326 Photosynthetic pigments: chemical structure, biological function and ecology

Part 5. TECHNIQUES OF CHLOROPHYLLSAND CAROTENOIDS IDENTIFICATION

Chapter 23. REFLECTANCE-BASED NON-DESTRUCTIVE ASSAYOF LEAF CHLOROPHYLLS AND CAROTENOIDS

Alexei Solovchenko, Olga Chivkunova, and Mark Merzlyak1

Contents

23.1. Introduction23.2. Common features of plant reflectance spectra23.3. Chlorophyll23.4. Carotenoids23.5. Chlorophyll-to-carotenoid ratio23.6. Summary and conclusions

References

23.1. Introduction

Chlorophylls (Chl) and carotenoids (Car) are essential pigments ofhigher plant assimilatory tissues (Lichtenthaler, 1987) responsible forabsorption of light energy and for initial steps of its photochemicalutilization; Car also fulfill a crucial function of protection of photosyn-thetic apparatus from photodestruction (Merzlyak and Solovchenko,2002; Young and Lowe, 2001; Choudhury and Behera, 2001; Demmig-Adams et al., 1999). Different content and composition of Chl and Cardetermine the variations of higher plant color from dark-green to yellow.Other pigments are often involved in leaf and fruit coloration such asflavonoids (yellow) and anthocyanins (red) (the latter two groups areout of the scope of the present paper; for more detail, see (Hughes,2011; Steele et al., 2009; Merzlyak et al., 2008)).

Deceased.

327Chapter 23. Reflectance-based non-destructive assay

of leaf chlorophylls and carotenoids

The absolute contents of the pigments as well as their ratio areimportant physiological characteristics, which could be recorded onthe level of a single leaf, whole plant, or even plant community. Thecontent of Chl, the dominant pigment of green leaves, determines to agreat extent the amount of Photosynthetic Active Radiation (PAR)absorbed by a leaf, the rate of photosynthesis, and plant productivity(Hallik et al., 2012; Hansson and Jensen, 2009; Merzlyak et al., 2009).Carotenoids augment Chl in light harvesting, stabilize pigment-proteincomplexes of photosynthetic apparatus (PSA) and prevent, via severalmechanisms, damages to plants by the excess of visible radiation(Lichtenthaler, 1987; Young, 1991). The pigment content undergoesdirectional and specific changes in the course of plant growth anddevelopment, during adaptation to unfavorable environmental condi-tions, as well as under various stresses and as a result of damages(Biswal, 1995; Demmig-Adams and Adams, 2006; Gitelson et al., 2002;Merzlyak and Solovchenko, 2002; Merzlyak et al., 1999; Merzlyak etal., 1998).

Traditionally, pigment analysis in plant physiological and bioche-mical studies is carried out with spectrophotometry of organic solventextracts. This method presumes destruction of the sample hence it istime-consuming and coupled with artifacts due to pigment instability,incomplete extraction, presence of light-absorbing impurities etc.(Merzlyak et al., 1996; Solovchenko et al., 2001). These circumstancesmake nondestructive estimation of pigment content with reflectancespectroscopy of intact tissues an attractive alternative to wet chemicalmethods. Indeed, both qualitative and quantitative changes in pigmentcontent of plant tissues should be inevitably apparent in tissue opticalproperties. Indeed, reflectance spectra of leaves undergo remarkablechanges as a result of mineral nutrition deficiency, pollutant intoxi-cation, senescence and stresses, in particular during acclimation tostrong solar irradiation, and in the course of senescence (Gitelson andMerzlyak, 1993; Merzlyak et al., 1999; Merzlyak et al., 1998; Merzlyakand Solovchenko, 2002; Merzlyak et al., 2008).

Nondestructive reflectance-based quantification of pigments has anumber of advantages such as rapid measurement of a large number ofsamples. Importantly, the leaves remain intact after reflectance mea-surements hence repeated measurement of the same sample turns tobe feasible making the reflectance-based pigment quantification highlysuitable for monitoring of plant objects.

Inexpensive portable reflectometers suitable for field measurements,providing reliable spectral data from both very small plant surfacearea and whole plants were designed (Penuelas and Filella, 1998;Solovchenko et al., 2010; Richardson et al., 2002). Reflectance spectro-scopy is of wide use in global remote monitoring of agro- and

328 Photosynthetic pigments: chemical structure, biological function and ecology

phytocenoses. In recent years these approaches have also been implemen-ted in “precision agriculture” technologies (Penuelas and Filella, 1998).

