remote sensing  

Article

Assessment of Portable Chlorophyll Meters for
Measuring Crop Leaf Chlorophyll Concentration

Taifeng Dong 1, Jiali Shang 1,*, Jing M. Chen 2,*, Jiangui Liu 1, Budong Qian 1, Baoluo Ma 1,
Malcolm J. Morrison 1, Chao Zhang 1, Yupeng Liu 1 , Yichao Shi 1, Hui Pan 1 and
Guisheng Zhou 3

1 Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6,
Canada; taifeng.dong@canada.ca (T.D.); jiangui.liu@canada.ca (J.L.); Budong.Qian@canada.ca (B.Q.);
baoluo.ma@canada.ca (B.M.); Malcolm.Morrison@Canada.ca (M.J.M.); Zhangc1700@yzu.edu.cn (C.Z.);
Ykevin.Liu@mail.utoronto.ca (Y.L.); Yichao.Shi@Canada.ca (Y.S.); Pan.Hui@agr.gc.ca (H.P.)

2 Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
3 College of Agriculture, Yangzhou University, Yangzhou 225009, China; gszhou@yzu.edu.cn
* Correspondence: jiali.shang@canada.ca (J.S.); jing.chen@utoronto.ca (J.M.C.)

Received: 16 October 2019; Accepted: 13 November 2019; Published: 19 November 2019 ����������
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Abstract: Accurate measurement of leaf chlorophyll concentration (LChl) in the field using a
portable chlorophyll meter (PCM) is crucial to support methodology development for mapping
the spatiotemporal variability of crop nitrogen status using remote sensing. Several PCMs have
been developed to measure LChl instantaneously and non-destructively in the field, however, their
readings are relative quantities that need to be converted into actual LChl values using conversion
functions. The aim of this study was to investigate the relationship between actual LChl and PCM
readings obtained by three PCMs: SPAD-502, CCM-200, and Dualex-4. Field experiments were
conducted in 2016 on four crops: corn (Zea mays L.), soybean (Glycine max L. Merr.), spring wheat
(Triticum aestivum L.), and canola (Brassica napus L.), at the Central Experimental Farm of Agriculture
and Agri-Food Canada in Ottawa, Ontario, Canada. To evaluate the impact of other factors (leaf
internal structure, leaf pigments other than chlorophyll, and the heterogeneity of LChl distribution)
on the conversion function, a global sensitivity analysis was conducted using the PROSPECT-D model
to simulate PCM readings under different conditions. Results showed that Dualex-4 had a better
performance for actual LChl measurement than SPAD-502 and CCM-200, using a general conversion
function for all four crops tested. For SPAD-502 and CCM-200, the error in the readings increases
with increasing LChl. The sensitivity analysis reveals that deviations from the calibration functions
are more induced by non-uniform LChl distribution than leaf architectures. The readings of Dualex-4
can have a better ability to restrict these influences than those of the other two PCMs.

Keywords: leaf chlorophyll concentration; portable chlorophyll meter; crop; PROSPECT-D; sensitivity
analysis; remote sensing; radiative transfer model

1. Introduction

Estimation of plant traits using remote sensing data, such as leaf nitrogen concentration, leaf
chlorophyll concentration (LChl) and leaf area index (LAI), is important for mapping the spatiotemporal
variability of crop and soil conditions, and modeling crop nutrient balance, and crop productivity [1–3].
LChl is the main light-harvesting pigment that determines leaf photosynthetic capacity, and it is highly
influenced by nitrogen fertilization [4–6]. Furthermore, incorporating LChl into process-based crop
models could improve model performance [7,8]. LChl varies with leaf positions, species, crop types,

Remote Sens. 2019, 11, 2706; doi:10.3390/rs11222706 www.mdpi.com/journal/remotesensing

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Remote Sens. 2019, 11, 2706 2 of 20

crop growth stages and crop managements [7,9,10]; thus, knowledge on the spatiotemporal variability
of LChl is important to understand the status of crop growth condition and productivity [8,11,12].

A number of studies have investigated the potential of remote sensing data in estimating LChl using
statistical or physical based approaches [13–15]. There has been rapid development in new satellite
sensors, such as multispectral satellite sensors with red-edge (680–750 nm) reflectance measurements
(e.g., Sentinel-2 and VENµS) [16,17], the VNIR-SWIR hyperspectral satellite sensors (e.g., HyspIRI and
EnMAP) [18,19], and the multi- and hyperspectral imaging systems mounted on a UAV system [19].
In particular, these sensors possess the red-edge or hyperspectral reflectance that is highly sensitive
to changes in LChl [4]. This allows for improved accuracy for LChl estimation from remote sensing
data at different spatial scales. Accurate in situ LChl measurements are essential for developing and
validating remote-sensing LChl estimation models.

Destructive and non-destructive methods are often used for LChl measurement. Both methods rely
on measured light absorption/transmission to determine LChl [2,20,21]. Conventionally, destructive
measurement is conducted using a wet-chemical method in a lab setting [4,22]. Leaves are harvested
from the plant and chlorophyll is extracted using organic solvents (e.g., acetone, methanol, ethanol,
dimethyl sulphoxide (DMSO), or N-dimethyl formamide (DMF) [4,22,23]. A spectrophotometer, a
fluorometer, or a high-performance liquid chromatography (HPLC) is often used to measure light
absorptions at a few wavelength ranges [4,23], which are then used to determine LChl. The lab-based
approach is costly, labour intensive and time consuming. In addition, destructive sampling does not
allow for tracking the temporal dynamics of LChl of the same leaves [5].

Non-destructive methods provide a cost-efficient way for frequent measurement of LChl over a
large area [2,9,10]. Studies have found that spectral indices derived from light absorbance or reflectance
at the visible and near infrared (NIR) regions have good correlations with LChl [24–27] and can be used to
develop non-destructive methods for LChl measurements [27,28]. Portable chlorophyll meters (PCMs),
such as the SPAD-502/501 (Soil Plant Analysis Development (SPAD) chlorophyll meter, Konica–Minolta,
Inc., Osaka, Japan), the CCM-200 (CCM-200 plus Chlorophyll Content Meter, Opti-Sciences, Inc.,
Hudson, NH), and the Dualex-4 (Dualex Scientific+TM Polyphenol & Chlorophyll Meter, FORCE-A,
Orsay, France), have been developed for non-destructive measurements of LChl and nitrogen in the
field [2,29,30]. The readings from the PCMs (meter reading) are relative quantities that need to be
converted to actual LChl. The transformation equations are usually established using meter readings
and lab-measured LChl of the same leaf area [21,29–32]. For instance, Markwell et al. [21] developed a
widely used exponential equation to estimate LChl from SPAD-502 readings, and Cerovic et al. [29]
subsequently developed a generic conversion function for SPAD-502 readings based on more data
collected in different studies (e.g., Markwell et al. [21], Richardson et al. [30] and Marenco et al. [33]).

It should be noted that factors other than LChl may also influence the light transmittance of
a leaf, such as leaf structure, water content and leaf pigment distribution [2,34,35]. Environmental
factors such as light intensity can also affect light transmittance of a leaf, resulting in measurement
errors of LChl [36,37]. Influences on light transmittance can be categorized into two groups [38–40].
The first is the detour effect (light scattering), caused primarily by non-chlorophyll components (e.g.,
leaf architecture and dry matter), which can result in an increase in the path length of light inside
a leaf [41]. The sieve effect occurs when light passes through leaf tissues without being absorbed,
thereby decreasing total absorption [39,40,42]. The distribution of chlorophyll molecules within a leaf
is usually non-uniform, associated with the structural organization of the grana within the chloroplasts,
chloroplasts within the cells, and cells within the tissue layers [43,44]. Furthermore, the influences
on light transmittance vary with wavelength. Since different PCMs are developed based on different
wavelengths, they may be impacted differently by different factors. Large uncertainties have been
reported when converting meter readings into LChl using a general conversion function for different
crops [29,39]. An in depth understanding of the mechanisms for the PCMs is useful for improving
protocols to obtain high-quality in situ LChl measurements. However, it is difficult to consider the
impacts from all leaf/canopy and environmental factors through field experiments. Using a leaf



Remote Sens. 2019, 11, 2706 3 of 20

radiative transfer model to simulate the complex light transmission processes inside a leaf may provide
a solution [14,35,43]. This study, therefore, was designed to address the following: (1) the performances
of different PCMs in estimating actual LChl, (2) the relationships between PCM reading and the actual
LChl, (3) the sources of errors in PCM measurements based on simulations of light transmission in a
leaf using radiative transfer model, and (4) the potential of deriving a generic conversion equation for
a specific PCM. To address these, an experiment was conducted to collect PCM readings of corn (Zea
mays L.), spring wheat (Triticum aestivum L.), soybean (Glycine max L. Merr.) and canola (Brassica napus
L.) using the three aforementioned instruments (SPAD-502, CCM-200 and Dualex-4) at the Central
Experimental Farm (CEF) of Agriculture and Agri-Food Canada (AAFC) in Ottawa, during the 2016
growing season.

