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Chlorophyll fluorescence can be remotely sensed in open fields via the Fraunhofer atmospheric absorption lines of oxygen and is termed Solar-Induced Fluorescence (SIF). SIF has been extensively related to carbon assimilation at global ecology scale and was interpreted as electron transport rate. However, SIF was shown to be unrelated directly to carbon assimilation at finer-scale resolution and may be related to other photosynthetic processes, such as non-photochemical quenching. This raises the question how exactly the SIF relates to actual photosynthetic activity. Based on a recently introduced spectral index that relates the photochemical fraction of SIF to the actual electron transport rate, this study presents the formulation of a structural equation, relating the remotely sensed electron transport rate index to fluorescence yield which considers the various fates of energetic quanta and electron excitation. The proposed structural equations are used to examine and interpret the relation between the novel spectral index and seasonal growth of corn (Z. mays Sh2, ‘super sweet’) on a platform of fertilization concentration gradient. Potential uses, practical and theoretical, for the proposed structural equations are discussed.

Plants transpire water through their tissues in order to move nutrients and water to the cells. Transpiration includes various mechanisms, primarily stomata movement, which controls the rate of CO2 and water vapor exchange between the tissues and the atmosphere. Assessment of stomatal conductance is available for gas exchange techniques at leaf level, yet these techniques are not scalable to the whole plant let alone a large vegetation area. Hyperspectral reflectance spectroscopy, which acquires hundreds of bands in a single scan, may capture a glimpse of the crop’s physiological activity and therefore meet the scalability challenge. In this study, classic chemometric analyses are used alongside advanced statistical learning algorithms in order to identify stomatal conductance cues in hyperspectral measurements of cotton plants experiencing a gradient of irrigation. Random forest of regression trees identified 23 wavelengths related to both structural properties of the plant as well as water content. Partial least squares regression succeeded in relating these wavelengths to stomatal conductance, but only partially (R2 < 0.2). An artificial neural network algorithm reported an R2 = 0.54 with an 89% error-free performance on the same data subset. This study discusses implementation of machine learning methodologies as a benchmark for deeper analysis of spectral information, such as required when searching for plant physiology-related attenuations embedded within reflectance spectra.

Photosynthesis performance can be assessed quantitatively with light response curves. These curves record the Electron Transport Rate (ETR) as a function of light intensity. Then, statistical fit on these curves parameterize light use efficiency, maximum photosynthetic activity and the reaction of the apparatus to stress. While this technique is performed with portable fluorometers in field conditions, it is difficult to scale it to the canopy level. The Fraunhofer line discrimination technique, which detects fluorescence signals emitted during photosynthesis, is a promising method to assess photosynthetic performance of canopies. In this study, we define a remote sensing ETR index based on a combination of three parameters: sun-induced fluorescence, normalized differential vegetation index and light intensity. Two representatives of C3 and C4 photosynthesis, L. sativa and Z. mays, experienced a fertilization concentrations gradient. ETR increased with light intensity in both crops. In L. sativa, ETR assumed a linear relationship between the photosynthetic activity and light intensity, with a correlation of R2 = 0.99 to the portable fluorometer. Additional parametrization revealed a resilience of its reaction centers to photoinhibition in maximum light intensities. When Z. mays experienced open field conditions, ETR correlated with the plant’s status. While the results of this study are promising, the index still requires validation in terms of temporal track and spatial variability.