Biophysically Informed Imaging Acquisition of Plant Water Status

Abstract

Vegetation controls carbon and water fluxes because of the fundamental tradeoff between carbon dioxide uptake and water loss occurring when stomata are open. Quantifying the rates of this exchange typically requires either intensive gas exchange or destructive harvesting of tissues and mass spectrometry analyses. Recent developments in high-throughput methods have enhanced our capacity to empirically test plant-environmental interactions. The vast integration characterizing satellite remote sensing methods masks organ-level physiological mechanisms limiting the predictive capability of current process models. Hence, more ground truth studies are necessary to determine the amount of mechanistic information needed to improve our understanding of forest, crop, and land management. Imaging methodologies, such as thermal and chlorophyll a fluorescence, are currently used to collect information for relevant traits such as water use, growth, and stress response. We tested these techniques during progressive drought across species with different susceptibility in controlled greenhouse conditions. We chose two highly represented tree species in North America: the gymnosperm Pinus ponderosa and the angiosperm Populus tremuloides. To better explore the whole drought response parameter space, we also tested a crop (Brassica rapa) and desert shrub (Artemisia tridentata). Thermal and fluorescence images of the canopy were coupled with leaf-level measurements as we performed three tests to predict drought response using (1) leaf temperature, (2) chlorophyll a fluorescence, and (3) the combination of the two. At 5 days of drought, leaf temperature increased 7 and 10%, accounting for 63 and 73% of the variation in stomatal conductance for both tree species, respectively. The fluorescence signal from images decreased ∼12% and ∼83% in moderately and severely droughted leaves respectively, reaching zero at mortality. Leaf water status was then predicted using a Bayesian approach that incorporated measurements' uncertainty and parsimony in the analysis of the parameters. Changes in canopy temperature provided confident predictions for the reductions of daily evapotranspiration at the onset of drought. Empirically combining thermal and fluorescence measurements improved predictions (R 2 = 0.81) of midday leaf water potential compared to univariate models. Our results represent an important step toward quantifying plant water status during drought using first principles that do not require species-specific information.

The love-hate relationship between chlorophyll a and water in PSII affects fluorescence products

“…The cut itself and the time spent at the FluorCAM could have in fact unsettled the in vivo leaf water status, with leaves starting to passively leak water from the wound and quick stomata shutdown. This was already implied by the poor predictions of stomatal conductance from RChlF obtained on the same experimental panel (Beverly et al 2020). So, we tested RChlF against the RWC finding an even higher linear correlation (R 2 = 0.85) than for LWC (Figs.…”

“…All environmental and soil data were recorded at 15-min intervals on CR3000 data loggers (Campbell Scientific Inc., Logan, UT). A total of 150 plants (n = 30 replications in each species) were spaced across the greenhouse and at least six replicates per species for droughted and fully watered (controls) plants were randomly selected for measurements over 52 d of progressive drought as reported in Beverly et al (2020).…”

“…To investigate the possible effects of leaf hydration on ChlF images, we followed plants naturally dried through a complete water withholding treatment for consecutive 52 d in controlled greenhouse conditions (Beverly et al 2020). Our experimental panel included species with extremely different leaf characteristics and diverse drought susceptibility: two crop types in the species B. rapa, two common North American woody species, and a shrub (Table 1).…”

AI-assisted image analysis and physiological validation for progressive drought detection in a diverse panel of Gossypium hirsutum L.

“…For instance, the Delta Pine16 genotype showed a leaf temperature mean almost 5°C lower than Tipo Chaco in the same WW conditions, while two morphologically dissimilar genotypes, namely, Dwarf Red Harrison and Siokara L23 —one with dark red/green, medium-size leaves and one with green, okra-like type of leaves—showed very similar leaf temperatures. It is known that leaf temperature is affected by changes in the microclimate at the canopy level and this can be somewhat variable in greenhouse conditions based on the spatial locations of the pots and on the time of the day ( Beverly et al., 2020 ). However, drawing significant relationships between leaf temperature per se and genotypic variation was not the scope of the current work, and the diverse experimental panel was used as a robust testbed for the development of the novel IP/ML software pipeline for thermal data.…”