Journal of Experimental Botany, Vol. 66, No. 18 pp. 5625–5637, 2015
doi:10.1093/jxb/erv270 Advance Access publication 1 June 2015
This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details)

RESEARCH PAPER

Comparing vegetation indices for remote chlorophyllmeasurement of white poplar and Chinese elm leaves withdifferent adaxial and abaxial surfaces
1
2

School of Geographical Sciences, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo 113–8657, Japan* These authors contributed equally to this work.
To whom correspondence should be addressed. E-mail: aomasa@mail.ecc.u-tokyo.ac.jp†

Received 13 March 2015; Revised 19 April 2015; Accepted 28 April 2015
Editor: Roland Pieruschka

Abstract
Quick non-destructive assessment of leaf chlorophyll content (LCC) is important for studying phenotypes related toplant growth and stress resistance. This study was undertaken to investigate the quantitative relationship between
LCC and different vegetation indices (VIs) on both adaxial and abaxial surfaces of white poplar (Populus alba), which
has dense tubular hairs on its abaxial surface, and Chinese elm (Ulmus pumila var. pendula), which does not show
obvious superficial differences except for lighter colour on the abaxial surface. Some published and newly developed
VIs were tested to relate them to LCC. The results showed that most of the published VIs had strong relationships with
LCC on the one-surface dataset, but did not show a clear relationship with LCC when both adaxial and abaxial surface
reflectance data were included. Among the reflectance indices tested, the modified Datt index, (R719−R726)/(R719−R743),
performed best and is proposed as a new index for remote estimation of chlorophyll content in plants with varying
leaf surface structures. It explained 92% of LCC variation in this research, and the root mean square error of the LCC
prediction was 5.23 μg/cm2. This new index is insensitive to the effects of adaxial and abaxial leaf surface structuresand is strongly related to the variation in reflectance caused by chlorophyll content.
Key words: Adaxial and abaxial leaf surfaces, chlorophyll, phenotyping, reflectance, remote sensing, vegetation index.

Introduction
The study of spectrometric remote sensing of leaves is relevant because the spectral features are related to nondestructive monitoring of plant growth and health (Cordón andLagorio, 2007) and are partly correlated with plant biochemical components (Vogelmann et al., 1993; Peñuelas et al., 1997;

Smith et al., 1997; Adams et al., 1999). Several attempts havebeen made to use remote spectral measurements to determine leaf chemistry at both leaf and canopy levels (Gitelsonand Merzlyak, 1994; Kim et al., 1994; Curran et al., 1995;
Peñuelas et al., 1995; Rondeaux et al., 1996; Blackburn, 1998a;

Abbreviations: CARI, chlorophyll absorption ratio index; Chl, chlorophyll; LCC, leaf chlorophyll content; MCARI, modified chlorophyll absorption ratio index; MDATT,
modified Datt index; ND, normalized difference; NDI, normalized difference index; NIR, near infrared; PSND, pigment-specific normalized difference; PSSR,
pigment-specific simple ratio; R2, coefficient of determination; RMSE, root mean square error; SD, simple difference; SPAD, soil plant analysis development; SR,
simple ratio; TCARI, transformed chlorophyll absorption in reflectance index; TCARI/OSAVI, transformed chlorophyll absorption in reflectance index/ optimized soiladjusted vegetation index; VI, vegetation index; VOG2, Vogelmann red edge index 2; Rλ, reflectance at wavelength λ.
© The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Shan Lu1,*, Xingtong Lu1,*, Wenli Zhao1, Yu Liu1, Zheyi Wang1 and Kenji Omasa2,†

5626 | Lu et al.
leaf surface may not be accurate enough (Lu and Lu, 2015).
Sims and Gamon (2002) also suggested that Chl detection
would tend to be limited to adaxial leaf layers. The authors’
research group has tested the validity of already-published
VIs on predicting SPAD (soil plant analysis development)
values of leaves on adaxial or abaxial surfaces (Lu and Lu,2015), but the performance of the VIs on both adaxial andabaxial surfaces was not evaluated, nor were effective models
superior to the already-published VIs suggested.
This study presents an analysis of the reflectance spectra of leaves belonging to two species (white poplar andChinese elm), which are very common plants in northeast China. These species possess very different phenotypic characteristics, especially with regard to leaf surface
structures; their study provides more general informationon the reflectance of plant leaves. The main objective of
this study is to find spectral indices for LCC estimation
on variously structured leaves that are not sensitive to thedifferences between adaxial and abaxial leaf surfaces. To
determine ‘universal’ Chl indices, i.e. indices applicable
to different species and leaf surfaces, the newly proposed
LCC indices were compared with the previously suggested
VIs and the most effective vegetation indices were foundfor measuring LCC over a wide range of plant species andleaf structures.

