Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

Abstract

The development of spectral methods of remote sensing, including measurement of a photochemical reflectance index (PRI), is a prospective trend in precision agriculture. There are many works which have investigated the connection between photosynthetic parameters and PRI; however, their results varied and were sometimes contradictory. For this paper, we performed a meta-analysis of works in this field. Here, only linear correlations of PRI with photosynthetic parameters—including quantum yield of photosystem II (ΔF/Fm’), nonphotochemical quenching of chlorophyll fluorescence (NPQ), and light use efficiency (LUE)—were investigated. First, it was shown that the correlations were dependent on conditions of PRI measurements (leaf or canopy; artificial light or sunlight). Second, it was shown that a minimal level of the photosynthetic stress, and the variation of this level among investigated plants, can influence the linear correlation of PRI with ΔF/Fm’ and NPQ; the effect was dependent on conditions of measurements. In contrast, the distribution of LUE among plants did not influence its correlation with PRI. Thus, the meta-analysis shows that the distribution of photosynthetic parameters among investigated plants can be an important factor that influences the efficiency of remote sensing on the basis of the PRI measurement.

1. Introduction

Plants growing under natural conditions can be affected by various environmental stressors, including drought [1,2,3], salt stress [4,5,6,7], temperature stress [2,8,9,10], light stress [10,11], etc. The stressors decrease the probability of survival and productivity of plants; in particular, they damage the photosynthetic process [12]. Early monitoring of these damages plays an important role in precision agriculture and ecological monitoring. As a result, remote sensing of the photosynthetic process is an important practical problem [13,14,15]. There are many methods that can be used for the analysis of the photosynthesis process in plants; in particular, pulse-amplitude-modulation (PAM)-fluorometry [16,17], JIP-test [18,19,20], and analysis of CO₂ exchange [21,22,23,24,25]. These methods are very effective in the laboratory. However, their use for remote sensing of the photosynthetic process under environmental conditions is very limited. Currently, remote sensing of photosynthetic parameters in plants is often based on reflectance indices. In particular, they include:

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a photochemical reflectance index (PRI), which shows changes in the xanthophyll cycle [26];

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a normalized difference vegetation index (NDVI) [27], an optimized soil-adjusted vegetation index (OSAVI) [28], and an enhanced vegetation index (EVI) [29,30], which quantitatively show a photosynthesizing biomass;

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a chlorophyll index (CI), which shows chlorophyll content in leaves [31,32]; and

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a structural independent pigment index (SIPI), which is connected to the ratio of carotenoids to chlorophylls [33].

There are other reflectance indices, see [34,35].

These indices are important tools for the remote sensing of the photosynthetic process in plants. In respect to the monitoring of fast changes in the photosynthetic process in plants (especially, photosynthetic stress), PRI is the most interesting reflectance index. This index, which is related to the fast transition in the xanthophyll cycle, is based on the rapid decrease of reflectance at 531 nm that is caused by the dissipation of light energy associated with xanthophyll de-epoxidation [26,36]. It is known that the de-epoxidation of xanthophylls plays an important role in the increase of nonphotochemical quenching of fluorescence of chlorophyll (NPQ) under stress conditions [12,37]. Thus, it can be expected that PRI is strongly connected with NPQ (and other photosynthetic parameters) under different environmental conditions.

There are numerous works that investigate the correlation between PRI and NPQ under different stressors [38,39,40,41,42,43,44]. Connections between PRI and other photosynthetic parameters, including a quantum yield of photosystem II (ΔF/F_m’) [38,41,42,45,46,47,48,49], photosynthetic light use efficiency (LUE) [3,48,50,51,52,53,54,55], and net CO₂ uptake [47,56,57,58,59], are actively being investigated. However, the results of these different works vary considerably, e.g., the linear correlation coefficients between PRI and NPQ can range from −0.90 [38,49,60] to +0.86 [41] in different investigations. It is probable that differences are mostly connected to the various conditions of the investigations, e.g., PRI seems to be more responsive to chlorophyll content then to the xanthophyll cycle over long time periods [48]. As a result, an analysis of factors influencing the connection between PRI and photosynthetic parameters is very important for the practical application of the photochemical reflectance index. A meta-analysis of literature data seems to be an effective method for finding a solution to this problem. There are several works [15,61] that are devoted to the meta-analysis of results of PRI measurements. In particular, these works investigated the influence of different spatial scales (leaves, canopy, or ecosystem) and time scales (daily or seasonal) of PRI measurements on photosynthetic parameters. A determination coefficient (R²) was used in the works [15,61] as the quantitative criterion for the description of the relationship between physiological processes and the photochemical reflectance index. However, these studies, which were the basis of the meta-analysis, used different regression curves (e.g., linear, logarithmic, or exponential functions), making their comparison with using R² more difficult. Analysis of only linear correlation coefficients can eliminate these difficulties. Another weakly studied factor is the influence of the distribution of photosynthetic parameters in investigated plants on PRI.

