Next Article in Journal
A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China
Previous Article in Journal
Dynamic Monitoring and Vibration Analysis of Ancient Bridges by Ground-Based Microwave Interferometry and the ESMD Method
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

Department of Biophysics, N.I. Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 771; https://doi.org/10.3390/rs10050771
Submission received: 6 April 2018 / Revised: 4 May 2018 / Accepted: 13 May 2018 / Published: 16 May 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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.

Graphical Abstract

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:
-
a photochemical reflectance index (PRI), which shows changes in the xanthophyll cycle [26];
-
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;
-
a chlorophyll index (CI), which shows chlorophyll content in leaves [31,32]; and
-
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/Fm’) [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. 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 PRI = R 531 R 570 R 531 + 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/Fm’), the nonphotochemical quenching of chlorophyll (NPQ), and the light use efficiency (LUE). ΔF/Fm’ 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/Fm’, or LUE) were sorted in ascending order, with each experimental value showing NPQ, ΔF/Fm’, or LUE for a single plant or single group of plants. After that, the minimal (Pmin) and maximal (Pmax) values of photosynthetic parameters (NPQ, ΔF/Fm’, or LUE) among investigated plants (or groups of plants) were calculated. Pmin and Pmax were calculated in each analyzed variant from literature data. Pmin of NPQ and Pmax of ΔF/Fm’ 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/Fm’, and LUE (ΔPabs = Pmax − Pmin). 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 ΔPabs and Pmin or Pmax. After that, they were divided into two approximately equal groups. The first group (“low”) included ΔPabs and Pmin or Pmax with values lower than the median value. The second group (“high”) included ΔPabs and Pmin or Pmax with values higher than the median value.
Finally, averaged ΔPabs and Pmin or Pmax and averaged correlation coefficients of PRI with NPQ, ΔF/Fm’, 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/Fm’ 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/Fm’, 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/Fm’ 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 Pmin of NPQ and Pmax of ΔF/Fm’ 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 Pmin or Pmax and were divided into two groups: low and high value of these parameters. A similar analysis was performed for ΔPabs, 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 Pmin (Pmax) and ΔPabs were significant (Figure 3, on the left). The correlation coefficients between quantum yield of photosystem II and PRI at high Pmax and ΔPabs (r = 0.75 and 0.73, respectively) were significantly higher than ones at low Pmax and ΔPabs (r = 0.47 and 0.49, respectively) (Figure 3a). The absolute correlation coefficients between NPQ and PRI at low Pmin and high ΔPabs (r = −0.61 and −0.63, respectively) were higher than ones at high Pmin and low ΔPabs (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 Pmax and ΔPabs. Thus, it was probable that the correlations between PRI and ΔF/Fm’ and PRI, and NPQ were higher in the analyzed variants that included plants with low photosynthetic stress (low Pmin of NPQ and high Pmax of ΔF/Fm’) and had high variation of the photosynthetic stress levels (high ΔPabs). 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/Fm’, 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 Pmin (Pmax) and ΔPabs 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 Pmax and ΔPabs (Figure 4a) and the correlation between PRI and NPQ was high at high ΔPabs and low Pmin (Figure 4b). In contrast, the correlation between LUE and PRI was high at both values of Pmax and ΔPabs (Figure 4c). The analysis of works that investigated PRI in canopy showed that significant differences between groups with low and high Pmin or Pmax and ΔPabs were absent (Figure 5). It should be additionally noted that absolute values of correlation coefficients of PRI with NPQ and ΔF/Fm’ 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/Fm’, 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 Pmin (Pmax) and ΔPabs 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/Fm’ and NPQ on Pmax or Pmin and ΔPabs (Figure 6a,b). Correlation coefficients of PRI with LUE did not significantly differ in groups with different Pmax and ΔPabs (Figure 6c). Similar trends were observed under artificial light (Figure 7). However, significant differences were shown only between correlation coefficients of PRI with ΔF/Fm’ in groups with low and high Pmax of the quantum yield of photosystem II. It should be noted that absolute values of correlation coefficients of PRI with NPQ and ΔF/Fm’ 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/Fm’ 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.
First, our results showed (Figure 2b, Figure 4a,b and Figure 5a,b, Table 2) that values of correlation coefficients of PRI with ΔF/Fm’ 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/Fm’, 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/Fm’ 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/Fm’, including mechanisms which are not connected to changes in PRI. Under these conditions, the connection of PRI with NPQ and ΔF/Fm’ 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/Fm’ 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/Fm’ (leaves or sunlight).
Influence of variation of the photosynthetic stress level among investigated plants (the difference between maximal and minimal values, ΔPabs) on the correlation of PRI with NPQ and ΔF/Fm’ was also observed (Figure 3, Table 2). The high correlation between PRI and photosynthetic parameters was at high ΔPabs and the low correlation was at the low ΔPabs. 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/Fm’ (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/Fm’; 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).

Author Contributions

E.S. and V.S. planned, designed, and performed the analysis. E.S. wrote the main manuscript and prepared tables. E.S. and V.S. prepared the figures. All authors contributed significantly to the final version of the manuscript.