Materials and methods
Plant materials
Sampling was carried out on the campus of Northeast Normal
University, China. Two deciduous tree species, white poplar(Populus alba) and Chinese elm (Ulmus pumila var. pendula),
were chosen because of their considerable leaf structural differences, with one individual of each sampled. Fifty-five leaf
samples of white poplar and 60 of Chinese elm were detachedrandomly from the stem, immediately packed and sealed into
plastic bags and placed inside a cooler to avoid desiccation anddecomposition of the Chl by light. All the measurements, including the reflectance measurements and Chl extraction, were carried out within 4 h after leaf harvesting to minimize changes in
Chl content.
Microscopic photographs of adaxial and abaxial leaf surfaces
were taken using an Olympus microscope equipped with a CCD
camera. The magnification was 10× the original size.
Spectral measurements
Immediately following leaf sample delivery to the laboratory, spectral reflectance was measured on adaxial and abaxial leaf surfaces
using an ASD FieldSpec® 3 portable spectrometer (Analytical
Spectral Devices, Boulder, CO, USA). The spectrometer can collectdata in the 350–2500 nm spectral region, with a sampling intervalof 1.4 nm in the 350–1000 nm wavelength range and 2 nm in the
1000–2500 nm wavelength range. Because the VIs involved in thisstudy were related only to wavelengths of 400–1000 nm, a subsetof the reflectance spectra were used within this range. Three scansfor each sample were conducted and the measurements were converted to reflectance after comparing with a 99% diffuse reflector(Spectralon®, Labsphere, North Sutton, NH, USA). The averagereflectance was taken as representative of the sample. The reflectance measurements were made with the help of a leaf clip equippedwith an internal halogen source directly attached to the leaf surface.

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Datt, 1998; Daughtry et al., 2000; Haboudane et al., 2002;
Richardson et al., 2002). Leaf chlorophyll (Chl) content, as
one of the most important vegetative parameters, provides
valuable information not only on the physiological status, butalso on the phenotypic manifestations of plants (Fiorani and
Schurr, 2013; Ainsworth et al., 2014). Hence, there is a needfor accurate, efficient and practical methodologies to estimate it (Levizou et al., 2005; Steele et al., 2008; Fiorani and
Schurr, 2013). Non-destructive remote determination of leaf
chlorophyll content (LCC) permits measurement of Chl variation over time for a single leaf and avoids time-consuming
and expensive traditional Chl content measurements (Sadras
et al., 2000; Sims and Gamon, 2002).
Decades of research have gone into finding Chl-sensitive
regions from the vegetation spectrum that can be nondestructively extracted (and quantified) using combinationsof wavebands [i.e. vegetation indices (VIs)] (Main et al.,2011). VIs have been introduced for large-scale phenotypingof biomass, as well as studies on greenness, nitrogen content,
pigment composition and photosynthetic status (Fiorani and
Schurr, 2013). Several studies have found that the wavelengths
adjoining the Chl maximum absorption bands (700 nm)
and the green (550 nm) bands are the most sensitive to awide range of Chl contents (Gitelson and Merzlyak, 1994,
1996; Lichtenthaler et al., 1996). Indices for Chl estimation
based on reflectances in narrow spectral bands have recently
received considerable attention and have been found to bewell correlated with the Chl content of leaves (e.g. 1/R700,
1/R550−1/R750, R800/R650, NDI: (R750−R705)/(R750+R705), D730
or D740) (Chappelle et al., 1992; Gitelson and Merzlyak, 1994,
1996; Peñuelas et al., 1994; Curran et al., 1995; Blackburn,
1998a, 1998b; Datt, 1998, 1999a, 1999b; Adams et al., 1999;
Zarco-Tejada et al., 2001; Richardson et al., 2002; Simsand Gamon, 2002; Mutanga and Skidmore, 2007). Several
researchers have also worked to develop an algorithm thatwould be minimally sensitive to differences in leaf structure
to avoid species-specific calibration (Datt, 1999b; Maccioni
et al., 2001; Gitelson et al., 2003). However, the authors areaware of few studies that have attempted to assess how theaccuracy of Chl content estimation depends on the adaxial(upper) and abaxial (lower) surfaces of leaf spectral bands.
While the remotely sensed data are being acquired, multiple scattering of higher-order canopy causes the incoming
solar radiation to be reflected from understory and otherleaves and enter the abaxial side of leaves. Furthermore, somefoliage may change its orientation, turning the adaxial leaf
surface away from the sun and exposing the abaxial leaf surface (Campbell et al., 2007). This would make the remotesensing data contain spectral information from both adaxial
and abaxial leaf surfaces. The phenotypic expressions (suchas leaf hair, wax, palisade tissues, spongy tissues, etc.) ofleaves vary not only between species, but also between the
adaxial and abaxial surfaces of one leaf. Whereas the reflectance signal registered by remote sensors reveals fairly definitive structural changes in leaves and vegetation canopy covers(Demarez et al., 1999; Slaton et al., 2001; Levizou et al., 2005;
Serrano, 2008; Rautiainen et al., 2010; Verrelst et al., 2012),
Chl content estimates made solely from VIs of the adaxial

Chlorophyll content indices insensitive to adaxial and abaxial leaf surfaces | 5627
The reflectance spectra were collected at a 45° angle from the nadir
direction (0°/45° irradiation/detection geometry). This configuration
was the same as in the research of Demarez et al. (1999). They suggested that this configuration could reduce some influence of leaf
specular reflectance. The measurements from other directions would
agree with the results of this research if a piece of leaf were considered as a Lambertian surface (Chelle, 2006). For the reflectancemeasurement, all the samples were put on a piece of black paper
to avoid transmission of extraneous reflectances from the reflected
light through the leaf.