2. Materials and Methods

2.1. Theoretical Basis of Portable Chlorophyll Meters

A portable chlorophyll meter measures the light transmittance of a leaf at two different wavelengths,
the index band and the reference band. The index band resides in a chlorophyll absorption region,
whereas the reference band is located in the NIR region. There is generally no light absorption in the
reference band, which is used to compensate for mechanical differences caused by leaf structure, such
as leaf thickness or/and leaf density [24,27,45].

The SPAD-502 (Konica–Minolta, Inc., Osaka, Japan) is the earliest and most widely used PCM.
It quantifies LChl based on the difference of light transmittance between the NIR band (centered at
940 nm with a full-width at half magnitude (FWHM) of about 10 nm) and the red band (centered at
650 nm with FWHM of about 30 nm). The reading is formulated as [21,40]:

SPAD = k
[
log (

I′940

I940
) − log (

I′650

I650
)

]
+ c = k[log (T940) − log (T650)] + c (1)

where k and c are calibration coefficients, I′940 and I′650 are transmitted light intensities at respective
wavelengths, I940 and I650 are light intensities of the LED light sources, and T940 and T650 are light
transmittances through the leaf. SPAD-502 has a 6 mm2 (2 mm × 3 mm) measurement aperture.
The claimed accuracy of the SPAD-502 reading is within ±1.0 unit for a range of 0 to 50 under normal
conditions. The value may be less accurate when the reading is greater than 50.

The CCM-200 PCM (Opti-Sciences, Inc., Hudson, NH, USA, Apogee Instruments 2011) measures
a chlorophyll content index (CCI)—the ratio of leaf light transmittance between the wavelength of 931
and 653 nm (T931 and T653) ([39]:

CCI =
I′931/I931

I′653/I653
=

T931

T653
(2)

where, I′931 and I′653 are the measured leaf light transmission intensities at respective wavelengths, and
I931 and I653 are the light intensities of the LED light source centered at 931 (FWHM about 25 nm) and
653 nm (FWHM about 50 nm), respectively. Calibration is required every time the instrument is turned
on. The CCM-200 has a sensing aperture of 71 mm2 (9.5 mm diameter). The readings of CCM-200
range from 0 to 200, with a resolution of ±1.0 CCI units.

The Dualex-4 Scientific (FORCE-A, Orsay, France) is a new-generation polyphenol and chlorophyll
meter that measures the leaf chlorophyll index (Chl), the flavonol index (Flav), the anthocyanin index
(Anth) and nitrogen balance index (NBI) [29,46]. Flav and Anth are relative measures of flavonol and
anthocyanins’ concentration, respectively [46–48]. NBI is the ratio between Chl and Flav, corresponding
to LChl corrected by dry leaf mass per unit area. Calculation of Chl uses a red-edge band centered at
710 nm and an NIR band centered at 850 nm. The reason for using a red-edge band is that indices
based on the red-edge and the NIR wavelength have better sensitivity to chlorophyll concentration



Remote Sens. 2019, 11, 2706 4 of 20

than the indices based on the red and NIR wavelengths [28,31,49]. Its circular sensing aperture has a
diameter of 5 mm (~20 mm2). The Chl readings range from 5 to 80 µg cm−2. The function of Chl [29] is:

Chl = k (
I′850/I850

I′710/I710
− 1) + c = k(

T850

T710
− 1) + c (3)

where k is the calibration coefficient to obtain leaf chlorophyll concentration in the unit of µg cm−2,
and c is a constant for correcting the potential bias of the model. Instrument calibration is required
prior to use and is done with no sample in the measuring head.

2.2. Leaf Chlorophyll Concentration Measurement

Field experiments were conducted at the Central Experimental Farm (45.38◦ N, 75.71◦ W, Figure 1)
in Ottawa, Ontario, Canada, which included a field experiment and a greenhouse experiment. Field
sampling for corn, soybean, canola and spring wheat was conducted three times during the growing
season on 21 June, 7 July and 4 August 2016. Measurements of LChl for corn and soybeans were
collected from a rotation experiment field with eight rotation patterns. The field was divided into 48
strips, each about 9 m wide and 17 m long. All the corn plots received three levels of nitrogen (N)
fertilizer (0, 100 and 200 kg N ha−1) with six replications for each level of N application. Soybean was
not fertilized. LChl were measured at the plots of continuous cropping with corn or soybean. Canola
was planted in a field adjacent to the rotation field (Figure 1). The field was divided into two sections
for two different experiments. The first experiment was designed to test eight rates of N application on
two canola varieties (InVigor L140P and InVigor 5440), each with four replicates. Each plot was 4.0 m
wide by 6.0 m long. The second experiment was designed to test 12 combinations of different rates of
N and Sulphur application, with three rates of N (0, 75, 150 kg ha−1) and four rates of Sulphur (0, 10,
20, 40 kg ha−1). Each treatment had four replicates, resulting in a total of 48 plots with the same size as
the first experiment. In this study, LChl was measured at plots receiving five levels of N application (0,
50, 100, 150 and 200 kg N ha−1) in the first experiment, and plots receiving 75 kg N ha−1 and four rates
of Sulphur (0, 10, 20, 40 kg ha−1) in the second experiment. The spring wheat field was adjacent to the
canola field (Figure 1). The field was treated with uniform nitrogen fertilization (150 kg N ha−1). LChl
was measured at 8 randomly selected plots.

Remote Sens. 2019, 11, x FOR PEER REVIEW 4 of 21 

 

𝐶ℎ𝑙 = 𝑘 𝐼 𝐼⁄𝐼 𝐼⁄ − 1 + 𝑐 = 𝑘 𝑇𝑇 − 1 + 𝑐 (3) 

where k is the calibration coefficient to obtain leaf chlorophyll concentration in the unit of µg cm-2, 
and c is a constant for correcting the potential bias of the model. Instrument calibration is required 
prior to use and is done with no sample in the measuring head. 

2.2. Leaf Chlorophyll Concentration Measurement 

Field experiments were conducted at the Central Experimental Farm (45.38° N, 75.71° W, Figure 
1) in Ottawa, Ontario, Canada, which included a field experiment and a greenhouse experiment. Field 
sampling for corn, soybean, canola and spring wheat was conducted three times during the growing 
season on 21 June, 7 July and 4 August 2016. Measurements of LChl for corn and soybeans were 
collected from a rotation experiment field with eight rotation patterns. The field was divided into 48 
strips, each about 9 m wide and 17 m long. All the corn plots received three levels of nitrogen (N) 
fertilizer (0, 100 and 200 kg N ha−1) with six replications for each level of N application. Soybean was 
not fertilized. LChl were measured at the plots of continuous cropping with corn or soybean. Canola 
was planted in a field adjacent to the rotation field (Figure 1). The field was divided into two sections 
for two different experiments. The first experiment was designed to test eight rates of N application 
on two canola varieties (InVigor L140P and InVigor 5440), each with four replicates. Each plot was 
4.0 m wide by 6.0 m long. The second experiment was designed to test 12 combinations of different 
rates of N and Sulphur application, with three rates of N (0, 75, 150 kg ha−1) and four rates of Sulphur 
(0, 10, 20, 40 kg ha−1). Each treatment had four replicates, resulting in a total of 48 plots with the same 
size as the first experiment. In this study, LChl was measured at plots receiving five levels of N 
application (0, 50, 100, 150 and 200 kg N ha−1) in the first experiment, and plots receiving 75 kg N ha−1 
and four rates of Sulphur (0, 10, 20, 40 kg ha−1) in the second experiment. The spring wheat field was 
adjacent to the canola field (Figure 1). The field was treated with uniform nitrogen fertilization (150 
kg N ha−1). LChl was measured at 8 randomly selected plots.  

 
Figure 1. Location of the four sampling fields in the Central Experimental Farm, Ottawa, Ontario, 
Canada; background satellite imagery (9 June 2018) obtained from the Google Earth Pro. 

In the greenhouse, canola was seeded on 2 May 2016. Five rates of N (0, 50, 75, 100, and 150 kg 
N ha−1) were applied with six replications per treatment to examine the response of crop growth to N 

Figure 1. Location of the four sampling fields in the Central Experimental Farm, Ottawa, Ontario,
Canada; background satellite imagery (9 June 2018) obtained from the Google Earth Pro.



Remote Sens. 2019, 11, 2706 5 of 20

In the greenhouse, canola was seeded on 2 May 2016. Five rates of N (0, 50, 75, 100, and
150 kg N ha−1) were applied with six replications per treatment to examine the response of crop growth
to N application. There were a total of 30 pots with a size of 15 cm in diameter and 15 cm in depth.
The soil used was a mixture of one-half sand and one-half potting soil to create soil N deficient.
The pots were placed on a bench 1 m above the ground, with a distance of 10 cm from each other.
The greenhouse was controlled at 25–15 ◦C day/night temperature and with a 16 h photoperiod. Plants
received both natural light, and light from fluorescence lamps with an intensity of 300 µmol m−2 s−1

during cloudy periods. Chlorophyll measurements using PCMs and leaf sampling were conducted
on 30 May and 16 June 2016, corresponding to the plant’s growth stage, with 5–6 leaves and early
flowering, respectively.

