Sand motion and dust emissions in arid environments contribute to increased amounts of fine particulate matter suspended in the atmosphere, with possible negative consequences for human health at the local and regional scale. Prior to implementation of dust control measures in 2002, dust emissions from Owens (dry) Lake, California were the single largest source of fine aerosol particulate matter with a diameter less than 10 μm (PM10) in the United States. Sarcobatus vermiculatus, a deep-rooted woody phreatophyte shrub, stabilizes the dune system surrounding Owens Lake and is highly effective at reducing sand motion and trapping wind-blown dust during high-wind events. Declines in S. vermiculatus growth or health due to drought or other disturbances that reduce resource availability could contribute to destabilization of the dune system and greater likelihood of erosion and poor air quality. Nonetheless, relationships between S. vermiculatus health and function and resource availability are not well understood in the Owens Lake vegetated dune system. The purpose of this study was to investigate the effects of surface water inputs (precipitation, runon) on the growth and function of S. vermiculatus after consecutive years of drought. The southwestern United States has been in the grips of a megadrought since 2000, with 2021 marking an exceptionally dry year. As surface conditions become increasingly dry, many plants invest in deeper roots for accessing water, assuming adequate carbon reserves are available for root growth. However, previous studies in S. vermiculatus have demonstrated strong co-limitation of plant growth by water and nutrients (which typically arrive from surface inputs), such that the species might maintain a mostly shallow root system to capture pulses of water and nutrients. Though, a complete shift in root distributions for S. vermiculatus is unlikely due to the presence and availability of regional shallow groundwater. To understand the vertical distribution of S. vermiculatus roots we collected soil cores across eight dune locations (depth range 1.8 to 7.6 m) and extracted roots at intervals of 30 and 45 cm. To understand spatiotemporal dynamics in soil moisture, especially in relation to surface water inputs, we carried out monthly neutron probe readings over 13 months across the same dune locations, with readings occurring at the same depth intervals as root sampling. Neutron probe data were converted to volumetric water content (VWC) using calibration equations. To understand spatiotemporal dynamics in shrub function, in relation to key environmental drivers, we collected predawn and midday stem xylem water potential (Ψ), predawn and midday chlorophyll fluorescence (Fv/Fm, ΦPSII) and midday leaf gas-exchange (net photosynthesis [Anet], stomatal conductance to water vapor [gs]) on 48 shrubs spread across eight dune locations (6 per dune location) at six timepoints. Fortuitously, our data collection spanned markedly different environmental conditions, capturing both the tail end of a multi-year drought and above-average summer precipitation. Across locations and timepoints, we found that VWC increased following precipitation and runon events and VWC in the top 0.9 m of soil was positively correlated (R2 = 0.76, p< 0.0001) with predawn stem water potential (Ψpd). This indicated that shrubs respond to and utilize surface soil moisture even after periods of prolonged drought. Increased Ψpd was associated with higher midday Anet, gs, and chlorophyll fluorescence, though leaf temperature and vapor pressure deficit (VPD) were the most important drivers of Anet regardless of Ψpd. Additionally, following a significant late summer rain event in September 2022, shrubs produced substantial new growth. This new growth and combined with increased C fixation could increase C availability for growth and reproduction leading into the next growing season. Collectively, our data suggest that surface water is a key resource for S. vermiculatus and, although shrubs access deep soil moisture and groundwater, precipitation events seem to be a key driver in their growth and function.

As a proxy for photosynthesis, ground-based and remotely-sensed measurements of solar-induced chlorophyll fluorescence (SIF) have enabled researchers to quantitatively interpretphotochemical activity of plants on a variety of scales. However, measuring SIF can be difficult, requiring fine-resolution spectral information and meticulous processing to tease apart fluorescence emissions from reflected solar radiation. Also, current SIF measuring instruments are expensive and require a high degree of technical interpretation. In recent studies, chlorophyll fluorescence has been measured in a different way by using multispectral light-emitting diodes (LED) products at night. By removing solar radiation and instead producing artificial LED light in the blue region of the electromagnetic spectrum (400-500 nm), these studies were successful in manipulating incoming light during nighttime conditions to induce fluorescence (a process known as LEDIF). While both SIF and LEDIF measurements explore light reemission in the red and far-red regions of the spectrum, it is worth further investigating the potential of LEDIF for its ability to capture ‘pure’ fluorescence characteristics at a lower cost. By deploying both a red/far-red photodiode sensor (Apogee Instruments, Utah, USA) and VIS-NIR spectrometer (FLAME, Ocean Insight, Florida, USA) during nighttime conditions, this study aims to capture temporal dynamics of chlorophyll fluorescence for the duration of an entire growing season in a vineyard. This research provides insight to further understand how plants respond to variations in environmental stress, such as drought, through their spectral responses and fluorescence emissions. By capturing spectra to quantify both reflectance-based vegetation indices (NDVI, NIRv, PRI, CCI, EVI) and fluorescence emissions (SIF/LEDIF), this research aims to explore differences in their individual performances and abilities to capture grape vineyard drought in California’s Central Valley. By simultaneously deploying in-situ LEDIF and SIF measuring instruments at a single site for an entire growing season, the strengths and weaknesses of each approach can be recognized, discussed, and learned from.