Data analysis
More than 30 published Chl indices (Table 1) for estimation fromspectral analysis were tested with the experimental data. Most of
the tested indices can be classified into five categories: (i) single-bandreflectance or single-difference (SD) index between the reflectanceof two bands (e.g. R680 or 1/R550−1/R750); (ii) simple-ratio (SR) index(e.g. R672/R550); (iii) normalized difference (ND) of reflectance (e.g.
NDVI: (R800−R650)/(R800+R650)); (iv) indices using reflectance derivatives (e.g. D730); and (v) other forms of indices (e.g. R705/(R717+R491)).
In addition, two-band indices were evaluated using a customdeveloped computer programme to traverse all band combinationsof the SD, SR and ND indices. These indices were calculated using
two random available wavebands (λ1 and λ2) in the 400–1000 nm
region to select the best two-band indices as well as the extent of
the effective two-band combination regions for assessment of Chlcontent, as shown in Eqs (1)–(3):SD( Rλ1 , Rλ2 ) = Rλ1 − Rλ2

SR( Rλ1 , Rλ2 ) =ND( Rλ1 , Rλ2 ) =

(1)

Rλ1
Rλ2

(2)

Rλ1 − Rλ2
Rλ1 + Rλ2

(3)Furthermore, three-band indices based on the Datt (1999b) principle were derived by introducing a third band (λ3) to the indicesaccording to Eq. (4):Modified Datt (MDATT) index ( Rλ1 , Rλ2 , Rλ3 ) =Rλ3 − Rλ1
Rλ3 − Rλ2

(4)This MDATT index was modified to compensate for high leaf surface (specular) reflectance and scattering from the mesophyll, which
tend to alter reflectance across the whole visible and near-infrared
spectrum. Adding a constant (the specular reflectance and scatteringfrom mesophyll) to all reflectance values changes the indices even

Results
Structure of adaxial and abaxial leaf surfaces
The smooth adaxial surface (Fig. 1A) of the white poplar
leaves differed greatly from the abaxial surface, which has large
amounts of tubular hair. The hair was so dense that the cuticular structure could not be seen from the microscopic photograph (Fig. 1B). Because leaf hair, as a major determinantof leaf surface relief, may change leaf surface scattering aswell as reflectance, the abaxial surface of white poplar appears
to have a white or silver colour. By contrast, the adaxial andabaxial surfaces of Chinese elm leaves had fewer superficial
structural differences (Fig. 1C, D), except for a little lighter
green on the abaxial surface. Studying the different leaf surfaces of plants may help to acquire more general informationon the reflectance of plant leaves and to obtain more applicable VIs to estimate LCC on variously structured leaves.

Spectral reflectance of adaxial and abaxial leaf
surfaces
The reflectance spectra for adaxial and abaxial surfaces of white
poplar and Chinese elm are presented in Fig. 2. It is apparent thatthe reflectance spectra were much lower in the visible wavelengths(400–680 nm) for the adaxial surface than for the abaxial surface.
However, the difference in the near infrared (NIR) wavelengths(760–1000 nm) was not significant. Furthermore, the difference
between the spectral reflectance of adaxial and abaxial surfaces
for the white poplar leaf (Fig. 2A) was significantly greater thanthat for Chinese elm (Fig. 2B) in the visible wavelengths.
The differences in reflectance between adaxial and abaxial
surfaces are shown in Fig. 3. A distinct difference could be foundbetween the surfaces in the white poplar leaves, but a smaller
variation was shown in the Chinese elm leaves, a result which
agreed with their leaf surface appearance and microscopic photographs. In white poplar leaves, the least reflectance difference
between adaxial and abaxial surfaces occurred at 735 nm wavelength and it occurred at 728 nm in Chinese elm leaves.

Relationship between LCC and new two-band indicesderived from both adaxial and abaxial surfaces of white
poplar and Chinese elm
The R2 values between the LCC and the SD, SR and ND
indices are shown in Fig. 4, where the x-axis represents λ1
and the y-axis λ2. In this figure, 230 samples comprising the

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Chlorophyll content measurements
For each leaf, Chl was extracted from the approximate location ofleaf disc used for reflectance measurements. The disc was cut into
small pieces and ground in the dark with a mortar and pestle in 95%(v/v) ethanol until the pulp turned white in colour and all pigments
were extracted. Thereafter, the leaf pigment mixture was moved to a
50 ml volumetric flask with 95% ethanol and one part of the mixture
was centrifuged in plastic tubes with a rotational speed of 3200 r/
min for 10 min. The supernatant was decanted from the tubes andits absorbance immediately measured with a Lambda 900 spectrophotometer (Perkin-Elmer, Waltham, MA, USA). Chl content (μg/cm2) was calculated according to Wintermans and De Mots (1965).

when there is no change in absorptance of tissues below the epidermis (Sims and Gamon 2002). The MDATT index was developed
here to remove this effect.
All two-band, MDATT and published indices derived from adaxial or abaxial surfaces (n=110 for the white poplar and n=120 for the
Chinese elm) and from both leaf surfaces for the two species together(n=230) were correlated with Chl content. The relationships with thebest-fit coefficient of determination (R2) and root mean square error(RMSE) were used to evaluate the effectiveness of each index in estimating Chl content and in selecting the most applicable indices thatare not sensitive to the leaf surface.

5628 | Lu et al.
Table 1. Chlorophyll indices used in this study
Classification

Vegetation index

References

Single-band reflectance or SD (simple difference) indices1/R700R680
1/R700−1/R750
1/R550−1/R750

Gitelson and Merzlyak, 1996
Blackburn, 1998b
Gitelson et al., 2003
Gitelson et al., 2003
This paper

SR (simple ratio) indices

Indices using reflectance derivatives

SR: Rλ1/Rλ2
NDI: (R750−R705)/(R750+R705)
PSNDb: (R800−R635)/(R800+R635)(R800−R650)/(R800+R650)(R800−R675)/(R800+R675)
ND: |(Rλ1−Rλ2)|/(Rλ1+Rλ2)
D754/D704
RII = ∫

