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
(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.
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.
4.5. 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.