Acknowledgments

The investigation was supported by the Russian Science Foundation (Project No. 17-76-20032).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Reddy, A.R.; Chaitanya, K.V.; Vivekanandan, M. Drought-induced responses of photosynthesis and antioxidant metabolism in higher plants. J. Plant Physiol. 2004, 161, 1189–1202. [Google Scholar] [CrossRef]
  2. Muñoz, R.; Quiles, M.J. Illumination in hibiscus plants. Int. J. Mol. Sci. 2013, 14, 5432–5444. [Google Scholar] [CrossRef] [PubMed]
  3. Peñuelas, J.; Llusia, J.; Piñol, J.; Filella, I. Photochemical reflectance index and leaf photosynthetic radiation-use-efficiency assessment in Mediterranean trees. Int. J. Remote Sens. 1997, 18, 2863–2868. [Google Scholar] [CrossRef]
  4. Allakhverdiev, S.I.; Nishiyama, Y.; Miyairi, S.; Yamamoto, H.; Inagaki, N.; Kanesaki, Y.; Murata, N. Salt stress inhibits the repair of photodamaged photosystem II by suppressing the transcription and translation of psbA genes in Synechocystis. Plant Physiol. 2002, 130, 1443–1453. [Google Scholar] [CrossRef] [PubMed]
  5. Murata, N.; Takahashi, S.; Nishiyama, Y.; Allakhverdiev, S.I. Photoinhibition of photosystem II under environmental stress. Biochim. Biophys. Acta 2007, 1767, 414–421. [Google Scholar] [CrossRef] [PubMed]
  6. Mehta, P.; Allakhverdiev, S.I.; Jajoo, A. Characterization of photosystem II heterogeneity in response to high salt stress in wheat leaves (Triticum aestivum). Photosynth. Res. 2010, 105, 249–255. [Google Scholar] [CrossRef] [PubMed]
  7. Zinnert, J.C.; Nelson, J.D.; Hoffman, A.M. Effects of salinity on physiological responses and the photochemical reflectance index in two co-occurring coastal shrubs. Plant Soil. 2012, 354, 45–55. [Google Scholar] [CrossRef]
  8. Mathur, S.; Allakhverdiev, S.I.; Jajoo, A. Analysis of high temperature stress on the dynamics of antenna size and reducing side heterogeneity of photosystem II in wheat leaves (Triticum aestivum). Biochim. Biophys. Acta 2011, 1807, 22–29. [Google Scholar] [CrossRef] [PubMed]
  9. Ivanov, A.G.; Allakhverdiev, S.I.; Huner, N.P.A.; Murata, N. Genetic decrease in fatty acid unsaturation of phosphatidylglycerol increased photoinhibition of photosystem I at low temperature in tobacco leaves. Biochim. Biophys. Acta 2012, 1817, 1374–1379. [Google Scholar] [CrossRef] [PubMed]
  10. Pospíšil, P. Production of reactive oxygen species by photosystem II as a response to light and temperature stress. Front. Plant Sci. 2016, 7, 1950. [Google Scholar] [CrossRef] [PubMed]
  11. Pospíšil, P.; Yamamoto, Y. Damage to photosystem II by lipid peroxidation products. Biochim. Biophys. Acta 2017, 1861, 457–466. [Google Scholar] [CrossRef] [PubMed]
  12. Ruban, A.V. Nonphotochemical chlorophyll fluorescence quenching: Mechanism and effectiveness in protecting plants from photodamage. Plant Physiol. 2016, 170, 1903–1916. [Google Scholar] [CrossRef] [PubMed]
  13. Garbulsky, M.F.; Peñuelas, J.; Papale, D.; Filella, I. Remote estimation of carbon dioxide uptake by a Mediterranean forest. Glob. Chang. Biol. 2008, 14, 2860–2867. [Google Scholar] [CrossRef]
  14. Prabhakar, M.; Prasad, Y.G.; Rao, M.N. Remote sensing of biotic stress in crop plants and its applications for pest management. In Crop Stress and Its Management: Perspectives and Strategies; Venkateswarlu, B., Shanker, A., Shanker, C., Maheswari, M., Eds.; Springer: Dordrecht, The Netherlands, 2012; pp. 517–545. [Google Scholar]
  15. Zhang, C.; Filella, I.; Garbulsky, M.F.; Peñuelas, J. Affecting factors and recent improvements of the photochemical reflectance index (PRI) for remotely sensing foliar, canopy and ecosystemic radiation-use efficiencies. Remote Sens. 2016, 8, 677. [Google Scholar] [CrossRef]
  16. Schreiber, U. Pulse-Amplitude-Modulation (PAM) fluorometry and saturation pulse method: An Overview. In Chlorophyll a Fluorescence. Advances in Photosynthesis and Respiration; Papageorgiou, G.C., Govindjee, Eds.; Springer: Dordrecht, The Netherlands, 2004; Volume 19, pp. 279–319. [Google Scholar]
  17. Kalaji, H.M.; Schansker, G.; Ladle, R.J.; Goltsev, V.; Bosa, K.; Allakhverdiev, S.I.; Brestic, M.; Bussotti, F.; Calatayud, A.; Dąbrowski, P.; et al. Frequently asked questions about in vivo chlorophyll fluorescence: Practical issues. Photosynth. Res. 2014, 122, 121–158. [Google Scholar] [CrossRef] [PubMed]
  18. Strasser, R.J.; Tsimilli-Michael, M.; Srivastava, A. Analysis of the chlorophyll a fluorescence transient. In Chlorophyll a Fluorescence. Advances in Photosynthesis and Respiration; Papageorgiou, G.C., Govindjee, Eds.; Springer: Dordrecht, The Netherlands, 2004; Volume 19, pp. 321–362. [Google Scholar]
  19. Stirbet, A. On the relation between the Kautsky effect (chlorophyll a fluorescence induction) and Photosystem II: Basics and applications of the OJIP fluorescence transient. J. Photochem. Photobiol. B 2011, 104, 236–257. [Google Scholar] [CrossRef] [PubMed]
  20. Goltsev, V.N.; Kalaji, H.M.; Paunov, M.; Bąba, W.; Horaczek, T.; Mojski, J.; Kociel, H.; Allakhverdiev, S.I. Variable chlorophyll fluorescence and its use for assessing physiological condition of plant photosynthetic apparatus. Russ. J. Plant Physiol. 2016, 63, 869–893. [Google Scholar] [CrossRef]
  21. Jones, H.G.; Stoll, M.; Santos, T.; de Sousa, C.; Chaves, M.M.; Grant, O.M. Use of infrared thermography for monitoring stomatal closure in the field: Application to grapevine. J. Exp. Bot. 2002, 53, 2249–2260. [Google Scholar] [CrossRef] [PubMed]
  22. Ferrara, G.; Flore, J. Comparison between different methods for measuring transpiration in potted apple trees. Biol. Plant. 2003, 46, 41–47. [Google Scholar] [CrossRef]
  23. Costa, J.M.; Grant, O.M.; Chaves, M.M. Thermography to explore plant–environment interactions. J. Exp. Bot. 2013, 64, 3937–3949. [Google Scholar] [CrossRef] [PubMed]
  24. Berghuijs, H.N.C.; Yin, X.; Hob, Q.T.; Driever, S.M.; Retta, M.A.; Nicolaï, B.M.; Struik, P.C. Mesophyll conductance and reaction-diffusion models for CO2 transport in C3 leaves; needs, opportunities and challenges. Plant Sci. 2016, 252, 62–75. [Google Scholar] [CrossRef] [PubMed]
  25. Gago, J.; de Menezes Daloso, D.; Figueroa, C.M.; Flexas, J.; Fernie, A.R.; Nikoloski, Z. Relationships of leaf net photosynthesis, stomatal conductance, and mesophyll conductance to primary metabolism: A multispecies meta-analysis approach. Plant Physiol. 2016, 171, 265–279. [Google Scholar] [CrossRef] [PubMed]
  26. Gamon, J.A.; Peñuelas, J.; Field, C.B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 1992, 41, 35–44. [Google Scholar] [CrossRef]
  27. Rouse, J.W., Jr.; Haas, R.H.; Schell, J.A.; Deering, D.W.; Harlan, J.C. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation; Type III Final Rep; The National Aeronautics and Space Administration (NASA)/Goddard Space Flight Center (GSFC): Greenbelt, MD, USA, 1974. [Google Scholar]
  28. Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 1996, 55, 95–107. [Google Scholar] [CrossRef]
  29. Huete, A.R.; Justice, C.; van Leeuwen, W. MODIS Vegetation Index (Mod13). Algorithm Theoretical Basis Document; Version 2; NASA Goddard Space Flight Center: Greenbelt, MD, USA, 1996. [Google Scholar]
  30. Huete, A.R.; Liu, H.Q.; Batchily, K.; van Leeuwen, W. A comparison of vegetation indices global set of TM images for EOS-MODIS. Remote Sens. Environ. 1997, 59, 440–451. [Google Scholar] [CrossRef]
  31. Gitelson, A.; Merzlyak, M.N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Plant Physiol. 1994, 143, 286–292. [Google Scholar] [CrossRef]
  32. Gamon, J.A.; Surfus, J.S. Assessing leaf pigment content and activity with a refectometer. New Phytol. 1999, 143, 105–117. [Google Scholar] [CrossRef]