750

705

Others( Rλ R705 − 1)d λ

D730
D710
D740
VOG2: (R734−R747)/(R715+R726)
CARI:(|(a*670+R670+b)|/(a2+1)0.5)*(R700/R670)[a=(R700−R550)/150; b=R550−(a*550)]
R672/(R550×R708)
R860/(R550×R708)
MCARI: [(R700−R670)−0.2*(R700−R550)]*(R700/R670)
TCARI/OSAVI:
3*[(R700−R670)−0.2*(R700−R550)*(R700/R670)]/[(1 + 0.16)*(R800–R670)/(R800+R670+0.16)]
TCARI: 3*[(R700−R670)−0.2*(R700−R550)*(R700/R670)]
R705/(R717+R491)
R434/(R496+R401)(R850−R710)/(R850−R680)
MDATT index: (Rλ3−Rλ1)/(Rλ3−Rλ2)

adaxial and abaxial surfaces of both species, were used to calculate R2. These maps provide an overview of the statisticalsignificance of the indices for all combinations of two wavelengths. They enable efficient extraction of significant peakwavelengths as well as the extent of the effective regions forassessment of Chl content. The results showed that the LCCsensitive regions mainly involved two areas. One was made up
of red edge and blue wavelengths, with λ1 ranging from 705 to
715 nm and λ2 from 420 to 440 nm.
The SD, SR and ND indices from two random bands that
had rather high R2 and selected published indices (R2>0.65,
n=230) are listed in Table 2 (see Supplementary Table S1 forthe performance of all the indices tested in this study). They

Lichtenthaler et al., 1996
Lichtenthaler et al., 1996
Datt, 1998
Datt, 1998
Blackburn, 1998aBlackburn, 1998aBlackburn, 1998b
Blackburn, 1998b
Zarco-Tejade et al., 2001
Zarco-Tejada et al., 2001
Zhu et al., 2007
This paper
Gitelson and Merzlyak, 1994
Blackburn, 1998aBlackburn, 1998b
Blackburn, 1998b
This paper
Takebe and Yoneyama, 1989
Richardson et al., 2002
Richardson et al., 2002
Mutanga and Skidmore, 2007
Mutanga and Skidmore, 2007
Vogelmann et al., 1993
Kim et al., 1994
Datt, 1998
Datt, 1998
Daughtry et al., 2000
Daughtry et al., 2000; Rondeauxet al., 1996
Haboudane et al., 2002
Tian et al., 2011
Tian et al., 2011
Datt, 1999b
This paper

were evaluated for their ability to predict Chl content basedon the datasets for the two species together (n=230) and foreach species separately (n=110 for white poplar and n=120 for
Chinese elm). The R2 and RMSE for each predictive model
are also provided in Table 2.
The two-band indices which performed best among the twoband spectral indices for the three datasets were the SD indices using the red edge and blue wavelength combination (seeFig. 4 and Table 2). The best-performing two-band spectral
index was the SD R709−R434 index (R2=0.81, RMSE=7.77 μg/
cm2) for both leaf surfaces in the two plant dataset, whereas
the SD R708−R434 index (R2=0.83, RMSE=8.07 μg/cm2) was
the best for both leaf surfaces of white poplar and the SD

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ND (normalized difference) indices

SD: Rλ1−Rλ2
R750/R550
R750/R700
R860/R550
R672/R550
PSSRa: R800/R680
PSSRb: R800/R635
R800/R650
R800/R675
R450/R550
R750/R710
R950/R680

Chlorophyll content indices insensitive to adaxial and abaxial leaf surfaces | 5629

Fig. 2. Average (the curves) and standard deviation (the shaded area) of reflectance spectra of adaxial and abaxial leaf surfaces for (A) white poplar(n=55) and (B) Chinese elm (n=60). λ is the wavelength.

R712−R426 index (R2=0.76, RMSE=7.35 μg/cm2) for both leaf
surfaces of Chinese elm.

Relationship between LCC and MDATT indices derived
from both adaxial and abaxial surfaces of white poplar
and Chinese elm
To identify optimal parameters for estimating LCC, further
analysis was conducted on the MDATT indices, which werebased on the same dataset analysed for the two-band indices.
Each combination of three bands was used to compose an
MDATT index that was then correlated with the LCC. The
best R2 values between the LCC and the MDATT indices

generated from combinations of wavelengths λ1, λ2 and λ3 areshown in Fig. 5, and the performance of the MDATT indices in estimating LCC is also shown in Table 2. The results
indicated that the MDATT indices that had good correlations with LCC were derived primarily from the red edge
wavelength regions. For example, the MDATT indices with
R2 greater than 0.90 were derived from the wavelengths of λ1(721~746 nm), λ2 (705~758 nm) and λ3 (699~798 nm); the indices with R2 greater than 0.92 were generated from the wavelengths of λ1 (726~728 nm), λ2 (743 nm) and λ3 (717~720 nm);and the best-performing index overall was (R719−R726)/(R719−
R743), which generated the most significant linear relationships with LCC (R2=0.92, RMSE=5.23 μg/cm2) (Table 2).

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Fig. 1. Optical microscopic reflectance images of leaf surfaces of white poplar and Chinese elm: (A) adaxial and (B) abaxial surfaces of white poplar; (C)adaxial and (D) abaxial surfaces of Chinese elm. Bar, 200 μm.

5630 | Lu et al.

Fig. 4. The map for coefficient of determination (R2) between the two-band simple difference (Eq. 1), simple ratio (Eq. 2) and normalized difference (Eq. 3)
indices and leaf chlorophyll content for both surfaces of both plant species. (A) simple difference, (B) simple ratio, (C) normalized difference.