A total of 220 plants were selected from the two experiments and from different dates (Table 1).
For each plant, a fully expanded leaf from the top, usually the second leaf, was selected for measurement.
Multiple readings were taken for a leaf depending on its size, and the average reading was used as the
representative value for the meter reading of each leaf. In detail, four to five readings were taken for
soybean and canola at the middle portion of the leaf and on two sides of the main rib (avoiding the
midrib and veins). For spring wheat, six to eight readings were taken at the two sides of the main
rib. Measurements with eight to ten readings for corn leaves were taken between the midrib and
the leaf margin about 20 cm from the stalk. Readings were taken at the same area on the leaf for the
three instruments.

Table 1. Description of the total number of samples for chlorophyll content measurement in each
crop type.

Crop Type Seeding Date May 30 June 16 June 21 July 07 August 04 Total

Experiment 1

Corn May 18 - - 19 18 18 55
Soybean May 12 6 9 10 25

Spring wheat April 27 - - 7 9 6 23
Canola May 6 30 27 Harvested 57

Experiment 2
(greenhouse) Canola May 2 30 30 - - - 60

Total - 30 30 62 64 34 220

For destructive sampling, four to six discs (1.09 cm2) were clipped from the same leaf at the same
area where readings were taken using the three PCMs. Leaf discs were placed into a 15 mL plastic
tube and kept cool. In the laboratory, the samples were stored in liquid nitrogen at −80 ◦C before
further processing. For chemical determination of pigment concentration, the samples from each site
were placed in 10 mL of ethanol solution (96%, v/v) and incubated at room temperature in the dark
for four days until leaf samples turned white completely. The solution for each site was put in three
cuvettes, and light absorption was measured for each cuvette using a Varian Cary 100 Bio UV-Visible
Spectrophotometer (Thermo Electron Corporation, Madison, WI, USA) at three wavelengths: 665, 649
and 470 nm. Chlorophyll a (Chla), chlorophyll b (Chlb) and total carotenoid concentrations (Car) were
calculated using the following equations (Lichtenthaler et al. [22]):

Chla(µg cm−2) = (13.95A665 − 6.88A649) ×V/TLA (4)

Chlb(µg cm−2) = (24.96A649 − 7.32A665) ×V/TLA (5)

LChl(µg cm−2) = (6.63A665 + 18.08A649) ×V/TLA (6)

Car(µg cm−2) = (1000A470 − 2.05Chla − 114.8Chlb)/245×V/TLA (7)

where A is the measured absorbance at different wavelengths given by the subscript (in nm); TLA
is the total leaf area (cm2) used, and V is the amount of ethanol (solvent, mL). The total chlorophyll
concentration (LChl) was the sum of Chla and Chlb. The composition of LChl (Chla/Chlb ratio) was



Remote Sens. 2019, 11, 2706 6 of 20

also calculated. Values for the three cuvettes of a site were averaged to obtain pigment concentrations
for the site.

2.3. Statistical Analysis

Among the 220 samples collected, 194 samples were retained for analysis. The other 26 samples
were excluded, as their measured absorbance at 649 nm was wrong (>1 absorbance unit). To identify
the difference in leaf pigment traits among the four crops, statistical indicators, including the average,
the minimum, the maximum, and the coefficient of variance (CV), of the meter readings and leaf
pigments’ concentrations were derived separately. Flav, Anth, and NBI, derived from the Dualex-4,
were also analyzed. Correlations among leaf pigment concentrations determined from the lab, and
linear or nonlinear regressions between meter readings and leaf pigment concentration (LChl and
Car) were then analyzed, respectively. The purpose of this analysis was to investigate whether other
pigments impacted on the conversion functions from optical reading to the actual LChl.

2.4. Sensitivity Analysis

Leaf reflectance and transmittance can be simulated using a leaf radiative transfer mode, by
taking into consideration LChl as well as interference factors (e.g, leaf structure and leaf pigments
other than LChl) [14,34,35,43]. PCM is developed based on sensing technologies through spectral
measurements. The PROSPECT-D [14], the newest version of the PROSPECT leaf optical model, was
used. The model simulates leaf directional–hemispherical reflectance and transmittance in the spectral
range 400–2500 nm using a set of leaf parameters, such as leaf anthocyanins (Canth), chlorophyll and
carotenoid concentration, leaf water concentration (Cw), leaf dry matter concentration (Cm), and leaf
structure parameter (Ns) [14,35].

To evaluate the detour effect, leaf–light transmittance was simulated using the PROSPECT-D
model. Leaf brown pigment (Cbp) was given a value of zero, as the analysis was performed on green
leaves only. Except for a constant value assigned to LChl, the other variables (Ns, Cw, Car, Cm and
Canth) were varied in a range determined from field measurements and the literature studies [7,14,35],
following a uniform distribution (Table 2). Light transmittances at wavelengths centered at 940, 931,
850, 710, 653, and 650 nm were then simulated. The spectral response functions of the LEDs used in the
PCMs [50–52] were also considered. A global sensitivity analysis (GSA) was conducted to determine
the contribution variation for each leaf parameter, using the SIMLAB (Simulation Environment for
Sensitivity and Uncertainty Analysis, http://simlab.jrc.ec.europa.eu/) software. Detailed information
on the variance-based GSA and its application in the simulation of PROSPECT model can be found in
the studies of Dong et al. [53] and Liu et al. [54].

Table 2. Summary of PROSPECT-D parameters used to simulate leaf transmittance.

Variable Constant Range Step Reference

Leaf structure parameter, Ns 1.55 1.0–2.8 0.2 [35]
Leaf chlorophyll concentration, LChl (µg cm−2) 48.39 10–80 5 Field collection
Leaf carotenoid concentration, Car (µg cm−2) 8.04 3.6–12.6 1.0 Field collection
Leaf water concentration, Cw (g cm−2) 0.0113 0.004–0.04 0.004 [35]
Leaf dry matter concentration, Cm (g cm−2) 0.0053 0.0017–0.0137 0.00133 [35]
Leaf anthocyanin concentration, Canth (µg cm−2) 1.0 0–14.0 1.4 [14]
Leaf brown pigment, Cbp 0.0 - - [55]

To investigate the sieve effects, the approach proposed by Uddling et al. [38] was used in the
PROSPECT-D simulations. It is assumed that the variation in actual LChl within the measured area
follows a normal distribution around the average value of LChl (µ). The standard deviation (σ) of
LChl was set within the range 10–50% of µ in steps of 10% of µ. The average LChl (µ) was varied
from 10 to 80 µg cm−2 with steps of 5 µg cm−2. For simplicity, other variables were given constant

http://simlab.jrc.ec.europa.eu/


Remote Sens. 2019, 11, 2706 7 of 20

values obtained from field measurements (e.g., Car) or the literature studies (e.g., Ns, Cm and Canth)
(Table 2). For instance, Car was assigned as 8.04 g cm−2, as that is the mean value of Car obtained in our
in situ measurements, and Ns was 1.55 as an average value of cereal crops [35,54]. The variations in
the log (T940/T650) (Equation (1)), the (T931/T653) (Equation (2)) and the (T850/T710 − 1) (Equation (3))
responses to different degrees of heterogeneity of leaf chlorophyll concentration distribution were
then analyzed.

3. Results

3.1. Variability of LChl

Statistics of meter readings and lab pigment concentration measurements for four crops together
are given in Table 3. Readings by CCM-200 (CCM-200-CCI) had a wider range and larger variation
(CV = 46.3%, 8.7–80.3) than those of SPAD-502 (CV = 18.3%, 25.5–70.3) and Dualex-4 (Dualex-4-Chl,
CV = 22.5%, 22.1–61.0). Large variations were also observed in Flav (CV = 42.5%), Anth (CV = 46.3%)
and NBI (CV = 65.3%) of Dualex-4. For lab chemical measurements, the absolute pigment concentration
ranged from 25.6 to 83.6 µg cm−2 for LChl (µ = 48.4 µg cm−2 and CV = 25.3%) and 3.6 to 13.3 µg cm−2

for Car (CV = 28.0%). These suggest that the variability of the SPAD-502 readings and the Dualex-4-Chl
readings was closer to the variability of actual LChl than that of the CCM-200-CCI. For the LChl
composition, Chla (19.9 to 62.8 µg cm−2) was generally greater than Chlb (4.6 to 21.1 µg cm−2), with a
ratio (Chla/Chlb) between 2.2 and 4.6 (µ = 3.3, CV = 14.4%). Car generally constituted about 14% of the
sum of LChl and Car, as all selected leaves were in dark-green.

Table 3. Statistics of leaf pigment concentration measurements of four crops (corn, soybean, spring
wheat and canola), using three portable chlorophyll meters and lab chemical methods.

LChl Types Mean CV (%) a Min b Max b

Portable chlorophyll meter

SPAD-502 46.6 18.3 25.5 67.8
CCM-200-CCI 33.1 46.3 8.7 75.8
Dualex-4-Chl 37.4 22.6 22.1 61.0
Dualex-4-Flav 1.2 42.5 0.2 2.0

Dualex-4-Anth 0.1 46.3 0.0 0.2

Dualex-4-NBI 41.1 65.3 11.6 122.5

Lab chemical measurement

Car (µg cm−2) 8.0 28.0 3.6 13.3
Chla (µg cm−2) 37.1 26.0 19.9 62.8
Chlb (µg cm−2) 11.3 25.9 4.6 21.1
Chla/Chlb ratio 3.3 14.4 2.2 4.6
LChl (µg cm−2) 48.4 25.3 25.6 83.6

a CV is the coefficient of variation, as the ratio of the standard error to the average value (n = 195); b Min and Max
are the minimum and the maximum values, respectively.