  33. Peñuelas, J.; Baret, F.; Filella, I. Semi-emperical indeces to asses caratenoids/chlorophyll ratio from leaf spectral reflectance. Photosynthetica 1995, 31, 221–230. [Google Scholar]
  34. Eitel, J.U.H.; Long, D.S.; Gessler, P.E.; Hunt, E.R. Combined spectral index to improve ground-based estimates of nitrogen status in dryland wheat. Agron. J. 2008, 100, 1694–1702. [Google Scholar] [CrossRef]
  35. Hernández-Clemente, R.; Navarro-Cerrillo, R.M.; Zarco-Tejada, P.J. Carotenoid content estimation in a heterogeneous conifer forest using narrow-band indices and PROSPECT+DART simulations. Remote Sens. Environ. 2012, 127, 298–315. [Google Scholar] [CrossRef]
  36. Gamon, J.A.; Field, C.B.; Bilger, W.; Björkman, O.; Fredeen, A.L.; Peñuelas, J. Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies. Oecologia 1990, 85, 1–7. [Google Scholar] [CrossRef] [PubMed]
  37. Müller, P.; Li, X.-P.; Niyogi, K.K. Non-photochemical quenching. A response to excess light energy. Plant Physiol. 2001, 125, 1558–1566. [Google Scholar] [CrossRef] [PubMed]
  38. Evain, S.; Flexas, J.; Moya, I. A new instrument for passive remote sensing: 2. Measurement of leaf and canopy reflectance changes at 531 nm and their relationship with photosynthesis and chlorophyll fluorescence. Remote Sens. Environ. 2004, 91, 175–185. [Google Scholar] [CrossRef]
  39. Inamullah; Isoda, A. Adaptive responses of soybean and cotton to water stress II. Changes in CO2 assimilation rate, chlorophyll fluorescence and photochemical reflectance index in relation to leaf temperature. Plant Prod. Sci. 2005, 8, 131–138. [Google Scholar]
  40. Peguero-Pina, J.J.; Morales, F.; Flexas, J.; Gil-Pelegrín, E.; Moya, I. Photochemistry, remotely sensed physiological reflectance index and de-epoxidation state of the xanthophyll cycle in Quercus coccifera under intense drought. Oecologia 2008, 156, 1–11. [Google Scholar] [CrossRef] [PubMed]
  41. Sarlikioti, V.; Driever, S.M.; Marcelis, L.F.M. Photochemical reflectance index as a mean of monitoring early water stress. Ann. Appl. Biol. 2010, 157, 81–89. [Google Scholar] [CrossRef]
  42. Yoshizumi, Y.; Li, M.S.; Akihiro, I. Assessment of photochemical reflectance index as a tool for evaluation of chlorophyll fluorescence parameters in cotton and peanut cultivars under water stress condition. Agric. Sci. China 2010, 9, 662–670. [Google Scholar]
  43. Osório, J.; Osório, M.L.; Romano, A. Reflectance indices as nondestructive indicators of the physiological status of Ceratonia siliqua seedlings under varying moisture and temperature regimes. Funct. Plant Biol. 2012, 39, 588–597. [Google Scholar] [CrossRef]
  44. Magney, T.S.; Eusden, S.A.; Eitel, J.U.H.; Logan, B.A.; Jiang, J.; Vierling, L.A. Assessing leaf photoprotective mechanisms using terrestrial LiDAR: Towards mapping canopy photosynthetic performance in three dimensions. New Phytol. 2014, 201, 344–356. [Google Scholar] [CrossRef] [PubMed]
  45. Naumann, J.C.; Young, D.R.; Anderson, J.E. Spatial variations in salinity stress across a coastal landscape using vegetation indices derived from hyperspectral imagery. Plant Ecol. 2009, 202, 285–297. [Google Scholar] [CrossRef]
  46. Ibaraki, Y.; Matsumura, K.; Gupta, S.D. Low-cost photochemical reflectance index measurements of micropropagated plantlets using image analysis. Comput. Electron. Agric. 2010, 71, 170–175. [Google Scholar] [CrossRef]
  47. Ripullone, F.; Rivelli, A.R.; Baraldi, R.; Guarini, R.; Guerrieri, R.; Magnani, F.; Peñuelas, J.; Raddi, S.; Borghetti, M. Effectiveness of the photochemical reflectance index to track photosynthetic activity over a range of forest tree species and plant water statuses. Funct. Plant Biol. 2011, 38, 177–186. [Google Scholar] [CrossRef]
  48. Porcar-Castell, A.; Garcia-Plazaola, J.I.; Nichol, C.J.; Kolari, P.; Olascoaga, B.; Kuusinen, N.; Fernández-Marín, B.; Pulkkinen, M.; Juurola, E.; Nikinmaa, E. Physiology of the seasonal relationship between the photochemical reflectance index and photosynthetic light use efficiency. Oecologia 2012, 170, 313–323. [Google Scholar] [CrossRef] [PubMed]
  49. Chou, S.; Chen, J.M.; Yu, H.; Chen, B.; Zhang, X.; Croft, H.; Khalid, S.; Li, M.; Shi, Q. Canopy-level photochemical reflectance index from hyperspectral remote sensing and leaf-level non-photochemical quenching as early indicators of water stress in maize. Remote Sens. 2017, 9, 794. [Google Scholar] [CrossRef]
  50. Filella, I.; Amaro, T.; Araus, J.L.; Peñuelas, J. Relationship between photosynthetic radiation-use efficiency of barley canopies and the photochemical reflectance index (PRI). Physiol. Plant. 1996, 96, 211–216. [Google Scholar] [CrossRef]
  51. Nichol, C.J.; Huemmrich, K.F.; Black, T.A.; Jarvis, P.G.; Walthall, C.L.; Grace, J.; Hall, F.G. Remote sensing of photosynthetic-light-use efficiency of boreal forest. Agric. For. Meteorol. 2000, 101, 131–142. [Google Scholar] [CrossRef]
  52. Serrano, L.; Peñuelas, J. Assessing forest structure and function from spectral transmittance measurements: A case study in a Mediterranean holm oak forest. Tree Physiol. 2005, 25, 67–74. [Google Scholar] [CrossRef] [PubMed]
  53. Nakaji, T.; Ide, R.; Takagi, K.; Kosugi, Y.; Ohkubo, S.; Nasahara, K.N.; Saigusa, N.; Oguma, H. Utility of spectral vegetation indices for estimation of light conversion efficiency in coniferous forests in Japan. Agric. For. Meteorol. 2008, 148, 776–787. [Google Scholar] [CrossRef] [Green Version]
  54. Liu, L.; Zhang, Y.; Jiao, Q.; Peng, D. Assessing photosynthetic light-use efficiency using a solar-induced chlorophyll fluorescence and photochemical reflectance index. Int. J. Remote Sens. 2013, 34, 4264–4280. [Google Scholar] [CrossRef]
  55. Hmimina, G.; Dufrêne, E.; Soudani, K. Relationship between photochemical reflectance index and leaf ecophysiological and biochemical parameters under two different water statuses: Towards a rapid and efficient correction method using real-time measurements. Plant Cell Environ. 2014, 37, 473–487. [Google Scholar] [CrossRef] [PubMed]
  56. Peñuelas, J.; Gamon, J.A.; Fredeen, A.L.; Merino, J.; Field, C.B. Reflectence indeces associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens. Environ. 1994, 48, 135–146. [Google Scholar] [CrossRef]
  57. Stylinski, C.D.; Gamon, J.A.; Oechel, W.C. Seasonal patterns of reflectance indices, carotenoid pigments and photosynthesis of evergreen chaparral species. Oecologia 2002, 131, 366–374. [Google Scholar] [CrossRef] [PubMed]
  58. Letts, M.G.; Phelan, C.A.; Johnson, D.R.E.; Rood, S.B. Seasonal photosynthetic gas exchange and leaf reflectance characteristics of male and female cottonwoods in a riparian woodland. Tree Physiol. 2008, 28, 1037–1048. [Google Scholar] [CrossRef] [PubMed]
  59. Buddenbaum, H.; Rock, G.; Hill, J.; Werner, W. Measuring stress reactions of beech seedlings with PRI, fluorescence, temperatures and emissivity from VNIR and thermal field imaging spectroscopy. Eur. J. Remote Sens. 2015, 48, 263–282. [Google Scholar] [CrossRef]
  60. Shrestha, S.; Brueck, H.; Asch, F. Chlorophyll index, photochemical reflectance index and chlorophyll fluorescence measurements of rice leaves supplied with different N levels. J. Photochem. Photobiol. B Biol. 2012, 113, 7–13. [Google Scholar] [CrossRef] [PubMed]
  61. Garbulsky, M.F.; Peñuelas, J.; Gamon, J.; Inoue, Y.; Filella, I. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies. A review and meta-analysis. Remote Sens. Environ. 2011, 115, 281–297. [Google Scholar] [CrossRef]
  62. Peñuelas, J.; Filella, I.; Gamon, J.A. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytol. 1995, 131, 291–296. [Google Scholar] [CrossRef]