The maps for R2 between the MDATT indices (λ3 fixed at
719 nm, 750 nm and 850 nm) and the LCC for both surfaces ofboth species combined are shown in Fig. 6. The (R719−R732)/(R719−R726) and (R719−R747)/(R719−R721) indices were strongly
related to LCC for the two-surface datasets of white poplar(R2=0.94, RMSE=4.67 μg/cm2) and Chinese elm (R2=0.91,
RMSE=4.53 μg/cm2).
The scatter plots for the 230 samples between LCC and thebest-performing two-band and MDATT indices are shown in
Fig. 7. It can also be seen that the MDATT index correlatedwith LCC better than the two-band indices.

Comparison of new LCC indices with publishedspectral indices derived from both adaxial and abaxial
surfaces of white poplar and Chinese elm
Some published indices performed relatively well with LCC
on the two-surface dataset, including the D754/D704 spectral
index and the (R850−R710)/(R850−R680) Datt index. The indiceswith better relationships to LCC than the other published VIs
are based on the red edge or the near infrared bands. They
performed similarly to or slightly better than the selected
ND or SR indices found in this study, as shown in Table 2.
However, they only achieved an R2 close to or less than 0.75

and an RMSE greater than 8.5 μg/cm2 and exhibited poorer
LCC estimation than the new proposed MDATT indices.

Relationship between LCC and the new proposed
and published spectral indices derived from adaxial or
abaxial surfaces of each plant
Analysis of the datasets for each leaf surface was also conducted separately for each species to verify the effect of leaf
surface on Chl content estimation. The distributions of R2 for
the MDATT indices when λ3 was set to 719 nm, 750 nm and
850 nm for white poplar and Chinese elm leaves are shown in
Figs 8 and 9. The LCC-sensitive region on adaxial or abaxial
surfaces was occupied by the whole bottom right area when
λ3 was 719 nm. However, the sensitive range was narrower
when λ3 was set to 750 nm and 850 nm. In addition, the LCCsensitive region was broader on adaxial than on abaxial leaf
surface.
The performance of the proposed and published indices in
predicting LCC for the adaxial or abaxial surface of each plant
species (R2>0.84) are shown in Table 3 (see Supplementary
Table S2 for the performance of all the indices tested in thisstudy). Almost all the indices had higher R2 on adaxial thanon abaxial leaf surfaces (Figs 8, 9, Table 3). The MDATT

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Fig. 3. Difference in reflectance between adaxial and abaxial surfaces of (A) white poplar and (B) Chinese elm.

Chlorophyll content indices insensitive to adaxial and abaxial leaf surfaces | 5631
Table 2. Relationships between vegetation indices and leaf chlorophyll content for both adaxial and abaxial data of two plants withdifferent leaf surfaces
Vegetation indices

Both plants

Chinese elm
R2

RMSE (μg/cm2)

4.67

MDATT:(R719−R747)/(R719−R721) *

0.91

4.53

0.83

8.07

SD: R712−R426

0.76

7.35

D754/D704

0.81

8.66

SR: R421/R700

0.75

7.42

8.85

SR:R434/R517

0.80

8.78

0.73

7.66

0.76

8.86

TCARI/OSAVI

0.78

9.26

ND: (R700−R420)/(R700+R420)
D754/D704

0.72

7.92

0.73

9.25

0.78

9.29

8.43

9.72
10.58

0.76
0.65

9.75
11.66

(R850−R710)/(R850−R680)
VOG2
D740

0.68

0.71
0.65

ND: (R516−R431)/(R516+R431)(R850−R710)/(R850−R680)
R705/(R717+R491)

0.67
0.67

8.47
8.49

RMSE (μg/cm2)

MDATT: (Rλ3−Rλ1)/(Rλ3−Rλ2)(R2>0.92; λ1,726~728; λ2,743~743; λ3, 717~720)(R2>0.90; λ1,721~746;
λ2,705~758; λ3, 699~798)(R719−R726)/(R719−R743) *

0.92

5.23

SD: Rλ1−Rλ2 (R2>0.73;
λ1,705−715; λ2, 420~440)
R709−R434

0.81

SR: Rλ1/Rλ2 (R2>0.70; λ1,400~680; λ2, 510~702)
R451/R604
D754/D704

Vegetation indicesWhite poplar
R2

RMSE (μg/cm2)

MDATT: (R719−R732)/(R719−R726) *

0.94

7.77

SD: R708−R434

0.78

8.51

0.76

ND: |(Rλ1-Rλ2)|/(Rλ1+Rλ2)(R2>0.60; λ1, 515~605 or
700~705; λ2, 410~435)(R583−R426)/(R583+R426)(R850−R710)/(R850−R680)
TCARI/OSAVI
R705/(R717+R491)*, the MDATT indices that performed best in each dataset. ‘Both plants’ dataset, n=230; white poplar dataset, n=110; Chinese elm dataset,
n=120.

abaxial (R2=0.95, RMSE=4.26 μg/cm2) leaf surfaces ofwhite poplar. Similar good relationships were also obtained
for Chinese elm, where the (R719−R761)/(R719−R493) MDATT
index gave significant linear relationships with LCC for adaxial surfaces (R2=0.95, RMSE=3.51 μg/cm2) and the (R719−
R742)/(R719−R732) index did so for abaxial surfaces (R2=0.93,
RMSE=4.07 μg/cm2).
In addition, the published spectral indices that achieved anequally good relationship to LCC were almost always basedon red edge region wavelengths such as VOG2. Althoughmost published spectral indices could give reasonable accuracy on the adaxial surface, they behaved relatively poorly on
the abaxial surface. For example, the VOG2 gave a R2 value of0.96 for the adaxial surface of white poplar dataset, but only
0.90 for the abaxial surface dataset.