The basic theory of diffuse reflectance developed by Kubelka andMunk for a homogenous layer of “infinite thickness” yields a simplerelationship between the intensity of reflected light and absorptionand scattering coefficients of the medium (see (Kortum, 1969)). A leafconsisting of several structures with different refraction indices (cuticle,epidermis, and mesophyll) and containing high amounts of pigmentsrepresents a complex optical system. Although detailed investigationsof leaf optical properties have appeared in the literature (Fukshansky,1981; Fukshansky et al., 1993; McClendon and Fukshansky, 1990),the most fruitful approaches for quantitative pigment analysis in situwere developed by considering the leaf as a “black box”. Significantamount of research was dedicated to the development of techniquesfor nondestructive analysis of plant pigments, and these issues attractedmuch attention during the last decade (Penuelas and Filella, 1998;Chappelle et al., 1992; Gilerson et al., 2010; Gitelson et al., 2009;Gitelson et al., 2006; Gitelson et al., 2003a). The foundation of theoptical reflectance-based approach successfully applied by the authorand his colleagues for in situ quantification of both photosyntheticand screening pigments has been laid in the works by Gitelson et al.(2009; 2006; 2003b; 2002; 1998). The present paper is a brief reviewof techniques for quantitative estimation of Chl and Car in leaves withreflectance spectroscopy, including those developed in the laboratoryof the authors.

23.2. Common features of plant reflectance spectra

Development of reflectance-based nondestructive techniques for plantpigments estimation requires a deep understanding of their in vivospectroscopy, localization, and patterns of their changes duringphysiological processes in plants. Senescing leaves featuring dramaticchanges in Chl and Car, are suitable for demonstration of influence ofpigments on leaf reflectance spectra (those of maple, as an example,are shown in Fig. 1). Leaves with low Chl content exhibit high reflec-tance (35–48%) at wavelengths longer than 600 nm. Measurementsperformed on leaves with extremely weak pigmentation showed thatleaf tissues possessed high reflectance without discernible spectralfeatures in the near infrared (NIR), the green and the red parts of thespectrum. These investigations also revealed that apparent absorptionof light by leaves (amounting to 10–15%) in the range of 750–800 nm,described in the literature, is likely to result from incomplete collectionof transmitted light with an integrating sphere (Merzlyak et al., 2002;Merzlyak et al., 2009). The presence of the pigments at very low

329

amounts, which are difficultto quantify analytically (e.g.,Chl content of 0.3–0.4 nmol/cm2), manifests itself as di-stinct bands in leaf and fruitreflectance spectra (Fig. 1, seealso (Merzlyak et al., 2002;Merzlyak et al., 2009). Withan increase in pigment con-tent up to 10–12 nmol/cm2, the spectra contain pronounced featuresof Chl (pale-green leaves) and Car (yellow leaves) absorption. Thereflectance spectra of green (Chl content ca. 30 nmol/cm2) and especiallyof dark-green leaves were poorly resolved.

The leaf reflectance in the main bands of Chl a absorption (near440–450 and 670–680 nm) became saturated at Chl content of 10–15nmol/cm2. However, reflectance in these spectral regions did not dropbelow 4–5% even at very high concentrations of the pigment (figure,see also (Gitelson et al., 2002; Merzlyak et al., 1999). This could beexplained by reflection of light by superficial leaf structures (cuticleand epidermis) containing very low amounts of pigments (see spectrain (Solovchenko and Merzlyak, 2003; Pfiindel et al., 2006). Distinctbands attributable to Car absorption could be distinguished only inreflectance spectra of senescing (yellow) leaves on terminal stages ofChl degradation (see uppermost curve in figure and spectra in (Merzlyaket al., 1999; Merzlyak et al., 1997).

It was found that reciprocal reflectance, 1/R(λ), of leaves (Gitelsonet al., 2006; Gitelson et al., 2003b; Gitelson et al., 2009) at certainwavelength relates to pigment contents. This feature was used in thedevelopment of models relating reflectance and pigment content. Onthe base of these models, algorithms for estimation of Chlorophyll andother pigments in leaves and fruit were developed (for review, see

Chapter 23. Reflectance-based non-destructive assayof leaf chlorophylls and carotenoids

Fig. 1. Reflectance spectra ofautumn maple (Acer platanoides L.)leaves. The chlorophyll content inleaves ranged from 0.1 to 60nmol/cm2 (see red-orange regionin upper and lower spectra, res-pectively). Spectra of anthocyanin-free (<1 nmol/cm2) leaves areshown by solid lines; broken linescorrespond to leaves with antho-cyanin content ranging from 2.1to 40.8 nmol/cm2 (upper and lowerspectra, respectively) (Merzlyak etal., 2003a).

