The Effect of Differential Growth Rates across Plants on Spectral Predictions of Physiological Parameters

Abstract

Leaves of various ages and positions in a plant's canopy can present distinct physiological, morphological and anatomical characteristics, leading to complexities in selecting a single leaf for spectral representation of an entire plant. A fortiori, as growth rates between canopies differ, spectral-based comparisons across multiple plants – often based on leaves' position but not age – becomes an even more challenging mission. This study explores the effect of differential growth rates on the reflectance variability between leaves of different canopies, and its implication on physiological predictions made by widely-used spectral indices. Two distinct irrigation treatments were applied for one month, in order to trigger the formation of different growth rates between two groups of grapevines. Throughout the experiment, the plants were physiologically and morphologically monitored, while leaves from every part of their canopies were spectrally and histologically sampled. As the control vines were constantly developing new leaves, the water deficit plants were experiencing growth inhibition, resulting in leaves of different age at similar nodal position across the treatments. This modification of the age-position correlation was characterized by a near infrared reflectance difference between younger and older leaves, which was found to be exponentially correlated (R2 = 0.98) to the age-dependent area of intercellular air spaces within the spongy parenchyma. Overall, the foliage of the control plant became more spectrally variable, creating complications for intra- and inter-treatment leaf-based comparisons. Of the derived indices, the Structure-Insensitive Pigment Index (SIPI) was found indifferent to the age-position effect, allowing the treatments to be compared at any nodal position, while a Normalized Difference Vegetation Index (NDVI)-based stomatal conductance prediction was substantially affected by differential growth rates. As various biotic and abiotic factors may form distinctions in growth, future precision agriculture studies should consider its spectral effect on physiological predictions.

Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment

“…Secondly, at the aerial and satellite levels, VIS reflectance values would have to be corrected for numerous biases that originate from leaf orientation, shadow/sunlit fractions, canopy architecture, viewing and illumination geometry, and background effects (Gamon et al, 1990;Hall et al, 2008;Hilker et al, 2008). Thirdly, the physiological variability within a tree's canopy -originating from the abovementioned factors and from biological and phenological differences (Rapaport et al, 2014) -would have to be taken into account. Fourthly, applying the WABIs through a single modern spectral instrument -designed to overcome the slowness and complexity of handling multiple sensors and analyzing their datasets -would probably incur high financial costs.…”

“…In order to strengthen the physiological findings throughout the first and second greenhouse experiments, supporting morphological measurements were also taken; these included calculations of total foliage areas according to the model of Rapaport et al (2014), and counting of the number of nodal positions along each vine's branch.…”

“…Normalized values of reflectance (q) were derived from supplementary readings of white reference standards (BaSO 4 tablets) and dark current (noise). In order to spectrally-represent an entire vine with a single leaf -considering the effect of water stress on the age variability within a plant's canopy -the reflectance profile method of Rapaport et al (2014) was applied at every measuring point. Twelve commonly used ''universal" narrow-band indices were calculated for each plant (Table 1), including two versions of the photochemical reflectance index (PRI; Gamon et al, 1992), the red-edge inflection point (REIP; Horler et al, 1983), the sum of red-edge first derivatives ( P dq; Filella and Penuelas, 1994), the modified normalized difference vegetation index (mNDVI; Gitelson et al, 1996), the structure insensitive pigment index (SIPI; Penuelas et al, 1995), the normalized difference vegetation index (NDVI; Jordan, 1969), the normalized difference infrared index (NDII; Hardisky et al, 1983), the normalized difference water index (NDWI; Gao, 1996), the water index (WI; Penuelas et al, 1993), the maximum difference water index (MDWI; Eitel et al, 2006) and the moisture stress index (MSI; Rock et al, 1986).…”

“…In order to validate the first experiment's leaf-scale regressions for the canopy level, the reflective signature of four spectrallyrepresentative leaves -selected according to the protocol of Rapaport et al (2014) -was obtained from each vine of the second experiment. Evaluation of index potential under the third experiment's field conditions included a selection of a small, representative foliage portion from each vine.…”

Examining the relationship between the Enhanced Vegetation Index and grapevine phenology

“…Of particular interest is the fact that this low or negative growth rate is usually followed by rapid growth rates, which can be explained by either physiological mechanisms that tend to offset the climate signal in the vine phenology or to newly favourable environmental conditions (optimal thermal conditions) necessary for enhanced vegetative growth [Hendrickson et al, 2004;Magalhães, 2008]. This demonstrates the usefulness of the rate of growth as an essential vegetation metrics that allows capturing the vegetative development [Rapaport et al, 2014]. As such, the obtained correlations reveal that the variability in the greenness measured by satellite derived vegetation metrics is able to capture the fluctuation in both phenological timings and intervals.…”