Thus, our work was devoted to the meta-analysis of the connection between PRI and photosynthetic parameters. Only linear Pearson correlation coefficients were analyzed in this work. Influence of the photosynthetic parameters on the photochemical reflectance index was also investigated.

2. Methods

2.1. Main Principles of Data Analysis

The analyzed works, which investigated the relationship between PRI and photosynthetic processes in plants, are shown in

Table 1. List of works, which were analyzed in the meta-analysis, and details of measurement of photochemical reflectance index (PRI) and photosynthetic parameters in each work. LUE = light use efficiency; ΔF/F_m’ = quantum yield of photosystem II; NPQ = nonphotochemical quenching of chlorophyll fluorescence.

Year	Reference	Scale	Source of Light	Species/Vegetation Type	Parameters
1994	Peñuelas et al. [56]	Canopy	Sunlight	Sunflower	LUE *
1995	Peñuelas et al. [62]	Leaves	Artificial light	Hedera canariensis, Phaseolus vulgaris, Rhus integrifolia, Heteromeles arbutifolia, Agave americana, Opuntia ficusindica and Cereus hexagonus	ΔF/Fm’, LUE ***
1996	Filella et al. [50]	Leaves/Canopy	Sunlight	Barley	LUE *
1997	Gamon et al. [63]	Canopy	Sunlight	Phaseolus vulgaris,	ΔF/Fm’ ***
				Gossypium barbadense,
				Helianthus annuus,
				Zea mays,
				Nicotiana tabacum,
				Trifolium repens,
				Aesculus californica,
				Cercis occidentalis,
				Platanus racemosa,
				Populus fremontii,
				Quercus lobata,
				Vitis californica,
				Vitis girdiana,
				Heteromeles arbutifolia,
				Ligustrum japonicum,
				Quercus ilex,
				Prunus ilicifolia,
				Quercus agrifolia,
				Quercus chrysolepis,
				Hedera canariensis,
1997	Peñuelas et al. [3]	Leaves	Sunlight	Quercus ilex, Phillyrea latifolia	ΔF/Fm’, LUE *
2000	Méthy [64]	Leaves	Artificial light	Quercus ilex	ΔF/Fm’ **
2000	Nichol et al. [51]	Canopy	Sunlight	Populus tremuloides, Corylus cornutta, Rosa woodsii, Pinus banksiana, Menyanthes trifoliata, Carex and Eriophorum spp, Betula pumila, Larix laricina, P. glauca, Arctostaphylos uva-ursi, Vaccinium vitis-idaea, Cladina spp, Alnus crispa, Picea mariana	LUE **
2002	Nichol et al. [65]	Canopy	Sunlight	Pinus sylvestris, Abies siberica, Picea	LUE **
				abies, Pinus siberica,
				Sorbus aucuparia, Abies siberica, and Betel pendula
2002	Strachan et al. [66]	Canopy	Sunlight	Maize	LUE **
2002	Trotter et al. [67]	Canopy	Artificial light	Hebe townsonii, Carex buchanani Осокa, Metrosideros excelsa, Pittosporum eugenioides, Hebe ‘Otari Delight’, Grisilinea littoralis, Hebe pimeleoides, Pittosporum tenuifolium ‘Shirley’	LUE **
2002	Winkel et al. [68]	Leaves	Sunlight	Chenopodium quinoa	ΔF/Fm’, LUE **
2004	Evain et al. [38]	Leaves/Canopy	Artificial light	Grapevine	ΔF/Fm’, NPQ ***
2005	Gamon [69]	Leaves	Sunlight	Anacardium excelsum, Carica papaya, Cecropia longipes, Enterolobium cyclocarpum, Ficus insipida, Luehea seemannii, Piper reticulatum, Pseudobombax septenatum and Maclura tinctoria	ΔF/Fm’ *
2005	Inamullah and Isoda [39]	Leaves	Sunlight	Soybean and cotton	ΔF/Fm’, NPQ *
2005	Nakaji et al. [70]	Canopy	Sunlight	Larix kaempferi	LUE *
2005	Raddi et al. [71]	Leaves	Artificial light	Medicago sativa,	NPQ **
				Phragmites australis,
				Rubus fruticosus,
				Silybum marianum
				Populus euroamericana,
				Fraxinus angustifolia,
				Alnus glutinosa
				Quercus ilex
				Pinus pinaster and
				Pinus pinea
2005	Serrano and Peñuelas [52]	Canopy	Sunlight	Quercus	LUE **
				ilex, Phyllirea latifolia,
				Arbutus unedo,
				Erica arborea, Juniperus oxycedrus and Cistus albidus
2006	Guo and Trotter [72]	Leaves	Artificial light	Ackama roseafolia,	ΔF/Fm’, LUE ***