Discussion
Fig. 5. Changes in the best coefficient of determinations (R2) between the
MDATT indices and leaf chlorophyll content as derived from combinationsof wavelengths of λ1, λ2 and λ3. (A) combination of λ1 and λ3, (B)combination of λ2 and λ3.

indices (λ3=719) also performed well in estimating LCC, as
did some SR indices.
The (R719−R731)/(R719−R647) and (R719−R746)/(R719−R718)
MDATT indices generated significant linear relationships
with LCC for adaxial (R2=0.96, RMSE=3.84 μg/cm2) andAnalysis of spectra of adaxial and abaxial leaf surfaces
The abaxial leaf surface had systematically larger visible
reflectance than the adaxial surface, whatever the species(Fig. 2). However, the distinction was notable only in visible wavelengths, whereas in the near infrared the reflectance
did not show an obvious difference between the two surfaces(Fig. 3). This reflectance difference exists perhaps because
most leaves have a distinct layer of long palisade parenchyma tissues in the upper part of the mesophyll and more

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Vegetation
indicesR2

5632 | Lu et al.

Fig. 7. Best-fit lines and experimental data for vegetation indices (VIs) and chlorophyll content relationships: (A) (R719−R726)/(R719−R743) versus Chl; (B)R709−R434 versus Chl; (C) R451/R604 versus Chl; (D) (R583−R426)/(R583+R426) versus Chl. Filled circles, adaxial surface data for white poplar; closed circles,abaxial surface data for white poplar; filled squares, adaxial surface data for Chinese elm; closed circles, abaxial surface data for Chinese elm.

irregularly shaped, loosely arranged spongy parenchyma tissues in the lower part of the mesophyll (Gates et al., 1965).
The abaxial side has larger aerial interspaces between themesophyll cells and a consequently greater extent for lightreflectance processes (Cordón and Lagorio, 2007). It was also
found that the reflectance at visible wavelengths for abaxial
surfaces of white poplar was much higher than for Chinese
elm because of the dense tubular hairs on the abaxial surfaceof white poplar.
Because all the VIs were derived from leaf spectra, one VI
value may be altered more or less according to the difference

between adaxial and abaxial spectra in one piece of leaf.
Therefore, it is necessary to ensure that a VI is stable enoughbefore using it to estimate LCC when reflectance spectrafrom both leaf surfaces are considered. The difference inreflectance between adaxial and abaxial surfaces in Fig. 3
indicates that the smallest change in reflectance occurred in
the red edge regions (735 nm for white poplar and 728 nm
for Chinese elm). It also suggests that robust VIs may bederived from these wavelengths or from wavelengths adjoining them. The MDATT index that was found to be least sensitive to the adaxial or abaxial surface included the 700~760 nm

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Fig. 6. The map for coefficient of determination (R2) between the MDATT indices and the leaf chlorophyll content for both surfaces of both species. (A)λ3=719 nm, (B) λ3=750 nm, (C) λ3=850 nm.

Chlorophyll content indices insensitive to adaxial and abaxial leaf surfaces | 5633

Table 3. Relationships between vegetation indices and leaf chlorophyll content for individual plant species
White poplar
Vegetation indices

Chinese elm
Adaxial surface
2

Vegetation
indices

Abaxial surface
R

RMSE (μg/cm2)

2Vegetation
indices

Adaxial surface
R

RMSE (μg/cm2)

2Vegetation
indices

Abaxial surface
R2

RMSE (μg/cm2)

R

RMSE (μg/cm2)

MDATT: (Rλ3−Rλ1)/(Rλ3−Rλ2)(R2>0.96: λ1, 715~751,λ2,
646~739; λ3, 400~788)(R719−R731)/(R719−R647)*

0.96

3.84

MDATT:(R719−R746)/(R719−R718) *

0.95

4.26

MDATT:(R719−R761)/(R719−R493) *

0.95

3.51

MDATT:(R719−R742)/(R719−R732) *

0.93

4.07

SR: Rλ1/Rλ2 (R2>0.83; λ1,
740~780; λ2, 700~750)R751/R720

0.96

3.78

SR: R747/R748

0.92

5.52

SR: R779/R709

0.95

3.48

SR: R742/R739

0.92

4.17

ND:|(Rλ1−Rλ2)|/(Rλ1+Rλ2)(R2>0.94; λ1, 740~750;λ2,730~750) (R740−R738)/(R740+R738)
R750/R710
VOG2

0.96

3.83

ND:
(R748−R747)/(R748+R747)

0.92

5.52

R750/R710

0.95

3.51

ND:
(R742−R739)/(R742+R739)

0.92

4.19

0.96
0.96

3.83
3.90

SD: R748−R747(R850−R710)/(R850−R680)

0.92
0.91

5.6
5.86

0.94
0.94

3.54
3.57

VOG2
SD:
R749−R746

0.92
0.91

4.21
4.45

SD: Rλ1−Rλ2 (R2>0.94; λ1,
740~750;λ2, 740~750)
R744−R742
RII
D740
R860/R550
1/R550−1/R750

0.95

4.30

VOG2

0.90

6.37

VOG2
ND:
(R749−R738)/(R749+R738)
RII

0.93

3.86

D740

0.91

4.51

0.95
0.95
0.95
0.95

4.27
4.41
4.49
4.54

D740
TCARI/OSAVI
D754/D704
D730

0.90
0.88
0.87
0.86

6.25
6.88
7.10
7.51

R750/R550
R860/R550
R750/R700SD:
R748−R747

0.93
0.93
0.93
0.93

3.87
3.88
3.94
4.00

R750/R710
D730
R750/R550
R860/(R550*
R708)

0.88
0.87
0.84
0.84

5.22
5.44
5.95
5.95

*, the MDATT indices that performed best in each dataset. White poplar dataset, n=55; Chinese elm dataset, n=60.

