Diversity in stomatal and hydraulic responses to post-flowering drought in common (Phaseolus vulgaris) and tepary (P. acutifolius) beans.

Plants differ widely in how soil drying affects stomatal conductance (gs) and leaf water potential (ψleaf), and in the underlying physiological controls. Efforts to breed crops for drought resilience would benefit from a better understanding of these mechanisms and their diversity. We grew 12 diverse genotypes of common bean (Phaseolus vulgaris L.) and four of tepary bean (P. acutifolius; a highly drought resilient species) in the field under irrigation and post-flowering drought, and quantified responses of gs and ψleaf, and their controls (soil water potential [ψsoil], evaporative demand [Δw] and plant hydraulic conductance [K]). We hypothesised that (i) common beans would be more "isohydric" (i.e., exhibit strong stomatal closure in drought, minimising ψleaf decline) than tepary beans, and that genotypes with larger ψleaf decline (more "anisohydric") would exhibit (ii) smaller increases in Δw, due to less suppression of evaporative cooling by stomatal closure and hence less canopy warming, but (iii) larger K declines due to ψleaf decline. Contrary to our hypotheses, we found that half of the common bean genotypes were similarly anisohydric to most tepary beans; canopy temperature was cooler in isohydric genotypes leading to smaller increases in Δw in drought; and that stomatal closure and K decline were similar in isohydric and anisohydric genotypes. gs and ψleaf were virtually insensitive to drought in one tepary genotype (G40068). Our results highlight the potential importance of non-stomatal mechanisms for leaf cooling, and the variability in drought resilience traits among closely related crop legumes.

Tracking canopy chlorophyll fluorescence with a low-cost light emitting diode platform.

Chlorophyll fluorescence measured at the leaf scale through pulse amplitude modulation (PAM) has provided valuable insight into photosynthesis. At the canopy- and satellite-scale, solar-induced fluorescence (SIF) provides a method to estimate the photosynthetic activity of plants across spatiotemporal scales. However, retrieving SIF signal remotely requires instruments with high spectral resolution, making it difficult and often expensive to measure canopy-level steady-state chlorophyll fluorescence under natural sunlight. Considering this, we built a novel low-cost photodiode system that retrieves far-red chlorophyll fluorescence emission induced by a blue light emitting diode (LED) light source, for 2 h at night, above the canopy. Our objective was to determine if an active remote sensing-based night-time photodiode method could track changes in canopy-scale LED-induced chlorophyll fluorescence (LEDIF) during an imposed drought on a broadleaf evergreen shrub, Polygala myrtifolia. Far-red LEDIF (720-740 nm) was retrieved using low-cost photodiodes (LEDIFphotodiode) and validated against measurements from a hyperspectral spectroradiometer (LEDIFhyperspectral). To link the LEDIF signal with physiological drought response, we tracked stomatal conductance (gsw) using a porometer, two leaf-level vegetation indices-photochemical reflectance index and normalized difference vegetation index-to represent xanthophyll and chlorophyll pigment dynamics, respectively, and a PAM fluorimeter to measure photochemical and non-photochemical dynamics. Our results demonstrate a similar performance between the photodiode and hyperspectral retrievals of LEDIF (R2 = 0.77). Furthermore, LEDIFphotodiode closely tracked drought responses associated with a decrease in photochemical quenching (R2 = 0.69), Fv/Fm (R2 = 0.59) and leaf-level photochemical reflectance index (R2 = 0.59). Therefore, the low-cost LEDIFphotodiode approach has the potential to be a meaningful indicator of photosynthetic activity at spatial scales greater than an individual leaf and over time.

Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications.

Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.

TSWIFT: Tower Spectrometer on Wheels for Investigating Frequent Timeseries for high-throughput phenotyping of vegetation physiology.

BACKGROUND: Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF). RESULTS: We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions. CONCLUSIONS: TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.

High-throughput drone-based remote sensing reliably tracks phenology in thousands of conifer seedlings.

Phenology is an important indicator of environmental variation and climate change impacts on tree responses. In conifers, monitoring phenology of photosynthesis through remote sensing has been unreliable, because needle foliage varies little throughout the year. This is challenging for modelling ecosystem carbon uptake and monitoring phenology for enhanced breeding (genomic selection) and forest health. Here, we demonstrate that drone-based carotenoid-sensitive spectral indices, such as the Chl/carotenoid index (CCI), can be used to track phenology in conifers by taking advantage of the close relationship between seasonally changing carotenoid levels and the variation of photosynthetic activity. Physiological ground measurements, including photosynthetic pigments and maximum quantum yield of Chl fluorescence, indicated that CCI tracked the variation of photosynthetic activity better than other vegetation indices for 30 white spruce seedlings measured over 1 yr. A machine-learning approach, using CCI derived from drone-based multispectral imagery, was used to model phenology of photosynthesis for the entire pedigree population (6000 seedlings). This high-throughput drone-based phenotyping approach is suitable for studying climate change impacts and environmental variation on the physiological status of thousands of field-grown conifers at unprecedented speed and scale.

Tracking the phenology of photosynthesis using carotenoid-sensitive and near-infrared reflectance vegetation indices in a temperate evergreen and mixed deciduous forest.