330 Photosynthetic pigments: chemical structure, biological function and ecology

(Merzlyak et al., 2003a)). Briefly, conceptual semi-analytical three-band model (Gitelson et al., 2006; Gitelson et al., 2003a) relatingreflectance and content of pigment of interest [P] was suggested in theform:

[P] ∝ (Rλ1

–1 – Rλ2

–1) × Rλ3 (Eq. 1)

The model contains reflectances in three spectral bands (λ1, λ

2, λ

3).

Reflectance in the spectral band λ1 is maximally sensitive to the pigment

of interest; however, it is also affected by absorption by other pigmentscontained in and scattering by plant tissue. To eliminate the effect ofabsorption by other pigments at reflectance, reflectance in spectralband has been used. is affected by absorption of other pigments andis minimally affected by absorption of the pigment of interest. Thus,the difference in Eq. 1 relates to the pigment of interest, however, isstill affected by the scattering. To minimize this effect, reflectance inspectral band λ

3 should be governed mainly by scattering of the sample

studied.The following strategy (Gitelson et al., 2006) allows to overcome

some of the complications inherent in nondestructive analysis of plantpigments using reflectance spectra and to employ the model (Eq. 1).

(1) The detection of reflectance spectral bands governed predomi-nantly by absorption of individual pigment and sensitive to this pigmentcontent.

(2) The development of algorithms relating reflectance at certainwavelengths with pigment content in the entire range of its variation.

(3) Finding a way for elimination of chlorophyll contribution intoreflectance that is necessary for other pigment analyses.

In the course of these studies, leaves of several plant species wereanalyzed at all stages of their development and in a wide range oftheir pigment content. The following criteria were used for validationof the approaches developed: (i) the algorithms should be sensitiveonly to pigment of interest and insensitive to other pigments ormorphological—anatomical features of plants and (ii) they should beapplicable to independently obtained data sets. For testing the secondcriterion, we used leaves of different species and collected in differentyears.

As a result of the analysis of reflectance spectra, the bands of insitu absorption of leaves of different species pigments were established(Gitelson et al., 2001; Gitelson et al., 2002; Merzlyak et al., 2003b;Gitelson and Merzlyak, 1996; Gitelson et al., 2006). The obtained resultsprovided evidence that the conceptual model (Eq. 1) is applicable foran accurate non-destructive estimation of certain screening pigmentcontent in leaves. The developed algorithms are (i) sensitive mainly topigment of interest and minimally sensitive to contents of other pig-

331

ments or morphological-anatomical features of plant samples, and (ii)applicable to independently obtained data sets (Giannopolitis and Ries,1977; Steele et al., 2009; Gitelson et al., 2009; Gitelson et al., 2003a;Gitelson et al., 2002).

23.3. Chlorophyll

In healthy anthocyanin-free leaves, Chl is the only pigment absorbingin the green to far-red spectral range (Lichtenthaler, 1987; Merzlyaket al., 2002). In earlier investigations, reflectance minimum at 670–680 nm was employed for Chl analysis. Although the algorithmsdeveloped for these wavelengths showed a good sensitivity and linearityat low Chl content, they lost sensitivity to Chl over 10–15 nmol/cm2

(Buschmann and Nagel, 1993; Gitelson and Merzlyak, 1994; Gitelsonet al., 1996a).

Actually, spectral regions where reflectance is sensitive to wide-range variations of Chl content (from 0 to 50–60 nmol/cm2) were foundaside from the red maximum of Chl absorption: in the green (a broadband near 550–600 nm) and in the red (a narrow band near 700–705 nm) parts of the spectrum. In particular, these regions were revealedin the spectrum of standard deviation of reflectance calculated forleaves with wide variation of Chl content (Gitelson et al., 1996a; Gitelsonet al., 2003b). Then, it was found that reflectances in these bands werehyperbolically related with Chl content (Fig. 2A). It should be notedthat Chl absorption coefficients are very low in these bands. The linearrelationship between inverse reflectance in certain spectral regionsand pigment content is likely the fundamental feature of leaf reflectancespectra. It was used as a basis in the development of algorithms forestimation of Chl and other pigments.