				Brachyglottis repanda, Fejoa selloiana, Rhaphiolepsis indica, Grisilinea littoralis, Corynocarpus laevigatus,
				Pseudopanax arboreus, Olearia ilicifolia, Pinus patula, Dodonaea viscose, Pinus radiata,
				Viburnum marisii and Populus deltoides
2006	Inoue and Peñuelas [73]	Leaves	Sunlight	Soybean	LUE *
2006	Nakaji et al. [74]	Canopy	Sunlight	Japanese larch	LUE *
2006	Nichol et al. [75]	Canopy	Sunlight	Rhizophora mangle	ΔF/Fm’, NPQ **
				and Avicennia germinans
2006	Sims et al. [76]	Canopy	Sunlight	Adenostoma fasciculatum, Adenostoma sparsifolium, Arctostaphylos pungens	LUE ***
2006	Weng et al. [77]	Leaves	Artificial light	Mangifera indica, podocarpus nagi, alnus formosana	ΔF/Fm’***, NPQ**
2008	Hall et al. [78]	Canopy	Sunlight	Douglas fir, western red cedar and	LUE **
				western hemlock
2008	Nakaji et al. [53]	Canopy	Sunlight	Japanese larch, Japanese cypress, hybrid larch and dwarf bamboo	LUE *
2008	Naumann et al. [79]	Canopy	Sunlight	Myrica cerifera	ΔF/Fm’ **
2008	Naumann et al. [80]	Canopy	Artificial light	Myrica cerifera	ΔF/Fm’ **
2008	Peguero-Pina et al. [40]	Canopy	Sunlight	Quercus coccifera	NPQ ***
2009	Busch et al. [81]	Leaves	Artificial light	Jack pine	ΔF/Fm’, NPQ **
2009	Middleton et al. [82]	Canopy	Sunlight	Douglas fir	LUE **
2009	Naumann et al. [45]	Canopy	Sunlight	Myrica cerifera and Iva frutescens	ΔF/Fm’ **
2010	Ibaraki et al. [46]	Leaves	Artificial light	Strawberry, lettuce and potato	ΔF/Fm’ **
2010	Ibaraki and Gupta [83]	Leaves	Artificial light	Potato	ΔF/Fm’ **
2010	Naumann et al. [84]	Canopy	Artificial light/ Sunlight	Elaeagnus umbellata	ΔF/Fm’ **
2010	Sarlikioti et al. [41]	Leaves	Artificial light	Tomato	ΔF/Fm’, NPQ ***
2010	Shahenshah et al. [42]	Leaves	Sunlight	Cotton and Peanut	ΔF/Fm’, NPQ *
2010	Weng et al. [85]	Leaves	Artificial light/ Sunlight	Mango	ΔF/Fm’ **
2010	Wu et al. [86]	Canopy	Sunlight	Wheat	LUE **
2011	Ripullone et al. [47]	Leaves	Sunlight	Arbutus unedo, Quercus ilex,	ΔF/Fm’ **
				Quercus pubescens, Quercus cerris,
				Quercus robur, Cannabis sativa,
				Fagus sylvatica and
				Populus euroamericana
2012	Ač et al. [87]	Canopy	Sunlight	(Festuca rubra, Hieracium sp., Plantago sp, Nardus stricta and Jacea pseudophrygia	LUE ***
2012	Osório et al. [43]	Leaves	Artificial light	Ceratonia siliqua	ΔF/Fm’ *
2012	Porcar-Castell et al. [48]	Leaves	Sunlight/ Artificial light	Pinus sylvestris	ΔF/Fm’, NPQ, LUE *
2012	Rahimzadeh-Bajgiran et al. [88]	Leaves	Artificial light	Solanum melongena	NPQ **
2012	Shrestha et al. [60]	Leaves	Artificial light	Rice	NPQ ***
2012	Weng et al. [89]	Leaves	Artificial light	Pinus taiwanensis, Stranvaesia	ΔF/Fm’ **
				niitakayamensis, two
				Miscanthus spp. and mango
2012	Zinnert et al. [7]	Canopy	Artificial light	Baccharis Halimifolia and	ΔF/Fm’, NPQ **
				Myrica cerifera
2013	Cheng et al. [90]	Canopy	Sunlight	Maize	LUE **
2013	Liu et al. [54]	Canopy	Sunlight	Maize and winter wheat	NPQ **
2013	Rossini et al. [91]	Canopy	Sunlight	Maize	ΔF/Fm’ **
2014	Hmimina et al. [55]	Leaves	Sunlight	Quercus robur and Fagus sylvatica	ΔF/Fm’, LUE **
2014	Magney et al. [44]	Leaves	Artificial light	Sunflower, wheat, Quercus	NPQ **
				macrocarpa, Betula papyrifera,
				and Populus tremuloides
2014	Soudani et al. [92]	Canopy	Sunlight	Quercus	LUE **
				robur, Quercus petraea, Quercus ilex, Carpinus betulus
2015	Rossini et al. [93]	Canopy	Sunlight	Maize	ΔF/Fm’ **
2015	Šebela et al. [94]	Leaves	Artificial light	Rice	ΔF/Fm’ *
2015	van Leeuwen et al. [95]	Canopy	Artificial light	Douglas fir	LUE **
2015	Wu et al. [96]	Canopy	Sunlight	Wheat	LUE **
2017	Chou et al. [49]	Canopy	Sunlight	Maize	ΔF/Fm’, NPQ **
2017	Zhang et al. [97]	Canopy	Sunlight	Erica multiflora	ΔF/Fm’ **
2017	Zhang et al. [98]	Leaves	Sunlight	Quercus ilex	ΔF/Fm’ **