Leaves of the four crops were different, according to the statistical characteristics of lab pigment
concentration measurements in each crop (Figure 2). Chla/Chlb of corn, with a mean value of 4.0
and range of 3.2–4.6, was higher than that of the other three crops, and Chla/Chlb for canola, spring
wheat and soybean crops were close to each other. Spring wheat had a larger mean value of pigment
concentrations (LChl, Chla, Chlb and Car) than other crops (Figure 2). Soybean had lower variation
(CV = 15.4% for LChl and 11.5% for Car) than the other three crops (Table A1). Pigment concentrations
of canola in the greenhouse were apparently lower than that of canola in the field. Similar differences
can be found in the Flav index of Dualex-4 between the greenhouse canola (Flav = 0.3) and the field
canola (Flav = 1.7) (Table A1). This may reflect the great difference in light conditions between field
and greenhouse, as the Flav index of Dualex-4 is an indicator of light intensity [36,47,56].



Remote Sens. 2019, 11, 2706 8 of 20Remote Sens. 2019, 11, x FOR PEER REVIEW 8 of 20 

 

   

   

   

Figure 2. Mean value and standard value in four crops for Car (a), LChl (b), Chla (c), Chlb, (d) Chla/Chlb 

ratio (e), SPAD-502 (f), Dualex-4-CCI (g) and CCM-200 (h). 

3.2. Correlation among Leaf Pigment Concentrations 

Examination of the correlation between Chla, Chlb, LChl and Car is helpful to understand the 

potential impact of other pigments on the conversion of PCM readings into actual LChl. Figure 3 

shows that there were strong linear correlations among leaf pigment concentrations determined by 

lab chemical method. The linear regression between Chla and Chlb for corn (C4 plant) was different 

from that of the other three crops (C3 plant), showing a greater Chla/Chlb. In comparison, the 

relationship between the Car and LChl was more universal among the four crops, and both Chla and 

Chlb had a strong correlation with Car.  

0

5

10

15
C

ar
(µ

g
 c

m
-2

)

(a) Car

0

20

40

60

80

100

L
C

h
l 

(µ
g
 c

m
-2

)

(b) LChl

0

20

40

60

80

C
h
l a

(µ
g
 c

m
-2

)

(c) Chla

0

10

20

C
h
l b

(µ
g
 c

m
-2

)

(d) Chlb

0

1

2

3

4

5

C
h
l a

/C
h
l b

ra
ti

o

(e) Chla/Chlb ratio

0

20

40

60

80

SPAD-502

M
et

er
 r

ea
d

in
g

(f) SPAD-502

0

20

40

60

80

Dualex-4-Chl

(g) Dualex-4

0

20

40

60

80

CCM-200-CCI

(h) CCM-200

Figure 2. Mean value and standard value in four crops for Car (a), LChl (b), Chla (c), Chlb, (d) Chla/Chlb
ratio (e), SPAD-502 (f), Dualex-4-CCI (g) and CCM-200 (h).

CCM-200 readings showed the largest variability for any specific crop (Table A1 and Figure 2).
Figure 2f–h show that average values of meter readings by each PCM were different among the four
crops. The relative differences of average values among the four crops for the SPAD-502 readings were
similar to those of the CCM-200-CCI, but were different from those of Dualex-4-Chl. The averages of
Dualex-4-Chl for the four crops showed similar relative differences to actual LChl (Figure 2b).

3.2. Correlation among Leaf Pigment Concentrations

Examination of the correlation between Chla, Chlb, LChl and Car is helpful to understand the
potential impact of other pigments on the conversion of PCM readings into actual LChl. Figure 3



Remote Sens. 2019, 11, 2706 9 of 20

shows that there were strong linear correlations among leaf pigment concentrations determined by lab
chemical method. The linear regression between Chla and Chlb for corn (C4 plant) was different from
that of the other three crops (C3 plant), showing a greater Chla/Chlb. In comparison, the relationship
between the Car and LChl was more universal among the four crops, and both Chla and Chlb had a
strong correlation with Car.

Remote Sens. 2019, 11, x FOR PEER REVIEW 9 of 21 

 

relationship between the Car and LChl was more universal among the four crops, and both Chla and 
Chlb had a strong correlation with Car.  

  

  

  

  

Figure 3. Scatterplots showing the linear relationship between leaf pigments in four different crops 
for (a) leaf chlorophyll a (Chla) vs. leaf chlorophyll b (Chlb), (b) leaf chlorophyll concentration 
(Chla+Chla, LChl) vs. leaf carotenoid concentration (Car), (c) Chla vs. Car and (d) Chlb vs. Ca. 

3.3. Estimation of Pigment Concentration from PCM Readings  

Figure 4 shows that both Car and LChl had a strong linear or nonlinear correlation with PCM 
readings. In general, the best regression model is nonlinear for SPAD-502 and CCM-200, but is linear 
for Dualex-4. It was observed that different crops had different relationships between SPAD-502 and 
CCM-200 readings and actual pigment concentrations (Table 4). Except for Dualex-4, errors of 
estimates in both LChl and Car increased with the increasing value of meter readings. For the same 
level of actual LChl (and Car), SPAD-502 and CCM-200 had lower readings for spring wheat and 
soybean than for corn and canola. The R2 values for SPAD-502 (R2 = 0.48 for LChl and R2 = 0.40 for 
Car) were larger than those for CCM-200-CCI (R2 = 0.40 for LChl and R2 = 0.33 for Car), suggesting that 
the readings of SPAD-502 were better at restricting interference from other factors (e.g., leaf 
architectures) than CCM-200-CCI. The actual LChl values higher than 60 µg cm-2 for corn and canola 
could not be well estimated from CCM-200-CCI. Dualex-4-Chl was the best PCM for consistent 
measurements of LChl for all the four crops (R2 = 0.74). A generic function was possible for converting 
Dualex-4-Chl readings into actual LChl for the four crops, with an average accuracy of 87%.  

y = 2.84x + 5.12
R² = 0.74

0

20

40

60

80

0 5 10 15 20 25

Ch
l a

(µ
g 

cm
-2

)

Chlb (µg cm-2)

Canola
Canola (greenhouse)
Corn
Spring wheat
Soybean

(a) Chlb vs. Chla

y = 0.16x + 0.12
R² = 0.79

0

5

10

15

0 20 40 60 80 100
C a

r
(µ

g 
cm

-2
)

LChl (µg cm-2)

(b) LChl vs. Car

y = 0.21x + 0.34
R² = 0.79

0

5

10

15

0 20 40 60 80

C a
r

(µ
g 

cm
-2

)

Chla (µg cm-2)

(c) Chla vs. Car

y = 0.62x + 1.11
R² = 0.64

0

5

10

15

0 5 10 15 20 25

C a
r

(µ
g 

cm
-2

)

Chlb (µg cm-2)

(d) Chlb vs. Ca

Figure 3. Scatterplots showing the linear relationship between leaf pigments in four different crops for
(a) leaf chlorophyll a (Chla) vs. leaf chlorophyll b (Chlb), (b) leaf chlorophyll concentration (Chla+Chla,
LChl) vs. leaf carotenoid concentration (Car), (c) Chla vs. Car and (d) Chlb vs. Ca.

3.3. Estimation of Pigment Concentration from PCM Readings

Figure 4 shows that both Car and LChl had a strong linear or nonlinear correlation with PCM
readings. In general, the best regression model is nonlinear for SPAD-502 and CCM-200, but is linear
for Dualex-4. It was observed that different crops had different relationships between SPAD-502 and
CCM-200 readings and actual pigment concentrations (Table 4). Except for Dualex-4, errors of estimates
in both LChl and Car increased with the increasing value of meter readings. For the same level of
actual LChl (and Car), SPAD-502 and CCM-200 had lower readings for spring wheat and soybean than
for corn and canola. The R2 values for SPAD-502 (R2 = 0.48 for LChl and R2 = 0.40 for Car) were larger
than those for CCM-200-CCI (R2 = 0.40 for LChl and R2 = 0.33 for Car), suggesting that the readings
of SPAD-502 were better at restricting interference from other factors (e.g., leaf architectures) than
CCM-200-CCI. The actual LChl values higher than 60 µg cm−2 for corn and canola could not be well
estimated from CCM-200-CCI. Dualex-4-Chl was the best PCM for consistent measurements of LChl



Remote Sens. 2019, 11, 2706 10 of 20

for all the four crops (R2 = 0.74). A generic function was possible for converting Dualex-4-Chl readings
into actual LChl for the four crops, with an average accuracy of 87%.