  63. Gamon, J.A.; Serrano, L.; Surfus, J.S. The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 1997, 112, 492–501. [Google Scholar] [CrossRef] [PubMed]
  64. Méthy, M. Analysis of photosynthetic activity at the leaf and canopy levels from reflectance measurements: A case study. Photosynthetica 2000, 38, 505–512. [Google Scholar] [CrossRef]
  65. Nichol, C.J.; Lloyd, J.; Shibistova, O.; Arneth, A.; Röser, C.; Knohl, A.; Matsubara, S.; Grace, J. Remote sensing of photosynthetic-light-use efficiency of a Siberian boreal forest. Tellus B 2002, 54, 677–687. [Google Scholar] [CrossRef]
  66. Strachan, I.B.; Pattey, E.; Boisvert, J.B. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sens. Environ. 2002, 80, 213–224. [Google Scholar] [CrossRef]
  67. Trotter, G.M.; Whitehead, D.; Pinkney, E.J. The photochemical reflectance index as a measure of photosynthetic light use efficiency for plants with varying foliar nitrogen contents. Int. J. Remote Sens. 2002, 23, 1207–1212. [Google Scholar] [CrossRef]
  68. Winkel, T.; Méthy, M.; Thénot, F. Radiation use efficiency, chlorophyll fluorescence, and reflectance indeces associated with ontogenic changes. Photosynthetica 2002, 40, 227–232. [Google Scholar] [CrossRef]
  69. Gamon, J.A. Diverse optical and photosynthetic properties in a neotropical dry forest during the dry season: Implications for remote estimation of photosynthesis. Biotropica 2005, 37, 547–560. [Google Scholar] [CrossRef]
  70. Nakaji, T.; Takeda, T.; Fujinuma, Y.; Oguma, H. Effect of autumn senescence on the relationship between the PRI and LUE of young japanese larch trees. Phyton 2005, 45, 535–542. [Google Scholar]
  71. Raddi, S.; Cortes, S.; Pippi, I.; Magnani, F. Estimation of vegetation photochemical processes: An application of the photochemical reflectance index at the San Rossore test site. In Proceedings of the 3rd ESA CHRIS/Proba Workshop, Frascati, Italy, 21–23 March 2005. [Google Scholar]
  72. Guo, J.M.; Trotter, C.M. Estimating photosynthetic light-use efficiency using the photochemical reflectance index: The effects of short-term exposure to elevated CO2 and low temperature. Int. J. Remote Sens. 2006, 27, 4677–4684. [Google Scholar] [CrossRef]
  73. Inoue, Y.; Peñuelas, J. Relationship between light use efficiency and photochemical reflectance index in soybean leaves as affected by soil water content. Int. J. Remote Sens. 2006, 27, 5109–5114. [Google Scholar] [CrossRef]
  74. Nakaji, T.; Oguma, H.; Fujinuma, Y. Seasonal changes in the relationship between photochemical reflectance index and photosynthetic light use efficiency of Japanese larch needles. Int. J. Remote Sens. 2006, 27, 493–509. [Google Scholar] [CrossRef]
  75. Nichol, C.J.; Rascher, U.; Matsubara, S.; Osmond, B. Assessing photosynthetic efficiency in an experimental mangrove canopy using remote sensing and chlorophyll fluorescence. Trees 2006, 20, 9–15. [Google Scholar] [CrossRef]
  76. Sims, D.A.; Luo, H.; Hastings, S.; Oechel, W.C.; Rahman, A.F.; Gamon, J.A. Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem. Remote Sens. Environ. 2006, 103, 289–303. [Google Scholar] [CrossRef]
  77. Weng, J.H.; Chen, Y.N.; Liao, T.S. Relationships between chlorophyll fluorescence parameters and photochemical reflectance index of tree species adapted to different temperature regimes. Funct. Plant Biol. 2006, 33, 241–246. [Google Scholar] [CrossRef]
  78. Hall, F.G.; Hilker, T.; Coops, N.C.; Lyapustin, A.; Huemmrich, K.F.; Middleton, E.; Margolis, H.; Drolet, G.; Black, T.A. Multi-angle remote sensing of forest light use efficiency by observing PRI variation with canopy shadow fraction. Remote Sens. Environ. 2008, 112, 3201–3211. [Google Scholar] [CrossRef]
  79. Naumann, J.C.; Anderson, J.E.; Young, D.R. Linking physiological responses, chlorophyll fluorescence and hyperspectral imagery to detect salinity stress using the physiological reflectance index in the coastal shrub, Myrica cerifera. Remote Sens. Environ. 2008, 112, 3865–3875. [Google Scholar] [CrossRef]
  80. Naumann, J.C.; Young, D.R.; Anderson, J.E. Leaf chlorophyll fluorescence, reflectance, and physiological response to freshwater and saltwater flooding in the evergreen shrub, Myrica cerifera. Environ. Exp. Bot. 2008, 63, 402–409. [Google Scholar] [CrossRef]
  81. Busch, F.; Hüner, N.P.A.; Ensminger, I. Biochemical constrains limit the potential of the photochemical reflectance index as a predictor of effective quantum efficiency of photosynthesis during the winter spring transition in Jack pine seedlings. Funct. Plant Biol. 2009, 36, 1016–1026. [Google Scholar] [CrossRef]
  82. Middleton, E.M.; Cheng, Y.-B.; Hilker, T.; Black, T.A.; Krishnan, P.; Coops, N.C.; Huemmrich, K.F. Linking foliage spectral responses to canopy-level ecosystem photosynthetic light-use efficiency at a Douglas-fir forest in Canada. Can. J. Remote Sens. 2009, 35, 166–188. [Google Scholar] [CrossRef]
  83. Ibaraki, Y.; Gupta, S.D. Nondestructive evaluation of the photosynthetic properties of micropropagated plantlets by imaging photochemical reflectance index under low light intensity. Cell. Dev. Biol. Plant. 2010, 46, 530–536. [Google Scholar] [CrossRef]
  84. Naumann, J.C.; Bissett, S.N.; Young, D.R.; Edwards, J.; Anderson, J.E. Diurnal patterns of photosynthesis, chlorophyll fluorescence, and PRI to evaluate water stress in the invasive species, Elaeagnus umbellata Thunb. Trees-Struct. Funct. 2010, 24, 237–245. [Google Scholar] [CrossRef]
  85. Weng, J.H.; Jhaung, L.H.; Lin, R.J.; Chen, H.Y. Relationship between photochemical efficiency of photosystem II and the photochemical reflectance index of mango tree: Merging data from different illuminations, seasons and leaf colors. Tree Physiol. 2010, 30, 469–478. [Google Scholar] [CrossRef] [PubMed]
  86. Wu, C.; Niu, Z.; Tang, Q.; Huang, W. Revised photochemical reflectance index (PRI) for predicting light use efficiency of wheat in a growth cycle: Validation and comparison. Int. J. Remote Sens. 2010, 31, 2911–2924. [Google Scholar] [CrossRef]
  87. Ač, A.; Malenovský, Z.; Urban, O.; Hanuš, J.; Zitová, M.; Navrátil, M.; Vráblová, M.; Olejníčková, J.; Špunda, V.; Marek, M. Relation of chlorophyll fluorescence sensitive reflectance ratios to carbon flux measurements of montanne grassland and norway spruce forest ecosystems in the temperate zone. Sci. World J. 2012, 2012, 705872. [Google Scholar] [CrossRef] [PubMed]
  88. Rahimzadeh-Bajgiran, P.; Munehiro, M.; Omasa, K. Relationships between the photochemical reflectance index (PRI) and chlorophyll fluorescence parameters and plant pigment indices at different leaf growth stages. Photosynth. Res. 2012, 113, 261–271. [Google Scholar] [CrossRef] [PubMed]
  89. Weng, J.H.; Wong, S.L.; Lai, K.M.; Lin, R.J. Relationships between photosystem II efficiency and photochemical reflectance index under different levels of illumination: Comparison among species grown at high- and low elevations through different seasons. Trees-Struct. Funct. 2012, 26, 343–351. [Google Scholar] [CrossRef]
  90. Cheng, Y.-B.; Middleton, E.M.; Zhang, Q.; Huemmrich, K.F.; Campbell, P.K.E.; Corp, L.A.; Cook, B.D.; Kustas, W.P.; Daughtry, C.S. Integrating solar induced fluorescence and the photochemical reflectance index for estimating gross primary production in a cornfield. Remote Sens. 2013, 5, 6857–6879. [Google Scholar] [CrossRef]
  91. Rossini, M.; Fava, F.; Cogliati, S.; Meroni, M.; Marchesi, A.; Panigada, C.; Giardino, C.; Busetto, L.; Migliavacca, M.; Amaducci, S.; et al. Assessing canopy PRI from airborne imagery to map water stress in maize. ISPRS J. Photogramm. Remote Sens. 2013, 86, 168–177. [Google Scholar] [CrossRef]
