent and
continuous long term monitoring, to inform statistical and biophysical
approaches to model vegetation dynamics.

\section*{Plain Language Summary}

{\label{plain-language-summary}}

Remote sensing has a long history of characterizing the distribution and
dynamics of vegetation in a wide variety of biomes, including the Arctic
tundra which is experiencing warming more rapidly than the global
average. Imaging spectroscopy (IS) - a rapidly advancing field of remote
sensing that measures reflected light in narrow, contiguous ``colors''
from satellites, aircraft, or towers - has demonstrated great promise to
``watch'' how key land surface properties vary across space and over
time. Because they are vast, remote, and have relatively little
infrastructure, currently available IS data from the Arctic tundra are
sporadic and intermittent. Hence, it has been challenging to study and
characterize these ecosystems across broad spatial scales and through
time. Furthermore, the climate and ecology of these ecosystems pose
unique challenges for employing and interpreting IS data. Inspired by a
forthcoming NASA satellite-based IS mission, we present an overview of
the current opportunities and challenges for the use of spectroscopy to
study Arctic tundra, informed by novel measurements across a range of
spatial and temporal scales. We share recommendations for how
researchers could leverage IS to resolve pressing ecological questions
and advance the design and sampling scheme of future instruments and
campaigns.

\section*{Key Points}

{\label{key-points}}

\begin{itemize}
\tightlist
\item
  Imaging spectroscopy (IS) can help measure critical Arctic tundra
  properties, physiological function, and temporal dynamics
\item
  Upcoming IS satellite missions including NASA's SBG will make imaging
  spectroscopy data widely available for Arctic tundra regions
\item
  To properly interpret IS data users must consider spectral complexity
  of tundra driven by composition, sensitivity to climate, and phenology
\end{itemize}

\section*{Introduction}

{\label{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
and\emph{in 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 \textgreater{} 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.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{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?

\section*{Dimensionality Analysis}

{\label{dimensionality-analysis}}

\subsection*{2.1. Intrinsic Dimensionality and Relevance to Arctic
Optical Diversity and
Ecosystems}

{\label{intrinsic-dimensionality-and-relevance-to-arctic-optical-diversity-and-ecosystems}}

Intrinsic dimensionality, the number of independent degrees of freedom
in a dataset, has been used to measure the information content of
spectral catalogues (Cawse-Nicholson et al., 2021; Thompson et al.,
2017). Roughly speaking, the dimensionality of the upwelling light field
indicates different physical and chemical properties apparent in the
terrain that are revealed in the measured radiance spectrum. Here we
characterize the differences in intrinsic dimensionality among different
areas of the Arctic, as represented in the airborne ABoVE dataset
acquired by AVIRIS-NG over Alaska and northwestern Canada. This
dimensionality analysis demonstrates the high degree of spectral
diversity of the Arctic tundra, highlighting the advantage of a
large-scale experiment such as ABoVE and the increased information
content provided by imaging spectrometers, as opposed to multispectral
sensors.

\subsection*{2.2. Dimensionality Analysis
Approach}

{\label{dimensionality-analysis-approach}}

We analyzed the AVIRIS-NG dataset acquired in 2017, consisting of over
200 different flightlines, segmented at \textasciitilde{}3 km intervals
(600 pixels at 5 m). The measured spectrum is calibrated to units of
absolute radiance as in (Chapman et al., 2019). We estimated surface
reflectance spectra using the approach of (Thompson et al., 2018).
Finally, we calculated the intrinsic dimensionality of each segment
independently using the strategy of (Thompson et al., 2017). Within each
segment, the intrinsic dimensionality was calculated from the image
stack, cloud fraction and the mean and standard deviation of Normalized
Difference of Vegetation Index (NDVI) were summarized from the imagery,
and the central latitude and longitude were extracted. We plotted the
frequency distribution of dimensionality for the cloud-free segments,
summarized by latitude and NDVI, to examine trends and patterns in
spectral dimensionality (Figure 2).

\subsection*{2.3. Dimensionality Analysis Results and
Implications}

{\label{dimensionality-analysis-results-and-implications}}

Dimensionality was calculated for a total of 14,519 segments, of which
12,626 were cloud-free and used in subsequent analysis. Dimensionality
values were positively skewed with a long tail of high values.
Generally, a broad range of dimensionality was observed across the
gradient of latitude and greenness. Above 62\selectlanguage{ngerman}° N, segments with moderate
NDVI values (0.25-0.75) consistently had higher dimensionality than
those with either low (\textless{} 0.25) or high (\textgreater{} 0.75)
NDVI. The lowest dimensionality values, \textless{} 20, were found
mostly in the low NDVI category corresponding to non-vegetated terrain
and open water. These systems were optically less diverse than the
vegetated areas. Inconsistent observing conditions, such as solar angle
and the amount of atmospheric haze, affect the sensor's ability to
resolve the subtlest features and probably play some role in the broad
spread of dimensionality values. Even excluding the largest values, the
modes of the distributions lie between 20 and 40, similar to previous
studies of midlatitude regions (Thompson et al., 2017). This
demonstrates that Arctic tundra exhibits considerable spectral
heterogeneity across the surveyed region. Unlocking the large amount of
information available in these dimensions can provide new insights into
tundra characteristics and function and will be the focus of future
studies. Considering that this analysis was restricted to one segment
size, it is quite likely that there is even more information embedded in
these spectra. Dimensionality analyses like this but conducted across a
range of segment sizes and with coincident finer-grained data provide an
important opportunity to inspect the spatial resolution of vegetation or
surface substrate patches in Arctic landscapes. Such analyses may be
necessary to understand the properties of interest and heterogeneity
within the mixture of non-vegetated and vegetated surfaces.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Locations of AVIRIS-NG flight line segments used in
this analysis and ground-based measurements shown in Figures 3-8 are
shown in the map (a). Frequency distribution from the dimensionality
analysis, binned by latitude and mean NDVI (b, c). Vertical bars in the
violin plots (b) indicate the inter-quartile range and median value.

\section*{Spectral Characteristics of
Tundra}

{\label{spectral-characteristics-of-tundra}}

\subsection*{3.1. Unique Characteristics of Tundra
Surfaces}

{\label{unique-characteristics-of-tundra-surfaces}}

Lichens, bryophytes, and vascular plants occur in different proportions
along gradients of climate, soil properties, and landscape history in
the Arctic (CAVM Team, 2003; Epstein et al., 2008, 2020) and possess
different physiologies and spectral reflectance patterns. This
variability poses unique challenges for remote sensing of tundra
vegetation properties, but an understanding of geographic patterns of
vegetation structure and function can help interpret such measurements.
(Walker et al., 2005) provide a framework to characterize the central
tendencies of Arctic tundra structure and composition by dividing the
biome into five bioclimatic subzones (A--E) distributed along gradients
of summer temperature. The subzones range from the coldest Subzone A,
found in coastal areas of the High Arctic with persistent summer sea
ice, to the warmest Subzone E, generally found in continental areas near
the northern limit of tree establishment. Subzone A, occasionally termed
``polar desert'' (Matveyeva, 1998) is characterized by discontinuous
vegetation cover that is typically dominated by nonvascular vegetation;
shrubs and sedges are usually absent, vascular plant diversity is very
low, and a large proportion of the ground surface is unvegetated. In
Subzone B, lichens and bryophytes dominate the cover and shrubs are
generally limited to only a few species (e.g., \emph{Salix arctica,
Dryas} spp.) with a prostrate growth form (\textless{} 5 cm height).
From Subzone C southward, vascular plants - particularly shrubs - occur
at greater abundances and species richness, and of higher stature. In
Subzone E, vegetation is typically continuous and forms a multi-layered
canopy, with shrubs commonly reaching heights of \textgreater{} 80 cm.
Near the southern boundary of Subzone E, broadleaf and needleleaf trees
are often present. The tundra-taiga ecotone (TTE) is typically a diffuse
transition zone where trees first occur as isolated patches within the
tundra matrix and become more abundant and spatially dense southward and
at lower elevations. In North American and European ecotones, tree cover
is generally dominated by evergreen species (e.g., \emph{Picea, Pinus}
), whereas deciduous needleleaf species (\emph{Larix} ) are dominant in
Siberian TTE. Within each bioclimatic subzone, there is a great deal of
heterogeneity in the relative abundance of plant functional types along
landscape-scale gradients of moisture, topography, permafrost, and soil
properties. Thus, IS applications must consider the relative abundance
of plant functional types along both circumpolar-scale climate gradients
and landscape-scale environmental gradients.

Furthermore, although plant functional types are expected to share
suites of similar traits, within plant functional types there can still
be enormous variation among traits that are important for ecosystem
function (Table 1). This trait diversity corresponds to spectral
variation within individual plant functional types in (Figure 3).