Remote Sens. 2019, 11, x FOR PEER REVIEW 10 of 21 

 

  

  

  
Figure 4. Scatterplots showing linear relationships between leaf carotenoid concentration (Car, µ 
cm−2), leaf chlorophyll concentration (LChl, µ cm−2) and meter readings for SPAD-502, Dualex-4 and 
CCM-200 in four different crops. 

Table 4. Regression analysis between readings from the handheld chlorophyll meters (meter reading, 
(x) and lab-measured leaf carotenoid and chlorophyll concentration (y); RMSE (µg cm−2) is the root–
mean–square error. 

 Handheld Chlorophyll 
Meter 

Leaf Chlorophyll 
Concentration 

Leaf Carotenoid 
Concentration 

 Regression R2 RMSE Regression R2 RMSE 

y = 2.41x + 27.29
R² = 0.39

0

20

40

60

80

0 5 10 15 20

SP
A

D
-5

02
 v

al
ue

Measured Car (µg cm-2)

Canola
Canola (greenhouse)
Corn
Spring wheat
Soybean

(a) SPAD-502 vs. Car

y = 23.64ln(x) - 44.27
R² = 0.48

0

20

40

60

80

0 20 40 60 80 100

SP
A

D
-5

02
 v

al
ue

Measured LChl (µg cm-2)

(b) SPAD-502 vs. LChl

y = 4.97x0.89

R² = 0.33

0

20

40

60

80

0 5 10 15 20

CC
M

-2
00

-C
CI

 v
al

ue

Measured Car (µg cm-2)

(c) CCM-200 vs. Car

y = 0.35x1.16

R² = 0.40

0

20

40

60

80

0 20 40 60 80 100

CC
M

-2
00

-C
CI

 v
al

ue

Measured LChl (µg cm-2)

(d) CCM-200 vs. LChl

y = 2.74x + 15.24
R² = 0.55

0

20

40

60

80

0 5 10 15 20

D
ua

le
x-

4-
Ch

l v
al

ue

Measured Car (µg cm-2)

(e) Dualex-4 vs. Car

y = 0.58x + 9.11
R² = 0.74

0

20

40

60

80

0 20 40 60 80 100

D
ua

le
x-

4-
Ch

l v
al

ue

Measured LChl (µg cm-2)

(f) Dualex-4 vs. LChl

Figure 4. Scatterplots showing linear relationships between leaf carotenoid concentration (Car, µ cm−2),
leaf chlorophyll concentration (LChl, µ cm−2) and meter readings for SPAD-502, Dualex-4 and CCM-200
in four different crops.



Remote Sens. 2019, 11, 2706 11 of 20

Table 4. Regression analysis between readings from the handheld chlorophyll meters (meter reading,
(x) and lab-measured leaf carotenoid and chlorophyll concentration (y); RMSE (µg cm−2) is the
root–mean–square error.

Handheld
Chlorophyll

Meter

Leaf Chlorophyll Concentration Leaf Carotenoid Concentration

Regression R2 RMSE Regression R2 RMSE

Canola
SPAD-502 y = 0.88x + 1.55 0.77 4.51 y = 0.21x − 2.78 0.80 0.99

CCM-200-CCI y = 0.45x + 27.25 0.75 5.93 y = 0.11x + 3.30 0.78 1.30
Dualex-4-Chl y = 1.14x + 4.28 0.83 3.86 y = 0.26x − 1.52 0.75 1.11

Corn
SPAD-502 y = 1.31x − 13.02 0.90 3.68 y = 0.17x − 0.47 0.74 0.93

CCM-200-CCI y = 0.72x + 23.18 0.81 5.04 y = 0.10x + 4.30 0.68 0.99
Dualex-4-Chl y = 1.21x + 0.37 0.69 6.33 y = 0.14x + 1.94 0.46 1.27

Soybean
SPAD-502 y = 1.91x − 21.71 0.88 2.79 y = 0.23x + 0.71 0.68 0.63

CCM-200-CCI y = 8.53x0.60 0.84 3.44 y = 3.22x0.37 0.59 0.72
Dualex-4-Chl y = 1.55x − 1.20 0.90 2.56 y = 0.18x + 3.45 0.64 0.66

Spring
wheat

SPAD-502 y = 10.71e0.04x 0.66 8.88 y = 2.54e0.03x 0.48 1.48
CCM-200-CCI y = 4.58x0.76 0.74 7.57 y = 1.20x0.62 0.58 1.28
Dualex-4-Chl y =1.52x − 5.57 0.72 5.12 y = 0.19x + 1.76 0.52 1.01

All crops
SPAD-502 y = 18.29e0.02x 0.48 9.31 y = 0.16x + 0.47 0.39 1.75

CCM-200-CCI y = 14.49x0.34 0.40 10.12 y = 2.18x0.37 0.33 1.88
Dualex-4-Chl y = 1.27x + 1.11 0.74 6.25 y = 0.20x + 0.51 0.55 1.48

3.4. Relationship of Meter Reading Averages and Deviations

Multiple readings were taken per plant sample using the PCMs, from which the average and the
standard deviation can be derived for each sample plant. Figure 5 shows the relationship between the
averages and the standard deviations of the PCM readings. The results of the greenhouse canola are
shown as an example, using SPAD-502 measurements. The standard deviation generally increased for
both SPAD-502 and CCM-200-CCI readings which suggests that the sources of measured error of the
two instruments increased with actual LChl. However, the standard deviation in the Dualex-4-Chl
readings was smaller and did not show an apparent increasing trend.

Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 21 

 

 

  

 

 

Figure 5. Relationship between meter readings and standard deviation error of measurements for 
SPAD-502, CCM-200 and Dualex-4; the meter reading was the average value of 4–5 readings of each 
leaf sample and the standard deviation value was derived from these readings; the standard deviation 
for SPAD-502 was only recorded for the canola in the Greenhouse.3.5. Factors Affecting Meter 
Readings. 

3.5.1. Influence of Leaf Parameters 

Results from the global sensitivity analysis (GSA) in Table 5 indicate that variability of light 
transmittance is primarily affected by LChl and Ns for the index band and by Ns and Cm for the 
reference band. Compared with the index band used in Dualex-4-CCI (710 nm), light transmittance 
in the index band used by both SPAD-502 (650 nm) and CCM-200-CCI (653 nm) is more sensitive to 
LChl, especially low LChl. Ns contributed >90% of the light transmittance variability for the reference 
band. Increasing Ns could increase light interaction probability within a leaf, and thereby boost light 
absorption and reduce light transmittance [14,40,57,58]. Increasing Cm would yield similar results but 
at a lower level. The impact of other parameters on light transmittance variability, including Car, Canth 
and Cw, were relatively small.  

Table 5. Results of the global sensitivity analysis (GSA). The first order index derived from the GSA 
represents the contribution of a parameter (Ns, LChl, Car, Cw, Cm and Canth in the first row) to a variable 
(T650, T653 etc. in the second column), and the interaction effects of all the parameters. 

  Ns LChl Car Cw Cm Canth Interactions 

Index  
band 

T650 (SPAD-502) 11.36 79.73 0.00 0.00 0.01 0.00 8.90 
T653 (CCM-200) 12.00 79.31 0.00 0.00 0.02 0.00 8.67 
T710 (Dualex-4) 42.57 55.96 0.00 0.00 0.33 0.00 1.14 

Reference 
band 

T940 (SPAD-502) 95.74 0.00 0.00 0.35 3.53 0.00 0.38 
T931 (CCM-200) 95.72 0.00 0.00 0.38 3.52 0.00 0.38 
T850 (Dualex-4) 95.85 0.00 0.00 0.01 3.75 0.00 0.39 

Ratio 
T940/T650 11.37 74.13 0.00 0.01 0.05 0.00 14.44 
Log(T940/T650) (SPAD-
502) 

7.50 91.68 0.00 0.01 0.03 0.00 0.78 

R² = 0.24

0

5

10

15

20

25

30

0 20 40 60 80

St
an

da
rd

 d
ev

ia
tio

n

SPAD-502 value

Canola (greenhouse)

(a) SPAD-502

R² = 0.38

0

5

10

15

20

25

30

0 20 40 60 80

St
an

da
rd

 d
ev

ia
tio

n

CCM-200-CCI value

Canola
Canola (greenhouse)
Corn
Spring wheat
Soybean

(b) CCM-200

R² = 0.01

0

5

10

15

20

25

30

0 20 40 60 80

St
an

da
rd

 d
ev

ia
tio

n

Dualex-4-Chl value

Canola
Canola (greenhouse)
Corn
Spring wheat
Soybean

(c) Dualex-4

Figure 5. Relationship between meter readings and standard deviation error of measurements for
SPAD-502, CCM-200 and Dualex-4; the meter reading was the average value of 4–5 readings of each leaf
sample and the standard deviation value was derived from these readings; the standard deviation for
SPAD-502 was only recorded for the canola in the Greenhouse.3.5. Factors Affecting Meter Readings.