  92. Soudani, K.; Hmimina, G.; Dufrêne, E.; Berveiller, D.; Delpierre, N.; Ourcival, J.-M.; Rambal, S.; Joffre, R. Relationships between photochemical reflectance index and light-use efficiency in deciduous and evergreen broadleaf forests. Remote Sens. Environ. 2014, 144, 73–84. [Google Scholar] [CrossRef]
  93. Rossini, M.; Panigada, C.; Cilia, C.; Meroni, M.; Busetto, L.; Cogliati, S.; Amaducci, S.; Colombo, R. Discriminating irrigated and rainfed maize with diurnal fluorescence and canopy temperature airborne maps. ISPRS Int. J. Geo-Inf. 2015, 4, 626–646. [Google Scholar] [CrossRef] [Green Version]
  94. Šebela, D.; Quiñones, C.; Olejníčková, J.; Jagadish, K.S.V. Temporal chlorophyll fluorescence signals to track changes in optical properties of maturing rice panicles exposed to high night temperature. Field Crops Res. 2015, 177, 75–85. [Google Scholar] [CrossRef]
  95. Van Leeuwen, M.; Kremens, R.L.; van Aardt, J. Tracking diurnal variation in photosynthetic down-regulation using low cost spectroscopic instrumentation. Sensors 2015, 15, 10616–10630. [Google Scholar] [CrossRef] [PubMed]
  96. Wu, C.; Huang, W.; Yang, Q.; Xie, Q. Improved estimation of light use efficiency by removal of canopy structural effect from the photochemical reflectance index (PRI). Agric. Ecosyst. Environ. 2015, 199, 333–338. [Google Scholar] [CrossRef]
  97. Zhang, C.; Filella, I.; Liu, D.; Ogaya, R.; Llusià, J.; Asensio, D.; Peñuelas, J. Photochemical reflectance index (PRI) for detecting responses of diurnal and seasonal photosynthetic activity to experimental drought and warming in a mediterranean shrubland. Remote Sens. 2017, 9, 1189. [Google Scholar] [CrossRef]
  98. Zhang, C.; Preece, C.; Filella, I.; Farré-Armengol, G.; Peñuelas, J. Assessment of the response of photosynthetic activity of mediterranean evergreen oaks to enhanced drought stress and recovery by using PRI and R690/R630. Forests 2017, 8, 386. [Google Scholar] [CrossRef]
  99. Panigada, C.; Rossini, M.; Meroni, M.; Cilia, C.; Busetto, L.; Amaducci, S.; Boschetti, M.; Cogliati, S.; Picchi, V.; Pinto, F.; et al. Fluorescence, PRI and canopy temperature for water stress detectionin cereal crops. Int. J. Appl. Earth Obs. Geoinf. 2014, 30, 167–178. [Google Scholar] [CrossRef]
  100. Moran, M.S.; Inoue, Y.; Barnes, E.M. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 1997, 61, 319–346. [Google Scholar] [CrossRef]
  101. Pinter, P.J.; Hatfield, J.L.; Schepers, J.S.; Barnes, E.M.; Moran, M.S.; Daughtry, C.S.T.; Upchurch, D.R. Remote sensing for crop management. Photogramm. Eng. Remote Sens. 2003, 69, 647–664. [Google Scholar] [CrossRef]
  102. Mahlein, A.-K. Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 2016, 100, 241–251. [Google Scholar] [CrossRef]
  103. Bogue, R. Sensors key to advances in precision agriculture. Sens. Rev. 2017, 37, 1–6. [Google Scholar] [CrossRef]
  104. Drolet, G.G.; Huemmrich, K.F.; Hall, F.G.; Middleton, E.M.; Black, T.A.; Barr, A.G.; Margolis, H.A. A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest. Remote Sens. Environ. 2005, 98, 212–224. [Google Scholar] [CrossRef]
  105. Berni, J.A.J.; Zarco-Tejada, P.J.; Suárez, L.; Fereres, E. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 2009, 47, 722–738. [Google Scholar] [CrossRef] [Green Version]
  106. Zhang, M.; Liu, X.; O’Neill, M. Spectral discrimination of Phytophthora infestans infections on tomatoes based on principal component and cluster analyses. Int. J. Remote Sens. 2002, 23, 1095–1107. [Google Scholar] [CrossRef]
  107. Mahlein, A.-K.; Steiner, U.; Dehne, H.-W.; Oerke, E.-C. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precis. Agric. 2010, 11, 413–431. [Google Scholar] [CrossRef]
  108. Filella, I.; Porcar-Castell, A.; Munné-Bosch, S.; Bäck, J.; Garbulsky, M.F.; Peñuelas, J. PRI assessment of long-term changes in carotenoids/chlorophyll ratio and short-term changes in de-epoxidation state of the xanthophyll cycle. Int. J. Remote Sens. 2009, 30, 4443–4455. [Google Scholar] [CrossRef]
  109. Barton, C.V.M.; North, P.R.J. Remote sensing of canopy light use efficiency using the photochemical reflectance index. Model and sensitivity analysis. Remote Sens. Environ. 2001, 78, 264–273. [Google Scholar] [CrossRef]
  110. Liu, L.-X.; Xu, S.-M.; Woo, K.C. Influence of leaf angle on photosynthesis and the xanthophyll cycle in the tropical tree species Acacia crassicarpa. Tree Physiol. 2003, 23, 1255–1261. [Google Scholar] [CrossRef] [PubMed]
  111. Jahns, P.; Latowski, D.; Strzalka, K. Mechanism and regulation of the violaxanthin cycle: The role of antenna proteins and membrane lipids. Biochim. Biophys. Acta 2009, 1787, 3–14. [Google Scholar] [CrossRef] [PubMed]
  112. Jajoo, A.; Mekala, N.R.; Tongra, T.; Tiwari, A.; Grieco, M.; Tikkanen, M.; Aro, E.-M. Low pH-induced regulation of excitation energy between the two photosystems. FEBS Lett. 2014, 588, 970–974. [Google Scholar] [CrossRef] [PubMed]
  113. Holzwarth, A.R.; Miloslavina, Y.; Nilkens, M.; Jahns, P. Identification of two quenching sites active in the regulation of photosynthetic light-harvesting studied by time-resolved fluorescence. Chem. Phys. Lett. 2009, 483, 262–267. [Google Scholar] [CrossRef]
  114. Zaks, J.; Amarnath, K.; Kramer, D.M.; Niyogi, K.K.; Fleming, G.R. A kinetic model of rapidly reversible nonphotochemical quenching. Proc. Natl. Acad. Sci. USA 2012, 109, 15757–15762. [Google Scholar] [CrossRef] [PubMed]
  115. Guadagno, C.R.; De Santo, A.V.; D’Ambrosio, N. A revised energy partitioning approach to assess the yields of non-photochemical quenching components. Biochim. Biophys. Acta 2010, 1797, 525–530. [Google Scholar] [CrossRef] [PubMed]
  116. Ahn, T.K.; Avenson, T.J.; Peers, G.; Li, Z.; Dall’Osto, L.; Bassi, R.; Niyogi, K.K.; Fleming, G.R. Investigating energy partitioning during photosynthesis using an expanded quantum yield convention. Chem. Phys. 2009, 357, 151–158. [Google Scholar] [CrossRef]
  117. Sato, R.; Ohta, H.; Masuda, S. Prediction of respective contribution of linear electron flow and PGR5-dependent cyclic electron flow to non-photochemical quenching induction. Plant Physiol. Biochem. 2014, 81, 190–196. [Google Scholar] [CrossRef] [PubMed]
  118. Pfannschmidt, T.; Bräutigam, K.; Wagner, R.; Dietzel, L.; Schröter, Y.; Steiner, S.; Nykytenko, A. Potential regulation of gene expression in photosynthetic cells by redox and energy state: Approaches towards better understanding. Ann. Bot. 2009, 103, 599–607. [Google Scholar] [CrossRef] [PubMed]
  119. Rochaix, J.-D.; Lemeille, S.; Shapiguzov, A.; Samol, I.; Fucile, G.; Willig, A.; Goldschmidt-Clermont, M. Protein kinases and phosphatases involved in the acclimation of the photosynthetic apparatus to a changing light environment. Philos. Trans. R. Soc. B 2012, 367, 3466–3474. [Google Scholar] [CrossRef] [PubMed]
  120. Johnson, G.N. Cyclic electron transport in C3 plants: Fact or artefact? J. Exp. Bot. 2005, 56, 407–416. [Google Scholar] [CrossRef] [PubMed]
  121. Latowski, D.; Kuczyńska, P.; Strzałka, K. Xanthophyll cycle–a mechanism protecting plants against oxidative stress. Redox Rep. 2011, 16, 78–90. [Google Scholar] [CrossRef] [PubMed]
  122. Porcar-Castell, A.; Tyystjärvi, E.; Atherton, J.; van der Tol, C.; Flexas, J.; Pfündel, E.E.; Moreno, J.; Frankenberg, C.; Berry, J.A. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: Mechanisms and challenges. J. Exp. Bot. 2014, 65, 4065–4095. [Google Scholar] [CrossRef] [PubMed]
  123. Allen, J.F. Cyclic, pseudocyclic and noncyclic photophosphorylation: New links in the chain. Trends Plant Sci. 2003, 8, 15–19. [Google Scholar] [CrossRef]