* the linear correlation coefficient was shown in this work; ** the linear correlation coefficient was calculated on the basis of the determination coefficient of linear regression in this work; *** the linear correlation coefficient was calculated on the basis of graphical dates from this work.

. For preparation of this list, we performed a wide search of works devoted to PRI investigation (including searching such sources as Web of Science and PubMed, Google searches, and searches in lists of references in articles). After that, we used the following criteria of for the selection of data for further analysis:

-

We used only the photochemical reflectance index calculated with an equation $\mathrm{PRI}=\frac{{\mathrm{R}}_{531}{-\mathrm{R}}_{570}}{{\mathrm{R}}_{531}+{\mathrm{R}}_{570}}$ where R₅₃₁ and R₅₇₀ were reflectance at 531 and 570 nm;

-

We analyzed correlations of PRI with the quantum yield of photosystem II (ΔF/F_m’), the nonphotochemical quenching of chlorophyll (NPQ), and the light use efficiency (LUE). ΔF/F_m’ and NPQ were used because these parameters show the efficiency of photosynthetic light reactions and the response of photosynthetic machinery to stressors. LUE was used for the estimation of efficiency of photosynthetic assimilation;

-

We used only linear correlations between PRI and photosynthetic parameters. These correlations were taken from papers or were calculated on the basis of the determination coefficient (in case of a linear regression) or were calculated on the basis of data from the articles. If correlation coefficients, determination coefficients for linear functions, or graphical data with changes of photosynthetic parameters and PRI were absent, we did not include these works in the analysis;

-

We analyzed the investigation of PRI on the levels of leaves and canopy. Data that were registered by satellites were not used in the analysis.

It should be noted that leaves and canopy levels are widely used scales of PRI measurements [15,61]. Measurements of PRI in leaves are often based on the application of spectrometers and specific systems of PRI measurement (e.g., PlantPen PRI 200) or systems of PRI imaging [15,61]. Measurements of PRI in the canopy of leaves (from single plant or group of plants) can be also based on the application of spectrometers or multispectral and hyperspectral cameras [15,61], which can be placed on a mobile platform (e.g., drone) or fixed at a certain distance from the canopy [35,38]. Photosynthetic parameters in leaves (in particular, ΔF/Fm’, NPQ, and LUE) can be measured by standard methods, including PAM-fluorometry [16,17] and analysis of CO₂ exchange [21,22,23,24,25]. However, the application of these methods on the canopy level is a very difficult problem. In this case, photosynthetic parameters are often measured in only some leaves from the canopy, which are used for PRI measurements [36,40].

In some works, the authors investigated several plants and/or analyzed the influence of different factors separately. In these cases, each connection between PRI and photosynthetic parameters was analyzed independently in each investigated variant. Averaged correlation coefficients and their standard errors were used for analysis. Significance of differences between groups was calculated using the Student’s test.