One of the requirements for reliable algorithms of pigment analysisis their low sensitivity to morphological-anatomical traits of planttissues. For leaves differing in pigment content, the lowest coefficientof variation of reflectance was found in the NIR region (Lichtenthaleret al., 1996; Gitelson and Merzlyak, 1994). Since leaf pigments possessno measurable absorption in the NIR, tissue reflectance in this regionis thought to be determined by “internal” optical properties related toleaf thickness, water content, and light scattering. The scattering withinplant tissues arises at interfacial boundaries separating phases withdifferent refraction indexes (Buschmann and Nagel, 1993; Merzlyaket al., 2002; Fukshansky, 1981).

Taking into account the above circumstances, the algorithms forestimation of Chl content were suggested in the form of simple ratiosof reflectance coefficients at certain wavelengths: R

NIR/R

550 and R

NIR/

R700

. Note that RNIR

is insensitive and R700

and R550

are highly sensitive

Chapter 23. Reflectance-based non-destructive assayof leaf chlorophylls and carotenoids

332 Photosynthetic pigments: chemical structure, biological function and ecology

to Chl content (see e.g. Fig. 3). Both ratios were highly sensitive toChl content in a wide range of its changes in leaves and fruits ofdiverse plant species and related linearly to the pigment content(Gitelson and Merzlyak, 1998; Lichtenthaler et al., 1996; Gitelson et

Fig. 2. Reflectances at 678, 700 and 800 nm versus chlorophyll content (A);reflectance at 700 nm vs. reflectance at 550 nm (B) in apple (Malus × domesticaBorkh.) fruits. For green to green-yellow fruits, R

550 vs. R

700 is linear with determination

coefficient higher than 0.95, whereas for anthocyanin-containing fruits, R550

< R700

and fair relationship between them was disturbed. Solid lines represent the best-fitfunctions; dashed lines represent STD in B (Merzlyak et al., 2003b).

333

al., 2003a). Furthermore,Chl determination could beperformed in broader spec-tral ranges: the algorithmsin form of [1/R(λ) – 1/R

NIR]

× RNIR

were shown to providehighly precise and linear es-timates of leaf Chl contentin the wavelength ranges of530–580 and 695–735 nm (Gitelson et al., 2006).

It is noteworthy that the RNIR

/R550

and RNIR

/R700

ratios possessedsimilar sensitivity to Chl content, which was due to high correlationbetween reflectance at 550 and 700 nm characteristic of healthyanthocyanin-free leaves. Furthermore, there is ground to believe thatthis correlation represents the universal feature of leaf reflectancespectra in the ranges predominantly or exclusively governed by Chlabsorption (Gitelson et al., 2003a; Gitelson and Merzlyak, 1998). Thus,accumulation of anthocyanins (Fig. 1; see also (Gitelson et al., 2009;Gitelson et al., 2001)]) leads to a significant decrease of R

550 relative

to R700

(Fig. 2B). This greatly complicates the application of the RNIR

/R

550 index for Chl determination in the red leaves. At the same time,

our studies showed that the RNIR

/R700

index could be used for Chlanalysis even at high anthocyanin content (Gitelson et al., 2001).Further details on development of the indices and their characteristicsare considered in (Gitelson and Merzlyak, 1996; Gitelson et al., 2003a).

Several other approaches allowing efficient analysis of Chl in leaveswere developed using reflectance in the red region of the spectrum.Thus, the pigment content could be retrieved from the amplitude andposition of the peak in the first derivative of a reflectance spectrumbetween 685 and 706 nm, so-called “red edge” (Ding et al., 2009; Gitelsonet al., 1996b).

23.4. Carotenoids

In green tissues the analysis of Car absorbing in the blue region ofthe spectrum is problematic due to a strong overlapping absorption ofChl present in high amounts in plant tissues (Merzlyak and Solovchenko,

Fig. 3. Relationships betweenthe index R

800/R

700 –1 and chloro-

phyll content in apple leaves (r2 >0.98) (Solovchenko A., unpubli-shed; in collaboration with Dr. L.Kozhina).