  124. Ashraf, M.; Harris, P.J.C. Photosynthesis under stressful environments: An overview. Photosynthetica 2013, 51, 163–190. [Google Scholar] [CrossRef]
  125. Rahman, A.F.; Gamon, J.A.; Fuentes, D.A.; Roberts, D.A.; Prentiss, D. Modeling spatially distributed ecosystem flux of boreal forest using hyperspectral indices from AVIRIS imagery. J. Geophys. Res. 2001, 106, 33579–33591. [Google Scholar] [CrossRef]
  126. Atherton, J.; Nichol, C.J.; Porcar-Castell, A. Using spectral chlorophyll fluorescence and the photochemical reflectance index to predict physiological dynamics. Remote Sens. Environ. 2016, 176, 17–30. [Google Scholar] [CrossRef]
  127. Kuusk, A. A Markov chain model of canopy reflectance. Agric. For. Meteorol. 1995, 76, 221–236. [Google Scholar] [CrossRef]
  128. Kuusk, A. A fast, invertible canopy reflectance model. Remote Sens. Environ. 1995, 51, 342–350. [Google Scholar] [CrossRef]
  129. Bousquet, L.; Lachérade, S.; Jacquemoud, S.; Moya, I. Leaf BRDF measurements and model for specular and diffuse components differentiation. Remote Sens. Environ. 2005, 98, 201–211. [Google Scholar] [CrossRef]
  130. Baranoski, G.V.G. Modeling the interaction of infrared radiation (750 to 2500 nm) with bifacial and unifacial plant leaves. Remote Sens. Environ. 2006, 100, 335–347. [Google Scholar] [CrossRef]
  131. Stuckens, J.; Verstraeten, W.W.; Delalieux, S.; Swennen, R.; Coppin, P. A dorsiventral leaf radiative transfer model: Development, validation and improved model inversion techniques. Remote Sens. Environ. 2009, 113, 2560–2573. [Google Scholar] [CrossRef]
  132. Vilfan, N.; van der Tol, C.; Muller, O.; Rascher, U.; Verhoef, W. Fluspect-B: A model for leaf fluorescence, reflectance and transmittance spectra. Remote Sens. Environ. 2016, 186, 596–615. [Google Scholar] [CrossRef]
  133. Jacquemoud, S.; Baret, F. PROSPECT: A Model of leaf optical properties spectra. Remote Sens. Environ. 1990, 34, 75–91. [Google Scholar] [CrossRef]
  134. Dawson, T.P.; Curran, P.J. The biochemical decomposition of slash pine needles from reflectance spectra using neural networks. Int. J. Remote Sens. 1998, 19, 1433–1438. [Google Scholar] [CrossRef]
  135. Féret, J.-B.; Gitelson, A.A.; Noble, S.D.; Jacquemoud, S. PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle. Remote Sens. Environ. 2017, 193, 204–215. [Google Scholar] [CrossRef]
  136. Sun, J.; Shi, S.; Yang, J.; Du, L.; Gong, W.; Chen, B.; Song, S. Analyzing the performance of PROSPECT model inversion based on different spectral information for leaf biochemical properties retrieval. ISPRS J. Photogramm. Remote Sens. 2018, 135, 74–83. [Google Scholar] [CrossRef]
  137. Hilker, T.; Coops, N.C.; Hall, F.G.; Black, T.A.; Wulder, M.A.; Nesic, Z.; Krishnan, P. Separating physiologically and directionally induced changes in PRI using BRDF models. Remote Sens. Environ. 2008, 112, 2777–2788. [Google Scholar] [CrossRef]
  138. Cheng, Y.-B.; Middleton, E.M.; Zhang, Q.; Corp, L.A.; Dandois, J.; Kustas, W.P. The photochemical reflectance index from directional cornfield reflectances: Observations and simulations. Remote Sens. Environ. 2012, 124, 444–453. [Google Scholar] [CrossRef]
  139. Gamon, J.A.; Field, C.B.; Fredeen, A.L.; Thayer, S. Assessing photosynthetic downregulation in sunflower stands with an optically-based model. Photosynth. Res. 2001, 67, 113–125. [Google Scholar] [CrossRef] [PubMed]
  140. Sukhov, V.; Sherstneva, O.; Surova, L.; Katicheva, L.; Vodeneev, V. Proton cellular influx as a probable mechanism of variation potential influence on photosynthesis in pea. Plant Cell Environ. 2014, 37, 2532–2541. [Google Scholar] [CrossRef] [PubMed]
  141. Sherstneva, O.N.; Vodeneev, V.A.; Katicheva, L.A.; Surova, L.M.; Sukhov, V.S. Participation of intracellular and extracellular pH changes in photosynthetic response development induced by variation potential in pumpkin seedlings. Biochem. Moscow 2015, 80, 776–784. [Google Scholar] [CrossRef] [PubMed]
  142. Sherstneva, O.N.; Vodeneev, V.A.; Surova, L.M.; Novikova, E.M.; Sukhov, V.S. Application of a mathematical model of variation potential for analysis of its influence on photosynthesis in higher plants. Biochem. Moscow Suppl. Ser. A 2016, 10, 269–277. [Google Scholar] [CrossRef]
  143. Sukhov, V. Electrical signals as mechanism of photosynthesis regulation in plants. Photosynth. Res. 2016, 130, 373–387. [Google Scholar] [CrossRef] [PubMed]
  144. Sukhov, V.; Surova, L.; Morozova, E.; Sherstneva, O.; Vodeneev, V. Changes in H+-ATP synthase activity, proton electrochemical gradient, and pH in pea chloroplast can be connected with variation potential. Front. Plant Sci. 2016, 7, 1092. [Google Scholar] [CrossRef] [PubMed]
  145. Surova, L.; Sherstneva, O.; Vodeneev, V.; Katicheva, L.; Semina, M.; Sukhov, V. Variation potential-induced photosynthetic and respiratory changes increase ATP content in pea leaves. J. Plant Physiol. 2016, 202, 57–64. [Google Scholar] [CrossRef] [PubMed]
  146. Sukhova, E.; Akinchits, E.; Sukhov, V. Mathematical models of electrical activity in plants. J. Membr. Biol. 2017, 250, 407–423. [Google Scholar] [CrossRef] [PubMed]
  147. Sukhova, E.; Mudrilov, M.; Vodeneev, V.; Sukhov, V. Influence of the variation potential on photosynthetic flows of light energy and electrons in pea. Photosynth. Res. 2018, 136, 215–228. [Google Scholar] [CrossRef] [PubMed]
  148. Grams, T.E.E.; Koziolek, C.; Lautner, S.; Matyssek, R.; Fromm, J. Distinct roles of electric and hydraulic signals on the reaction of leaf gas exchange upon re-irrigation in Zea mays L. Plant Cell Environ. 2007, 30, 79–84. [Google Scholar] [CrossRef] [PubMed]
  149. Białasek, M.; Gуrecka, M.; Mittler, R.; Karpiński, S. Evidence for the involvement of electrical, calcium and ROS signaling in the systemic regulation of non-photochemical quenching and photosynthesis. Plant Cell Physiol. 2017, in press. [Google Scholar]
Figure 1. The common design of the analysis of influence of photosynthetic parameter distribution on investigated plants for their connection with PRI. Pmin is the minimal value of NPQ among plants (or groups of plants) in each analyzed variant; Pmax is the maximal value of ΔF/Fm’ or LUE among plants (or groups of plants) in each analyzed variant; ΔPabs is the difference between Pmax and Pmin for ΔF/Fm’, NPQ, and LUE in each analyzed variant.
Figure 1. The common design of the analysis of influence of photosynthetic parameter distribution on investigated plants for their connection with PRI. Pmin is the minimal value of NPQ among plants (or groups of plants) in each analyzed variant; Pmax is the maximal value of ΔF/Fm’ or LUE among plants (or groups of plants) in each analyzed variant; ΔPabs is the difference between Pmax and Pmin for ΔF/Fm’, NPQ, and LUE in each analyzed variant.
Remotesensing 10 00771 g001
Figure 2. The connection of PRI with the quantum yield of photosystem II (ΔF/Fm’), nonphotochemical quenching (NPQ), and light use efficiency (LUE). (a) Average correlation coefficients of PRI with ΔF/Fm’ (n = 110), LUE (n = 63), and NPQ (n = 50); (b) Average correlation coefficients of PRI with ΔF/Fm’, LUE, and NPQ for measurements of the photochemical reflectance index in leaves (n = 86, n = 15, n = 38, respectively) and canopy (n = 24, n = 48, n = 12, respectively); (c) Average correlation coefficients of PRI with ΔF/Fm’, LUE, and NPQ with measurements under sunlight (n = 52, n = 54, n = 33, respectively) or artificial light (n = 58, n = 9, n = 17, respectively). * the groups significantly differed from another one (p < 0.05, Student’s test).