\textbf{Table 1} . Summary of heights, patch sizes, dominant taxa, and
distributional patterns of plant functional types in Arctic tundra
ecosystems and forest-tundra ecotones. For patch sizes, minimum values
refer to typical individual plants, and maximum values refer to
contiguous areas in which the functional type forms the top of the
canopy.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Functional type & Height (cm) & Patch size (m\textsuperscript{2}) &
Description \& distributional patterns\tabularnewline
Lichens & 0--5 & 0.001--100 & Diverse nonvascular plants consisting of
fungal and algal symbionts, often distinguished by growth form (foliose,
fruticose, crustose) or color group. Intermixed ``reindeer lichens''
(\emph{Cladonia} spp.) and other fruticose taxa (e.g.,
\emph{Flavocetraria, Alectoria,} and \emph{Bryoria} spp.) can form
extensive mats on undisturbed, well drained sites.\tabularnewline
Bryophytes & 0--5 & 0.001--100 & Nonvascular plants including mosses and
liverworts. Found throughout Arctic; common mesic taxa include branched
``feathermosses'' (e.g., \emph{Hylocomnium splendens, Pleurozium
schreberi}) and single-stemmed mosses (e.g., \emph{Dicranum} and
\emph{Polytrichum} spp.). Peat mosses (\emph{Sphagnum} spp.) can form
continuous carpets in wet areas. Liverworts much less abundant, but form
extensive cryptogamic crusts in High Arctic.\tabularnewline
Graminoids & 10--70 & 0.01--0.25 & Sedges and grasses. Sedges common
throughout tundra except in coldest parts of High Arctic. Tall
cottongrass (\emph{Eriophorum angustifolium}) and water sedge
(\emph{Carex aquatilis}) often dominate wet sites. Large areas of
tussock tundra dominated by Arctic cottongrass (\emph{Eriophorum
vaginatum}) occur in Low Arctic on mesic soils. Grasses superficially
resemble sedges and occur throughout Arctic, but cover is usually low;
most common on floodplains and disturbed sites. Pendantgrass
(\emph{Arctophila fulva}) is a common marsh species.\tabularnewline
Forbs & 0--50 & 0.01--0.05 & Diverse group of non-graminoid herbaceous
flowering plants found throughout the Arctic, but cover is typically
low. Common forbs include Arctic lupine (\emph{Lupinus arcticus}),
Arctic sweet coltsfoot (\emph{Petasites frigidus}), and ``cushion''
plants such as purple mountain saxifrage (\emph{Saxifraga
oppositifolia}).\tabularnewline
Deciduous shrubs & 0--500 & 0.01--100 & Multi-stemmed, broadleaf woody
plants; common erect species include dwarf birch (\emph{Betula
nana})\emph{,} diamondleaf willow (\emph{Salix pulchra})\emph{,} and bog
blueberry (\emph{Vaccinium uligonosum}). Dwarf shrubs such as Arctic
willow (\emph{Salix arctica}) occur throughout tundra biome except in
coldest parts of High Arctic. Tall stands are restricted to warmer parts
of Low Arctic, where typically found on floodplains (e.g., feltleaf
willow \emph{Salix alaxensis}) and mesic slopes (e.g., Siberian alder
\emph{Alnus viridis} ssp. \emph{fruticosa}).\tabularnewline
Evergreen shrubs & 0--20 & 0.01--10 & Widespread dwarf shrubs, except in
High Arctic. Common species include entireleaf mountain-avens
(\emph{Dryas integrifolia})\emph{,} mountain heather (\emph{Cassiope
tetragona}), lingonberry (\emph{Vaccinium vitis-idaea})\emph{,} and
Labrador tea (\emph{Ledum decumbens}).\tabularnewline
Deciduous trees & 150--1,000 & 1--25 & The deciduous conifer, larch
(\emph{Larix} spp.), is the dominant tree in Siberian taiga-tundra
ecotones. Poplar (\emph{Populus balsamifera}) can occur on Low Arctic
floodplains and south-facing slopes. Trees are typically widely spaced.
Conifers typically have columnar growth form with small canopies;
broadleaf trees often have larger canopies.\tabularnewline
Evergreen trees & 150--1,000 & 1--10 & Evergreen conifers such as spruce
(\emph{Picea} spp.) are dominant in North American and European
taiga-tundra ecotones.\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3} . Median (black), 75\% (dark ribbon), and 90\% (grey
ribbon) quartiles of spectral reflectance for eight plant functional
types from the Arctic tundra biome. Sample size (\emph{n} ) is shown
parenthetically. Sentinel-2 bandpasses are indicated with vertical bars
to illustrate the advantage of imaging spectrometers with contiguous
bands over multispectral instruments. Spectra were collected in the
field with leaf clip or contact probe and illumination source across
Alaska between 2010-2019, primarily 2017-2019. Most of the data were
collected with a Spectral Evolution PSR+3500 under AVIRIS-NG flight
lines +/- 14 days of flight in most cases. Spectra were collected at 1
nm resolution and trimmed to 450-2400 nm to remove sensor artifacts.

\subsection*{3.2. Lichens}

{\label{lichens}}

Lichens reach high diversity, cover, and biomass in certain tundra
ecosystems and play a significant role in biogeochemical and physical
processes, such as land-atmosphere radiative exchange, hydrological
buffering, and nitrogen (N) cycling (Cornelissen et al., 2007). The
genus \emph{Cladonia} (reindeer lichens) create dominant carpets across
the Arctic that likely represent the majority of lichen cover and
biomass. Other genera do contribute significant biomass and cover, such
as \emph{Cetraria} , \emph{Flavocetraria} and \emph{Stereocaulon} all
which grow mostly upright and intermixed with bryophytes, lichens and
other plants. However, talus slopes and other rock surfaces are often
covered with very different genera (eg. \emph{Rhizocarpon}
and\emph{Aspcilia} , both crustose or stain-like growth forms that can
cover boulders and talus fields), which creates complexity in estimating
the total cover of lichens. Lichens contribute substantial ground cover
in periglacial environments, stabilizing soils (Makoto \& Klaminder,
2012). Albedo varies widely among lichen groups, with implications for
heat exchange with fractional cover variability (Aartsma et al., 2021).
A large fraction of biodiversity of terrestrial vegetation in the tundra
is composed of lichen species. Most caribou and reindeer survive in
northern climates, in part, by eating mostly lichens throughout winter
months (Heggberget et al., 2002; Joly et al., 2007). A major opportunity
for SBG to enhance wildlife habitat mapping will be to use the unique
spectral signatures to separate lichen groups (Macander et al., 2020;
Nelson et al., 2013; Petzold \& Goward, 1988; Rees et al., 2004).

Physiological differences between lichens and vascular plants affect
their spectral reflectances. Lichens have more broadly different
cellular structure than vascular plants. The upper surfaces of most
lichens, composed of fungal cells of one or sometimes two fungi
(Spribille et al., 2016), often with pigments, protect the next inner
layer of cells, usually composed of the photobiont (algae,
cyanobacteria, or both). The upper cortical cells of lichens are usually
dense and have high concentrations of pigments produced by one or both
fungi that are attributed to photoprotection. These fungal pigments
protect the algal photosynthesis machinery by dealing with reactive
oxygen species produced by high irradiance by dissipating excess energy
as thermal wavelengths (Beckett et al., 2021). Under the cortex, a thin
layer of photobiont (algae, cyanobacteria, or both) receives sufficient
light for photosynthesis. The parts of the spectral signature of lichens
similar to vascular plants belies the presence of the photobiont(s).
After the photobiont, little if any light likely penetrates in the
fungal structural backbone of a lichen body, the medulla, which is often
thick, white or pale. Amongst the \textgreater{} 12,000 species of
lichens, there is a diversity of mixtures of cortical cell structure,
chemistry and photobiont that contribute to the spectral signatures of
lichens.

Lichens are spectrally variable both within and among species, but
compared with vascular plants, tend to have higher reflectance in the
visible range and lower reflectance in the NIR (Figure 3). Hundreds of
compounds, many with pigments detectable in the visible range, can be
found across the diversity of tundra lichens. These complex molecules
aid in differentiating lichens from vascular plants but also make
modeling lichens as a group difficult. However, most mapping efforts
have treated lichens as a monolithic group, focused on one relatively
homogenous color group (e.g., light) (Macander et al., 2020) or at most
treated lichens in a few color groups (Nelson et al., 2013). Lichen
spectral signatures indicate high degrees of variability within and
among species (Kuusinen et al., 2020; Petzold \& Goward, 1988; Rees et
al., 2004). Lichens have no true vascular tissue therefore hydration is
based on short term meteorological conditions which in turn drives short
term metabolic activity of lichens (Lange et al., 1996). Nonvascular
plants, including lichens and bryophytes (i.e., mosses, hornworts, and
liverworts), lack true vascular tissue (parenchyma) and therefore
passively dessicate and rehydrate (poikilohydry) (Walter, 1931). The
hydration status of lichens greatly influences the overall magnitude of
reflectance as well as spectrum shape (Kuusinen et al., 2020; Rees et
al., 2004) but the difference between dry and wet lichen spectra varies
both across wavelengths and species. Water content can be estimated for
lichens (Granlund et al., 2018) but uses wavelengths beyond those
proposed for SBG (i.e., \textgreater{} 5000 nm). A key challenge for SBG
in the Arctic will be accounting for water content in spectral profiles
of the lichen (and bryophyte) mat since photosynthesis and respiration
are both tied to hydration. Rapid changes in hydration make observations
of productivity fleeting and unstable in non-vascular plants. To address
the impact of hydration state on the reflectance profiles of
non-vascular plant communities, diurnal and seasonal spectral
measurements with high temporal density colocated with in situ moisture
probes are needed.

Lichens tend to be very small organisms but in the tundra can form
confluent patches of varying sizes and mixtures of patches with
different species and other organisms. Studies of tundra with coincident
imagery of different spatial resolutions suggest pixels smaller than 3 m
are needed to accurately classify patches (R\selectlanguage{ngerman}äsänen \& Virtanen, 2019)
with a loss of 30\% absolute accuracy associated with declining
resolution (2-20 m) (Virtanen \& Ek, 2014). Another key challenge for
leveraging observations from SBG will be the fact that the composition
of surfaces in 30 m pixels will have a wide range of pure patch sizes,
from centimeters to meters.

There are few measurements on the phenology of pure lichen patches.
Measurements of tundra mixtures with abundant lichens display limited
seasonal variability (John A. Gamon et al., 2013) with spectral changes
mostly associated with moisture status. This may be one of the few
positive features of lichens for remote sensing and SBG. To take
advantage of this, SBG could use observations after snow melt but before
green up and then after leaf-off but before first snow to observe lichen
(and bryophyte) dynamics in more detail. At those times, non-vascular
vegetation would have less over-topping vegetation, reducing occlusion
from nadir-viewing sensors.

\subsection*{3.3. Bryophytes}

{\label{bryophytes}}

One of the main features of the tundra are the bryophytes, which can be
found growing on most surfaces and conditions, from fully immersed in
water to exposed rock or bare soil. Bryophytes (i.e., mosses, hornworts,
and liverworts) usually appear as mats or patches of miniature plants
formed by multiple individuals. Bryophytes can form the primary
understory vegetation in many tundra plant communities, from wet, acidic
bogs where \emph{Sphagnum} spp. dominate to the fine matrix of moist
tundra where numerous species of bryophyte form dense mats interspersed
with lichens and vascular plants. In wet environments,
\emph{Sphagnum}spp. can create large colonies with deep accumulation of
senescent material storing carbon as peat. In less hydric sites,
\emph{Hylocomium splendens} (stair step moss) and \emph{Pleurozium
schreberi} (big red stem) are dominant. They have exceptional hydrologic
and thermal buffering qualities and are tied to the formation and
stability of permafrost (Blok et al., 2011; Shur \& Jorgenson, 2007).
Bryophytes such as \emph{Polytrichum} spp. and \emph{Ceratadon
purpureus} can also form short-lived but extensive colonies post-fire
which aid in stabilizing carbon recovery. They are crucial to carbon
sequestration and storage, protecting the permafrost layer while also
forming a living layer beneath a sparse vascular plant canopy. Despite
their obvious importance to Arctic ecosystems, bryophytes have been
largely neglected in remote sensing except for narrow cases like
\emph{Sphagnum} spp. (Angela Harris \& Bryant, 2009; Huemmrich et al.,
2013).