2.2. Analysis of the Influence of Distribution of Photosynthetic Parameters among Investigated Plants on Connection of these Parameters with PRI

Figure 1 shows a common design for analysis of the influence of distribution of photosynthetic parameters among investigated plants on connection of these parameters with PRI. First, in each analyzed variant, all experimental values of photosynthetic parameters (NPQ, ΔF/F_m’, or LUE) were sorted in ascending order, with each experimental value showing NPQ, ΔF/F_m’, or LUE for a single plant or single group of plants. After that, the minimal (P_min) and maximal (P_max) values of photosynthetic parameters (NPQ, ΔF/F_m’, or LUE) among investigated plants (or groups of plants) were calculated. P_min and P_max were calculated in each analyzed variant from literature data. P_min of NPQ and P_max of ΔF/F_m’ and LUE showed the minimal level of photosynthetic stress among investigated plants, because the action of stressors increases nonphotochemical quenching and decreases the quantum yield of photosystem II [7,41,47,99] and light use efficiency [55,56]. Another parameter that was used was the difference between maximal and minimal values of NPQ, ΔF/F_m’, and LUE (ΔP_abs = P_max − P_min). We assumed that the difference reflected the variation of the photosynthetic stress level among investigated plants (or groups of plants) in the analyzed variant.

Second, all analyzed variants were sorted from minimum to maximum of ΔP_abs and P_min or P_max. After that, they were divided into two approximately equal groups. The first group (“low”) included ΔP_abs and P_min or P_max with values lower than the median value. The second group (“high”) included ΔP_abs and P_min or P_max with values higher than the median value.

Finally, averaged ΔP_abs and P_min or P_max and averaged correlation coefficients of PRI with NPQ, ΔF/F_m’, or LUE, and their standard errors were calculated for each group. Significance of differences between groups were calculated using the Student’s test. In a similar manner, we also analyzed data with specific conditions of measurements of PRI (leaves or canopy, artificial light, or sunlight).

3. Results

3.1. Connection of PRI with Photosynthetic Parameters under Different Measurement Conditions

Figure 2a shows that the linear correlation coefficients between PRI and photosynthetic parameters, which were calculated on the basis of all investigated variants, were moderate, and had absolute values from 0.5 to 0.6. Correlations between PRI and ΔF/F_m’ and PRI, and LUE were positive, whereas the correlation between PRI and NPQ was negative.

Further, we investigated correlations between PRI and photosynthetic parameters when the reflected light was measured from the leaves or canopy surface. Figure 2b shows that the correlation coefficient between PRI and NPQ for canopy measurements was higher than the coefficient for leaves measurements. A similar tendency was observed for the correlation coefficient between PRI and ΔF/F_m’, although it was not significant. In contrast, the correlation coefficient between PRI and LUE for leaves measurements was higher than the coefficient at canopy measurements.

The analysis of the influence of the light source (artificial light or sunlight) on correlations between PRI and photosynthetic parameters was performed later. It could be seen that the correlation coefficients of PRI with ΔF/F_m’ and LUE were significantly higher under artificial light than under sunlight (Figure 2c). The difference between the correlation coefficients of PRI and NPQ was not significant. However, we did observe a tendency of correlation increase under artificial light (Figure 2c).

3.2. Influence of Distribution of Photosynthetic Parameters among Investigated Plants on Connection of These Parameters with PRI

First, we analyzed the influence of the P_min of NPQ and P_max of ΔF/F_m’ and LUE, which showed the minimal level of photosynthetic stress among investigated plants in each analyzed variant (see details in Section “Analysis of the Influence of Distribution of Photosynthetic Parameters among Investigated Plants on Connection of these Parameters with PRI” and Figure 1), on the connection of photosynthetic parameters and PRI. All analyzed variants were sorted in accordance to their P_min or P_max and were divided into two groups: low and high value of these parameters. A similar analysis was performed for ΔP_abs, which shows the variation of photosynthetic stress levels among investigated plants in each analyzed variant.

It was shown that the differences of photosynthetic parameters between groups with low and high absolute values of P_min (P_max) and ΔP_abs were significant (Figure 3, on the left). The correlation coefficients between quantum yield of photosystem II and PRI at high P_max and ΔP_abs (r = 0.75 and 0.73, respectively) were significantly higher than ones at low P_max and ΔP_abs (r = 0.47 and 0.49, respectively) (Figure 3a). The absolute correlation coefficients between NPQ and PRI at low P_min and high ΔP_abs (r = −0.61 and −0.63, respectively) were higher than ones at high P_min and low ΔP_abs (r = −0.36 and −0.34, respectively) (Figure 3b). In the case of LUE (Figure 3c), we did not observe significant differences between the groups with low and high P_max and ΔP_abs. Thus, it was probable that the correlations between PRI and ΔF/F_m’ and PRI, and NPQ were higher in the analyzed variants that included plants with low photosynthetic stress (low P_min of NPQ and high P_max of ΔF/F_m’) and had high variation of the photosynthetic stress levels (high ΔP_abs). This effect was not observed for correlations between LUE and PRI.