Remote Sens. 2019, 11, 2706 12 of 20

3.4.1. Influence of Leaf Parameters

Results from the global sensitivity analysis (GSA) in Table 5 indicate that variability of light
transmittance is primarily affected by LChl and Ns for the index band and by Ns and Cm for the
reference band. Compared with the index band used in Dualex-4-CCI (710 nm), light transmittance
in the index band used by both SPAD-502 (650 nm) and CCM-200-CCI (653 nm) is more sensitive to
LChl, especially low LChl. Ns contributed >90% of the light transmittance variability for the reference
band. Increasing Ns could increase light interaction probability within a leaf, and thereby boost light
absorption and reduce light transmittance [14,40,57,58]. Increasing Cm would yield similar results but
at a lower level. The impact of other parameters on light transmittance variability, including Car, Canth

and Cw, were relatively small.

Table 5. Results of the global sensitivity analysis (GSA). The first order index derived from the GSA
represents the contribution of a parameter (Ns, LChl, Car, Cw, Cm and Canth in the first row) to a
variable (T650, T653 etc. in the second column), and the interaction effects of all the parameters.

Ns LChl Car Cw Cm Canth Interactions

Index band
T650 (SPAD-502) 11.36 79.73 0.00 0.00 0.01 0.00 8.90
T653 (CCM-200) 12.00 79.31 0.00 0.00 0.02 0.00 8.67
T710 (Dualex-4) 42.57 55.96 0.00 0.00 0.33 0.00 1.14

Reference
band

T940 (SPAD-502) 95.74 0.00 0.00 0.35 3.53 0.00 0.38
T931 (CCM-200) 95.72 0.00 0.00 0.38 3.52 0.00 0.38
T850 (Dualex-4) 95.85 0.00 0.00 0.01 3.75 0.00 0.39

Ratio

T940/T650 11.37 74.13 0.00 0.01 0.05 0.00 14.44
Log(T940/T650)
(SPAD-502) 7.50 91.68 0.00 0.01 0.03 0.00 0.78

(T931/T653)
(CCM-200) 10.81 79.96 0.00 0.02 0.05 0.00 9.16

(T850/T710)
(Dualex-4) 10.91 84.39 0.00 0.00 0.17 0.00 4.53

Note: Ns: leaf structure parameter; LChl (µ cm−2): leaf chlorophyll concentration; Car (µ cm−2): leaf carotenoid
concentration; Cw (µ cm−2): leaf water concentration; Cm (µ cm−2): leaf dry matter concentration and Canth (µ cm−2):
leaf anthocyanin concentration.

Table 5 also shows that the ratio between NIR and VIS transmittance can suppress the interaction
effects of leaf structure (e.g., Ns and Cm) on meter readings. The effect is more apparent for T850/T710
in Dualex-4-Chl. The influence of leaf parameters on T940/T650 used in SPAD-502 is not different
from that on T931/T653 used in CCM-200. This is because the center wavelengths of the two bands
used in SPAD-502 are close to that used in the CCM-200. However, it is important to note that the
logarithmic transformation of T940/T650 used in the SPAD-502 instrument reduces the influence of Ns

and other parameters, and thereby improves its sensitivity to LChl measurement, compared with the
ratio of T940/T650 and the ratio of T931/T653. Compared to CCM-200 and Dualex-4, this increases the
sensitivity of SPAD-502 to LChl. The meter readings of Dualex-4 are more sensitive to LChl compared
with that of CCM-200.

Further analysis (Figure 6) showed that the variability of PCM readings caused by other interference
factors (Ns, Cw, Car, Cm and Canth) increases with LChl, consistent with the studies by Uddling et al. [38]
and Nauš et al. [40]. The result in Figure 6(d) shows that log(T940/T650) used in SPAD-502 could have
the best performance in reducing the influences of light scattering caused by interference factors (e.g.,
leaf structure), especially at a high LChl. The change in CV was more stable for Dualex-4 (T850/T710;
12.21–21.35%) and SPAD-502 (log (T940/T650); 7.66–12.21%). However, the change in CV for SPAD-502
(log (T940/T650)) was generally lower than for Dualex-4 (T850/T710).



Remote Sens. 2019, 11, 2706 13 of 20

Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 21 

 

(T931/T653) (CCM-200)  10.81 79.96 0.00 0.02 0.05 0.00 9.16 
(T850/T710) (Dualex-4) 10.91 84.39 0.00 0.00 0.17 0.00 4.53 

Note: Ns: leaf structure parameter; LChl (µ cm−2): leaf chlorophyll concentration; Car (µ cm−2): leaf 
carotenoid concentration; Cw (µ cm−2): leaf water concentration; Cm (µ cm−2): leaf dry matter 
concentration and Canth (µ cm−2): leaf anthocyanin concentration. 

Table 5 also shows that the ratio between NIR and VIS transmittance can suppress the interaction 
effects of leaf structure (e.g., Ns and Cm) on meter readings. The effect is more apparent for T850/T710 
in Dualex-4-Chl. The influence of leaf parameters on T940/T650 used in SPAD-502 is not different 
from that on T931/T653 used in CCM-200. This is because the center wavelengths of the two bands 
used in SPAD-502 are close to that used in the CCM-200. However, it is important to note that the 
logarithmic transformation of T940/T650 used in the SPAD-502 instrument reduces the influence of 
Ns and other parameters, and thereby improves its sensitivity to LChl measurement, compared with 
the ratio of T940/T650 and the ratio of T931/T653. Compared to CCM-200 and Dualex-4, this increases 
the sensitivity of SPAD-502 to LChl. The meter readings of Dualex-4 are more sensitive to LChl 
compared with that of CCM-200. 

Further analysis (Figure 6) showed that the variability of PCM readings caused by other 
interference factors (Ns, Cw, Car, Cm and Canth) increases with LChl, consistent with the studies by 
Uddling et al. [38] and Nauš et al. [40]. The result in Figure 6(d) shows that log(T940/T650) used in 
SPAD-502 could have the best performance in reducing the influences of light scattering caused by 
interference factors (e.g., leaf structure), especially at a high LChl. The change in CV was more stable 
for Dualex-4 (T850/T710; 12.21–21.35%) and SPAD-502 (log (T940/T650); 7.66–12.21%). However, the 
change in CV for SPAD-502 (log (T940/T650)) was generally lower than for Dualex-4 (T850/T710).  

  

  
Figure 6. Uncertainty of the readings (shown by the error bars) due to interference factors other than 
leaf chlorophyll for the three instruments (a–c) and the relative error as a function of leaf chlorophyll 

0

30

60

90

120

150

0 30 60 90 120 150

Re
fe

re
nc

e 
 4

0×
lo

g(
T9

40
/T

65
0)

40×log(T940/T650)

(a) SPAD-502

0

40

80

120

0 40 80 120

Re
fe

re
nc

e 
0.

5×
(T

93
1/

T6
53

)

0.5×(T931/T653)

(b) CCM-200

0

20

40

60

80

0 20 40 60 80

Re
fe

re
nc

e 
20

×(
T8

50
/T

71
0-

1)

20×(T850/T710-1)

(c) Dualex-4

0%

20%

40%

60%

0 20 40 60 80 100

Co
ef

fic
ie

nt
 o

f v
ar

ia
nc

e 
(%

)

Leaf chlorophyll concentration (µg cm-2)

T940/T650
40×Log(T940/T650)
0.5×(T931/T653)
20×(T850/T710-1)

(d) Variation of CV

Figure 6. Uncertainty of the readings (shown by the error bars) due to interference factors other than
leaf chlorophyll for the three instruments (a–c) and the relative error as a function of leaf chlorophyll
concentration (d). The solid gray lines represent the 1:1 relationship (no impact from other factors).
Parameters of the PROSPECT-D simulation are given in Table 2.

3.4.2. The Influence of Non-Uniform LChl Distribution

Figure 7 shows that the impact of non-uniform distribution of LChl on meter readings becomes
more apparent with increasing LChl. Meter readings decrease with increasing heterogeneity of LChl
distribution, in particular at high LChl values. This is more apparent for CCM-200 and less apparent
for Dualex-4. The readings of Dualex-4 were the least affected by the non-uniform distribution of LChl,
as the sensitivity of LChl to light transmission at 710 nm was weaker than at the red wavelengths
(Table 5).



Remote Sens. 2019, 11, 2706 14 of 20

Remote Sens. 2019, 11, x FOR PEER REVIEW 14 of 21 

 

concentration (d). The solid gray lines represent the 1:1 relationship (no impact from other factors). 
Parameters of the PROSPECT-D simulation are given in Table 2. 

3.5.2. The Influence of Non-Uniform LChl Distribution 

Figure 7 shows that the impact of non-uniform distribution of LChl on meter readings becomes 
more apparent with increasing LChl. Meter readings decrease with increasing heterogeneity of LChl 
distribution, in particular at high LChl values. This is more apparent for CCM-200 and less apparent 
for Dualex-4. The readings of Dualex-4 were the least affected by the non-uniform distribution of 
LChl, as the sensitivity of LChl to light transmission at 710 nm was weaker than at the red 
wavelengths (Table 5). 

   

Figure 7. Influence of non-uniform distribution of leaf chlorophyll concentration on the readings of 
portable chlorophyll meters: (a) SPAD-502; (b) CCM-200; (c) Dualex-4; the solid gray lines (1:1 line) 
represent the change of light transmittance under the same amount of leaf chlorophyll concentration 
with uniform distribution. Parameters of the PROSPECT-D simulation are shown in Table 2. 

