Early Detection of Sage (Salvia officinalis L.) Responses to Ozone Using Reflectance Spectroscopy

Abstract

Advancements in techniques to rapidly and non-destructively detect the impact of tropospheric ozone (O3) on crops are required. This study demonstrates the capability of full-range (350–2500 nm) reflectance spectroscopy to characterize responses of asymptomatic sage leaves under an acute O3 exposure (200 ppb for 5 h). Using partial least squares regression, spectral models were developed for the estimation of several traits related to photosynthesis, the oxidative pressure induced by O3, and the antioxidant mechanisms adopted by plants to cope with the pollutant. Physiological traits were well predicted by spectroscopic models (average model goodness-of-fit for validation (R2): 0.65–0.90), whereas lower prediction performances were found for biochemical traits (R2: 0.42–0.71). Furthermore, even in the absence of visible symptoms, comparing the full-range spectral profiles, it was possible to distinguish with accuracy plants exposed to charcoal-filtered air from those exposed to O3. An O3 effect on sage spectra was detectable from 1 to 5 h from the beginning of the exposure, but ozonated plants quickly recovered after the fumigation. This O3-tolerance was confirmed by trends of vegetation indices and leaf traits derived from spectra, further highlighting the capability of reflectance spectroscopy to early detect the responses of crops to O3.

Hyperspectral Reflectance of Light-Adapted Leaves Can Predict Both Dark- and Light-Adapted Chl Fluorescence Parameters, and the Effects of Chronic Ozone Exposure on Date Palm (Phoenix dactylifera)

“…This might be due to a moderate O 3 susceptibility of date palm, but better outputs might be reached by raising the experimental/plant replications adopted for the hyperspectral phenotyping (this is especially true under field conditions, usually characterized by highly variable growth environments). On the one hand, these results confirm the capability of this approach (i.e., hyperspectral phenotyping through the analyses of spectral signatures) to detect O 3 effects on plants, as previously reported for various other abiotic and for biotic stressors (e.g., [ 8 , 17 , 51 , 52 ]). On the other hand, the present results confirm that the efficiency of this spectral approach is dependent on the sensitivity of the plants/cultivars to O 3 , as well as to the magnitude at which this environmental pressure is imposed on vegetation.…”

“…Spectral approaches have been shown as a valid alternative to standard measurements of photosynthetic activity in plants since they are able to estimate photosynthetic processes through VIs correlated with photosynthetic processes (e.g., the xanthophyll cycle by PRI [ 39 , 40 ]). In addition, specific and commonly used photosynthetic traits such as net photosynthesis, stomatal conductance, the maximum rate of carboxylation, and the maximum rate of electron transport [ 17 , 41 , 42 , 43 , 44 ] could directly be predicted from spectral data. Some attempts to evaluate the relations between VIs and a few ChlF parameters have also been reported (e.g., [ 19 , 20 ]).…”

“…Nevertheless, it is not surprising that best predictions of most of the modeled ChlF parameters were obtained using only the wavelengths from 600 to 900 nm (this also means that these parameters could be estimable by inexpensive optical instrumentation [ 9 ]). The use of narrower ranges, including only specific absorption wavelengths for the trait to be estimated, sometimes leads to better predictions than using wider ranges, since the incorporation of other spectral regions may reduce the prediction ability of trait-specific wavelengths [ 15 , 17 ]. Indeed, the 600–900 nm spectral range finally used for estimations of F 0 , F m , F v /F m , F 0 ′, Φ PSII , F v ’/F m ’, qP, qN, NPQ, qL, and D includes the red chlorophyll absorption peaks around 700 nm [ 45 ], the whole re-emission ChlF spectrum of a leaf ranging from 650 to 800 nm [ 46 ], as well as the red-edge at 700–750 nm [ 47 ].…”

“…Another ever-expanding approach regards the use of multivariate methods to directly model commonly used plant traits as a function of the spectral profiles. Advances in the sensitivity and portability of hyperspectral spectrometers, as well as in computational capacity and chemometric modeling (e.g., using partial least squares regression, PLSR [ 12 ]), have enabled the use of this approach to estimate a variety of commonly investigated plant traits and physiological processes based on foliar optical properties, including morphological, physiological, and biochemical parameters (e.g., [ 13 , 14 , 15 , 16 , 17 ]). The model calibration is accomplished by pairing leaf spectra, collected using a uniform and stable light source in a consistent manner, with independent and reliable reference measurements.…”

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

“…However, light intensity also influences leaf thickness [10], and then it often makes the output of the meter obscure [11]. In contrast, hyperspectral reflectance measurements may be used for detecting various responses from crops [12] or evaluating vegetation properties [13,14], and the use of reflectance for estimating chlorophyll content is being seriously considered. Some pigments, such as chlorophylls and carotenoids, absorb energy strongly in the ultraviolet, blue, and red regions, and then the reflectance and transmittance are weak [15].…”