Figure 4 . Spectral signature at varying moisture saturation levels measured as a fraction of the saturated water mass of S. capillifolium (left) and 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 H2O at interval * g total H2O-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 inSphagnum spp. and Pleurocarpous mosses, such as Hylocomium splendens and 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 Sphagnum species under high light conditions may develop photoprotective pigments that will affect their reflectance. Studies of open-growing 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 measurementsin 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 (< 1 m) and there are sufficient and appropriate bandpasses (Räsänen & 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.

3.4. 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., 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 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 Salix spp. (willows) orAlnus 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 Eriophorum vaginatum (cottongrass) is a dominant species over very large areas throughout the Arctic (0.9x 106 km2; (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., 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., Picea and Pinus ), deciduous needleleaf (e.g., Larix ), and deciduous broadleaf (e.g., 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 (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.

3.5. 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 ~ 5 m2) 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 (> 80% between 400 - 900 nm, with values > 50% between 900 – 1200 nm), while clean ice, as from a glacier, has relatively high reflectivity (> 60% between 400 – 600nm, steadily decreasing to < 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 (~650 nm although this varies by sensor), or PRI which uses green (~ 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.

3.6. 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.