4. Discussion 

The four crops were selected to explore the capability of the three PCMs for crop LChl 
measurement. Leaf pigment contents were different among the four crops (Figure 2 and Table A1). 
In particular, the composition of LChl (i.e., Chla/Chlb ratio, Table A1) was obviously different between 
C4 plants (corn) and C3 plants (spring wheat, canola and soybean) because of their difference in 
photosynthesis pathways [39,56,59,60]. Actual LChl, especially high LChl, had a larger linear 
correlation coefficient with the Dualex-4-Chl readings than with the SPAD-502 and the CCM-200-CCI 
for an individual crop and for the four crops combined (Table 4). Both SPAD-502 and CCM-200-CCI 
tend to have a nonlinear relationship with actual LChl, as they are sensitive to LChl at low LChl rates 
but are easily saturated at high LChl rates [61]. These results are consistent with the results from 
Cerovic et al. [29] and Casa et al. [32], in which Dualex-4-Chl performed better than the other two 
instruments. More importantly, there is a greater potential to develop a generic calibration function 
for LChl estimates of the four crops using Dualex-4-Chl than using SPAD-502 and CCM-200-CCI. 
Dualex-4 could be more accurate and applicable than the other two PCMs in measuring LChl over a 
wide, dynamic range using a generic conversion function. Time and effort taken to recalibrate the 
concversion fuction for different crops would be largely reduced by using the Dualex-4 compared to 
the other two PCMs. Moreover, Dualex-4 has lower uncertainties at high LChl rates (Figure 5 and 
Figure 7). LChl generally shows an increasing trend during the vegetative stage, and reaches its 
maximum at the peak growing stage, which is an improtant inditor for asessing crop nitrogen uptake 
and crop yield [7,62]. Thus, Dualex-4 might be a better PCM to accuartely measure LChl at the peak 
growth stage. 

Sensitivity analysis using the PROSPECT-D model simulation revealed that the leaf structure 
parameter (Ns) had a strong impact on the variability of PCM readings. The literature showed similar 

0

20

40

60

80

100

0 20 40 60 80 100

40
×l

og
(T

94
0/

T6
50

) n
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fo
rm

40×log(T940/T650) uniform

(a) SPAD-502

0

20

40

60

80

0 20 40 60 80

0.
5×

(T
93

1/
T6

53
) n

on
-u

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0.5×(T931/T653) uniform

(b) CCM-200

0

20

40

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0 20 40 60 80

20
×(

T8
50

/T
71

0-
1)

 n
on

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20×(T850/T710 -1) uniform

σ = 10%
σ = 20%
σ = 30%
σ = 40%
σ = 50%

(c) Dualex-4

Figure 7. Influence of non-uniform distribution of leaf chlorophyll concentration on the readings of
portable chlorophyll meters: (a) SPAD-502; (b) CCM-200; (c) Dualex-4; the solid gray lines (1:1 line)
represent the change of light transmittance under the same amount of leaf chlorophyll concentration
with uniform distribution. Parameters of the PROSPECT-D simulation are shown in Table 2.

4. Discussion

The four crops were selected to explore the capability of the three PCMs for crop LChl measurement.
Leaf pigment contents were different among the four crops (Figure 2 and Table A1). In particular,
the composition of LChl (i.e., Chla/Chlb ratio, Table A1) was obviously different between C4 plants
(corn) and C3 plants (spring wheat, canola and soybean) because of their difference in photosynthesis
pathways [39,56,59,60]. Actual LChl, especially high LChl, had a larger linear correlation coefficient
with the Dualex-4-Chl readings than with the SPAD-502 and the CCM-200-CCI for an individual
crop and for the four crops combined (Table 4). Both SPAD-502 and CCM-200-CCI tend to have a
nonlinear relationship with actual LChl, as they are sensitive to LChl at low LChl rates but are easily
saturated at high LChl rates [61]. These results are consistent with the results from Cerovic et al. [29]
and Casa et al. [32], in which Dualex-4-Chl performed better than the other two instruments. More
importantly, there is a greater potential to develop a generic calibration function for LChl estimates of
the four crops using Dualex-4-Chl than using SPAD-502 and CCM-200-CCI. Dualex-4 could be more
accurate and applicable than the other two PCMs in measuring LChl over a wide, dynamic range
using a generic conversion function. Time and effort taken to recalibrate the concversion fuction for
different crops would be largely reduced by using the Dualex-4 compared to the other two PCMs.
Moreover, Dualex-4 has lower uncertainties at high LChl rates (Figures 5 and 7). LChl generally shows
an increasing trend during the vegetative stage, and reaches its maximum at the peak growing stage,
which is an improtant inditor for asessing crop nitrogen uptake and crop yield [7,62]. Thus, Dualex-4
might be a better PCM to accuartely measure LChl at the peak growth stage.

Sensitivity analysis using the PROSPECT-D model simulation revealed that the leaf structure
parameter (Ns) had a strong impact on the variability of PCM readings. The literature showed similar
results; PCM readings were significantly affected by leaf internal architecture such as leaf thickness,
specific leaf mass, and leaf succulence [12,27,33,58,63]. Variability in the leaf structure parameter greatly
influenced light interactions within the leaf, resulting in significant changes in light transmittance in
the VIS and NIR ranges [27,35,57]. Our study showed that the influence of leaf structure parameters
was different on both the reference and the index bands. The influence of the parameters on the index
band (710 nm) for Dualex-4-Chl was much greater than for SPAD-502 (650 nm) and CCM-200-CCI
(653 nm), while the influence of the parameters on the reference band was comparable for all three
PCMs (Table 5). The influence of multiple scattering of light by leaf tissues could be further reduced by
taking a simple ratio of NIR to VIS transmittance [26,27,39,58,64]. The ratio of T850/T710 was better
to restrict the influence of leaf structure parameters than T940/T650 and T937/T653. The logarithmic
function applied to the ratio of 940/650 can restrict the influence of leaf structure parameters, especially
at high LChl, and improve the sensitivity to Cab. However, the overall ability of the three PCMs to
reduce the impacts was limited (<15%), and their difference was small. The non-uniform distribution of



Remote Sens. 2019, 11, 2706 15 of 20

LChl within the measured area was another important factor influencing the variation of PCM readings.
The sensing area of CCM-200 (71 mm2) is much larger than the sensing area of SPAD-502 (6 mm2)
and Dualex-4 (20 mm2), hence CCM-200 can be more susceptible to greater non-uniformity in LChl
distribution. In particular, CCM-200-CCI had larger variations at high LChl, compared with SPAD-200
and Dualex-4 (Figure 4), which is consistent with the observations of Padilla et al. [65]. Our simulation,
using the method by Uddling et al. [38], showed that CCM-200-CCI was most sensitive to the degree
of non-uniformity of the LChl distribution, especially when LChl was high (Figure 7). Increased
heterogeneity of LChl distribution across leaf area can result in an increased light transmittance and
decreased light absorption at the red wavelengths [38]. Although similar wavelengths are employed
in both SPAD-502 and CCM-200-CCI, the variability of SPAD-502 readings is not largely influenced
by the heterogeneity distribution of LChl within the measured area. This could be attributed to the
logarithmic transformation used in the SPAD-502, which helps reduce the divergence of non-uniform
distribution of LChl [39]. Dualex-4-Chl was the best at restricting the influence of the non-uniform
LChl distribution. The data in this study showed that the meter readings for both SPAD-502 and
CCM-200 had crop-specific relationships with LChl (Figure 4). In particular, the deviations apparently
increased at high rates of LChl. In addition, the errors of meter readings within the measured area
increased with increasing LChl (Figure 5). The results in both Figures 4 and 5 are more consistent
with Figure 7 than with Figure 6, suggesting that uncertainty in PCM readings was more due to
non-uniform distribution of LChl than to leaf structure parameters. The studies of Parry et al. [39]
and Richardson et al. [30] also showed that the non-uniform distribution of LChl at high LChl had
an apparent influence on both SPAD-502 and CCM-200 readings. Previous studies [20,63] found that
the heterogeneity of the pigment distribution was greater for leaves with a higher pigment content,
as leaves with high chlorophyll concentration tend to have a high chlorophyll density in chloroplasts,
rather than develop more chloroplasts [20].