Chapter 23. Reflectance-based non-destructive assayof leaf chlorophylls and carotenoids

334 Photosynthetic pigments: chemical structure, biological function and ecology

2002; Gitelson et al., 2002; Demmig-Adams et al., 1996). Additionalobstacles to the Car analysis in plants are due to complex compositionof these pigments undergoing transformation during fruit ontogenyand upon their adaptation to high-light conditions (Solovchenko et al.,2006). To estimate effect of Car on reflection spectra, one needs toremove a significant effect of Chl absorption. Normalization of reci-procal reflectance to reflectance at 678 nm (red Chl absorption band)removes in certain degree Chl effect, thus, the spectrum [R

800/R(λ)]/

R678

depends on other factors but Chl (Merzlyak et al., 2003a; Gitelsonet al., 2002). The quantitative Car estimation became feasible usingthe same 3-band model (Eq. 1) with λ

1 in the range 510–530 nm (Merz-

lyak et al., 1998). To subtract effect of Chl absorption on reflectancein spectral band λ

1, λ

2 was found to be optimal in either the green

range (around 550 nm) or red edge range (700 nm). As for Chl andAnC retrieval, optimal λ

3 was in the NIR range beyond 750 nm. Two

Carotenoid Reflectance Indexes (CRI) developed for leaves (Gitelson etal., 2002) were suggested as

CRI1 = (R

520–1 – R

700–1) × R

800, (Eq. 2)

orCRI

2 = (R

520–1 – R

550–1) × R

800, (Eq. 3)

where the first term in the parentheses associates with combinedabsorption by Car and Chl, and the second one relates to Chl absorption.However, it should be mentioned that CRI is not applicable to antho-cyanin-pigmented objects. In addition, flavonoids when accumulatedin high quantities influence considerably optical spectra and theirabsorption might extend quite far into the visible spectrum. Therefore,one using reflectances for non-destructive determination of higherplant pigments absorbing in the visible range should be aware ofobstacles which could be caused by flavonoids when they are present inhigh amounts (Merzlyak et al., 2005).

23.5. Chlorophyll-to-carotenoid ratio

The proportion between Car and Chl is an important characteristicof plant photosynthetic apparatus. The most dramatic changes in thecontent of these pigments occur at terminal stages of leaf and fruitdevelopment in many plant species. Frequently, at these stages planttissues retain certain amounts of Car or Car synthesis is induced onthe background of Chl degradation. In chloroplasts of senescing planttissues, plastoglobules rather than thylakoids become the predominantsites of Car localization (Merzlyak and Solovchenko, 2002; Merzlyaket al., 1999; Tevini and Steinmuller, 1985; Steinmuller and Tevini,

335

1985; Solovchenko et al., 2010). The analysis of green leaves withdifferent pigment content revealed a strong correlation between reflec-tance in the red maximum of Chl absorption (near 678 nm) and in thespectral band near 500 nm governed by combined absorption of Chland Car (Merzlyak et al., 1997; Merzlyak et al., 1999). In senescingcoleus (Coleus blumei Benth.) leaves, characterized by a remarkablysynchronous disappearance of both Chl and Car resulting in whitishleaf coloration, a high correlation of reflectances at these wavelengthswas retained until advanced stages of Chl breakdown. By contrast,during chlorophyll degradation in yellowing leaves of deciduous trees(maple and chestnut) and in ripening fruits (e.g., apples and lemons)R

678 increased significantly higher than R

500. As a result, a close

correlation between reflectance at these wavelengths characteristic oftissues with high chlorophyll content was broken (Merzlyak et al.,1997; Merzlyak et al., 1999).

For detection of relative changes in Chl and Car content, PlantSenescence Reflectance Index (PSRI) was suggested that uses reflectanceat 500 and 678 nm along with NIR reflectance: (1/R

678 – 1/R

500)/R

NIR(Gilerson et al., 2010). This index exhibited high correlation with themolar Car/Chl ratio in senescent maple leaves (Gilerson et al., 2010).

In leaves containing high amounts of Chl, R500

was somewhat higherthan R

678 that resulted in negative PSRI values. As mentioned earlier,

the reflectance at 678 nm increased faster in the course of leafsenescence than that at 500 nm, which made PSRI positive. Therefore,the stage when PSRI turns to zero (i.e., R

678 = R

500) could serve as a

criterion for the onset of senescence in plants exhibiting Car retention.Our experiments showed that the induction of Car synthesis occurs atdifferent stages of Chl degradation and the rates of relative changesin Chl and Car content vary between plant species (Gilerson et al.,2010).

23.6. Conclusions and prospects

The results obtained during the last decade considerably extendedpossible applications of reflectance spectroscopy for estimation ofpigment content and for assessment of physiological status of plants.These achievements are really impressive, because some time agoreflectance spectroscopy was considered to be unable to provide usefulinformation about plant organisms due to their low reflectance andpoorly resolved spectra that seemed similar in different species (Gamonand Surfus, 1999). We consider these similarities as evidence of commonorganization of photosynthetic apparatus and uniformity of its changesoccurring during plant development and stress responses in higherplants.