Bryophyte physiology differs vastly from vascular plants, primarily due
to reduced-to-absent vascular tissue. By virtue of this, bryophytes can
absorb large amounts of water, but are not able to actively regulate
moisture content via a root system like vascular plants. Instead,
bryophytes form colonies, sometimes only with one species but often with
many species, which together determine hydration through water holding
capacity of the living layer. As a result, bryophytes may hydrate or
dessicate quickly. Similar to lichens, bryophyte hydration status is
known to significantly influence spectral reflectance, with many changes
observable in the visible to short-wave infrared spectra (Van Gaalen et
al., 2007; Vogelmann \& Moss, 1993) (Figure 4).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4} . Spectral signature at varying moisture saturation
levels measured as a fraction of the saturated water mass of \emph{S.
capillifolium} (left) and \emph{S. lenense} (right). Spectra for both
species were collected at regular intervals using a SVC - HR-1024i with
light source at 100\% under a progressive drying experiment. Fraction
calculated as mass of water in samples divided by total water mass (g
H\textsubscript{2}O at interval * g total
H\textsubscript{2}O\textsuperscript{-1}). Sentinel-2 bandpasses are
indicated with vertical bars to illustrate the advantage of imaging
spectrometers with contiguous bands over multispectral instruments.

In addition to spectral changes, metabolic activity of bryophytes is
also significantly influenced by moisture content with primary
production decreasing as moisture decreases (Green \& Lange, 1995);
however, decoupling of reflectance and productivity has been noted
in\emph{Sphagnum} spp. and Pleurocarpous mosses, such as
\emph{Hylocomium splendens} and \emph{Pleurozium schreberi} with
spectral indices such as NDVI returning to near-initial values within
minutes after rehydration, but primary production response lagging for
more than 24 hours (May et al., 2018).

Given the generally low canopy cover across the arctic, bryophytes are
likely driving spectral reflectance of mixed pixels across large
extents, making timing of data collection and awareness of moisture
content crucial for interpreting IS observations. For this reason, early
and late summer provide opportunities for IS of bryophytes. Though there
are many lab studies of bryophyte physiology (Green \& Lange, 1995), the
few studies scaling bryophyte spectral signatures for classification and
chemical analysis show promise for estimating water, N, C, and P
(Thomson et al., 2021). Translating bryophyte spectra to trait maps
using remote sensing is an important opportunity to better constrain
ecosystem models (Wullschleger et al., 2014).

Bryophyte reflectance spectra differ from vascular vegetation by
exhibiting a wider and taller peak in the green to yellow, a gentler red
edge, and a greater variability in the NIR (Figure 3). Additionally, the
SWIR region is very responsive to moisture content with large increases
in reflectance under drier conditions. Bryophytes also produce
photoprotective compounds that influence the spectral profiles. For
example, many \emph{Sphagnum} species under high light conditions may
develop photoprotective pigments that will affect their reflectance.
Studies of open-growing \emph{Sphagnum} have shown that they are
photo-inhibited in full sun and exhibit faster vertical growth under
lower (e.g., shaded) illumination (Harley et al., 1989; Murray et al.,
1993) Little is known about the variability of pigments among bryophytes
species across the extent of the Arctic. Reflectance
measurements\emph{in situ} indicate broad diversity both within and
among bryophyte species that will be further complicated by the impact
of variable hydration status.

Though short in stature, bryophytes can form small but highly visible
homogeneous patches, carpets and hummocks. Bryophyte mixtures are very
commonly intermixed with vascular plants (dwarf shrubs and grass-like
plants) and lichens, in the understory living-mat matrix. The mixtures
of patch sizes of each species and degree of heterogeneity combined with
vascular plant canopy cover make it challenging to separate them
spectrally. Similar to lichens, classification accuracy of bryophytes
can be high if pixels are small (\textless{} 1 m) and there are
sufficient and appropriate bandpasses (R\selectlanguage{ngerman}äsänen \& Virtanen, 2019). For
context, researchers found that increasing to 20 m pixels reduced the
absolute accuracy of their plant classification of remotely sensed
spectra by 50\% compared to 2 m pixels (Thomson et al., 2021) . Like
lichens, small patch sizes of bryophytes present a challenge for SBG
that will need to be met with scaling studies to understand within-pixel
variation.

Bryophytes generally do not display strong seasonal patterns in their
reflectance, although there are few studies of pure bryophyte patch
phenology. Vegetation classes with high fractional cover by bryophytes
do show some phenological variability but this is likely primarily due
to the non-bryophyte fraction in the vegetation class (Rautiainen et
al., 2011). In the spring, following snowmelt, bryophytes are green and
photosynthetically active well before the deciduous vascular plants
begin greening up (Huemmrich et al., 2010). New annual growth of many
bryophyte species appears much lighter green than older growth.
Bryophytes in shaded vs open areas also show different chlorophyll and
other pigment concentrations (Niinemets \& Tobias, 2014). Bryophyte
reproductive structures develop annually in many species and these
tissues display apparent coloration distinct from the vegetative tissue.
Bryophyte phenological variation may occur at scales at which IS could
be useful in detecting physiological changes relevant to ecosystem
processes.

\subsection*{3.4. Vascular plants}

{\label{vascular-plants}}

Living vascular plant tissue shows remarkable similarities as a group in
the general shape of spectral response, specifically characterized by a
modest increase in reflectance in the green (relative to blue and red)
and a steep ``red edge,'' followed by a plateau across the NIR (Figure
3). Variation in spectral profiles amongst vascular plants is often most
notable in the inflection point of the NIR and features of the SWIR,
which in turn inform the derivation of many important functional
attributes (e.g., phenology, photoprotective pigmentation, water
content, disease). The long history of research into the relationships
between leaf-level biochemistry, traits, and spectra (discussed in
Section 4.2) points to science just scratching the surface of the
potential spectra to inform plant science. Spectral profiles are
evolutionarily conserved (Meireles et al., 2020), which provides a basis
for assuming the ability to separate species using spectra. Reflectance
profiles have recently been used to separate species and even genotypes
among co-occurring plants (e.g., \emph{Dryas} sp., one of the most
common vascular plant genera in the Arctic) (Stasinski et al., 2021).
This level of distinction is likely beyond the capacity of SBG but
points to the profoundly strong linkage between vascular plants and
their reflectance profiles.

Vegetation in the Arctic occurs largely in confluent mixtures, where the
boundary between an individual and group blurs. Viewing this problem in
terms of pure patches of a single species helps describe the challenge
for remote sensing. Patch size varies by species across several orders
of magnitude, from individual plants (cm scale) to confluent forest or
shrub canopies (10 m scale) or continuous patches of a single type (km
scale) such as tussock tundra dominated by \emph{Eriophorum vaginatum} .
Snow, wind and ice scour the landscape and force shrubs to form thickets
that can cover very large areas but change in size and shape across
species of dominant shrubs, like \emph{Salix} spp. (willows)
or\emph{Alnus} spp. (Alder). The sparse distribution of trees presents
unique challenges to spectral remote sensing, particularly for coarse
spatial resolution imagery where tree crowns may be widely spaced and
collectively constitute on average 30\% of a 30 m pixel (Paul Mannix
Montesano et al., 2016). In contrast, some regions of the TTE are
characterized by clumps of dense tree cover with minimal spacing between
crowns across otherwise open tundra vegetation. As with non-vascular
plants, many vascular plant patches are smaller than the likely pixel
size of SBG (30 m). This underscores the need to measure features at
high spatial and spectral resolution with coordinated field campaigns to
validate SBG pixels and fully utilize the spectral resolution of SBG to
estimate vegetation composition and function.

Vascular plants exhibit strong variation in phenology across groups,
from fully dormant species such as forbs that are absent aboveground or
buried under snow in the winter to persistent year-round tissues of
evergreen trees and shrubs. The brief growing season results in very
rapid progression of plant phenological stages, which elicits the common
perception by observers that changes in reflectance are visually
apparent at a daily time scale. Most studies have focused on summer
reflectance for peak photosynthetic activity, but imaging at other times
of year provides opportunities to characterize the important features of
green up and senescence. For most plants snowmelt defines the onset of
annual growth and initiation of myriad phenological processes including
flowering and leaf-out. Characterizing differences in phenology among
plant functional types may help separate co-occurring plant groups with
similar reflectance profiles during peak summer (Beamish et al., 2017).
Spatial variation in onset of green up (earlier at lower latitudes,
south facing aspects, and lower elevations) and senescence (earlier at
higher latitudes, north-facing aspects, and higher elevations) provides
both a challenge and an opportunity for SBG to capture the important
spectral information about the biophysical changes in tundra vegetation.
In shoulder seasons when understory vegetation is buried under snow but
tree crowns protrude above the snowpack, lower albedo distinguishes
these patches from surrounding snow-covered tundra. However, to detect
phenological events in sparsely treed regions, indices that can account
for background effects - namely the coincidence of snow with vegetation
phenology - are critical (see Section 4.4).

Vascular plants generally become increasingly important, more diverse,
and larger with decreasing latitude and altitude in the Arctic. By
subzone C and south vascular plants become more prevalent than
nonvascular plants, with increasing diversity of growth forms/functional
types, graminoids, forbs, cushion plants, and deciduous and evergreen
shrubs and trees that represent general life history strategies
characterized by specific traits (with many exceptions) that influence
ecosystem and spectral properties. For example, evergreen shrubs and
trees are characterized by long-lived leaves, low photosynthetic rates,
low leaf nitrogen but high leaf mass per unit leaf area (LMA), and
tolerance to water stress. Forbs and deciduous shrubs tend to have
short-lived leaves, high photosynthetic rates and leaf nitrogen
contents, and low LMA. Graminoids may span the entire spectrum.

Graminoids (mainly sedges) form a large component of boreal and tundra
herbaceous vegetation, ranging from dry ridges to wet areas and standing
water. Reflectance profiles of graminoids are broadly similar to other
vascular plants with some distinctive features in the SWIR and overall
lower green values. However, fine spatial mixtures of living and dead
tissue in graminoid end members present a different spectral challenge
for remote sensing. Collecting clean graminoid spectral signatures in
the field under controlled light conditions is difficult due to the
shape and size of the leaves. For this reason, most measurements of
graminoids in the field are taken with a larger FOV under ambient and
therefore often have dead leaves and stems that remain mixed in with
living graminoid tissue thereby creating the mixture of living and dead
tissue in the spectral profiles for this group.