The variations of light intensity can greatly influence chloroplast movement inside a
leaf [37,40,43,66]. This can lead to variations of leaf optical properties (reflectance, absorbance and
fluorescence) for the same amount of LChl, and therefore difference in PCM readings. Nauš et al. [40]
found that the movement of chloroplast from the cell walls perpendicular to the incident light (face
position) to the cell walls parallel to the incident light (the side position) could induce approximately
35% of the difference in SPAD-502 readings. Padilla et al. [36] reported that measurement time in
day determines light intensity and can have a strong impact on SPAD-502 and CCM-200 readings.
The ratio of Chla/Chlb was considered to be an indicator of light intensity that has strong influence on
chloroplast movement [56,67]. Strong relationships between the Chla/Chlb ratio and SPAD-502 readings
were observed by Netto et al. [67] and Li et al. [63]; however, we did not find similar relationships
between the Chla/Chlb ratio and PCM readings in the present study, similar to the result obtained
by Parry et al. [39]. The large difference in light conditions between the field and the greenhouse for
canola was revealed by the Flav index of Dualex-4 measurements, which was another indicator of light
intensity [36,47,56]. In addition, the chloroplast movement is closely linked with the combined effects
of light scattering (detour effect) and non-uniform chlorophyll distribution (sieve effect) [40,43,66].
For instance, Nauš et al. [40] reported that the impact of chloroplast movement on the relationship
between SPAD readings and actual LChl was different between old and young tobacco leaves with
different leaf structures (e.g., leaf area mass and leaf thickness). Our study results indirectly support
this finding. In particular, the variability of the CCM-200-CCI readings was greatly affected by both
light scattering and non-uniform LChl distribution. More recently, Stuckens et al. [68] developed a
Dorsiventral Leaf Model (DLM) to simulate leaf radiative transfer by considering the influence of leaf
asymmetry that is modeled by assigning non-uniform distributions of pigments, water and dry matter
to palisade and mesophyll layers and by simulating different amounts of light diffusion for adaxial and
abaxial leaf surfaces. Baránková et al. [43] developed a Simple Explicitly Non-Linear Empirical model
for Leaf Optical Properties (SENLELOP model) to investigate the influence of chloroplast movement on
the optical properties of green tobacco leaves. In future studies, a specially designed field experiment



Remote Sens. 2019, 11, 2706 16 of 20

integrated with the SENLELOP model, DLM, or other, similar models, could lead to an improved
understanding of the mechanistic relationship between optical readings and LChl.

5. Conclusions

In this study, we evaluated the performances of three commonly used portable chlorophyll meters
(SPAD-502, CCM-200 and Dualex-4) in measuring the leaf chlorophyll concentration (LChl) of four
different crops. Analyses were conducted based on field measurements of four crops (corn, soybean,
spring wheat and canola), to explore the relationships between the actual LChl measured in the lab
and readings from the portable chlorophyll meters (PCM). Simulation of leaf transmittance using
the PROSPECT-D model was used to further explore the driving factors of light transmission on the
used wavelengths, including leaf pigments other than chlorophyll, leaf internal structure, and the
heterogeneity of LChl distribution. Major conclusions can be drawn as follows:

(1) SPAD-502 and CCM-200 readings of this study had larger dynamic ranges than Dualex-4 readings.
The sources of error for both SPAD-502 and CCM-220 readings increased with increasing LChl,
whereas they were relatively stable for Dualex-4;

(2) Relationships between SPAD-502 and CCM-200 readings and the actual LChl were more sensitive
to crop type than the relationships between Dualex-4 and LChl;

(3) The sieve effect (caused by the heterogeneity of LChl distribution) would have more influence on
PCM readings than the detour effect (caused by leaf parameters, such as leaf pigments and leaf
internal structure) does. The ratio of light transmittance between the index and reference bands
used in the Dualex-4-Chl was generally better at minimizing the interference factors;

(4) Our results suggest that Dualex-4 is a better choice for collecting LChl measurements for different
crops in the field, compared with the SPAD-502 and the CCM-200.

Author Contributions: Conceptualization, J.S. and J.L.; methodology, T.D., J.S., J.M.C., and J.L.; formal analysis,
T.D., J.S., J.M.C., J.L. and B.Q.; investigation, T.D., J.S., J.M.C., J.L. and B.Q.; writing—original draft preparation,
T.D.; writing—review and editing, all authors (T.D., J.S., J.M.C., J.L., B.Q., B.M., M.J.M., C.Z., Y.L., Y.S., H.P., G.Z.);
supervision, J.S., J.M.C., and J.L.; project administration, J.S., and J.M.C.

Funding: This work was supported by Agriculture and Agri-Food Canada’s Lang Productivity (#1130) and Crop
Harvest Monitoring (#1313) projects, as well as the Canadian Space Agency grant (#17SUSOARTO).

Acknowledgments: The authors thank the anonymous reviewers for the constructive criticism to improve the
quality of the manuscript.

Conflicts of Interest: The authors declare no conflict of interest.

Appendix A

Table A1. Statistics of leaf pigment content measurements by crop type (canola, corn, soybean and
spring wheat).

Crop Types Mean CV (%) a Range (Min–Max) b

Canola (Field, n = 57)

Chlorophyll
meter

SPAD-502 53.9 12.5 32.8–67.8
CCM-200-CCI 47.2 32.1 13.4–75.8
Dualex-4-Chl 38.7 15.8 22.3–58.8
Dualex-4-Flav 1.7 13.3 1.1–2.0
Dualex-4-Anth 0.1 50.4 0.0–0.1
Dualex-4-NBI 24.1 23.7 11.6–36.6

Lab chemical
measurement

Car (µg cm−2) 8.7 18.7 4.8–12.0
Chla (µg cm−2) 36.4 18.4 20.0–52.6
Chlb (µg cm−2) 12.0 19.2 6.4–16.6
Chla/Chlb ratio 3.1 6.6 2.7–3.8
LChl (µg cm−2) 48.6 18.4 26.4–69.2



Remote Sens. 2019, 11, 2706 17 of 20

Table A1. Cont.

Crop Types Mean CV (%) a Range (Min–Max) b

Canola (Greenhouse,
n = 41)

Chlorophyll
meter

SPAD-502 41.1 13.4 31.8–53.6
CCM-200-CCI 23.0 34.3 8.7–46.9
Dualex-4-Chl 31.2 21.1 22.8–52.9
Dualex-4-Flav 0.3 19.3 0.2–0.5
Dualex-4-Anth 0.1 31.9 0.0–0.1
Dualex-4-NBI 90.7 12.2 65.6–122.5

Lab chemical
measurement

Car (µg cm−2) 5.3 19.4 3.6–8.3
Chla (µg cm−2) 27.6 14.9 19.9–40.2
Chlb (µg cm−2) 9.6 16.6 7.8–15.9
Chla/Chlb ratio 2.9 9.5 2.2–3.3
LChl (µg cm−2) 30.3 37.0 11.4–53.2

Corn (n = 52)

Chlorophyll
meter

SPAD-502 46.7 18.4 25.5–62.2
CCM-200-CCI 34.5 42.6 20.4–68.2
Dualex-4-Chl 39.2 20.9 21.1–52.4
Dualex-4-Flav 1.4 22.4 0.7–1.8
Dualex-4-Anth 0.1 33.4 0.0–0.2
Dualex-4-NBI 30.3 29.5 12.4–53.8

Lab chemical
measurement

Car (µg cm−2) 7.7 22.1 4.8–10.9
Chla (µg cm−2) 38.6 24.1 20.8–56.5
Chlb (µg cm−2) 9.7 24.0 4.6–14.0
Chla/Chlb ratio 4.0 5.8 3.2–4.6
LChl (µg cm−2) 49.0 25.1 25.6–70.5

Soybean (n = 25)

Chlorophyll
meter

SPAD-502 39.3 10.2 31.4–48.3
CCM-200-CCI 21.1 23.8 12.1–34.7
Dualex-4-Chl 35.3 14.2 25.0–46.2
Dualex-4-Flav 1.47 8.2 1.2–1.7
Dualex-4-Anth 0.1 40.7 0.0–0.1
Dualex-4-NBI 24.1 12.4 16.8–29.9

Lab chemical
measurement

Car (µg cm−2) 9.8 11.5 7.6–11.6
Chla (µg cm−2) 41.3 15.3 27.2–51.8
Chlb (µg cm−2) 12.2 15.9 7.7–15.9
Chla/Chlb ratio 3.4 4.4 3.0–3.8
LChl (µg cm−2) 53.2 15.4 34.9–67.3

Spring wheat (n = 20)

Chlorophyll
meter

SPAD-502 50.8 10.8 39.7–59.6
CCM-200-CCI 34.5 23.2 17.7–47.5
Dualex-4-Chl 47.8 16.9 31.2–61.0
Dualex-4-Flav 1.2 8.2 1.1–1.5
Dualex-4-Anth 0.1 48.4 0.0–0.1
Dualex-4-NBI 38.9 19.6 25.1–56.6

Lab chemical
measurement

Car (µg cm−2) 10.5 18.9 5.5–13.3
Chla (µg cm−2) 49.8 20.6 26.3–62.8
Chlb (µg cm−2) 15.5 22.9 9.2–21.1
Chla/Chlb ratio 3.2 6.3 2.9–3.6
LChl (µg cm−2) 64.4 27.0 32.3–97.8

a CV (%) is the coefficient of variation, as the ratio of the standard error to the average value (n = 195); b Min and
Max are the minimum and the maximum values, respectively.

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	Introduction 
	Materials and Methods 
	Theoretical Basis of Portable Chlorophyll Meters 
	Leaf Chlorophyll Concentration Measurement 
	Statistical Analysis 
	Sensitivity Analysis 

	Results 
	Variability of LChl 
	Correlation among Leaf Pigment Concentrations 
	Estimation of Pigment Concentration from PCM Readings 
	Relationship of Meter Reading Averages and Deviations 
	Influence of Leaf Parameters 
	The Influence of Non-Uniform LChl Distribution 


	Discussion 
	Conclusions 
	
	References