Chapter 23. Reflectance-based non-destructive assayof leaf chlorophylls and carotenoids

336 Photosynthetic pigments: chemical structure, biological function and ecology

The results presented in this review show that reflectance spectro-scopy could be a useful and efficient tool for pigment analysis in plants.Remarkably, to retrieve nondestructively Chl and Car, the reflectancein only four spectral bands is sufficient. However, the possibilities ofapplication of this technique to leaves of other plant species need furtherverification. Another problem, which remains to be solved is the findingof approaches for selective determination of Chl a and b. All thisfacilitates the extensive application of reflectance spectroscopy forsolving various issues of plant physiology on the level of individualleaves. The developed algorithms could be employed in remote sensingof vegetation status (Gitelson et al., 1997; Gitelson et al., 1996a).Fundamental spectral features of leaf reflectance, revealed in thesestudies, provide a basis for the development of this technology.

References

Biswal, B. 1995. Carotenoid catabolism during leaf senescence and its controlby light. Journal of Photochemistry & Photobiology, B: Biology 30 (1): 3-13.

Buschmann, C., and Nagel, E. 1993. In vivo spectroscopy and internaloptics of leaves as basis for remote sensing of vegetation. Int J Remote Sens14 (4): 711-722.

Chappelle, E., Kim, M., and McMurtrey III, J. 1992. Ratio analysis ofreflectance spectra(RARS): An algorithm for the remote estimation of theconcentrations of chlorophyll a, chlorophyll b, and carotenoids in soybeanleaves. Remote Sens Environ 39 (3): 239-247.

Choudhury, N., and Behera, R. 2001. Photoinhibition of photosynthesis:Role of carotenoids in photoprotection of chloroplast constituents.Photosynthetica 39 (4): 481-488.

Demmig-Adams, B., Adams III, W., Ebbert, V., and Logan, B. 1999 Eco-physiology of the xanthophyll cycle. In: Frank H, Young A, Britton G, CogdellR (eds) The photochemistry of carotenoids. pp 245–269.

Demmig-Adams, B., and Adams, W. 2006. Photoprotection in an ecologicalcontext: the remarkable complexity of thermal energy dissipation. New Phytol172 (1): 11-21.

Demmig-Adams, B., Gilmore, A., and Adams, W. 1996. In vivo functionof carotenoids in higher plants. The FASEB Journal 10 (4): 403-412.

Ding, P., Fuchigami, L., and Scagel, C. 2009. Simple Linear Regressionand Reflectance Sensitivity Analysis Used to Determine the OptimumWavelengths for the Nondestructive Assessment of Chlorophyll in Fresh LeavesUsing Spectral Reflectance. J Am Soc Hort Sci 134 (1): 48-57.

Fukshansky, L. 1981 Optical properties of plant tissue. In: Smith H (ed)Plants and the Daylight Spectrum. Springer, Berlin, pp 37-303

Fukshansky, L., Remisowsky, A., McClendon, J., Ritterbusch, A., Richter, T.,and Mohr, H. 1993. Absorption spectra of leaves corrected for scattering anddistributional error: a radiative transfer and absorption statistics treatment.Photochem Photobiol 57 (3): 538-555.

337

Gamon, J., and Surfus, J. 1999. Assessing leaf pigment content and activitywith a reflectometer. New Phytol: 105-117.

Giannopolitis, C.N., and Ries, S.K. 1977. Superoxide Dismutases:I. Occurrence in Higher Plants. Plant Physiol 59 (2): 309-314.

Gilerson, A., Gitelson, A., Zhou, J., Gurlin, D., Moses, W., Ioannou, I.,and Ahmed, S. 2010. Algorithms for remote estimation of chlorophyll-a incoastal and inland waters using red and near infrared bands. Opt Express 18(23): 24109-24125.

Gitelson, A., Chivkunova, O., and Merzlyak, M. 2009. Nondestructiveestimation of anthocyanins and chlorophylls in anthocyanic leaves. Am J Bot96 (10): 1861.

Gitelson, A., Gritz, Y., and Merzlyak, M. 2003a. Non destructive chlorophyllassessment in higher plant leaves: algorithms and accuracy J Plant Physiol160: 271-282.