Figure 2. The connection of PRI with the quantum yield of photosystem II (ΔF/Fm’), nonphotochemical quenching (NPQ), and light use efficiency (LUE). (a) Average correlation coefficients of PRI with ΔF/Fm’ (n = 110), LUE (n = 63), and NPQ (n = 50); (b) Average correlation coefficients of PRI with ΔF/Fm’, LUE, and NPQ for measurements of the photochemical reflectance index in leaves (n = 86, n = 15, n = 38, respectively) and canopy (n = 24, n = 48, n = 12, respectively); (c) Average correlation coefficients of PRI with ΔF/Fm’, LUE, and NPQ with measurements under sunlight (n = 52, n = 54, n = 33, respectively) or artificial light (n = 58, n = 9, n = 17, respectively). * the groups significantly differed from another one (p < 0.05, Student’s test).
Remotesensing 10 00771 g002
Figure 3. The influence of the distribution of ΔF/Fm’ (a); NPQ (b) and LUE (c) among investigated plants for the connection between these photosynthetic parameters with PRI at all variants of measurements. Average values of Pmin (Pmax) and ΔPabs are shown on left panels, average correlation coefficients are shown on right panels. The label “low” indicates groups with low Pmin (Pmax) and ΔPabs; the label “high” indicates groups with high Pmin (Pmax) and ΔPabs. “Low” groups had n = 55 (ΔF/Fm’), n = 25 (NPQ), and n = 31 (LUE); “high” groups had n = 55 (ΔF/Fm’), n = 25 (NPQ), and n = 32 (LUE). * the group significantly differed from another one (p < 0.05, Student’s test).
Figure 3. The influence of the distribution of ΔF/Fm’ (a); NPQ (b) and LUE (c) among investigated plants for the connection between these photosynthetic parameters with PRI at all variants of measurements. Average values of Pmin (Pmax) and ΔPabs are shown on left panels, average correlation coefficients are shown on right panels. The label “low” indicates groups with low Pmin (Pmax) and ΔPabs; the label “high” indicates groups with high Pmin (Pmax) and ΔPabs. “Low” groups had n = 55 (ΔF/Fm’), n = 25 (NPQ), and n = 31 (LUE); “high” groups had n = 55 (ΔF/Fm’), n = 25 (NPQ), and n = 32 (LUE). * the group significantly differed from another one (p < 0.05, Student’s test).
Remotesensing 10 00771 g003aRemotesensing 10 00771 g003b
Figure 4. The influence of the distribution of ΔF/Fm’ (a); NPQ (b) and LUE (c) on investigated plants for the connection between these photosynthetic parameters with PRI at measurements of the photochemical reflectance index in leaves. Average values of Pmin (Pmax) and ΔPabs are shown on left panels, average correlation coefficients are shown on right panels. The label “low” indicates groups with low Pmin (Pmax) and ΔPabs; the label “high” indicates groups with high Pmin (Pmax) and ΔPabs. “Low” groups had n = 43 (ΔF/Fm’), n = 19 (NPQ), and n = 8 (LUE); “high” groups had n = 43 (ΔF/Fm’), n = 19 (NPQ), and n = 7 (LUE). * the group significantly differed from another one (p < 0.05, Student’s test).
Figure 4. The influence of the distribution of ΔF/Fm’ (a); NPQ (b) and LUE (c) on investigated plants for the connection between these photosynthetic parameters with PRI at measurements of the photochemical reflectance index in leaves. Average values of Pmin (Pmax) and ΔPabs are shown on left panels, average correlation coefficients are shown on right panels. The label “low” indicates groups with low Pmin (Pmax) and ΔPabs; the label “high” indicates groups with high Pmin (Pmax) and ΔPabs. “Low” groups had n = 43 (ΔF/Fm’), n = 19 (NPQ), and n = 8 (LUE); “high” groups had n = 43 (ΔF/Fm’), n = 19 (NPQ), and n = 7 (LUE). * the group significantly differed from another one (p < 0.05, Student’s test).
Remotesensing 10 00771 g004aRemotesensing 10 00771 g004b
Figure 5. The influence of the distribution of ΔF/Fm’ (a); NPQ (b) and LUE (c) on investigated plants for the connection between these photosynthetic parameters with PRI at measurements of the photochemical reflectance index in canopy. Average values of Pmin (Pmax) and ΔPabs are shown on left panels, average correlation coefficients are shown on right panels. The label “low” indicates groups with low Pmin (Pmax) and ΔPabs; the label “high” indicates groups with high Pmin (Pmax) and ΔPabs. “Low” groups had n = 12 (ΔF/Fm’), n = 6 (NPQ), and n = 24 (LUE); “high” groups had n = 12 (ΔF/Fm’), n = 6 (NPQ), and n = 24 (LUE). * the group significantly differed from another one (p < 0.05, Student’s test).
Figure 5. The influence of the distribution of ΔF/Fm’ (a); NPQ (b) and LUE (c) on investigated plants for the connection between these photosynthetic parameters with PRI at measurements of the photochemical reflectance index in canopy. Average values of Pmin (Pmax) and ΔPabs are shown on left panels, average correlation coefficients are shown on right panels. The label “low” indicates groups with low Pmin (Pmax) and ΔPabs; the label “high” indicates groups with high Pmin (Pmax) and ΔPabs. “Low” groups had n = 12 (ΔF/Fm’), n = 6 (NPQ), and n = 24 (LUE); “high” groups had n = 12 (ΔF/Fm’), n = 6 (NPQ), and n = 24 (LUE). * the group significantly differed from another one (p < 0.05, Student’s test).
Remotesensing 10 00771 g005
Figure 6. The influence of the distribution of ΔF/Fm’ (a); NPQ (b) and LUE (c) on investigated plants for the connection between these photosynthetic parameters with PRI with measurements under sunlight. Average values of Pmin (Pmax) and ΔPabs are shown on left panels, average correlation coefficients are shown on right panels. The label “low” indicates groups with low Pmin (Pmax) and ΔPabs; the label “high” indicates groups with high Pmin (Pmax) and ΔPabs. “Low” groups had n = 26 (ΔF/Fm’), n = 16 (NPQ), and n = 27 (LUE); “high” groups had n = 26 (ΔF/Fm’), n = 17 (NPQ), and n = 27 (LUE). * the group significantly differed from another one (p < 0.05, Student’s test).
Figure 6. The influence of the distribution of ΔF/Fm’ (a); NPQ (b) and LUE (c) on investigated plants for the connection between these photosynthetic parameters with PRI with measurements under sunlight. Average values of Pmin (Pmax) and ΔPabs are shown on left panels, average correlation coefficients are shown on right panels. The label “low” indicates groups with low Pmin (Pmax) and ΔPabs; the label “high” indicates groups with high Pmin (Pmax) and ΔPabs. “Low” groups had n = 26 (ΔF/Fm’), n = 16 (NPQ), and n = 27 (LUE); “high” groups had n = 26 (ΔF/Fm’), n = 17 (NPQ), and n = 27 (LUE). * the group significantly differed from another one (p < 0.05, Student’s test).
Remotesensing 10 00771 g006aRemotesensing 10 00771 g006b
Figure 7. The influence of the distribution of ΔF/Fm’ (a); NPQ (b) and LUE (c) on investigated plants for the connection between these photosynthetic parameters with PRI at measurements under artificial light. Average values of Pmin (Pmax) and ΔPabs are shown on left panels, average correlation coefficients are shown on right panels. The label “low” indicates groups with low Pmin (Pmax) and ΔPabs; the label “high” indicates groups with high Pmin (Pmax) and ΔPabs. “Low” groups had n = 29 (ΔF/Fm’), n = 8 (NPQ), and n = 4 (LUE); “high” groups had n = 29 (ΔF/Fm’), n = 9 (NPQ), and n = 5 (LUE). * the group significantly differed from another one (p < 0.05, Student’s test).
Figure 7. The influence of the distribution of ΔF/Fm’ (a); NPQ (b) and LUE (c) on investigated plants for the connection between these photosynthetic parameters with PRI at measurements under artificial light. Average values of Pmin (Pmax) and ΔPabs are shown on left panels, average correlation coefficients are shown on right panels. The label “low” indicates groups with low Pmin (Pmax) and ΔPabs; the label “high” indicates groups with high Pmin (Pmax) and ΔPabs. “Low” groups had n = 29 (ΔF/Fm’), n = 8 (NPQ), and n = 4 (LUE); “high” groups had n = 29 (ΔF/Fm’), n = 9 (NPQ), and n = 5 (LUE). * the group significantly differed from another one (p < 0.05, Student’s test).