The tussock-forming sedge \emph{Eriophorum vaginatum} (cottongrass) is a
dominant species over very large areas throughout the Arctic
(0.9\textbf{x} 10\textsuperscript{6} km\textsuperscript{2}; (Oechel et
al., 1993)). Its unique tussock growth form provides an unusual surface
topography that introduces shadows and at low observation angles may
obscure vegetation on the opposite side. Cottongrass and many other
graminoid species also have predominantly vertically-oriented leaves
that present a challenge for top-down, nadir remote sensing because most
of the leaf area is not apparent to the sensor; further. Again, a key
challenge for remote sensing of graminoids will be accounting the amount
of dead material in spectral profiles of these plants.

Forbs are the dominant vascular plants in snow banks and snow beds,
where the annual growing season is brief but water and nutrient supplies
are high and present in dry to semi-aquatic habitats throughout the
Arctic. They are non-woody non-graminoids that typically present only
leaves and flowering stalks above the soil surface during the growing
season. Forbs show broad similarity to shrubs in their spectral
profiles, but with more variability in the visible range and more
symmetrical variation about the median in the SWIR (Figure 3).
Separating forbs from other vascular vegetation may be a challenge for
SBG but one opportunity may be during the autumn, when the spectacular
variation in pigments of Arctic tundra forbs and dwarf shrubs becomes
strikingly apparent.

The expansion of deciduous shrubs is one of the most apparent responses
of tundra ecosystems to climate warming. Deciduous shrub species have
high environmental plasticity and are unique among tundra plant
functional types in the Low Arctic, because they can achieve canopy
heights of 2 m or more and greatly overtop other vascular plants.
Therefore, the development of upright, woody canopies in tundra
landscapes strongly influences biophysical processes throughout the
year. Shrubs promote a strong positive winter feedback by trapping
drifting snow in the winter that insulates the soil; subsequently warmer
soils allow faster decomposition; decomposition releases nutrients that
promote further shrub growth (Sturm et al., 2005). In warmer parts of
the Low Arctic, the large size attained by individual deciduous shrubs,
and their tendency to develop dense canopy patches in favorable
landscape positions provides opportunities for IS to sample a relatively
pure spectral signal, which is otherwise not possible in most tundra
landscapes dominated by small, intermixed, low-statured plants.
Deciduous shrubs exhibit limited variation in the visible range and a
notable plateau in the NIR (Figure 3).

Evergreen shrubs present a different set of challenges and opportunities
for IS. In moist acidic and dry tundra, dwarf evergreen shrubs are a
major component of the vegetation, often as an understory layer above
bryophyte species (e.g., \emph{Vaccinium vitis-idaea} L.). The evergreen
growth form is associated with low nutrient habitats where conservative
use of nutrients is favored. Evergreen shrubs retain leaves for 1-5 or
more years (Shaver, 1981) and thus have the potential to photosynthesize
whenever conditions are able to sustain it, even under snow (Starr \&
Oberbauer, 2003), especially during the shoulder seasons. Most
evergreens produce photoprotective pigments that protect the leaves
during the cold season and strongly affect spectral reflectance of these
plants (explored further in section 4.2).

Even in otherwise tundra-dominated landscapes, trees can persist in
sparse numbers across the tundra domain. The primary example of this is
along the tundra-taiga ecotone (TTE), which is an often diffuse (rather
than abrupt) transition between denser boreal forest tree cover to
tundra-dominated plant cover. Common tree genera of the TTE include a
mix of evergreen needleleaf (e.g., \emph{Picea} and \emph{Pinus} ),
deciduous needleleaf (e.g., \emph{Larix} ), and deciduous broadleaf
(e.g., \emph{Betula and Populus} ). Having more structural complexity
than forbs, bryophytes, or lichens, trees exhibit different effects on
radiative transfer within canopies, particularly affecting multiple
scattering in the NIR and SWIR regions. For example, conifer needles
have similar reflectance to deciduous in the VNIR, but their IR
reflectances are lower than deciduous due to morphological
characteristics of needles (Hovi et al., 2017). Observed and simulated
radiative transfer of conifer needles infer that part of the spectral
differences between deciduous leaves may be due to variation in leaf
angle with both convex and flat leaf sides to their needles (J. Wang et
al., 2020). Conifer arrangement in shoots, and the presence of woody
material in twigs and boles that alter multiple scattering likely also
differs between deciduous trees. Evergreen needleleaf trees in the TTE
tend to have exceptionally narrow crowns (maximum 1-2 m in diameter),
and black spruce (\emph{Picea mariana} ) can often have sparse foliage
clustered at the top of the crown, especially in regions where fire had
caused non-lethal disturbance. Due to their upright structure and
tendency to be widely spaced in much of the TTE, the interaction of high
solar zenith angles with tree stems and canopies cast extended shadows
on surrounding tundra vegetation. The vertical distribution of foliage
along narrow crowns causes problems for nadir viewing of trees in the
TTE to characterize gradients in foliar properties (Moorthy et al.,
2008). In addition to the structural complexity of trees, deciduous vs.
evergreen species experience strong phenological differences which may
complicate interpretation of spectral information in mixed-forest stands
(Pierrat et al., 2021) (Section 4.4). As with shrub-dominated
landscapes, understory tundra vegetation may be obscured from
measurement by nadir-viewing sensors in regions with denser tree cover.
Similar to the case of shrubs, encroachment of trees into tundra
landscapes influences biophysical processes such as snow distribution,
wind patterns, and soil active layer depth (F. K. Holtmeier \& Broll,
2007). Characterization of geographic position, composition, and
condition of the TTE is important for detecting expansion or retreat of
tree species across the tundra domain (F.-K. Holtmeier \& Broll, 2019;
Paul M Montesano et al., 2020; Stumberg et al., 2014).

Though lidar is often the primary tool for delineating the TTE and
characterizing the structure of trees in this zone, spectroscopy can
provide valuable information on phenology, physiological state, and
heterogeneity among trees (Montesano et al., 2016a; Montesano et al.,
2016b). Spectroscopy is particularly useful for characterizing
photosynthetic dynamics of trees in the tundra domain since these
individuals tend to be especially slow growing at the northern range
limit for their species distribution (hence limited structural change
detectable by repeated lidar campaigns) but contribute a substantial
amount to landscape-scale carbon flux.

\subsection*{3.5. Non-vegetated Surfaces}

{\label{non-vegetated-surfaces}}

The Arctic tundra is characterized by low leaf area and sparse
vegetation cover, resulting in other materials, such as snow, water,
bare ground, and dead or burned material comprising significant portions
of the landscape. Each of these materials have unique spectral
characteristics which can confound retrievals of vegetation. Remote
sensing instruments with fine to moderately sized pixels (e.g.,
AVIRIS-NG \textasciitilde{} 5 m\textsuperscript{2}) can capture multiple
landscape components within a single pixel, producing a mixed spectral
signal that can be difficult to interpret. Our ability to tease apart
vegetation signals from these non-vegetated tundra landscape components
is important not only for understanding vegetation, but also for
understanding the condition of the landscape itself and its feedbacks on
vegetation dynamics. An additional complication is that many surfaces
are non-vegetated for only part of the year due to snow pack, snow melt,
or flooding; at other seasons understory vegetation in the form of
lichens, bryophytes, or biocrusts becomes visible from above making the
timing of signal retrievals an important component of Arctic vegetation
dynamics.

Remote sensing of the cryosphere has been a key focus of Arctic remote
sensing. Snow, ice, and permafrost are important drivers of tundra
ecosystem structure and function, impacting components such as the depth
of the soil active layer, freshwater availability, and the formation of
important landscape features such as thermokarst lakes. Fresh snow has
very high reflectivity in the visible and near-infrared (\textgreater{}
80\% between 400 - 900 nm, with values \textgreater{} 50\% between 900
-- 1200 nm), while clean ice, as from a glacier, has relatively high
reflectivity (\textgreater{} 60\% between 400 -- 600nm, steadily
decreasing to \textless{} 10\% for 1000 -- 1200 nm) (Tedesco, 2015). The
reflectivity of ice and snow is reduced over time as dirt accumulates
and darkens the surface. Snow can interfere significantly with
vegetation spectral retrieval as snow can both accumulate over
vegetation canopies (i.e., obscuring direct visibility) and snow
reflectance can saturate any vegetative signal. The timing of snowmelt,
a key driver of tundra phenology, can vary drastically throughout the
tundra (Kelsey et al., 2021), making snow dynamics both an important
process to study but also a confounding factor in vegetation remote
sensing (further explored in Section 4.4).

Permafrost thaw in the Arctic tundra is one of the most concerning
results of climate change due to the biogeochemical feedbacks which
drive increased greenhouse gas emissions. The spatial dynamics of
permafrost thaw are complex, involving interactions between multiple
processes including biogeochemical cycles, hydrology, and climate
(Grosse et al., 2013). Vegetation cover can provide insulation from
summer warming, with different types of cover providing varying levels
of protection against thaw, which makes vegetation change detection an
important component of understanding permafrost thaw changes (Anderson
et al., 2019). Vegetation feedbacks between the permafrost and
vegetation provide a key geophysical connection for SBG in studying the
Arctic because the high spectral resolution will allow separation of
more kinds of tundra attributes. However, permafrost features have
highly patterned features, often considerably finer scale than the 30 m
resolution of SBG, requiring field campaigns to describe patterns in the
permafrost at higher spatial resolution. Permafrost thaw can also impact
vegetation cover through landscape transformation.

One of the most rapid and noticeable landscape features of permafrost
thaw is the development of thermokarst lakes (Grosse et al., 2013).
Thermokarst lakes form from the degradation of ice wedges in continuous
permafrost areas, leaving standing water and unfrozen ground, called
taliks, underneath the lake. The presence of thermokarst lakes, which
have been forming in the Arctic since the Last Glacial Maximum, have
been increasing and existing lakes have been expanding. Thermokarst
lakes increase the amount of standing water that is present in the
Arctic tundra. Standing water poses a challenge for tundra vegetation
remote sensing. The tundra is studded with thermokarst lake depressions
that form due to the freeze-thaw cycle of permafrost, and in the summer
much of the tundra is covered with standing water. Water most strongly
interferes with the retrieval of vegetation reflectance in the visible
range (400 -- 700 nm), but it can also cause a reduction across the
entire spectrum. This can potentially influence vegetation signals
retrieved from vegetation indices such as NDVI which use red reflectance
(\textasciitilde{}650 nm although this varies by sensor), or PRI which
uses green (\textasciitilde{} 531 nm). Liquid water absorbs light in the
NIR, reducing vegetation reflectance in that region, thus dampening
vegetation signals in pixels with standing water (Lang et al., 2015).
Remote sensing instruments with finer spatial resolution can help to
solve this problem by improving pixel purity.

The amount of vegetation cover varies significantly across the Arctic
tundra due to differences in topography and soil properties such as
nutrient content (Liu et al., 2017). Exposed bedrock and bare soil are
common and bare soil can be intermixed with sparse vegetation cover.
Soil and rock spectra vary depending on the type and color of the
substrate and moisture content. Most dark colored soils are more
strongly absorptive in the visible range than vegetation, but the
vegetation signal is more strongly reflective in the NIR than soil. As
with water, interference in the red and NIR can confound commonly used
vegetation indices such as NDVI. Another complication is senescent or
dry vegetation, which can have a spectral signal similar to bare soil
(Liu et al., 2017). In the high Arctic, tundra vegetation can have a
very brief growing season, so it is important that remote sensing
measurements have short revisit times to capture phenological changes
appropriately, and tease apart vegetation from bare soil or litter.

Tundra fires have a sparse historical record, but recent data and model
projections indicate that tundra fires will increase in frequency and
severity under climate change (French et al., 2015). Fire has become a
growing concern as a source of tundra change. Spectrally, burned
vegetation reflectance is high in the shortwave NIR which can help
distinguish it from green vegetation, but bare soil which is exposed
during burning can interfere with vegetation retrieval (Boelman et al.,
2011). Alternative vegetation indices have been proposed to assess
burned vegetation areas, but full spectral data will help to tease apart
burned areas from green vegetation.

\subsection*{3.6. Mixed Composition
Observations}

{\label{mixed-composition-observations}}

Although many tundra vegetation communities can often contain both
vascular and non-vascular species, the combined spectral signature can
be distinct enough to allow for separability among communities. For
example, (Davidson et al., 2016) successfully distinguished among eight
different tundra vegetation communities including bryophyte-shrub,
bryophyte-lichen, and tussock-shrub utilizing the Blue (450-510 nm), Red
(640-692 nm) and Red Edge (705-745 nm) regions (Figure 5). (Bratsch et
al., 2016) distinguished among four tundra plant communities at Ivotuk,
Alaska (particularly early in the growing season), using Blue, Red, and
Near-Infrared hyperspectral bands.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5} . Spectral reflectance of mixed-composition pixels
from representative tundra sites in Alaska. Figure adapted from
(Davidson et al., 2016). Sample size (n) is shown parenthetically.
Sentinel-2 bandpasses are indicated with vertical bars to illustrate the
advantage of imaging spectrometers with contiguous bands over
multispectral instruments. Dots are opaque to show the density of
observations. Spectra were collected using a UniSpec DC Analysis System
(PP Systems, Amesbury, MA, USA) with a spatial resolution of 50 cm.

\section*{Uses of Spectroscopy for Tundra
Studies}

{\label{uses-of-spectroscopy-for-tundra-studies}}

\subsection*{4.1. Long-term Vegetation Changes with
NDVI}

{\label{long-term-vegetation-changes-with-ndvi}}

Long-term satellite data has revealed ``greening'' of Arctic tundra
since the 1970s based on increases to NDVI derived from Landsat and
AVHRR time-series data (Myers-Smith et al., 2020; Wang \& Friedl, 2019).
While tundra greening remains the most common trend across the Arctic,
``browning'', represented by a decreasing trend in NDVI values, has
occurred in various regions and scales across the tundra (Myers-Smith et
al., 2020). Greening and browning trends were one of the first
indications that the Arctic tundra was being significantly impacted by
climate change. Variations in greening/browning over different years
have most commonly been attributed to climate warming (e.g., (Berner et
al., 2020; Bhatt et al., 2021; Cooper, 2014)), herbivory by small
mammals (Olofsson et al., 2012), and vegetation disturbance and
subsequent recovery after extreme warming events (Bokhorst et al.,
2012). However, there are significant limitations of the sensitivity of
NDVI to high latitude ecosystem change (Huemmrich et al., 2021). For
example, recent evidence suggests that some of these changes' impacts
are fine-scale in nature (i.e., \textless{} 5-30 m), making many common
remote sensing platforms impractical for studying these dynamics
(Myers-Smith et al., 2020; Niittynen et al., 2020). Moving beyond
greening and browning into the shifting landscape of numerous other
metrics unlocked by IS, such as changes in land cover type and
biophysical traits, will provide key insights into the magnitude and
nature of high latitude ecosystem change.

From the outset, advanced IS data collections, such as from SBG, should
be organized and calibrated to allow for future analysis of multi-year
trends. In addition, improved land cover descriptions from SBG will
enhance the interpretation of the existing NDVI trend analyses by
establishing the capacity of different land cover types to respond to
environmental change and for that change to be reflected by observable
changes in NDVI. Ground measurements collected over extended time series
will improve our understanding of the nature of spectral reflectance
change associated with measured land cover change and inform remote
sensing needs.

\subsection*{4.2. Land Cover and Vegetation
Classification}

{\label{land-cover-and-vegetation-classification}}

Surface reflectance data have long been used to classify and map
vegetation types from landscape to global scales. Accurate data
identifying the distribution of and changes to land cover types provide
a significant opportunity for understanding Arctic environmental change.
Improved mapping and classification of circumpolar land cover and its
changes will be key to understanding the effects of global environmental
change on Arctic ecosystems (Sections 4.2-4.4). Overcoming the
challenges associated with mapping land cover at appropriate levels of
thematic, spatial, and temporal detail will ultimately provide a
significant advancement in our understanding of Arctic ecosystems.

Mapping Arctic vegetation types at high spatial resolution and with
sufficient thematic detail has been challenging in part due to a
relative sparsity of spectroscopic data. Global-scale land cover maps,
such as the MODIS land cover product (Sulla-Menashe et al., 2019), are
typically produced at a level of thematic detail that cannot distinguish
between functionally distinct landforms (e.g., low- versus high-centered
polygons) and vegetation types (e.g., low versus tall shrublands)
present in Arctic tundra. Different arctic vegetation types are often
combined into simpler, but less effective classes, or are represented by
inappropriate classes (e.g., ``grassland'') which do not reflect tundra
ecosystem composition. The utility of land cover maps for tracking
Arctic environmental change hinges on improving land cover
classification, as subtle changes in vegetation properties, such as
increased shrub abundance, do not necessarily involve a transition from
one class to another within a mapped pixel.

Moving beyond land cover types and into the mapping of plant functional
types or finer taxonomic groups from spectra may be possible at
continental scales if IS data with large spatial coverage (e.g., ABoVE
airborne campaigns (Section 2) and SBG) are harnessed and developed.
Acquiring and applying more detailed spectroscopic data for Arctic
vegetation types will enable mapping with improved thematic detail,
particularly if they are analyzed in tandem with ancillary high spatial
resolution datasets that capture important environmental covariates such
as topography (e.g., ArcticDEM) and edaphic characteristics (e.g.,
seasonal inundation, snow depth and hardness, active layer thickness,
depth to water). Few studies have yet applied detailed IS data to map
Arctic vegetation types (Smith et al., 2021; Thomson et al., 2021), but
an increase in available imagery may enable future work in this area.

Land cover maps with classifications designed for Arctic vegetation
types are typically limited in spatial or temporal range (Chasmer et
al., 2014; Greaves et al., 2019), precluding comprehensive study of
Arctic vegetation dynamics, or are coarse in spatial or temporal
resolution (e.g., gridded 1 km CAVM) (Raynolds et al., 2019), precluding
accurate characterization of the high level of spatial heterogeneity and
temporal variability in Arctic vegetation. (Bartsch et al., 2016)
suggested that a 30 m spatial grain, which is the proposed spatial
resolution for SBG, is sufficient for capturing many of the dynamics of
Arctic land cover. However, depending on whether species-level or
functional type-level maps are being generated, even higher spatial
resolution (e.g., 3 m from Planet) may be insufficient to distinguish
Arctic vegetation except at broad thematic levels (e.g., trees vs. shrub
vs. water). Therefore, the use and further development of advanced
subpixel mixture analysis will enable high accuracy vegetation
classifications with reasonable instrument spatial resolution and broad
spatial coverage (Thomson et al., 2021). Tapping the information content
of higher spatial resolution data (e.g., Section 2) will be essential to
preparing the algorithms and analysis pipelines to utilize a spaceborne
imaging spectrometer such as SBG that has a finer spectral resolution
occurring at an intermediate spatial resolution to map Arctic vegetation
(Section 5).

Another key limitation to mapping vegetation in Arctic tundra is the
lack of high-quality, georeferenced training data. Existing observations
are scattered across numerous countries, land management agencies, and
historical datasets. Disparate datasets often do not capture similar
levels of detail, and thus can be challenging to integrate. Land cover
maps, and the algorithms and data that go into producing them, are only
as credible as the underlying training data. Typically, land cover maps
are trained on datasets of land cover type that are produced by visual
interpretation of very high spatial resolution imagery (e.g., using
Google Earth), but the availability of suitable (midsummer) imagery is
extremely limited in the Arctic tundra (Section 1). Field data provide
the most reliable source of georeferenced Arctic ground verification,
but they are inherently limited in scope and are spatially biased
towards areas with a long history of research (e.g., northern Alaska's
Dalton Highway corridor). Airborne data (including UAS observations) can
bridge the scaling from field data to spatially extensive gridded
datasets (Assmann et al., 2020). This scaling will ultimately enable
training of machine learning algorithms to effectively map Arctic
vegetation at continental scales.

Finally, the unique seasonal characteristics of the Arctic impose
additional challenges on mapping tundra vegetation at scale.
Phenological differences can help to separate co-occurring and
spectrally similar plant functional types (Macander et al., 2017), but
the phenology itself is highly variable through space and time since it
is sensitive to moisture status and interannual variability in
meteorologic conditions (Sections 4.4 and 4.5). Land cover mapping
algorithms may misinterpret spectral changes caused by interannual
variation as real land cover change. The brief snow-free season in the
Arctic tundra may inhibit sufficient characterization of
phenology-driven spectral changes, which further reduces our ability to
identify spurious change detection. A sufficiently large and
representative training dataset, as described above, will help prevent
vegetation mapping algorithms from misclassifying changes in moisture
status and phenology with changes in land cover in the Arctic Tundra.

\subsection*{4.3. Retrieval of Biophysical Properties and Plant
Traits}

{\label{retrieval-of-biophysical-properties-and-plant-traits}}

The strong connection between IS and the biophysical properties of plant
leaves and canopies makes it possible to retrieve a host of important
vegetation properties with spectroscopy (Serbin \& Townsend, 2020).
Particularly, the mapping of plant functional traits, i.e., the
morphological, biochemical, phenological, and physiological attributes
of leaves and canopies (Violle et al., 2007), has been a priority and
key focal area of study (Asner et al., 2015; Asner \& Martin, 2008;
Cawse-Nicholson et al., 2021; Singh et al., 2015; Z. Wang et al., 2019,
2020) . These functional traits, which are closely related to vegetation
establishment, growth, and functioning, are key to understanding
vegetation responses to climate change, as well as process modeling of
terrestrial ecosystems (Gamon et al., 2019; Myers-Smith et al., 2019; Xu
\& Trugman, 2021; Zakharova et al., 2019). For example, traits that
describe leaf photosynthetic capacity (e.g., foliar pigments, nitrogen,
and V\textsubscript{cmax}), biogeochemistry (e.g., ligno-cellulose,
carbon, and macronutrients), and water cycling (e.g., stomatal
conductance) are important to characterize ecosystem carbon, water, and
energy cycling and response to climate change (Chapin, 2003; Myers-Smith
et al., 2019; Ollinger \& Smith, 2005; Rogers et al., 2017; Tang et al.,
2018; Woodward \& Diament, 1991) . Similarly, traits related to
vegetation structure, such as leaf area and canopy height, are important
for determining ecosystem energy partitioning (e.g., through surface
albedo and temperature), as well as surface-atmosphere interactions
(Aalto et al., 2018) that feedback to the global climate system (Zhang
et al., 2018).

In the Arctic, plant functional traits vary remarkably within and across
plant species and over space and time, controlled by the complex,
fine-scale gradients in climate, topography, water, and nutrients
(Andresen \& Lougheed, 2021; Bjorkman et al., 2018; Black et al., 2021;
Chen et al., 2020; Thomas et al., 2020). In particular, traits that
confer differing competitive advantages, such as those related to plant
size and resource economics (e.g., leaf area, seed mass, height, LMA, N,
LDMC) (Thomas et al., 2020), are highly sensitive to changes in
micro-environments, making them difficult to characterize with
traditional field surveys (Metcalfe et al., 2018; Schimel et al., 2015).
In addition, the photosynthetic capacity (\emph{V}
\textsubscript{cmax}and \emph{J} \textsubscript{max}) and response to
environmental conditions of Arctic plants are significantly different
from the current assumptions in the process models used to forecast
Arctic change (Rogers et al., 2017).

Non-vascular plants which dominate large areas of the Arctic, have very
different biochemical attributes and possess morphologies that are not
yet easily measured (Sections 3.2-3.3) (Holt \& Nelson, 2021). Water
content varies in non-vascular plants based almost entirely on
environmental conditions since they do not actively conduct water, which
greatly influences their spectral signatures (Figure 4). Variable water
content in the non-vascular ground layer visible to remote sensing
instruments presents a primary challenge and significant opportunity to
understand ecosystem function where they dominate. Methods using a
combination of VNIR, SWIR and MIR show promise for addressing water
content in non-vascular plants (Granlund et al., 2018; Neta et al.,
2010). Testing these estimations of water content at large spatial
scales remains a challenge. Most traits in non-vascular plants exhibit
different spectral responses from those of vascular plants (Cornelissen
et al., 2007), precluding direct use of existing trait retrieval
approaches developed for vascular plants. Recent work by (Thomson et
al., 2021) shows that chemometric estimation in non-vascular plants
using remote sensing is possible but there are only a few species
studied over a small area. Collectively these challenges have created
significant uncertainties in our understanding and modeling of Arctic
ecosystems (Fisher et al., 2018; Metcalfe et al., 2018; Myers-Smith et
al., 2019). Developing algorithms to estimate properties of non-vascular
plants using spectra and remote sensing will enable more accurate
quantification of plant functional traits.

IS can provide a tool to spatially map a variety of plant functional
traits across scales (e.g., from watershed to biome) which has been
demonstrated in many other biomes (e.g., Asner \& Martin, 2008; Martin
et al., 2008; Singh et al., 2015; Z. Wang et al., 2019, 2020). The
launch of SBG and other IS missions (e.g., EnMAP) will provide important
data to further enable spatiotemporal mapping of traits across the
broader Arctic tundra biome (Cawse-Nicholson et al., 2021) .
Simultaneously, spectral data from aircraft (e.g., Miller et al., 2019)
and low-altitude, near-surface platforms, including automated trams
(John A. Gamon et al., 2006; Goswami et al., 2011; Healey et al., 2014),
tower-mounted instruments (e.g., Drolet et al., 2014; Hilker et al.,
2011), and unoccupied aerial systems (Assmann et al., 2020; Cunliffe et
al., 2021; Shiklomanov et al., 2019; Yang et al., 2020), have increased
in northern high latitudes. These diverse spectral platforms are likely
to revolutionize our means for collecting trait information, which could
usher a new paradigm in our understanding and modeling of Arctic
vegetation dynamics. For example, using traits derived at watershed and
larger scales, the spatial variation in traits across plant species,
plant functional types (PFTs), communities, and even ecosystems can be
easily characterized (Figure 6). The drivers of spatial variation in
plant traits can also be investigated in combination with other core
remote sensing datasets, such as topography, climate, soil properties,
and vegetation maps, which is a key to understanding plant responses to
climate change (Dur\selectlanguage{ngerman}án et al., 2019) In addition, as a critical
uncertainty in process models (Rogers et al., 2017), the spatial
information on plant traits and biophysical properties inferred from IS
could be directly integrated with models to inform and improve
predictions (Fer et al., 2018; Shiklomanov et al., 2021), thereby
reducing current predictive uncertainties (Dietze et al., 2014).

The high spatial heterogeneity in vegetation composition, structure, and
abiotic environments (Section 3) pose a unique challenge to retrieve
plant traits using spectroscopy in the Arctic, as compared to other
biomes (Thomson et al., 2021; Yang et al., 2021). Traditional radiative
transfer model-based retrieval assumes the underlying vegetation layer
to be homogeneous (Jacquemoud et al., 2009), which is not met in tundra
landscapes. Empirical modeling that builds statistical relationships
between field trait observations and remote sensing spectra using
machining learning or latent variable techniques is a powerful
alternative (Curran et al., 1997; Singh et al., 2015; Z. Wang et al.,
2020; Wold et al., 2001). However, to construct an empirical model, a
plot-to-pixel connection is required. This requirement can be easily met
in forest or managed ecosystems where a single tree can occupy one or
multiple image pixels or a vegetation layer is homogenous across
relatively large areas. The Arctic poses challenges to plot-to-pixel
connections given the high level of species mixing in imagery pixels of
\textgreater{} 5 m resolution, which, combined with the remote and
meteorologically harsh environment, restricts the collection of
quantitative plot observations to develop trait models.

Unoccupied Aircraft Systems (UAS) remote sensing collects spectral data
at a very high spatial resolution and has shown great promise to serve
as an intermediate data source to connect ground and high-altitude
platforms (Thomson et al., 2021). In addition to the high spatial
heterogeneity, the common presence of non-vegetated surfaces (e.g.,
water, soil, rocks, and litter) and their highly variable spectral
characteristics (Section 3.5), present additional challenges to the
mapping of traits. Typically, non-vegetated surfaces can be excluded
over the course of trait model development and application in
low-latitude ecosystems (e.g., Wang et al., 2019), but non-vegetated
surfaces are highly mixed with vegetation surfaces in the Arctic, which
must be accounted for in trait model development. Lastly, the short
growing season and harsh environment means that vegetation spectra and
traits can change rapidly during the growing season (Section 4.4).
Therefore, trait models built from data collected at a certain time of
year may only be applicable to a small time window (e.g., \textless{} 1
month), as compared to low-latitude ecosystems where vegetation growth
peaks can persist for several months. SBG and other time-series spectral
platforms (e.g., PACE, CHIME, DESIS, EnMAP) hold great potential to
address this issue by facilitating the development of time-series models
that capture seasonal trait dynamics.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 6.} Example full shortwave (i.e., 350-2500 nm) albedo and
leaf nitrogen map, spectral variation and trait distribution across main
plant functional types or plant community types (Serbin et al.,
unpublished). Observations were collected from AVIRIS-NG at the Seward
Peninsula, AK. The PFT spectra shown in the bottom-left panel are
derived from AVIRIS-NG pixels that are at least 85\% dominated by the
(PFTs). Evergreen tree (ET), deciduous tree (DT), deciduous tall
shrub-alder (DTSA), deciduous tall shrub-willow (DTSW), deciduous low
shrub (DLS), deciduous dwarf shrub (DDS), evergreen shrub (ES), forb
(FO), dry graminoid (DG), wet graminoid (WG), moss (MO), and lichen
(LI).

\subsection*{4.4. Phenology}

{\label{phenology}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Phenological change in the tundra is characterized by rapid transition
seasons with volatile weather patterns. Snow cover over the winter
months and along the transition seasons complicates our ability to use
remote sensing metrics to detect such phenological change. Vegetation
indices that track both chlorophyll content (e.g., NDVI, NIRv, and EVI)
as well as photosynthetic capacity (e.g., PRI and CCI) are all sensitive
to the presence of snow cover and emergent senescent vegetation (Gamon
et al., 2013; Luus et al., 2017; Pierrat et al., 2021) (Figure 7).
Further, photosynthesis of Arctic tundra vegetation may occur under snow
cover (Parazoo et al., 2018; Starr \& Oberbauer, 2003), thereby severely
hindering the utility of spectroscopy for even detecting primary
productivity throughout the year. Reliance on these measures without
appropriate snow cover correction significantly inhibits their utility
to determine phenological change over winter and transition seasons. For
many tundra species, especially lichens, bryophytes, and evergreen
shrubs and trees exhibiting limited intra-annual biomass production,
changes in structural indices such as NDVI, NIRv, and EVI may better
capture changes in snow on/off periods than actual changes in biomass
(Figure 7) (Gamon et al., 2013; Luus et al., 2017; Pierrat et al.,
2021). Cold temperatures and the lack of liquid water can force dormancy
and limit photosynthesis, but if the vegetation remains green, changes
in NDVI may be nominal. Tundra species have been shown to acclimatize to
winter conditions by increasing the size of their pool of xanthophyll
cycle pigments and by maintaining that pool largely as antheraxanthin
and zeaxanthin (Verhoeven, 2014), which manifests as an increase in
total carotenoid pigments, and can be captured by the CCI (Gamon et al.,
2016; Wong et al., 2020). In evergreen needleleaf trees, strong seasonal
variation in photoprotective pigments can be detected via PRI and CCI -
attuned to variation in xanthophyll and bulk carotenoid pigments,
respectively (John A. Gamon et al., 2016; Wong \& Gamon, 2015b, 2015a).
Strong linkages between sensitivity of cessation of radial stem growth
in TTE spruce trees to end-of-season meteorology is also detectable by
changes in PRI (Eitel et al., 2019, 2020). Similar investigations of
PRI/CCI-growth and photosynthesis relationships on (non-tree) tundra
vegetation would help advance understanding of Arctic tundra phenology.
In addition, plant pigment composition serves as an important indicator
of the timing of autumn entry into this seasonally downregulated
(\emph{i.e.,} dormant) state (Figure 7). Hence, phenological analysis of
tundra vegetation requires integration of multiple spectral metrics,
preferably including narrowband measurements related to photoprotective
pigment variation, to isolate seasonal change in plant structural and
functional dynamics from confounding variation in snow cover.

\textbf{Figure 7} . a) Shows phenocam images from different points
during the year with varying degrees of snow cover on understory/tundra
vegetation at NEON Delta Junction, AK. b)-c) Shows commonly used
vegetation indices (NDVI, NIRv, PRI, and CCI) measured from a
tower-based spectrometer system PhotoSpec (Grossmann et al., 2018)
observing three understory tundra targets at a 30-minute resolution. d)
Shows daily average PAR and SZA. For b)-d), plotted is the 5-day moving
mean of the measured quantity. Shaded error bars indicate the standard
deviation of diurnal variability. Shaded blue regions indicate the
presence of snowfall on the understory as determined visually from
phenocam images.

Many spaceborne instruments are temporally incompatible with the rapid
phenological progression of tundra within a compressed growing season.
Historically, analyses of seasonal change across the Arctic may leverage
time series observations by the Landsat missions. However, the 16-day
revisit frequency precludes accurate detection of timing of important
events to quantify interannual variability in phenology. The similar
temporal resolution (14-day revisit) proposed for SBG may yield similar
challenges for phenology applications. Furthermore, due to the
prevalence of cloud cover, infrequent observations reduce the
opportunity for clear-sky imaging resulting in seasonally sparse or
irregular observations. Both these issues are made apparent by
tower-based observations (Figure 7), which enable continuous or high
frequency observations but lack the spatial coverage of spaceborne
observations. Tower-based observations in the boreal forest showed a 29
day difference in the timing of the springtime onset of photosynthesis
between evergreen and deciduous tree species (Pierrat et al., 2021) Such
temporal asynchrony - including among evergreen and deciduous tundra
plants - may not be adequately captured by spatially and temporally
coarse resolution spaceborne data. Thus, tower-based observations will
play an integral role in understanding Arctic phenological change.
Co-incident UAS observations can help bridge the spatiotemporal gap
through repeated measurements at a lower temporal resolution than
tower-based but at a much higher spatial range.

\subsection*{4.5. Diurnal variation}

{\label{diurnal-variation}}

The primary intrinsic mechanisms driving diurnal changes in spectral
reflectance are related to plant pigment composition, which regulate the
efficiencies of photochemistry through dynamic changes in
photoprotective pigment pools (xanthophylls, lutein) via sustained and
rapidly reversible non-photochemical quenching (Adams et al., 2004), and
hydration status for non-vascular vegetation. Dynamics among a cycling
group of carotenoids, violaxanthin, antheraxanthin and zeaxanthin (V, A,
and Z, respectively), known as the xanthophyll cycle, are especially
informative in this regard (Demmig-Adams et al., 1996). During the
photosynthetically active season, the state of the xanthophyll cycle
responds to diurnal variation in incoming light via enzymatically
regulated conversions between Z + A and V. These dynamics are often
captured using spectral indices sensitive to changes at 531 nm (the
photochemical reflectance index, PRI, (Gamon et al., 1992)). However,
most other vegetation spectral changes are not associated with diurnal
physiological dynamics; hence, these spectral indices (i.e., NDVI, NIRv,
and CCI) can remain relatively invariant (Figure 8) with the exception
of changes in moisture status for non-vascular vegetation (Figure 4).
Most spectral changes in the VIS-SWIR range throughout the course of the
day are associated with changes in viewing-illumination geometries, as
illustrated in subplots of NDVI, NIRv, CCI in Figure 8.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 8} . Diurnal cycles of commonly used vegetation indices
(NDVI, NIRv, PRI, CCI), PAR, and SZA collected from PhotoSpec at NEON
Delta Junction, AK for three vegetated understory targets. Diurnal
cycles are the average cycle over the 20 day period indicated and shaded
error bars are the standard deviation of measured quantities over the 20
day period.

The interaction of orbital mechanics with diurnal and seasonal variation
in vegetation indices results in possible bias due to overpass timing of
spaceborne instruments (Xiao et al., 2021). As shown in Figure 8, some
indices are diurnally invariant (e.g., NDVI, NIRv, and CCI) whereas PRI
is not. Consistent observation in the morning versus afternoon may
result in discrepancies in comparing observations from multiple
instruments and platforms. Relying exclusively on observations from
either morning or afternoon may obscure important diurnal processes at
work that govern plant productivity (see discussion of xanthophyll cycle
dynamics, above). On a seasonal basis, the extended diurnal photoperiod
experienced by high latitudes provides an opportunity for higher
frequency observation (i.e., multiple per day) of vegetation spectra by
spaceborne instruments during the peak season; however, this potential
advantage of deploying sensors that can collect multiple daily
observations rapidly diminishes in the shoulder seasons and winter when
photoperiod is abbreviated or non-existent (i.e., polar winter, Figure
8). Therefore, interpreting time series vegetation indices from arctic
tundra requires careful accounting for overpass timing, photoperiod, and
all associated responses to diurnal physiology and viewing/ illumination
geometry.

\section*{Recommendations}

{\label{recommendations}}

In ecosystems characterized by low accessibility and challenging
terrain, including the Arctic tundra, remote sensing observations
provide the only practical approach for observing, monitoring, and
quantifying changes in vegetation structure, function, and distribution.
However, to make the best use of these data and provide useful
information for ecological research and specifically process modeling
requires converting the remotely sensed observations (e.g., surface
radiance or reflectance) to derived biophysical or functional quantities
of interest (e.g., leaf area index, leaf functional traits). A range of
approaches have been used to convert spectroscopic measurements to plant
properties (Cawse-Nicholson et al., 2021; J. A. Gamon et al., 2019;
Serbin \& Townsend, 2020). However, the distinctive characteristics of
the Arctic as described above requires separate approaches, differing
from algorithms trained using data from temperate or tropical regions.
Thus, developing effective scaling approaches to allow for mapping
Arctic vegetation composition and function is a critical need and
challenge.

To address this challenge, we recommend that a multi-scale approach
(Table 2), including a mix of observations from laboratory, field, and
novel platform studies (e.g., UAS, tower-mounted, sensor network
including SpecNet) is used in coordination with satellite overpasses
when possible. These observations must then be assessed cohesively,
together with appropriate statistical and radiative transfer modeling
(Figure 9, Table 2). Leaf-scale and controlled laboratory studies are
often used to identify fundamental, underlying drivers of variation in
leaf optical properties to aid in the development of algorithms for
estimating leaf functional traits or evaluating leaf stress (e.g., F\selectlanguage{ngerman}éret
et al., 2011; J. A. Gamon et al., 1997; Hunt \& Rock, 1989). However,
such work has historically been limited in the Arctic in comparison with
other ecosystems, suggesting that considerable uncertainty will remain
through efforts to tie spectral observations to vegetation function. To
efficiently address this issue, future spectroscopy campaigns should
engage with laboratory and field studies to determine where multi-scale
observations could be established.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{Figure 9} . Framework for advancing understanding of Arctic
tundra ecosystem properties and dynamics through spectroscopy.

\textbf{Table 2} . Example recommended spectroscopic observations across
spatial scales that, coupled with spaceborne missions like SBG, would
improve understanding of Arctic tundra ecology.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Measurement type\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Spatial scale\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Temporal Scale\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Purpose\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Methods\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Example Citations\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Laboratory or leaf-level\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textless{}\textless{} 1 m\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Snapshot\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Variation in leaf-level optical properties; spectral response to stress
(e.g., drydown); development of leaf-scale functional trait models\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Collection of leaf and canopy spectra in controlled, manipulation
environments and in situ; leaf spectroscopy; collection of leaf
endmember spectra\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Hunt \& Rock, 1989; Serbin et al., 2019; Stasinski et al., 2021)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Field spectroscopy\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textless{}1 m - 10 m Canopy\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Snapshot\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Variation in spectral profiles by species at different scales;
developing scaling approaches\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Near-surface, non-contact measurement of plant canopy reflectance\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
(Davidson et al., 2016; Karl Fred Huemmrich et al., 2013)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Tower or automated tram\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
100s of m Canopy - Landscape\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Continuous (hourly) over seasons\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Diurnal and seasonal variation; variation between species; developing
scaling approaches\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Repeated manual or automated measurement of plant canopy
reflectance\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
(Hilker et al., 2011; Pierrat et al., 2021)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
UAS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0.2 - 1 m Landscape\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Snapshots\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Fine-scale spatial information at landscape to watershed scales;
scaling; seasonal variation\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Collection of surface reflectance and other composition and structural
information from unoccupied platforms\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
(Thomson et al., 2021; Yang et al., 2021)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Piloted aircraft\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0.5 - 5 m Landscape - regional\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Snapshots every few years\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Regional mapping and intermediate scale of observation\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Imaging spectroscopy\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
(Maguire et al., 2021; Singh et al., 2015)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Spacecraft\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
10m - 8 km Regional - continental\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Repeating daily - weekly\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Regional to continental-scale mapping, monitoring of coarse spatial
resolution phenological change\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Imaging spectroscopy, change detection and time series analysis\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
(Puletti et al., 2016; J. A. Wang et al., 2020)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Given the strong seasonality of Arctic vegetation (see Section 4.4),
additional consideration of the timescale of measurement and underlying
phenomena are critical to mapping efforts. Coupled observations across
spatial scales that can be conducted with high observation frequency
across seasons will help resolve this challenge (Table 2). Further, both
seasonal and interannual evaluations of change in the Arctic tundra must
consider the constraints of winter in terms of both sampling design and
physiological effects. The rapid seasonal progression (as discussed in
Section 4.4) imposes tremendous challenges for benchmarking the onset of
seasonal photosynthetic activity and tissue growth, quantifying
sensitivity to shoulder season stress, and detecting legacy effects on
productivity in subsequent seasons. In particular, the strong
seasonality of photoprotective pigments in evergreens (see Section 3.4
and 4.4), which complicate interpretation of spectral reflectance,
requires further research across the Arctic tundra domain to improve
parameterization of models of primary production. Additionally,
deciduous shrub species exhibit strong autumn leaf coloration with
photoprotective pigments present (and chlorophyll content declining)
during leaf senescence that may facilitate remotely sensed
quantification of species cover values. For example the red autumn
leaves of birch continue to actively photosynthesize even though
chlorophyll pigments may be less evident by traditional greeness-based
remote sensing (Patankar et al., 2013). Spectroscopy is well suited to
address these challenges, and could likely help disentangle the timing
of vegetation responses among plant functional types.

The use of optical remote sensing information over large regions (i.e.,
across continents) and through time (i.e., multiple decades) has
increased considerably in recent years (Ustin \& Middleton, 2021). This
includes IS data in the Arctic (e.g., (Langford et al., 2019)), given
the increased availability of these datasets (Miller et al., 2019).
However, new approaches for access, use, and analysis of large IS
datasets will be needed given the growing volume of remote sensing
observations across scales. For example, fusing high volume data from
novel UAS and ground-based platforms and expanded use of datasets across
scenes and locations will greatly increase the overall volume of data
for any given project. Seasonal weather conditions and sun-sensor
geometry changes in the Arctic mean that a considerable fraction of data
may have variable data quality over scenes or across scenes in a study
area. Similarly, current methods for retrieval of IS data require manual
search, collection and combining of data across different locations by
end-users. To ease and expand use of IS data for Arctic researchers, it
is recommended that data systems provide analysis-ready (e.g.,
geo-rectified and consistent atmospheric correction) and cloud-optimized
data storage formats (e.g., cloud-optimized GeoTIFF). In addition, files
should be accessible on storage buckets (i.e., basic container that
stores bulk data, usually used for organizing combinations of similar
datasets, e.g., S3 or Google cloud bucket) through cloud-based tools to
facilitate rapid search, filter, and extraction of data across specific
locations, regions and scenes. Similarly, it is recommended that
cloud-based tools facilitate basic analyses, data transformation,
subsetting, and application of mapping algorithms without downloading
large volumes of IS datasets but instead the final derived products or
results of the cloud pre-processing. For example, this could be
facilitated through the use of a cloud storage location within Google
Earth Engine (GEE) or GEE within the Python or R environments. By moving
IS data access to the cloud would also facilitate easy combination with
other remote sensing data or even multiscale observations, including UAS
data. This would also reduce the data latency from collection to
community use and allow more users to facilitate discovery of novel and
important patterns in phenomena in the Arctic biome.

We described important attributes of tundra ecosystems that impose
challenges for conducting spectroscopy, including plant functional type
and pixel-composition characteristics, intrinsic dimensionality, and
capacity for land cover classification, change detection, time series
observations, and characterizing biophysical properties. Future
spectroscopy missions such as SBG would be well-advised to consider the
challenges of complex biomes such as the Arctic tundra during mission
development and especially for data product generation. To address these
challenges, an optimized mixture of narrow and broad bands should be
considered for SBG to accurately characterize Arctic vegetation.

\section*{Acknowledgements}

{\label{acknowledgements}}

PRN and MJM were supported by NASA Arctic Boreal Vulnerability
Experiment (ABoVE) Grant 80NSSC19M0112. AJM was supported by an
appointment to the NASA Postdoctoral Program at the Jet Propulsion
Laboratory, administered by Universities Space Research Association
under contract with NASA. ZP was supported by the National Science
Foundation (NSF) Graduate Research Fellowship under Grant No.
DGE-1650604 and DGE-2034835. Any opinion, findings, and conclusions or
recommendations expressed in this material are those of the authors(s)
and do not necessarily reflect the views of the NSF. ELO and TSM were
supported by NASA ABoVE project 80NSSC19M0129. DY and SS were supported
by the Next-Generation Ecosystem Experiment in the Arctic (NGEE Arctic)
project that is supported by the Office of Biological and Environmental
Research in the United States Department of Energy, Office of Science,
and through the Department of Energy contract No. DE-SC0012704 to
Brookhaven National Laboratory, and SS was also partially supported by
the NASA Surface Biology and Geology (SBG) mission study (under contract
\#NNG20OB24A). GVF was partially supported by NASA ABoVE Grant
NNH16CP09C. SFO and SU were partially supported by NSF Office of Polar
Programs award number 1836898. SVZ, PKE, and KFH were partially
supported by NASA ABoVE grant NNX17AC58A, and SVZ was also partially
supported by NSF ITEX-AON 1836861. MV was partially supported by NOAA
NA16SEC4810008. AVIRIS-NG data was supported by the National Aeronautics
and Space Administration, Earth Science Division, and acquired by the
NASA ABoVE project. The data shown in Figure 5 were collected under a
project funded by the OPP of the NSF awarded to Donatella Zona (award
number 1204263) with additional logistical support funded by the NSF
OPP, and Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE),
an Earth Ventures (EV-1) investigation, under contract with NASA. A
portion of this research was carried out at the Jet Propulsion
Laboratory, California Institute of Technology, under a contract with
NASA (80NM0018D0004).

\section*{Data Availability
Statements}

{\label{data-availability-statements}}

Orthorectified radiances in Figure 2 from AVIRIS-NG's ABoVE campaign
acquisitions are available with documentation from the ORNL DAAC
(\emph{https://doi.org/10.3334/ORNLDAAC/1569}). They were analyzed with
the ISOFIT atmospheric correction codebase
(\emph{https://github.com/isofit/isofit}). This software is also
available via the DOI\emph{https://doi.org/10.5281/zenodo.4614338}).
Data for Figure 2 are cited in the text with the references below:

AVIRIS-NG data citation:

Miller, C.E., R.O. Green, D.R. Thompson, A.K. Thorpe, M. Eastwood, I.B.
Mccubbin, W. Olson-duvall, M. Bernas, C.M. Sarture, S. Nolte, L.M. Rios,
M.A. Hernandez, B.D. Bue, and S.R. Lundeen. 2019. ABoVE: Hyperspectral
Imagery from AVIRIS-NG, Alaskan and Canadian Arctic, 2017-2019. ORNL
DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1569

ISOFIT citation:

Thompson, David R., Vijay Natraj, Robert O. Green, Mark C. Helmlinger,
Bo-Cai Gao, and Michael L. Eastwood. ''Optimal estimation for imaging
spectrometer atmospheric correction.'' Remote Sensing of Environment 216
(2018): 355-373.\emph{https://doi.org/10.1016/j.rse.2018.07.003}

Reflectance spectra for Figure 3 are available on Github
(\emph{https://github.com/nelsopet/lecospec}) and are in the queue for
archiving at the ORNL DAAC (\emph{https://daac.ornl.gov/}). These data
will also be made available on EcoSIS (\emph{https://ecosis.org/}).

Reflectance spectra and hydration data for bryophytes in Figure 4 will
be archived at the Arctic Data Center by NCEAS repository
(\emph{https://www.nceas.ucsb.edu/arctic-data-center}\textbf{)}

Data for Figure 5 came from Arctic Vegetation Plots in Flux Tower
Footprints, North Slope, Alaska, 2014. This dataset provides spectral,
carbon flux, vegetation, environmental, and soil data collected from
plots located in the footprints of eddy covariance flux towers along a
300 km north-south latitudinal gradient from Barrow, to Atqasuk, and to
Ivotuk across the North Slope of Alaska in 2014. Within each of the five
flux tower footprints, 1x1-m quadrats were placed subjectively within
widespread habitat or micro-habitat types to map the dominant vegetation
communities and site environmental factors. Specific attributes included
species cover data and environmental, soil and spectral data (active
layer thaw depth, moss layer depth, organic horizon layer depth,
standing water depth, soil moisture status, vegetation height, LAI).

Data for Figure 5 are available at NASA's Earth Observing System Data
and Information System (EOSDIS) (Registration
required)\emph{https://doi.org/10.3334/ORNLDAAC/1546}with additional
information
at\emph{https://daac.ornl.gov/ABOVE/guides/Flux\_Tower\_Zona\_Veg\_Plots.html}.
This dataset is openly shared, without restriction, in accordance with
the EOSDIS Data Use Policy

Data for Figure 5 are cited in the text using the references below:

Davidson, S.J., and D. Zona. 2018. Arctic Vegetation Plots in Flux Tower
Footprints, North Slope, Alaska, 2014. ORNL DAAC, Oak Ridge, Tennessee,
USA. https://doi.org/10.3334/ORNLDAAC/1546

Davidson, S.J., Santos, M.J., Sloan, V.L., Watts, J.D., Phoenix, G.K.,
Oechel, W.C. and Zona, D. (2016) Mapping Arctic Tundra Vegetation
Communities Using Field Spectroscopy and Multispectral Satellite Data in
North Alaska, U.S.A., Remote Sensing, 8(12), 978;
https://doi.org/10.3390/rs8120978

Data archiving is underway for data presented in Figure 6 and will be
made available at the NGEE-Arctic data
portal\emph{https://ngee-arctic.ornl.gov/}

Data archiving is underway for data presented in Figures 7 and 8 and
will be made available at\emph{https://zenodo.org/}. Data was collected
and retrieved using PhotoSpec (Grossmann et al., 2018) installed at
Delta Junction Alaska as part of NASA ABoVE project 80NSSC19M0129.

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