Photosynthetic phenology is an important indicator of annual gross primary productivity (GPP). Assessing photosynthetic phenology remotely is difficult for evergreen conifers as they remain green year-round. Carotenoid-based vegetation indices such as the photochemical reflectance index (PRI) and chlorophyll/carotenoid index (CCI) are promising tools to remotely track the invisible phenology of photosynthesis by assessing carotenoid pigment dynamics. PRI, CCI and the near-infrared reflectance of vegetation (NIRV ) index may act as proxies of photosynthetic efficiency (ɛ), an important parameter in light-use efficiency models, or direct proxies of photosynthesis. To understand the physiological mechanisms reflected by PRI and CCI and the ability of vegetation indices to act as proxies of photosynthetic activity for estimating GPP, we measured leaf pigment composition, PRI, CCI, NIRV and photosynthetic activity at the leaf and canopy scales over 2 years in an evergreen and mixed deciduous forest. PRI and CCI captured the large seasonal carotenoid/chlorophyll ratio changes and good relationships were observed between PRI-ɛ and CCI-photosynthesis and NIRV -photosynthesis. PRI-, CCI- and NIRV -based models effectively tracked observed seasonal GPP. We propose that carotenoid-based and near-infrared reflectance vegetation indices may provide useful proxies of photosynthetic activity and can improve remote sensing-based models of GPP in evergreen and deciduous forests.

A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers.

In evergreen conifers, where the foliage amount changes little with season, accurate detection of the underlying "photosynthetic phenology" from satellite remote sensing has been difficult, presenting challenges for global models of ecosystem carbon uptake. Here, we report a close correspondence between seasonally changing foliar pigment levels, expressed as chlorophyll/carotenoid ratios, and evergreen photosynthetic activity, leading to a "chlorophyll/carotenoid index" (CCI) that tracks evergreen photosynthesis at multiple spatial scales. When calculated from NASA's Moderate Resolution Imaging Spectroradiometer satellite sensor, the CCI closely follows the seasonal patterns of daily gross primary productivity of evergreen conifer stands measured by eddy covariance. This discovery provides a way of monitoring evergreen photosynthetic activity from optical remote sensing, and indicates an important regulatory role for carotenoid pigments in evergreen photosynthesis. Improved methods of monitoring photosynthesis from space can improve our understanding of the global carbon budget in a warming world of changing vegetation phenology.

Zeaxanthin-independent energy quenching and alternative electron sinks cause a decoupling of the relationship between the photochemical reflectance index (PRI) and photosynthesis in an evergreen conifer during spring.

In evergreen conifers, the winter down-regulation of photosynthesis and its recovery during spring are the result of a reorganization of the chloroplast and adjustments of energy-quenching mechanisms. These phenological changes may remain undetected by remote sensing, as conifers retain green foliage during periods of photosynthetic down-regulation. The aim was to assess if the timing of the spring recovery of photosynthesis and energy-quenching characteristics are accurately monitored by the photochemical reflectance index (PRI) in the evergreen conifer Pinus strobus. The recovery of photosynthesis was studied using chlorophyll fluorescence, leaf gas exchange, leaf spectral reflectance, and photosynthetic pigment measurements. To assess if climate change might affect the recovery of photosynthesis, seedlings were exposed to cold spring conditions or warm spring conditions with elevated temperature. An early spring decoupling of the relationship between photosynthesis and PRI in both treatments was observed. This was caused by differences between the timing of the recovery of photosynthesis and the timing of carotenoid and chlorophyll pool size adjustments which are the main factors controlling PRI during spring. It was also demonstrated that zeaxanthin-independent NPQ mechanisms undetected by PRI further contributed to the early spring decoupling of the PRI-LUE relationship. An important mechanism undetected by PRI seems to involve increased electron transport around photosystem I, which was a significant energy sink during the entire spring transition, particularly in needles exposed to a combination of high light and cold temperatures.

The photochemical reflectance index provides an optical indicator of spring photosynthetic activation in evergreen conifers.

In evergreens, the seasonal down-regulation and reactivation of photosynthesis is largely invisible and difficult to assess with remote sensing. This invisible phenology may be changing as a result of climate change. To better understand the mechanism and timing of these hidden physiological transitions, we explored several assays and optical indicators of spring photosynthetic activation in conifers exposed to a boreal climate. The photochemical reflectance index (PRI), chlorophyll fluorescence, and leaf pigments for evergreen conifer seedlings were monitored over 1 yr of a boreal climate with the addition of gas exchange during the spring. PRI, electron transport rate, pigment levels, light-use efficiency and photosynthesis all exhibited striking seasonal changes, with varying kinetics and strengths of correlation, which were used to evaluate the mechanisms and timing of spring activation. PRI and pigment pools were closely timed with photosynthetic reactivation measured by gas exchange. The PRI provided a clear optical indicator of spring photosynthetic activation that was detectable at leaf and stand scales in conifers. We propose that PRI might provide a useful metric of effective growing season length amenable to remote sensing and could improve remote-sensing-driven models of carbon uptake in evergreen ecosystems.

Three causes of variation in the photochemical reflectance index (PRI) in evergreen conifers.

The photochemical reflectance index (PRI) reflects diurnal xanthophyll cycle activity and is also influenced by seasonally changing carotenoid : Chl pigment ratios. Both changing pigment pools and xanthophyll cycle activity contribute to photoprotection in evergreen conifers exposed to boreal winters, but they operate over different timescales, and their relative contribution to the PRI signal has often been unclear. To clarify these responses and their contribution to the PRI signal, leaf PRI, pigment composition, temperature and irradiance were monitored over 2 yr for two evergreen conifers (Pinus contorta and Pinus ponderosa) in a boreal climate. PRI was affected by three distinct processes operating over different timescales and exhibiting contrasting spectral responses. Over the 2 yr study period, the greatest change in PRI resulted from seasonally changing carotenoid : Chl pigment ratios, followed by a previously unreported shifting leaf albedo during periods of deep cold. Remarkably, the smallest change was attributable to the xanthophyll cycle. To properly distinguish these three effects, interpretation of PRI must consider temporal context, physiological responses to evolving environmental conditions, and spectral response. Consideration of the separate mechanisms affecting PRI over different timescales could greatly improve efforts to monitor changing photosynthetic activity using optical remote sensing.