This thesis investigates novel remote sensing approaches to monitor and predict plant physiology and biochemistry in response to environmental stressors and seasonal changes. Divided into two chapters, each explores a distinct remote sensing technique and its application in understanding the connection between plant physiology and remote sensing.

Chapter 1 introduces a novel nighttime low-cost photodiode method, which was tested during a drought response experiment of LED-induced canopy-level chlorophyll a fluorescence (LEDIF) in Polygala myrtifolia. Far-red LEDIF (720 nm - 740 nm) was retrieved using low-cost photodiodes (LEDIFphotodiode) and a hyperspectral instrument (LEDIFhyperspectral). To link the LEDIF signal with physiological drought response, we tracked stomatal conductance (gsw) using a porometer as an indicator of plant water status, two leaf-level vegetation indices — photochemical reflectance index, PRI; normalized difference vegetation index, NDVI— to represent chlorophylls and xanthophyll pigment dynamics, respectively, and a pulse-amplitude modulation (PAM) device to measure photochemical and non-photochemical dynamics of photochemistry. Our results demonstrate a similar performance between the photodiode and hyperspectral retrievals of LEDIF (R2=0.77, P < 0.01). Furthermore, LEDIFphotodiode closely tracked drought responses with photochemical quenching (PQ, R2=0.69, P < 0.001), Fv/Fm (R2=0.59, P < 0.001), and leaf-level PRI (R2= 0.59, P<0.05). The results demonstrate the potential of this cost-effective method to accurately track changes in photosynthetic status and overall plant health, offering valuable insights into the relationships between the physiological mechanisms of photosynthesis and chlorophyll fluorescence.

Chapter 2 employs hyperspectral reflectance data to predict an array of chlorophyll and carotenoid pigment concentrations in Pinus palustris (Longleaf Pine) using a partial least squares regression modeling approach. This study took place in north-central Florida, at the Ordway Swisher Biological Station (OSBS), and more specifically, the National Ecological Observation Network (NEON) flux tower site within it. The research site is dominated by mature Longleaf Pines and low-lying perennial grasses. From six Longleaf Pine trees, branches were harvested, and needles were either 1) immediately stored for later pigment extraction and 2) made into needle mats to retrieve reflectance measurements with our hyperspectral spectroradiometer’s leaf clip. Needle mats were assembled by laying individual needles flat and continuously side by side until they were approximately 5-6 cm wide, and were held together by two pieces of tape, arranged at the top and bottom of the needles. Using a PLSR modeling approach, our prediction task for each PLSR model was to predict the average pigment content of a tree, using a hyperspectral reflectance measurement of a single needle mat (for each pigment/pool) per tree. Our results reveal the potential of hyperspectral remote sensing to estimate plant pigments accurately and efficiently; our PLSR models successfully predicted the concentrations with R² values > 50% for eight of the ten pigments/pools. We were able to best predict lutein and neoxanthin, as well as chlorophyll b and a (R2 = 0.91, RMSE = 0.01; R2 = 0.77, RMSE = 0.0; R2 = 0.68, RMSE = 0.02; R2 = 0.65, RMSE = 0.05, respectively). This research demonstrates the value of leaf-level remote sensing in advancing our understanding of the physiological status of evergreen species and their underlying pigments.

Collectively, these chapters showcase the power of remote sensing for monitoring and predicting plant responses to environmental stressors and seasonal changes. They offer valuable insights into the relationships between plant physiology and remote sensing, paving the way for improved strategies to assess plant health and resilience in the face of changing environmental conditions.

Plain language summary

Background

Results

Conclusions