3.3. Influence of Distribution of Photosynthetic Parameters among Investigated Plants on Connection of These Parameters with PRI Measurements in Leaves and Canopy

Further, we examined the influence of the photosynthetic parameter distribution among investigated plants on correlations between PRI and ΔF/F_m’, PRI and NPQ, and PRI and LUE with measurements of PRI in leaves and canopy. In this case, we analyzed only experiments that investigated PRI in leaves or only experiments that investigated PRI in canopy. Analysis of each group (leaves or canopy) was analogous to the previous analysis (see above). It should be noted that differences of photosynthetic parameters between groups with low and high absolute values of P_min (P_max) and ΔP_abs were significant for all measurements (Figure 4 and Figure 5, on the left).

On the basis of works that investigated leaves, we showed that the correlation between PRI and quantum yield was high at high P_max and ΔP_abs (Figure 4a) and the correlation between PRI and NPQ was high at high ΔP_abs and low P_min (Figure 4b). In contrast, the correlation between LUE and PRI was high at both values of P_max and ΔP_abs (Figure 4c). The analysis of works that investigated PRI in canopy showed that significant differences between groups with low and high P_min or P_max and ΔP_abs were absent (Figure 5). It should be additionally noted that absolute values of correlation coefficients of PRI with NPQ and ΔF/F_m’ were high (about 0.75–0.85) in both groups with measurement in canopy. Influence of photosynthetic parameter distribution among investigated plants on correlations between PRI and LUE were absent in all variants.

3.4. Influence of Distribution of Photosynthetic Parameters among Investigated Plants on Connection of These Parameters with PRI at Measurements under Sunlight and Artificial Light

Finally, we investigated the influence of the photosynthetic parameter distribution among investigated plants on correlations of PRI with ΔF/F_m’, NPQ, and LUE with measurement of photosynthetic parameters and the photochemical reflectance index under sunlight and artificial light. The analysis was similar to the analysis that was described in the previous section. It should be noted that differences of photosynthetic parameters between groups with low and high absolute values of P_min (P_max) and ΔP_abs were significant at all light conditions (Figure 6 and Figure 7, on the left).

Under sunlight, we observed dependencies of correlations of PRI with ΔF/F_m’ and NPQ on P_max or P_min and ΔP_abs (Figure 6a,b). Correlation coefficients of PRI with LUE did not significantly differ in groups with different P_max and ΔP_abs (Figure 6c). Similar trends were observed under artificial light (Figure 7). However, significant differences were shown only between correlation coefficients of PRI with ΔF/F_m’ in groups with low and high P_max of the quantum yield of photosystem II. It should be noted that absolute values of correlation coefficients of PRI with NPQ and ΔF/F_m’ under artificial light (about 0.6–0.8) were higher than ones under sunlight (about 0.2–0.7).

These results are in accordance with the results of analysis in the previous section: the correlations of PRI with ΔF/F_m’ and NPQ were affected by the photosynthetic parameter distribution among investigated plants; however, this effect was reduced with a strong connection between PRI and these photosynthetic parameters (investigations under artificial light). Influence of the photosynthetic parameter distribution among investigated plants on correlations between PRI and LUE was absent in all variants.

4. Discussion

Precision agriculture [14,100,101,102,103] requires the development of methods of remote sensing of fields and fast analysis of the derived data. The prospective direction of field monitoring is in the application of spectral indices [41,104,105] due to their connection to physiological processes [15,61] and the damage caused by stressors and pathogens in plants [102,106,107]. These indices can potentially be used for the detection of different types of stressors in the early stages of their action [102,107]. The application of a combination of spectral indices can be an additional tool for the improvement of the identification of plant stressors.

Measurement of the photochemical reflectance index is a potentially effective tool for the remote sensing of plants in the field [15,63,108]. There are numerous experimental studies [49,54,66,86,90,93,96] that were devoted to the analysis of the connection between PRI and photosynthetic parameters. The results require theoretical investigations that analyze the current experimental data. The meta-analysis of literature data is an important tool for this analysis [15,61]. In particular, the meta-analysis can reveal the influence of various factors on the connection between PRI and photosynthetic parameters. The meta-analysis in our work shows several important points which are briefly summarized in

Table 2. Average correlation coefficients of the photochemical reflectance index with photosynthetic parameters and influence of distribution of these parameters among investigated plants on the connection between PRI and ΔF/F_m’, NPQ, and LUE with different conditions of measurements.