Gitelson, A., Kaufman, Y., and Merzlyak, M. 1996a. Use of a green channelin remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ58 (3): 289-298.

Gitelson, A., Keydan, G., and Merzlyak, M. 2006. Three-band model fornoninvasive estimation of chlorophyll, carotenoids, and anthocyanin contentsin higher plant leaves. Geophys Res Lett 33: L11402.

Gitelson, A., and Merzlyak, M. 1993. Spectral reflectance changes associatedwith autumn senescence of Aesculus hippocastanum L. and Acer platanoides L.leaves. Spectral features and relation to chlorophyll estimation. J Plant Physiol143 (3): 286-292.

Gitelson, A., and Merzlyak, M. 1994. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and mapleleaves. J Photochem Photobiol B 22 (3): 247-252.

Gitelson, A., and Merzlyak, M. 1996. Signature analysis of leaf reflectancespectra: algorithm development for remote sensing of chlorophyll. J PlantPhysiol 148 (3-4): 494-500.

Gitelson, A., and Merzlyak, M. 1998. Remote sensing of chlorophyllconcentration in higher plant leaves. Adv Space Res 22 (5): 689-692.

Gitelson, A., Merzlyak, M., and Lichtenthaler, H. 1996b. Detection of rededge position and chlorophyll content by reflectance measurements near 700nm. J Plant Physiol 148 (3-4): 501-508.

Gitelson, A., Stark, R., and Dor, I. 1997. Quantitative near-surface remotesensing of wastewater quality in oxidation ponds and reservoirs: a case studyof the Naan system. Water Environ Res 69 (7): 1263-1271.

Gitelson, A.A., Gritz, Y., and Merzlyak, M.N. 2003b. Relationships betweenleaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol160 (3): 271-282.

Gitelson, A.A., Merzlyak, M.N., and Chivkunova, O.B. 2001. Opticalproperties and nondestructive estimation of anthocyanin content in plant leaves.Photochem Photobiol 74 (1): 38-45.

Gitelson, A.A., Zur, Y., Chivkunova, O.B., and Merzlyak, M.N. 2002.Assessing carotenoid content in plant leaves with reflectance spectroscopy.Photochem Photobiol 75 (3): 272-281.

Chapter 23. Reflectance-based non-destructive assayof leaf chlorophylls and carotenoids

338 Photosynthetic pigments: chemical structure, biological function and ecology

Hallik, L., Niinemets, U., and Kull, O. 2012. Photosynthetic acclimationto light in woody and herbaceous species: a comparison of leaf structure,pigment content and chlorophyll fluorescence characteristics measured in thefield. Plant Biol 14 (1): 88-99.

Hansson, A., and Jensen, P. 2009. Chlorophyll limitation in plants remodelsand balances the photosynthetic apparatus by changing the accumulation ofphotosystems I and II through two different approaches. Physiol Plant 135:214-228.

Hughes, N.M. 2011. Winter leaf reddening in ‘evergreen’ species. NewPhytol 190 (3): 573-581.

Kortum, G. (1969) Reflectance spectroscopy: principles, methods,applications. Springer, Berlin, Heidelberg, New York

Lichtenthaler, H. (1987) Chlorophyll and carotenoids: pigments of photo-synthetic biomembranes. Methods of Enzymology, vol 148.

Lichtenthaler, H., Gitelson, A., and Lang, M. 1996. Non-destructive deter-mination of chlorophyll content of leaves of a green and an aurea mutant oftobacco by reflectance measurements. J Plant Physiol.

McClendon, J., and Fukshansky, L. 1990. On the interpretation of absorptionspectra of leaves-II. the non-absorbed ray of the sieve effect and the meanoptical pathlength in the remainder of the leaf. Photochem Photobiol 51 (2):211-216.

Merzlyak, M., Chivkunova, O., Zhigalova, T., and Naqvi, K. 2009. Lightabsorption by isolated chloroplasts and leaves: effects of scattering and‘packing’. Photosynthesis Res 102 (1): 31-41.

Merzlyak, M., Gitelson, A., Chivkunova, O., and Rakitin, V. 1999. Non-destructive optical detection of pigment changes during leaf senescence andfruit ripening. Physiol Plant 106 (1): 135-141.

Merzlyak, M., Gitelson, A., Chivkunova, O., Solovchenko, A., and Pogosyan,S. 2003a. Application of reflectance spectroscopy for analysis of higher plantpigments. Russ J Plant Physiol 50 (5): 704-710.