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Fig. 8. The map for coefficient of determination (R2) between the MDATT index [MDATT=(Rλ3−Rλ1)/(Rλ3−Rλ2)] and leaf chlorophyll content for the adaxial
and abaxial surfaces of white poplar. MDATT indices for adaxial surface with λ3 equal to (A) 719 nm, (B) 750 nm and (C) 850 nm. MDATT indices forabaxial surface with λ3 equal to (D) 719 nm, (E) 750 nm and (F) 850 nm.

5634 | Lu et al.

wavelengths that were very similar to the zero-change reflectance wavelength.

Relationships of VIs with LCC on adaxial or abaxial
surfaces
When the two species were analysed separately, almost all theVIs had stronger relationships with LCC on adaxial surfaces
than on abaxial surfaces because the large aerial interspacesin the spongy tissues and the dense hairs of abaxial surfaces
increased reflectance and resulted in greater errors in Chl content prediction than on adaxial surfaces.
In particular, many of the published indices studied in thispaper performed well when only adaxial surface data were
tested (Table 3). For example, the VOG2 index showed astrong correlation with LCC for the adaxial surfaces in thewhite poplar dataset, with a high R2 value of 0.96 and an
RMSE of 3.90 μg/cm2. However, for the abaxial surfaces in
the white poplar dataset, the R2 value was 0.90 and the RMSE
was 6.37 μg/cm2. The other published indices also showed thesame tendency. Although the published VIs that performed
well in estimating LCC measured using the SPAD chlorophyll
meter were also discussed in Lu and Lu’s (2015) study, theirresults were not as accurate as those presented in this paper
because the Chl meter included some error in measuring the
LCC [the R2 between the SPAD index and LCC was only 0.90
for white poplar and 0.85 for Chinese elm (Lu and Lu, 2015)].
It can be concluded that these indices are valid if they areused only on adaxial surfaces under exactly the same conditions for which they were designed, on the species examined in
the study. However, they did not generate good results for thedataset of abaxial surfaces. The most likely reason is that theindices were developed by regression analysis between LCC

and adaxial leaf surface reflectance. The observed reflectancedifference between adaxial and abaxial leaf surfaces was nottaken into account when the indices were developed.

Stable VIs for predicting LCC independent of leaf
species or surfaces
A helpful comparison of VI performance can be made using
a dataset that mixes both adaxial and abaxial leaf surfaces.
Empirical models for estimating LCC by MDATT indices forboth adaxial and abaxial surfaces are largely based on thereflectance band of red edge regions (700~760 nm) (Fig. 5).
The figure showed that MDATT indices based on these wavelength reflectances had good applicability for LCC prediction
if the reflectance from different phenotypic surfaces, such as
the abaxial surfaces of white poplar leaves with their very
dense hair, is considered. A possible explanation is that thereflectances are most similar between adaxial and abaxial leaf
surfaces. (R719−R726)/(R719−R743) could be the best VI for estimating leaf Chl content, whatever the leaf side or species. The(R719−R732)/(R719−R726) and (R719−R747)/(R719−R721) indices,which are composed of very similar bands to the (R719−R726)/(R719−R743) index, were selected for white poplar and Chinese
elm, respectively, because they possessed the strongest relationship to LCC.
Note that the MDATT indices combined with the red edge
wavelengths performed better than combinations with otherwavelengths between 400 and 1000 nm (Figs 5, 6). The most
likely reason is that the differences in reflectance between
adaxial and abaxial surfaces were least within the red edge
region. Datt (1999b) proposed a (R850−R710)/(R850−R680) index
to predict the LCC of Eucalyptus plants. This index only generated an R2 value of 0.73 when estimating LCC in a dataset

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Fig. 9. The map for coefficient of determination (R2) between the MDATT index [MDATT=(Rλ3−Rλ1)/(Rλ3−Rλ2)] and leaf chlorophyll content for the adaxial
and abaxial surfaces of Chinese elm. MDATT indices for adaxial surface with λ3 equal to (A) 719 nm, (B) 750 nm and (C) 850 nm. MDATT indices forabaxial surface with λ3 equal to (D) 719 nm, (E) 750 nm and (F) 850 nm.

Chlorophyll content indices insensitive to adaxial and abaxial leaf surfaces | 5635

R = Rs + S exp ( − kiCi )(5)where Rs is the reflectance at the leaf surface, S represents
the scattering effects of the leaf mesophyll structure on reflectance, and ki and Ci are, respectively, the specific absorption
coefficient and the concentration of leaf biochemical i (Datt,1999b). Rs and S are thought to be the main factors influencing LCC estimation variability between different samples
because they depend on the differences in leaf surface andinternal mesophyll structure of different samples, but do notvary between samples due to leaf biochemicals.
For the development of a specular reflectance and scatter
insensitive Chl index, three wavelengths were used, for which
Eq. (5) can be rewritten as follows:
Rλ1 = Rs + S exp ( − kchl ( λ1 )Cchl )(6)

Rλ 2 = Rs + S exp ( − kchl ( λ 2 )Cchl )(7)

Rλ3 = Rs + S exp ( − kchl ( λ3 )Cchl )(8)where Cchl is the chlorophyll content and kchl(λ1), kchl(λ2) andkchl(λ3) are the specific absorption coefficients for Chl at λ1, λ2
and λ3, respectively.
Taking differences between Eqs (6) and (8) and Eqs (7) and(8) and dividing the results gives:(Rλ − Rλ ) = exp( −kchl ( λ )Cchl ) − exp( −kchl ( λ )Cchl )(Rλ − Rλ ) exp( −kchl ( λ )Cchl ) − exp( −kchl ( λ )Cchl )
3