Conditions of Measurement		Analyzed Parameter or Effect	ΔF/Fm’	NPQ	LUE
Scale	Leaves	Average correlation coefficient	0.58±0.05	−0.40±0.08	0.77±0.03
		Influence of Pmax (Pmin)	+++	+++	−
		Influence of ΔPabs	+++	+++	−
	Canopy	Average correlation coefficient	0.72±0.05	−0.77±0.05	0.46±0.08
		Influence of Pmax (Pmin)	−	+	−
		Influence of ΔPabs	−	−	−
Source of light	Sunlight	Average correlation coefficient	0.50±0.08	−0.41±0.08	0.50±0.07
		Influence of Pmax (Pmin)	+++	+++	−
		Influence of ΔPabs	+++	+++	−
	Artificial light	Average correlation coefficient	0.71±0.04	−0.65±0.11	0.75±0.04
		Influence of Pmax (Pmin)	+++	−	−
		Influence of ΔPabs	−	−	−

“+++”, the effect was significant (p < 0.05); “+”, tendency was observed (0.05 < p < 0.1); “−”, the effect was not significant (p > 0.1). Red color shows a low correlation coefficient (0.3–0.5); blue color shows a moderate correlation coefficient (0.5–0.7); green color shows a high correlation coefficient (0.7–0.9).

.

First, our results showed (Figure 2b, Figure 4a,b and Figure 5a,b, Table 2) that values of correlation coefficients of PRI with ΔF/F_m’ and NPQ, when PRI was registered in canopy, were higher than the coefficients when PRI was registered in leaves. It is probable that this effect was caused by the decrease of noise in PRI measurements due to the averaging of data in the investigation on the canopy level. In contrast, the correlation coefficient of PRI with LUE was minimal for the investigation of the photochemical reflectance index in canopy and maximal at its investigation in leaves. These results may be due to methodological reasons because measurement of CO₂ assimilation, which is the basis of the LUE calculation [62,65], is mainly analyzed in leaves under controlled conditions (CO₂ and H₂O concentrations, light intensity and spectrum, temperature often regulated). That is, the analysis of LUE and PRI at the leaves level tends to be more accurate than the comparison between PRI in canopy and LUE in leaves.

Second, we showed that the correlation coefficients between PRI and photosynthetic parameters under artificial light were higher than those coefficients under sunlight (Figure 2c, Figure 6 and Figure 7, Table 2). It can be presumed that the positive effect of artificial light is caused by the minimization of fluctuations of PRI, ΔF/F_m’, NPQ, and LUE. In contrast, measurements under sunlight can be disturbed by fluctuation of light intensity [42,70,81,85], changes in angle of incidence of light [82,109,110], etc.

Third, the photosynthetic parameter distribution among investigated plants can strongly influence the connection of PRI with ΔF/F_m’ and NPQ (Figure 3, Table 2). However, the influence of the LUE distribution among investigated plants on the connection of PRI with this photosynthetic parameter was not observed (Figure 3c, Figure 4c, Figure 5c, Figure 6c and Figure 7c, Table 2).

In particular, it was shown that the correlation coefficients were increased with a decrease of the minimal level of photosynthetic stress among investigated plants in the analyzed variants. The effect may be due to the complex mechanisms of photosynthetic stress in plants. It is known that changes in PRI are mainly connected with redox processes in the xanthophyll cycle [26,36], which is regulated by pH in the lumen of chloroplasts [111]. Transitions in the xanthophyll cycle can influence the nonphotochemical quenching and the quantum yield of photosystem II [111,112]. However, these photosynthetic parameters can be also affected by other mechanisms. In particular, different components of NPQ can be affected by the pH-dependent protonation of PsbS proteins [37,111], state transition [37,113,114], and photoinhibition [115]. The contribution of these processes to the total NPQ depends on environmental conditions [115,116] and the time of their development [117]. The quantum yield of photosystem II is connected with all components of NPQ [113,118,119] as well as with the ratio of the linear and cyclic electron flows [117,120], production of reactive oxygen species [121], etc. Also, there are additional factors which can complicate interaction between photosynthetic parameters and PRI under the action of stressors. In particular, an increase in transthylakoid ΔpH, which can be stimulated during photosynthetic stress, causes chloroplast shrinkage, and this shrinkage probably participates in PRI changes in the range of seconds [15,38]. In contrast, very long-term stress can change the content of chlorophyll and the pool size of the xanthophyll cycle pigments. It is known that similar changes can also influence PRI [48,122].

Thus, it can be speculated that the investigation of plants with high photosynthetic stress (with the high minimal level of the photosynthetic stress among these plants) must be accompanied by numerous mechanisms of changes in NPQ and ΔF/F_m’, including mechanisms which are not connected to changes in PRI. Under these conditions, the connection of PRI with NPQ and ΔF/F_m’ can be disturbed. It is very probable that this effect can be stimulated by fluctuations of environmental conditions at measurement (in particular, changes in light intensity). That is, it should be low at the high correlation between PRI and photosynthetic parameters and it should be high at the low correlation. In reality, our results showed (Figure 4, Figure 5, Figure 6 and Figure 7, Table 2) that the influence of the minimal level of photosynthetic stress on the connection of PRI with NPQ and ΔF/F_m’ was low at the high correlation between the photochemical reflectance index and photosynthetic parameters (canopy or artificial light). In contrast, the influence was high at the moderate correlation of PRI with NPQ and ΔF/F_m’ (leaves or sunlight).