Merzlyak, M., Gitelson, A., Pogosyan, S., Chivkunova, O., Lehimena, L.,Garson, M., Buzulukova, N., Shevyreva, V., and Rumyantseva, V. 1997.Reflectance spectra of leaves and fruits during their development and senes-cence and under stress. Russ J Plant Physiol 44 (5): 614-622.

Merzlyak, M., Gitelson, A., Pogosyan, S., Lekhimena, L., and Chivkuno-va, O. 1998. Light-induced pigment degradation in leaves and ripening fruitsstudied in situ with reflectance spectroscopy. Physiol Plant 104 (4): 661-667.

Merzlyak, M., Khozin, I., and Cohen, Z. 1996. Spectrophotometric analysisof carotenoids in plant extracts based on elimination of chlorophyll absorption.Phytochem Anal 7 (6): 294-299.

Merzlyak, M., and Solovchenko, A. 2002. Photostability of pigments inripening apple fruit: a possible photoprotective role of carotenoids duringplant senescence. Plant Sci 163 (4): 881-888.

Merzlyak, M., Solovchenko, A., and Gitelson, A. 2003b. Reflectance spectralfeatures and non-destructive estimation of chlorophyll, carotenoid and antho-cyanin content in apple fruit. Postharvest Biol Technol 27 (2): 197-212.

Merzlyak, M.N., Chivkunova, O.B., Melo, T.B., and Naqvi, K.R. 2002.Does a leaf absorb radiation in the near infrared (780-900 nm) region? A new

339

approach to quantifying optical reflection, absorption and transmission ofleaves. Photosynthesis Res 72 (3): 263-270.

Merzlyak, M.N., Chivkunova, O.B., Solovchenko, A.E., and Naqvi, K.R.2008. Light absorption by anthocyanins in juvenile, stressed, and senescingleaves. J Exp Bot 59 (14): 3903-3911.

Merzlyak, M.N., Solovchenko, A.E., Smagin, A.I., and Gitelson, A.A. 2005.Apple flavonols during fruit adaptation to solar radiation: spectral featuresand technique for non-destructive assessment. J Plant Physiol 162 (2): 151-160.

Penuelas, J., and Filella, I. 1998. Visible and near-infrared reflectancetechniques for diagnosing plant physiological status. Trends Plant Sci 3 (4):151-156.

Pfiindel, E., Agati, G., and Cerovic, Z. (2006) Optical properties of plantsurfaces. Biology of the Plant Cuticle, vol 23. Blackwell Publishing, Oxford

Richardson, A., Duigan, S., and Berlyn, G. 2002. An evaluation ofnoninvasive methods to estimate foliar chlorophyll content. New Phytol: 185-194.

Solovchenko, A., Avertcheva, O., and Merzlyak, M. 2006. Elevated sunlightpromotes ripening-associated pigment changes in apple fruit. Postharvest BiolTechnol 40 (2): 183-189.

Solovchenko, A., Chivkunova, O., Merzlyak, M., and Reshetnikova, I. 2001.A spectrophotometric analysis of pigments in apples. Russ J Plant Physiol 48(5): 693-700.

Solovchenko, A., and Merzlyak, M. 2003. Optical properties and contributionof cuticle to UV protection in plants: experiments with apple fruit.Photochemical and Photobiological Sciences 2 (8): 861-866.

Solovchenko, A.E., Merzlyak, M.N., and Pogosyan, S.I. 2010. Light-induceddecrease of reflectance provides an insight in the photoprotective mechanismsof ripening apple fruit. Plant Sci 178 (3): 281-288.

Steele, M., Gitelson, A., Rundquist, D., and Merzlyak, M. 2009. Nonde-structive estimation of anthocyanin content in grapevine leaves. AmericanJournal of Enology and Viticulture 60 (1): 87-92.

Steinmuller, D., and Tevini, M. 1985. Composition and function of pla-stoglobuli. Planta 163 (2): 201-207.

Tevini, M., and Steinmuller, D. 1985. Composition and function of pla-stoglobuli. Planta 163 (1): 91-96.

Young, A. 1991. The photoprotective role of carotenoids in higher plants.Physiol Plant 83 (4): 702-708.

Young, A., and Lowe, G. 2001. Antioxidant and prooxidant properties ofcarotenoids. Arch Biochem Biophys 385 (1): 20-27.

Chapter 23. Reflectance-based non-destructive assayof leaf chlorophylls and carotenoids