1

3

1

3

2

3

2

(9)

Equation (9) is now related to Chl absorption only and isindependent of the additive and multiplicative effects ofleaf structure (Datt, 1999b). Therefore, the MDATT indiceshave removed the effects of Rs and S by taking the difference
in reflectance between two wavelength bands and then taking the ratio of two such differences. Datt (1999b) developedthe (R850−R710)/(R850−R680) index by taking advantage of thelack of absorption by leaf pigments at 850 nm, although theresults obtained here did not show that taking 850 nm as
the λ3 wavelength was effective in removing the effect of thereflectance differences between adaxial and abaxial surfaces.
Taking account of the three bands within the red edge region,
which correlates with LCC, was shown to be highly superior
to the Datt index in predicting LCC.
The relationship of the SD VIs (R2=0.81 for R709−R434
for both plants, 0.83 for R708−R434 for white poplar and 0.76
for R712−R426 for Chinese elm, respectively) to LCC (shownin Table 2) seems to be less satisfactory compared to the
MDATT indices, but better than the other two-band indices.
If Eq. (5) was substituted into the SD indices, then Eq. (10)would explain the possible reasons for the poorer performance of the SD indices:SD( Rλ1 , Rλ 2 ) = Rλ1 − Rλ 2 = S exp( −kchl ( λ1 )Cchl ) − S exp ( −kchl ( λ2 )Cchl )(10)

In the SD indices, the leaf surface effect (Rs) on the LCC estimates could be removed, but the influence of scattering frommesophyll structures (Rs) would still remain. As for the SR
and NR indices, neither the effect of Rs nor that of S waseliminated.
In addition, the scatter plots for the 230 samples between
LCC and the best-performing MDATT, SD, SR and ND
indices, as shown in Fig. 7, revealed that the MDATT index
correlates better with LCC than the two-band indices. For
example, the MDATT (R719−R726)/(R719−R743) index had thehighest correlation with LCC (R2=0.92), whereas the SD
R709−R434 index (R2=0.81), the SR R451/R604 index (R2=0.78)
and the ND (R583−R426)/(R583+R426) index (R2=0.76) showedmuch lower linear R2. Hence, caution is necessary whenselecting not only applicable bands, but also suitable VI types
to predict LCC.
Most of the published indices performed poorly, especiallyin the dataset that mixed different leaf surfaces. The D754/D704
and (R850−R710)/(R850−R680) indices gave the least reasonable
performance among all the published indices for white poplar, Chinese elm, or both species combined, independent of
the leaf surface. This occurred perhaps because all these indices also have the same form as the MDATT index. Taking
D754/D704 as an example, D754 and D704 are also the reflectancedifferences near the 754 nm and 704 nm wavelengths. As aresult, D754/D704 is also the type of VI that takes the ratio of
two differences in reflectance between two wavelength bands,
similarly to the MDATT index proposed in this paper. It might
have removed some effects of Rs and S. The main distinction
between D754/D704 and the MDATT index was the wavelength
selection. Although D754/D704 also selected the red edge bands,
it was not as effective as the MDATT index proposed in thisstudy in estimating LCC when considering both adaxial andabaxial leaf surfaces. D754/D704 showed R2 values of only 0.76,
0.81 and 0.72 for the two-surface dataset of the two species
combined, white poplar and Chinese elm, respectively. The
other published indices did not generate good results withthe observed data from leaves of the two plants combined
because of their limited generality for addressing different
leaf structures, which may be strongly impacted by epidermis
and mesophyll structures or phenotypic characteristics.

Downloaded from http://jxb.oxfordjournals.org/ at Kokusai Hoken Keikakugaku (UNIV OF TOKYO) on October 29, 2015of the two surfaces of the two species (Table 2). The selectionof the 680 nm and 850 nm wavelengths in the red edge region,
which showed relatively greater differences in reflectance
between the two leaf surfaces as well as spectrometer noise at
the longer wavelength (850 nm), may have caused the decreasein R2 for the Datt index. Although the results of Datt’s study
showed that (R850−R710)/(R850−R680) was effective in estimating LCC for Eucalyptus plants, it was not very applicable to
LCC prediction, including adaxial and abaxial reflectanceinformation used in this study. However, the MDATT indiceswith a fixed λ3 at 850 nm still could generate R2 greater than
0.8 if λ1 and λ2 were set within the red edge region (Fig. 5).
Leaf reflectance (R) was modelled by Baret et al. (1988)
using the following formula:

5636 | Lu et al.
Note that the developed VIs are more generally useful andapplicable to LCC prediction for leaves with structures similar to those in this study. When estimating leaves with otherphenotypic characteristics, the validity of the VIs must be
confirmed.

Conclusions

Supplementary data
Supplementary data is available at JXB online.
Supplementary Table S1. Relationships between vegetation
indices and leaf chlorophyll content for combined adaxial
and abaxial data of two species with different leaf surfaces.
Supplementary Table S2. Relationships between vegetation
indices and leaf chlorophyll content for separate adaxial andabaxial data of two species with different leaf surfaces.

Acknowledgments
This work was supported by the National Natural Science Foundationof China (grant no: 41001258), Science and Technology Planning
Project of ‘12th Five-Year Plan’ in the Department of Education of
Jilin province and the Japan Society for the Promotion of Science (grantno: 24248043).

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