Influence of variation of the photosynthetic stress level among investigated plants (the difference between maximal and minimal values, ΔP_abs) on the correlation of PRI with NPQ and ΔF/F_m’ was also observed (Figure 3, Table 2). The high correlation between PRI and photosynthetic parameters was at high ΔP_abs and the low correlation was at the low ΔP_abs. This effect was observed (Figure 4, Figure 5, Figure 6 and Figure 7, Table 2) at the moderate correlation of PRI with NPQ and ΔF/F_m’ (leaves or sunlight) and was absent at the high correlation (canopy or artificial light). This result seems expected because the influence of fluctuations on the correlation coefficient should be decreased with the increase of variation of the photosynthetic stress level among investigated plants. For the practical problem of field remote sensing, the results show that application of PRI can be more effective in the investigation of the effects of strong stressors than in the investigation of weak stressors. However, the minimal level of the photosynthetic stress among investigated plants should be low (see above), i.e., measurements of control plants, which are not affected by stressors, are also necessary.

The reasons for the absence of the influence of the minimal level of the photosynthetic stress among investigated plants and its variation on the correlation of PRI with LUE (Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7, Table 2) require future analysis. It cannot be excluded that this absence is caused by a complicated connection between changes in xanthophyll de-epoxidation (i.e., PRI) and changes in CO₂ assimilation (i.e., LUE). The de-epoxidation can directly change NPQ and ΔF/F_m’; however, its influence on CO₂ assimilation is not direct. Changes in linear and cyclic electron flows, transthylakoid proton gradient, and synthesis of Adenosine Triphosphate (ATP) and Nicotinamide Adenine Dinucleotide Phosphate (NADPH) [123] can participate in the induction of changes in CO₂ assimilation after changes in the xanthophyll de-epoxidation.

5. Conclusions

As a whole (Figure 8), our meta-analysis shows that the linear correlation coefficients between PRI and photosynthetic parameters depend on variable conditions of the environment, including scale of measurements (leaves or canopy) and light conditions (sunlight or artificial light). Further, the distribution of photosynthetic parameters among plants (a minimal rate of photosynthetic stress and a variation of the photosynthetic stress level among investigated plants) can influence the linear correlation of PRI with the photosystem II quantum yield and nonphotochemical quenching; the effect is also dependent on conditions of measurements. In contrast, the distribution of light use efficiency among plants did not influence its correlation with PRI.

It is known that the photosynthetic parameters can be modified by numerous factors, including light intensity, temperature, drought, etc. [124]. It is very probable that even a crude guess of the range of photosynthetic parameters can allow one to estimate the efficiency of the PRI in an accurate analysis of photosynthetic stress in plants. The mathematical modeling of photosynthetic processes and PRI can be potentially used for a crude guess of the photosynthetic parameters under specific conditions. Moreover, the modeling can be an additional tool for the analysis of the connection between reflectance indices and photosynthetic parameters [109,125,126]. Development of these models can be used as a solution to the fundamental and applied problems in the field of remote sensing with PRI.

Presently, there are several mathematical models describing the optic properties of leaves and canopy [127,128,129,130,131,132] and connection of these properties with the content of photosynthetic pigments in leaves [133,134,135,136]. Detailed models of PRI, which include a description of the geometry and discontinuity of canopy and different depth penetrations of light into the canopy, are developed on the basis of these models [109,137,138]. Also, linear and nonlinear regressions are widely used to describe the connection between PRI and photosynthetic parameters [62,76,124,137,139]. Development of mechanistic models of PRI and photosynthetic processes is another important method of PRI simulation [109]. In light of the strong connection between PRI and photosynthetic stress [39,40,49], development of detailed models of the relationship between PRI and NPQ is a very important task. Only a few models of the connection between PRI and NPQ have been developed [126]; thus, the problem is very topical.

Finally, it should be noted that the development of PRI analysis methods (on the basis of meta-analysis, simulation, etc.) can reveal a new field for use of the photochemical reflectance index. In particular, PRI can potentially be used for fast and remote investigations of systemic photosynthetic responses induced by long-distance stress signals, including electrical [140,141,142,143,144,145,146,147], hydraulic [148], and Reactive Oxygen Species (ROS) [149] signals which strongly influence photosynthetic processes (e.g., the nonphotochemical quenching).