Remotesensing 10 00771 g007
Figure 8. An expected efficiency of application of PRI for investigation of NPQ and ΔF/Fm’ at different conditions of measurements and different distribution of photosynthetic stress levels among investigated plants.
Figure 8. An expected efficiency of application of PRI for investigation of NPQ and ΔF/Fm’ at different conditions of measurements and different distribution of photosynthetic stress levels among investigated plants.
Remotesensing 10 00771 g008
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/Fm’ = quantum yield of photosystem II; NPQ = nonphotochemical quenching of chlorophyll fluorescence.
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/Fm’ = quantum yield of photosystem II; NPQ = nonphotochemical quenching of chlorophyll fluorescence.
YearReferenceScaleSource of LightSpecies/Vegetation TypeParameters
1994Peñuelas et al. [56]CanopySunlightSunflowerLUE *
1995Peñuelas et al. [62]LeavesArtificial lightHedera canariensis, Phaseolus vulgaris, Rhus integrifolia, Heteromeles arbutifolia, Agave americana, Opuntia ficusindica and Cereus hexagonusΔF/Fm, LUE ***
1996Filella et al. [50]Leaves/CanopySunlightBarleyLUE *
1997Gamon et al. [63]CanopySunlightPhaseolus 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,
1997Peñuelas et al. [3]LeavesSunlightQuercus ilex, Phillyrea latifoliaΔF/Fm, LUE *
2000Méthy [64]LeavesArtificial lightQuercus ilexΔF/Fm **
2000Nichol et al. [51]CanopySunlightPopulus 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 marianaLUE **
2002Nichol et al. [65]CanopySunlightPinus sylvestris, Abies siberica, PiceaLUE **
abies, Pinus siberica,
Sorbus aucuparia, Abies siberica, and Betel pendula
2002Strachan et al. [66]CanopySunlightMaizeLUE **
2002Trotter et al. [67]CanopyArtificial lightHebe townsonii, Carex buchanani Осокa, Metrosideros excelsa, Pittosporum eugenioides, Hebe ‘Otari Delight’, Grisilinea littoralis, Hebe pimeleoides, Pittosporum tenuifolium ‘Shirley’LUE **
2002Winkel et al. [68]LeavesSunlightChenopodium quinoaΔF/Fm, LUE **
2004Evain et al. [38]Leaves/CanopyArtificial lightGrapevineΔF/Fm, NPQ ***
2005Gamon [69]LeavesSunlightAnacardium excelsum, Carica papaya, Cecropia longipes, Enterolobium cyclocarpum, Ficus insipida, Luehea seemannii, Piper reticulatum, Pseudobombax septenatum and Maclura tinctoriaΔF/Fm *
2005Inamullah and Isoda [39]LeavesSunlightSoybean and cottonΔF/Fm, NPQ *
2005Nakaji et al. [70]CanopySunlightLarix kaempferiLUE *
2005Raddi et al. [71]LeavesArtificial lightMedicago sativa,NPQ **
Phragmites australis,
Rubus fruticosus,
Silybum marianum
Populus euroamericana,
Fraxinus angustifolia,
Alnus glutinosa
Quercus ilex
Pinus pinaster and
Pinus pinea
2005Serrano and Peñuelas [52]CanopySunlightQuercusLUE **
ilex, Phyllirea latifolia,
Arbutus unedo,
Erica arborea, Juniperus oxycedrus and Cistus albidus
2006Guo and Trotter [72]LeavesArtificial lightAckama 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
2006Inoue and Peñuelas [73]LeavesSunlightSoybeanLUE *
2006Nakaji et al. [74]CanopySunlightJapanese larchLUE *
2006Nichol et al. [75]CanopySunlightRhizophora mangleΔF/Fm, NPQ **
and Avicennia germinans
2006Sims et al. [76]CanopySunlightAdenostoma fasciculatum, Adenostoma sparsifolium, Arctostaphylos pungensLUE ***
2006Weng et al. [77]LeavesArtificial lightMangifera indica, podocarpus nagi, alnus formosanaΔF/Fm***, NPQ**
2008Hall et al. [78]CanopySunlightDouglas fir, western red cedar andLUE **
western hemlock
2008Nakaji et al. [53]CanopySunlightJapanese larch, Japanese cypress, hybrid larch and dwarf bambooLUE *
2008Naumann et al. [79]CanopySunlightMyrica ceriferaΔF/Fm **
2008Naumann et al. [80]CanopyArtificial lightMyrica ceriferaΔF/Fm **
2008Peguero-Pina et al. [40]CanopySunlightQuercus cocciferaNPQ ***
2009Busch et al. [81]LeavesArtificial lightJack pineΔF/Fm, NPQ **
2009Middleton et al. [82]CanopySunlightDouglas firLUE **
2009Naumann et al. [45]CanopySunlightMyrica cerifera and Iva frutescensΔF/Fm **
2010Ibaraki et al. [46]LeavesArtificial lightStrawberry, lettuce and potatoΔF/Fm **
2010Ibaraki and Gupta [83]LeavesArtificial lightPotatoΔF/Fm **
2010Naumann et al. [84]CanopyArtificial light/ SunlightElaeagnus umbellataΔF/Fm **
2010Sarlikioti et al. [41]LeavesArtificial lightTomatoΔF/Fm, NPQ ***
2010Shahenshah et al. [42]LeavesSunlightCotton and PeanutΔF/Fm, NPQ *
2010Weng et al. [85]LeavesArtificial light/ SunlightMangoΔF/Fm **
2010Wu et al. [86]CanopySunlightWheatLUE **
2011Ripullone et al. [47]LeavesSunlightArbutus unedo, Quercus ilex,ΔF/Fm **
Quercus pubescens, Quercus cerris,
Quercus robur, Cannabis sativa,
Fagus sylvatica and
Populus euroamericana
2012Ač et al. [87]CanopySunlight(Festuca rubra, Hieracium sp., Plantago sp, Nardus stricta and Jacea pseudophrygiaLUE ***
2012Osório et al. [43]LeavesArtificial lightCeratonia siliquaΔF/Fm *
2012Porcar-Castell et al. [48]LeavesSunlight/ Artificial lightPinus sylvestrisΔF/Fm, NPQ, LUE *
2012Rahimzadeh-Bajgiran et al. [88]LeavesArtificial lightSolanum melongenaNPQ **
2012Shrestha et al. [60]LeavesArtificial lightRiceNPQ ***
2012Weng et al. [89]LeavesArtificial lightPinus taiwanensis, StranvaesiaΔF/Fm **
niitakayamensis, two
Miscanthus spp. and mango
2012Zinnert et al. [7]CanopyArtificial lightBaccharis Halimifolia andΔF/Fm, NPQ **
Myrica cerifera
2013Cheng et al. [90]CanopySunlightMaizeLUE **
2013Liu et al. [54]CanopySunlightMaize and winter wheatNPQ **
2013Rossini et al. [91]CanopySunlightMaizeΔF/Fm **
2014Hmimina et al. [55]LeavesSunlightQuercus robur and Fagus sylvaticaΔF/Fm, LUE **
2014Magney et al. [44]LeavesArtificial lightSunflower, wheat, QuercusNPQ **
macrocarpa, Betula papyrifera,
and Populus tremuloides
2014Soudani et al. [92]CanopySunlightQuercusLUE **
robur, Quercus petraea, Quercus ilex, Carpinus betulus
2015Rossini et al. [93]CanopySunlightMaizeΔF/Fm **
2015Šebela et al. [94]LeavesArtificial lightRiceΔF/Fm *
2015van Leeuwen et al. [95]CanopyArtificial lightDouglas firLUE **
2015Wu et al. [96]CanopySunlightWheatLUE **
2017Chou et al. [49]CanopySunlightMaizeΔF/Fm, NPQ **
2017Zhang et al. [97]CanopySunlightErica multifloraΔF/Fm **
2017Zhang et al. [98]LeavesSunlightQuercus ilexΔF/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.
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/Fm’, NPQ, and LUE with different conditions of measurements.
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/Fm’, NPQ, and LUE with different conditions of measurements.
Conditions of MeasurementAnalyzed Parameter or EffectΔF/Fm’NPQLUE
ScaleLeavesAverage correlation coefficient0.58±0.05−0.40±0.080.77±0.03
Influence of Pmax (Pmin)++++++
Influence of ΔPabs++++++
CanopyAverage correlation coefficient0.72±0.05−0.77±0.050.46±0.08
Influence of Pmax (Pmin)+
Influence of ΔPabs
Source of lightSunlightAverage correlation coefficient0.50±0.08−0.41±0.080.50±0.07
Influence of Pmax (Pmin)++++++
Influence of ΔPabs++++++
Artificial lightAverage correlation coefficient0.71±0.04−0.65±0.110.75±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).

Share and Cite

MDPI and ACS Style

Sukhova, E.; Sukhov, V. 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. Remote Sens. 2018, 10, 771. https://doi.org/10.3390/rs10050771

AMA Style

Sukhova E, Sukhov V. 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. Remote Sensing. 2018; 10(5):771. https://doi.org/10.3390/rs10050771

Chicago/Turabian Style

Sukhova, Ekaterina, and Vladimir Sukhov. 2018. "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" Remote Sensing 10, no. 5: 771. https://doi.org/10.3390/rs10050771

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop