Tree Physiology 36, 368–379
doi:10.1093/treephys/tpv133
Methods paper
A comparison of methods to estimate photosynthetic
light absorption in leaves with contrasting morphology
Beñat Olascoaga1,3, Alasdair Mac Arthur2, Jon Atherton1 and Albert Porcar-Castell1
1Department
of Forest Sciences, University of Helsinki, PO Box 27, 00014 Helsinki, Finland; 2NERC/NCEO Field Spectroscopy Facility, GeoScience, University of Edinburgh,
The King’s Buildings, EH9 3FE Edinburgh, UK; 3Corresponding author ([email protected])
Received June 22, 2015; accepted November 20, 2015; published online February 3, 2016; handling Editor Ülo Niinemets
Accurate temporal and spatial measurements of leaf optical traits (i.e., absorption, reflectance and transmittance) are paramount
to photosynthetic studies. These optical traits are also needed to couple radiative transfer and physiological models to facilitate
the interpretation of optical data. However, estimating leaf optical traits in leaves with complex morphologies remains a challenge. Leaf optical traits can be measured using integrating spheres, either by placing the leaf sample in one of the measuring
ports (External Method) or by placing the sample inside the sphere (Internal Method). However, in leaves with complex morphology (e.g., needles), the External Method presents limitations associated with gaps between the leaves, and the Internal Method
presents uncertainties related to the estimation of total leaf area. We introduce a modified version of the Internal Method, which
bypasses the effect of gaps and the need to estimate total leaf area, by painting the leaves black and measuring them before
and after painting. We assess and compare the new method with the External Method using a broadleaf and two conifer species.
Both methods yielded similar leaf absorption estimates for the broadleaf, but absorption estimates were higher with the External
Method for the conifer species. Factors explaining the differences between methods, their trade-offs and their advantages and
limitations are also discussed. We suggest that the new method can be used to estimate leaf absorption in any type of leaf
independently of its morphology, and be used to study further the impact of gap fraction in the External Method.
Keywords: conifer, integrating sphere, reflectance, transmittance.
Introduction
Photosynthesis starts with the absorption of photosynthetically
active radiation (PAR) by plant pigments. An accurate estimation
of light absorption in leaves is, therefore, paramount to study the
spatial and temporal variations of photosynthesis. Estimating
light absorption is, however, not straightforward. At the leaf level,
light absorption can be measured using an integrating sphere.
An integrating sphere is a device with a highly reflective inner
coating, which promotes the multiple scattering of light, so that
the photon flux density inside the sphere is evenly distributed
over all the internal surface of the sphere.
In addition to being absorbed, light that impinges onto a leaf
can also be reflected and/or transmitted. The shape of the reflectance (and transmittance) spectrum over the PAR wavelengths
is largely determined by the absorption of light by ­photosynthetic
pigments and the morphology of the leaf. For this reason, reflectance has been widely used as a proxy of ­absorption to study
photosynthesis without the limitations imposed by scale. For
example, reflectance data can be used to infer the chemical composition and physiological status of leaves and their assemblages
(e.g., ­
Gamon et al. 1992, ­
Gitelson and M
­ erzlyak 1997,
Lichtenthaler et al. 1998, ­
­
Peñuelas and ­
Filella 1998,
­Jacquemoud and U
­ stin 2001). Reflectance and transmittance
data are also needed to calibrate and validate leaf radiative
transfer models, which in turn are used to infer vegetation properties from remotely sensed data (­Jacquemoud and ­Baret 1990,
­Dawson et al. 1998, ­Ganapol et al. 1998, ­Di ­Vittorio 2009,
­Jacquemoud et al. 2009, ­Mac ­Arthur et al. 2012). Clearly, the
precise estimation of leaf light absorption, reflectance and
© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]
A comparison of methods to assess leaf absorption 369
t­ransmittance is important for the understanding of photosynthesis at the leaf level, and for their applications in remote sensing approaches.
Traditionally, two approaches have been used to estimate leaf
light absorption using integrating spheres: those where the
­sample is placed inside the sphere (Internal Method) and those
where the sample is placed outside the sphere (External
Method). Historically, the Internal Method is based on the seminal work by V.R. Ulbricht to measure total radiant fluxes, and the
External Method is based on the work by A.H. Taylor to estimate
total reflectance of a surface (­Taylor 1920, see also review by
­Hanssen and ­Snail 2002).
In the Internal Method, total leaf light absorption (AT) is typically estimated by measuring and comparing the photon flux
density in the sphere under three different conditions: (i) when
the sphere is empty, (ii) when the leaf is inside the sphere and
(iii) when the sample is replaced by a piece of black paper of
known area and absorptivity (­Öquist et al. 1978, ­Idle and ­Proctor
1983, ­McKiernan and ­Baker 1991, ­Long et al. 1993). The limitation of this approach is that accurate estimation of total leaf
area is needed, which can be difficult to obtain for leaves with
complex morphologies (e.g., conifer needles), or when shoots
or small plants are measured instead (­Öquist et al. 1978,
­Serrano et al. 1997). In the External Method, leaf light absorption (AH) is estimated from reflectance and transmittance as:
AH = 1 − RH − TH
Figure 1. Example of spruce needles arranged as a mat separated by
gaps (black regions within the central circle) for the estimation of hemispherical reflectance (RH) and transmittance (TH) factors. The lighter
grey shades in each of the needles are epicuticular waxes. A white mark
(top right corner) was placed on the frame (black area surrounding the
central circle) to ensure the same orientation of the needles when repositioning the sample for the different configurations required.
(1)
where RH and TH are the leaf hemispherical reflectance and transmittance factors, respectively. RH is obtained by comparing the
photon flux density in the sphere when a leaf or a highly reflective
reference panel is placed in the sphere’s measuring port opposite
to the light source. Similarly, TH is obtained by placing the leaf
between the light source and the sphere, and by comparing the
photon flux density with and without the leaf. The requisite for the
estimation of RH and TH, and by extension of AH, is that the area
of the measurement surface (either leaf or reference panel) needs
to be equal. This requisite can be easily met when measuring
broadleaf species whose leaves fully cover the measuring port
(e.g., ­Woolley 1971, ­Daughtry and W
­ althall 1998, ­Daughtry et al.
2000, ­Zarco-Tejada et al. 2005). However, fully covering the
measuring port with small leaves and/or leaves with complex morphology (e.g., conifer needles) is not possible without leaving
gaps or clumping several layers of leaves.
Gaps are known to influence RH and TH estimations (­Daughtry
et al. 1989, ­Middleton et al. 1996, ­Mesarch et al. 1999) because
the resulting spectral measurements are a composite of radiation
interacting with the sample and passing through the gaps. To
address this limitation, needle samples have been arranged in
holders to create a mat of needles separated by gaps (Figure 1).
Subsequently, the effect of the gaps on the measured RH and TH
is corrected during post-processing (e.g., ­Middleton et al. 1996,
­ esarch et al. 1999, ­Stimson et al. 2005, ­Malenovský et al.
M
2006, ­Lukeš et al. 2013, ­Olascoaga et al. 2014, ­Yáñez-Rausell
et al. 2014). Despite these corrections, the resulting RH and TH
still depend on the gap fraction of the sample (­Middleton et al.
1996, ­Mesarch et al. 1999). Clearly, these limitations will also
affect the estimation of leaf light absorption.
We compare the Internal and External Methods to estimate
leaf light absorption in leaves with contrasting morphology (a
broadleaf and two conifer species). To this end, we introduce a
new version of the Internal Method that dispenses with the need
to estimate total leaf area, and is thus expected to yield higher
accuracy. The new method consists of painting the leaves black
and measuring them inside the integrating sphere before and
after painting. We also compare an additional alternative using
bidirectional reflectance (hereafter referred to as the Reflectance
Method). Limitations, trade-offs and potential applications of
each of the methods are discussed.
Materials and methods
Plant material
Current-year leaves from adult Scots pine (Pinus sylvestris L.),
blue spruce (Picea pungens Engelm.) and silver birch (Betula
pendula Roth.) trees growing in the University of Helsinki,
­Finland (60°13′N, 25°01′E), were used. A single tree was
Tree Physiology Online at http://www.treephys.oxfordjournals.org
370 Olascoaga et al.
selected per species, and leaves from low south-west facing
branches were collected during September 2013, well before
leaf senescence. Measurements were replicated five times with
freshly collected leaves for each tree and for each method.
Leaf light absorption methods
External Method Leaf light absorption (referred to as AH as it is
derived from hemispherical data) was estimated from RH and TH
following Eq. (1). We used a spectroradiometer (FieldSpec HH
VIS-NIR; ASD Inc., Boulder, USA, with spectral range of 325–
1075 nm, sampling interval of 1.6 nm and 3.5 nm FWHM) connected to an integrating sphere (RTS-3ZC; ASD Inc.) through a
fibre optic bundle. The photon flux densities necessary for the
calculation of RH and TH were recorded by the spectroradiometer
as digital counts, proportional to the photon flux densities through
calibration factors given by the manufacturer. Black cardboard
sample holders after ­Malenovský et al. (2006) and ­Yáñez-Rausell
et al. (2014) were manufactured, and used to arrange the needles as a mat large enough to cover the integrating sphere measurement port (Figure 1). A collimated light source (Figure 2)
with a 10-W halogen bulb (Osram GmbH, Munich, Germany) was
used, and allowed to warm up for 10 min before the measurements were made. The spectroradiometric integration time (IT)
was set to 4.35 s, and the spectra averaging for each of the
recorded spectra (AS) was set to five scans. A dark-current (DC)
measurement was conducted every two spectra.
Estimations of RH and TH were conducted as in Figure 3,
where each spectrum consisted of three independent spectroradiometric measurements of the photon flux density within the
sphere (for Sample Reflectance or Transmittance, White Reference and Stray Light configurations).
After the measurements, the sample in the holder was monochromatically scanned (Canon ImageRunner Advance C5051i,
Canon Inc., Tokyo, Japan) at a resolution of 600 dpi. The gap
fraction (GF) was estimated from the scanned images using
Adobe Photoshop CS6, after applying a greyscale threshold to
discriminate between sample and gaps (­Mesarch et al. 1999).
The GF slightly differed between the abaxial (low) and adaxial
(top) sides of the same samples (e.g., GF of pine, abaxial side:
0.062 ± 0.017; adaxial side: 0.077 ± 0.007, mean ± SD),
which was likely caused by the geometric interaction of the
scanner light source and the sample, as pine and spruce needles
have a complex morphology, and are not round in cross section
but semicircular and rhomboidal, respectively. Nevertheless,
none of the GF differences was significant (all P > 0.05).
Leaf RH was calculated as:
RH =
(IS _ RH − ISTR _ RH )RSP /(IW _ RH − ISTR _ RH )
1 − GF
(2)
where IS_RH, ISTR_RH and IW_RH are the spectroradiometric measurements of the photon flux densities (measured as digital counts)
Tree Physiology Volume 36, 2016
Figure 2. Shape of the normalized emission spectra for the halogen light
sources used in the study. Because the shapes were similar, we assume
that the light penetration within the leaf was comparable.
in Sample Reflectance, Stray Light and White Reference configurations, respectively (Figure 3). GF is the gap fraction, and RSP is
the reflectance of a standard panel, equal to 0.98 for the spectral range in this study.
Similarly, TH was calculated as:
TH =
[IS _ TH /(IW _ TH − ISTR _ TH )] − Rw GF
1 − GF
(3)
where IS_TH, ISTR_TH and IW_TH are the spectroradiometric measurements of the photon flux densities (measured as digital counts)
in Sample Transmittance, Stray Light and White Reference configuration, respectively (Figure 3). The reflectance of the integrating sphere wall (R w) was assumed to be equal to one.
Finally, AH was computed from RH and TH as in Eq. (1).
The GF effect on RH, TH and AH was studied in pine needles
subjected to two contrasting GF: 0.07 ± 0.01 and 0.17 ± 0.02
(mean ± SD). Note that as broadleaves fully cover the port of
the sphere, GF = 0, and thus, Eqs (2) and (3) are simplified.
The effect of the leaf side on RH, TH and AH was studied in
pine and birch leaves, where surface geometry and morphology
differed between the abaxial and adaxial sides. Spruce needles
were not considered because they do not present differences
between sides. Altogether, mounting, measuring RH and TH for
both sample sides and sample scanning took 35–40 min.
Internal Method The Internal Method (after ­Öquist et al.
1978) is based on three independent spectroradiometric measurements of the photon flux density (measured as digital
counts) inside the sphere: without leaf sample (IW), with leaf
sample (IS) and with a reference with known absorptivity and
same surface area as the sample (IB). In our study, we measured
IB by painting the same leaf sample in black (Figure 4). Adding
the blackened sample measurements bypasses the need to estimate total leaf area which, apart from being time consuming, is
A comparison of methods to assess leaf absorption 371
Figure 3. Configuration of the ports (from [A] to [E]) for hemispherical reflectance (upper row) (RH) and transmittance (lower row) (TH) factor measurements with an ASD RTS-3ZC integrating sphere. Estimating RH requires three independent spectroradiometric measurements of the photon flux
density (measured as digital counts) inside the sphere: a White Reference reflectance measurement using a white panel (IW_RH), a Sample Reflectance
measurement (IS_RH) and a Stray Light measurement using a light trap (ISTR_RH). Estimating TH requires: a White Reference transmittance measurement
using an empty holder (IW_TH), a Sample Transmittance measurement, keeping the sample between the light source and the illumination port (IS_TH) and
a Stray Light measurement using a light trap (ISTR_TH). The elements necessary are: (1) light source, (2) light trap, (3) dummy sample held in a holder,
(4) white panel, (5) empty holder, (6) port plug, (7) sample held in a holder, (8) transmittance spacer and (9) fibre optic holder. During RH measurements, the integrating sphere ports [D] and [E] are not used. During TH measurements, the integrating sphere ports [A] and [E] are not used. The
dotted arrows show the light path from the light source.
also challenging and a source of additional errors in leaves with
complex morphologies.
The spectroradiometer and fibre optic bundle were here
attached to a 4-in. diameter integrating sphere (LabSphere
4P-GPS-040-SF) coated with Spectraflect®, and presenting four
ports orthogonally oriented in a single plane.
The total leaf absorption obtained through the Internal Method
(AT) was computed using Eq. (4) (see Appendix for equation
derivation), as:
AT =
(IW − IS )IB ABLACK
(IW − IB )IS
(4)
where ABLACK is the absorption of the black spray paint used in
this study (Citadel Miniatures Ltd, Nottingham, UK), found to
remain stable at 0.96 ± 0.001 (mean ± SD) along the PAR
region. ABLACK was estimated by painting a piece of cardboard,
and applying the External Method described above.
The light source (Figure 2) consisted of a halogen bulb
mounted in a 2-in. diameter LabSphere integrating sphere
connected to the main sphere (Figure 4). The light source
was controlled with a stable power supply (Manson EP-613,
Manson Engineering Industrial Ltd., Hong Kong, China), and
the halogen bulb was warmed for 10 min before any measurements were made. We mounted the light source inside this
additional sphere to diffuse the incident light and, thus, minimize the potential effect of sample repositioning between
measurements (i.e., before and after painting). For the same
purpose, we designed a special fibre optic holder so that the
fibre pointed to the sphere wall, and the leaf sample was not
directly included in the field of view. The spectroradiometric
settings for the Internal Method were IT = 2.18 s and AS = 5
scans. A DC measurement was done prior to each spectrum.
The amount of leaf material to place inside the sphere was
optimized to the internal surface area of the sphere by characterizing the integrating capacity of the sphere. Saturation of the
integrating capacity of the sphere was characterized by measuring the parameter IB/IW (terminology in Eq. (4)), using pieces of
paper of different areas painted black. Deviation from linearity,
shown in Figure 5, was interpreted as a sign of saturation. Subsequently, the greatest leaf area for which IB/IW remained in the
linear part of the function (corresponding to IB/IW = 0.75, or
sample area = 3 cm2) was selected as optimum. To define the
optimal leaf area for each species, we measured IB/IW in one,
Tree Physiology Online at http://www.treephys.oxfordjournals.org
372 Olascoaga et al.
Figure 4. Direct estimation of total leaf absorption (AT) with a LabSphere integrating sphere. The spectroradiometric measurement of the photon flux
density (measured as digital counts) inside the sphere is first recorded with the empty sphere containing only a white thread (IW). An optimal number
of needles (or any other leaf sample) mounted on the thread, regularly spaced to avoid shading, is hung inside the sphere (IS). Finally, the needles are
sprayed with a black paint and moved along the thread so that the sample is hung again from the thread (IB). The elements are as follows: (1) light
source in a 2-in. diameter integrating sphere, (2) light channel, (3) port plug with thread holder, (4) port plug, (5) port plug with thread and fibre optic
holders, (6) white thread, (7) sample and (8) blackened sample. The dotted arrows show the light path from the light source inside the attached 2-in.
diameter integrating sphere.
three, five and eight needles of pine and spruce, and in half, one
and two leaves of birch painted black. The optimum number of
samples was found to be six and eight needles for pine and
spruce needles, respectively, and half a leaf for birch leaves.
Additionally, we also estimated averaged PAR (400–700 nm)
AT in birch leaves using a PAR quantum sensor (LI-190, LI-COR
Inc., Lincoln, NE, USA) connected to a light metre (Li-250A;
LI-COR Inc.) instead of the fibre optic bundle and spectroradiometer. Mounting and measuring AT using the Internal Method took
20–25 min per sample.
Figure 5. Sphere saturation function (y = −0.191 ln(x) + 0.935; solid
line), obtained by estimating the decrease in the photon flux density
inside the sphere relative to the empty sphere (IB/IW) as a function of
blackened sample area (dots).
Tree Physiology Volume 36, 2016
Reflectance Method Leaf light absorption was estimated
from bidirectional reflectance factors (RB) (­Schaepman-Strub
et al. 2006) instead of RH. We here used a contact plant probe
(ASD Inc.) containing a 6.5-W halogen light source (Figure 2)
coupled to the same ASD spectroradiometer via the fibre optic
bundle.
A comparison of methods to assess leaf absorption 373
Leaf light absorption by the Reflectance Method (AB) was
computed from the leaf RB, assuming zero transmittance, as:
AB = 1 − RB = 1 −
IS _ RB
IW _ RB
(5)
where RB was estimated by dividing the spectroradiometric measurement of the photon flux density (measured as digital counts)
associated with the sample (IS_RB) with respect to that associated with a white Spectralon® reference (IW_RB).
Samples were arranged in a leaf clip (Hansatech Ltd, Norfolk,
UK) originally designed for dark acclimation in fluorescence
measurements. The clip, which fitted in front of the tip of the
plant probe, was used to reduce the sample area viewed by the
plant probe from 12.5 to 1.5 cm2, and facilitate the arrangement
of the needles. The clip was painted with the same black spray
described above to minimize its relative contribution to RB, as
parts of the clip were included in the fibre optic’s field of view.
We have used this method before to estimate relative variations
in reflectance or reflectance indices (­Porcar-Castell et al. 2012,
­Olascoaga et al. 2014). However, in this study, we needed to
use absolute RB and, therefore, needed to correct for the contribution effect of the clip. We conducted measurements with the
empty clip, and derived the following correction function:
RB = 1.11 (Runcorrected − 0.097) (6)
The spectroradiometric settings in the Reflectance Method
were IT = 544 ms and AS = 5 scans. A DC measurement was
conducted every 10 spectra.
Both the abaxial and adaxial sides of the pine and birch leaves
were measured, and the measurements were averaged. Additionally, two pine needle arrangements were evaluated: by placing the needles carefully side by side in a one layer mat, and by
clumping a bunch of needles. Mounting and measuring IS_RB and
IW_RB took 1–5 min per sample.
Statistical analysis
Non-parametric statistical analyses were used to test for statistical differences between samples using IBM SPSS Statistics
(v.18) software. Wilcoxon signed-rank test (for paired data) and
Mann–Whitney U-test (for unpaired data) were used to detect
differences between two groups, and the Kruskal–Wallis H-test
was used to assess differences between more than two groups.
Post hoc analyses were conducted from pairwise Mann–Whitney
U-tests.
Results
External vs Internal Method to estimate leaf light
absorption
Both methods produced similar absorption spectra in birch
leaves, which fully covered the port of the integrating sphere
Figure 6. Absorption spectra of pine (a), spruce (b) and birch (c) leaves assessed through the External (dotted line, AH) and Internal (solid line, AT)
Methods. Hemispherical reflectance (RH), transmittance (TH) and absorption (AH) factors of pine (d), spruce (e) and birch (f) leaves measured through
the External Method. The data show mean ± SD (dark grey bands), n = 5.
Tree Physiology Online at http://www.treephys.oxfordjournals.org
374 Olascoaga et al.
(Figure 6c and Table 1). However, for pine and spruce needles,
the External Method produced higher absorption spectra along
the PAR region (Figure 6a and b and Table 1).
In the External Method, leaf light absorption (AH) spectra
were estimated for each leaf side in pine and birch leaves, and
averaged a posteriori for comparisons with the total leaf absorption (AT) spectra derived through the Internal Method, which
integrates all sides (Figures 6 and 7). Although the leaf light
absorption spectra with the External Method differed between
adaxial and abaxial sides of birch leaves, mainly due to differences in the hemispherical reflectance factor (RH) (Figure 7c),
the shape of the side-averaged absorption spectrum was similar
to that obtained with the Internal Method (Figures 6 and 7d).
Spruce needles had the largest RH among the three species,
especially in the 400–500 nm region, due to their bluish bloom
(­Reicosky and ­Hanover 1978). Importantly, the hemispherical
transmittance factor (TH) in the PAR region was lower and flatter
for conifers, and yielded negative values despite the gap fraction
correction using Eq. (3) (Figure 6d and e).
We tested the effect of the gap fraction on the estimation of
RH, TH and AH, when using Eq. (3) to correct for the effect of the
gaps. Increasing the gap fraction from 0.07 to 0.17 in a mat of
pine needles increased the apparent needle TH both in the PAR
and the near-infrared (NIR) regions (Figure 8a and b). In contrast, increasing the gap fraction decreased RH, especially in the
NIR region (Figure 8a and b). Consequently, AH, averaged for the
PAR region (400–700 nm), was significantly higher for
GF = 0.07 relative to GF = 0.17 (Wilcoxon signed-rank test,
n1 = n2 = 5, Z = −2.02, P = 0.04) (Figure 8c and Table 1). Comparing PAR absorption estimates with those obtained through
Table 1. Averaged PAR (400–700 nm) absorption for each of the species and methods. The values represent mean ± SD, n = 5; GF, gap fraction; n.t.,
not tested.
Reflectance Method
Pine
Spruce
Birch
External Method
Internal Method
Clustered
Side by side
Small GF
Big GF
Spectroradiometer
PAR sensor
0.888 ± 0.011
n.t.
n.t.
0.854 ± 0.003
0.779 ± 0.004
0.924 ± 0.007
0.904 ± 0.011
0.825 ± 0.020
0.823 ± 0.021
0.645 ± 0.083
n.t.
n.t.
0.815 ± 0.025
0.676 ± 0.036
0.811 ± 0.020
n.t.
n.t.
0.778 ± 0.033
Figure 7. Hemispherical reflectance (RH), transmittance (TH) and absorption (AH) factors of pine (a) and birch (c) leaves for the abaxial (solid line)
and adaxial (dash-dotted line) sides derived through the External Method. Absorption spectra of pine (b) and birch (d) leaves for the abaxial, adaxial,
their average (dotted line) and through the Internal Method (thick solid line, AT). The data show mean ± SD (dark grey bands), n = 5.
Tree Physiology Volume 36, 2016
A comparison of methods to assess leaf absorption 375
Figure 8. Hemispherical reflectance (RH), transmittance (TH) and absorption (AH) factors of pine needles exposed to a gap fraction of GF = 0.07 (a)
and GF = 0.17 (b), averaged for both sides of the needles. Comparison of averaged PAR (400–700 nm) absorption with the Internal Method, and the
External Method using the previous two GF (c). The data show mean ± SD (dark grey bands and bars), n = 5.
the Internal Method, used here as a reference, revealed that PAR
absorption was overestimated when measured with GF = 0.07,
and underestimated with a GF = 0.17 (Kruskal–Wallis H, χ2(2,
n = 15) = 12.50, P = 0.00, and three pairwise Mann–Whitney
U-tests) (Figure 8c and Table 1).
We also tested the effect of leaf side (abaxial vs adaxial) on
RH, TH and AH in pine and birch leaves (Figure 7). The abaxial
side displayed higher RH than the adaxial side in both species,
but especially in birch. In contrast, differences in TH between
sides were only observed in pine (Figure 7a). As a result, AH was
consistently larger when estimated for the adaxial compared
with the abaxial side in both species (Figure 7b and d).
Alternative methods
In an attempt to assess faster and more straightforward alternatives to measure leaf light absorption, we investigated the performance of a method based on computing leaf absorption from
the bidirectional reflectance factor (RB) (Reflectance Method), as
well as that of a variant of the Internal Method using a quantum
sensor instead of a spectroradiometer.
In the PAR region, RB was found to be lower than RH for spruce
and birch leaves, but not for pine (Figure 9a, c and e). In the NIR
region, RH was higher than RB in all of the species. When comparing two ways to arrange needles in the leaf clip (clumped
arrangement vs side-by-side arrangement), RB was lower when
the needles were clumped (Figure 9a) compared with a side-byside arrangement. Consequently, the type of arrangement
affected the PAR absorption estimates (Wilcoxon signed-rank
test, n1 = n2 = 5, Z = 2.02, P = 0.04).
Overall comparison
The three methods tested (External, Internal and Reflectance)
yielded different PAR absorption estimates both in pine (Kruskal–
Wallis H, χ2(3, n = 20) = 17.10, P = 0.00, and six pairwise
Mann–Whitney U-tests) and spruce (Kruskal–Wallis H, χ2(2,
n = 15) = 12.50, P = 0.00, and three pairwise Mann–Whitney
U-tests) needles, with the highest PAR absorption estimates
obtained using the External Method, and the lowest using the
Internal Method (Figure 9b and d). In birch leaves, both the
External and Internal Methods yielded similar PAR absorption
estimates (Figure 9f), but PAR absorption estimates with the
Reflectance Method were higher than with the remaining two
methods (Kruskal–Wallis H, χ2(3, n = 20) = 13.47, P = 0.00,
and six pairwise Mann–Whitney U-tests). Substituting the spectroradiometer by a PAR quantum sensor in the Internal Method
did not result in significant differences in PAR absorption estimates (Wilcoxon signed-rank test, n1 = n2 = 5, Z = −1.48,
P = 0.14).
Discussion
We here present a new method to estimate light absorption
using an integrating sphere that can be applied to leaves with
contrasting morphologies. We compared it with the External
Method. The External Method measures the leaf out of the
sphere, and leaf light absorption is indirectly computed from
hemispherical reflectance and transmittance factors. In contrast,
the Internal Method gives a direct estimate of total leaf light
absorption. The Internal Method is, therefore, independent of
errors and uncertainties associated with the effects of gap fraction in samples with complex morphologies, such as conifer
needles. The Internal Method estimates total light absorption of
a leaf sample by measuring the photon flux density in the
sphere in three conditions: with a leaf sample, without the leaf
sample and with a black reference of equal area to that of the
leaf sample. The black reference serves to normalize for the
size and properties of the integrating sphere. Black references
have previously been estimated by substituting the leaf with a
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376 Olascoaga et al.
Figure 9. Hemispherical (RH) and bidirectional (RB) reflectance factors for pine (a), spruce (c) and birch (e) leaves, and averaged PAR (400–700 nm)
absorption from pine (b), spruce (d) and birch (f) leaves using the different methods evaluated. For pine, we also assessed the effect of needle arrangement in the leaf clip (side-by-side arrangement vs clumped arrangement). For birch, averaged PAR absorption using the Internal Method was estimated
both with a spectroradiometer and a PAR quantum sensor. The data show mean ± SD (dark grey bands and bars). In (b), (d) and (f), the letters indicate
statistically different values, α = 0.005, n = 5.
reference of high and known absorptivity, and equal area to that
of the leaf (­Öquist et al. 1978, ­Idle and ­Proctor 1983). However, estimating total leaf area is far from straightforward in
leaves with complex morphologies such as needles, a step that
adds significant uncertainty to the measurements (­Serrano
et al. 1997). Here, this step has been bypassed by painting the
leaf sample with a black spray, so that the same leaf sample
becomes the black reference. When applied to birch leaves,
which fully cover the port of the integrating sphere, the Internal
Tree Physiology Volume 36, 2016
Method yielded very similar absorption spectra to those of the
External Method (Figure 6c). Because the External Method is
the standard when applied to broadleaves, these results suggest that the Internal Method and our painting technique produce valid results. We then used the Internal Method as
benchmark to assess the performance of the External Method
in conifer needles, and found that the External Method tended
to overestimate light absorption in conifer needles (Figure 6a
and b).
A comparison of methods to assess leaf absorption 377
In our study, we selected a painting spray of low viscosity
designed for undercoating metal and plastic models. Accordingly, we assumed that the area of the leaf sample before and
after painting would remain constant. If the area of the sample
would significantly increase after painting it black, then the calculated absorption would be an underestimation of the real
absorption of the leaf (see Appendix). Yet, these errors could
not explain the differences in leaf absorption estimates between
the External and Internal Methods that were obtained in pine and
spruce because the differences found in birch leaves were only
minimal. In other words, assuming that the differences obtained
in birch were entirely caused by the painting (i.e., were the maximum expression of the error), these differences would explain
only a minor fraction of the differences found in pine and spruce
(Figure 6 and Table 1).
The sensitivity of the External Method to the gap fraction was
assessed by estimating hemispherical reflectance, transmittance
and absorption factors using needle samples with different gap
fractions. The External Method was found to be very sensitive to
the size of the gap fraction, especially for hemispherical transmittance factors, which yielded physically unrealistic negative
transmittance factors in the PAR region when measuring under
small gap fractions (Figure 8). Negative transmittance factors
have previously been reported in studies comparing samples
with different gap fractions (­Middleton et al. 1996), as well as in
studies comparing different methods for gap fraction estimation
(­Mesarch et al. 1999). Interestingly, in these studies, the gap
fraction was indirectly correlated with transmittance, whereas
our measurements suggest a direct correlation between these
two variables. The complex interaction between the light path,
the three-dimensional shape of the needles and the distance
between needles (i.e., proportional to the gap fraction) can generate variable degrees of forward and backward scattering
through the gaps, which would affect the estimation of hemispherical reflectance and transmittance factors through the
External Method. These effects could explain why the optical
spectra from needles exposed to two contrasting gap fractions
did not converge after correcting for the gap fraction (Figure 8).
All these results clearly indicate that estimates of hemispherical
transmittance factors for conifer needles are error prone, and
thus, they should be treated with caution because they are
highly dependent on the gap fraction, and because this dependency may be in turn controlled by additional factors, such as
sample morphology or the shape of the leaf cross section.
Despite the fact that the Internal Method presented here cannot be used to estimate hemispherical reflectance and transmittance factors, we suggest that it could be used to benchmark the
External Method by providing a gap-insensitive estimate of leaf
absorption. This benchmark could be used to study further the
impact of gap fraction (­Mesarch et al. 1999) and to improve the
estimations of hemispherical reflectance and transmittance
­factors in needles. These factors are essential parameters to
construct radiative transfer models for upscaling photosynthesis
from the leaf to the canopy level and beyond.
An additional application for our Internal Method is that it can
be used to estimate total sample area. This application may be
particularly interesting for complex leaves, or even small shoots,
and it represents an alternative to the method proposed by
­Serrano et al. (1997). Once the sphere saturation function is
known (Figure 5), the sample can be painted in black and IB and
IW measured to estimate IB/IW and retrieve sample total area
(­Figure 5).
Leaf absorption spectra estimated with the Internal Method
measures the total absorption of the leaf, which is integrated
over its entire surface. In contrast, the External Method utilizes
hemispherical reflectance and transmittance factors from individual leaf sides to calculate leaf light absorption (e.g.,
­Daughtry et al. 1989, ­Lukeš et al. 2013). The hemispherical
reflectance factor of each of the sides of birch leaves differed,
a common phenomenon in broadleaves, which have evolved to
maximize internal scattering and light absorption when illuminated from the adaxial side. Nevertheless, when both sides
were averaged, the absorption spectra measured using the
External Method converged with that from the Internal Method.
This supports the assumption that the estimates obtained with
the Internal Method are representative of the overall all-sided
absorption of the sample.
In addition to the Internal and External Methods described
above, we also assessed the efficacy of two alternative methods
to estimate leaf light absorption. The first alternative, the Reflectance Method, used a plant probe and a leaf clip instead of an
integrating sphere. This method consistently resulted in higher
PAR absorption estimates than those obtained through the Internal Method, but overestimation was highest in birch leaves. The
Reflectance Method is based on bidirectional reflectance factors
(­Schaepman-Strub et al. 2006), which are strongly dependent
on measurement geometry (­Jacquemoud and U
­ stin 2001), and
so they can be more difficult to replicate and interpret. For example, we found significant effect of needle arrangement on the
resulting bidirectional reflectance factors (Figure 9a and b and
Table 1). The Reflectance Method has an inherent bias because
it assumes zero transmittance. Accordingly, the overestimation
effect will decrease in optically thick samples, as seen in ­Figure 9
when comparing the thinner birch leaves with thicker pine or
spruce needles. Nevertheless, the Reflectance Method was
much faster and straightforward than the methods using integrating spheres, and could represent a reasonable option for
field studies that seek to track seasonal changes rather than
absolute levels.
Finally, using a PAR quantum sensor instead of a spectroradiometer in birch leaves yielded similar averaged PAR absorption
estimates compared with both the Internal and External Methods
(Figure 9f). This alternative approach to the Internal Method can
be more economical and straightforward than the other methods
Tree Physiology Online at http://www.treephys.oxfordjournals.org
378 Olascoaga et al.
Table 2. Summary of the trade-offs for each of the methods tested to estimate leaf optical properties in the PAR region. AT, total leaf light absorption;
GF, gap fraction; IS, integrating sphere; RB, bidirectional reflectance factor; RH, hemispherical reflectance factor; TH, hemispherical transmittance factor.
External Method
Optimal leaf type
Time per sample (min)
Optical profiles
Equipment
Benefits
Limitations
Broadleaves
35–40
RH, TH
IS, fibre optic, spectroradiometer,
laptop, scanner, frames
Standard method, RH and TH for
each leaf side
Effect of GF
Internal Method
Spectroradiometer
PAR sensor
Any kind of leaves
20–25
AT
IS, fibre optic, spectroradiometer,
laptop
Independent of GF
Direct measure of total leaf AT
No RH and TH
No side-specific AH
Any kind of leaves
20–25
PAR AT
IS, PAR sensor
at the expense of losing spectral information. Overall, the suitability of the different approaches will depend on the application
and resources, which are summarized in Table 2.
Acknowledgments
We are thankful to Drs Petr Lukeš, Christopher J. MacLellan,
­Caroline Nichol and Zbyněk Malenovský for technical help.
Conflict of interest
None declared.
Funding
This work was supported by Academy of Finland (1138884,
12720412) and University of Helsinki Funds (490116). The
study was preliminarily planned during a EUROSPEC Cost Action
ES0903 STSM granted to B.O., and hosted by the NERC/NCEO
Field Spectroscopy Facility, GeoScience, University of Edinburgh.
References
Daughtry CST, Walthall CL (1998) Spectral discrimination of Cannabis
sativa L. leaves and canopies. Remote Sens Environ 64:192–201.
Daughtry CST, Biehl LL, Ranson KJ (1989) A new technique to measure
the spectral properties of conifer needles. Remote Sens Environ
27:81–91.
Daughtry CST, Walthall CL, Kim MS, Brown de Colstown E, McMurtrey JE
III (2000) Estimating corn leaf chlorophyll concentration from leaf and
canopy reflectance. Remote Sens Environ 74:229–239.
Dawson TP, Curran PJ, Plummer SE (1998) LIBERTY—modeling the
effects of leaf biochemical concentration on reflectance spectra.
Remote Sens Environ 65:50–60.
Di Vittorio AV (2009) Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of
total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance. Remote Sens Environ 113:1948–1966.
Gamon JA, Peñuelas J, Field CB (1992) A narrow-waveband spectral
index that tracks diurnal changes in photosynthetic efficiency. Remote
Sens Environ 41:35–44.
Tree Physiology Volume 36, 2016
Reflectance Method
No spectroradiometer
needed
Same as previous
No spectral
information
Optically thick leaves
1–5
RB
Fibre optic, spectroradiometer,
plant probe, leaf clip, laptop
Economical and fast
Provides spectral information
Not valid for leaves with
significant TH
Ganapol BD, Johnson LF, Hammer PD, Hlavka CA, Peterson DL (1998)
LEAFMOD: a new within-leaf radiative transfer model. Remote Sens
Environ 63:182–193.
Gitelson AA, Merzlyak MN (1997) Remote estimation of chlorophyll content in higher plant leaves. Int J Remote Sens 18:2691–2697.
Hanssen LM, Snail KA (2002) Integrating spheres for mid- and near-­
infrared reflection spectroscopy. In: Chalmers JM, Griffiths PR (eds) Handbook of vibrational spectroscopy. John Wiley & Sons Ltd, Chichester, UK,
pp 1175–1191.
Idle DB, Proctor CW (1983) An integrating sphere leaf chamber. Plant
Cell Environ 6:437–439.
Jacquemoud S, Baret F (1990) PROSPECT: a model of leaf optical properties spectra. Remote Sens Environ 34:75–91.
Jacquemoud S, Ustin SL (2001) Leaf optical properties: a state of the
art. In: Proceedings of the 8th International Symposium of Physical
Measurements & Signatures in Remote Sensing, CNES, Aussois,
France, pp 223–232.
Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada PJ, Asner
GP, François C, Ustin SL (2009) PROSPECT+SAIL models: a review
of use for vegetation characterization. Remote Sens Environ
113:S56–S66.
Lichtenthaler HK, Wenzel O, Buschmann C, Gitelson A (1998) Plant
stress detection by reflectance and fluorescence. Ann NY Acad Sci
851:271–285.
Long JP, Postl WF, Bolhár-Nordenkampf HR (1993) Quantum yields of
uptake of carbon dioxide in C3 vascular plants of contrasting habitats
and taxonomic groupings. Planta 189:226–234.
Lukeš P, Stenberg P, Rautianen M, Mõttus M, Vanhatalo KM (2013) Optical properties of leaves and needles for boreal tree species in Europe.
Remote Sens Lett 4:667–676.
Mac Arthur A, MacLellan CJ, Malthus T (2012) The fields of view and
directional response functions of two field spectroradiometers. IEEE
Trans Geosci Remote Sens 50:3892–3907.
Malenovský Z, Albrechtová J, Lhotáková Z, Zurita-Milla R, Clevers
JGPW, Schaepman ME, Cudlín P (2006) Applicability of the PROSPECT model for Norway spruce needles. Int J Remote Sens
27:5315–5340.
McKiernan M, Baker NR (1991) Adaptation to shade of the light-harvesting apparatus in Silene dioica. Plant Cell Environ 14:205–212.
Mesarch MA, Walter-Shea EA, Asner GP, Middleton EM, Chan SS (1999)
A revised measurement methodology for conifer needles spectral
­optical properties: evaluating the influence of gaps between elements.
Remote Sens Environ 68:177–192.
Middleton EM, Chan SS, Mesarch MA, Walter-Shea EA (1996) A revised
measurement methodology for spectral optical properties of conifer
needles. In: Proc IEEE IGARSS 1996, Lincoln, NE, USA, pp 1005–1009.
A comparison of methods to assess leaf absorption 379
Olascoaga B, Juurola E, Pinho P, Lukeš P, Halonen L, Nikinmaa E, Bäck J,
Porcar-Castell A (2014) Seasonal variation in the reflectance of photosynthetically active radiation from epicuticular waxes of Scots pine
(Pinus sylvestris) needles. Boreal Environ Res 19:132–141.
Öquist G, Hällgren J-E, Brunes L (1978) An apparatus for measuring
photosynthetic quantum yields and quanta absorption spectra of
intact plants. Plant Cell Environ 1:21–27.
Peñuelas J, Filella I (1998) Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci
3:151–156.
Porcar-Castell A, García-Plazaola JI, Nichol CJ et al. (2012) Physiology of the seasonal relationship between the photochemical reflectance index and photosynthetic light use efficiency. Oecologia
170:313–323.
Reicosky DA, Hanover JW (1978) Physiological effects of surface waxes:
I. Light reflectance for glaucous and nonglaucous Picea pungens. Plant
Physiol 62:101–104.
Schaepman-Strub G, Schaepman ME, Painter TH, Dangel S, Martonchik
JV (2006) Reflectance quantities in optical remote sensing—definitions and case studies. Remote Sens Environ 103:27–42.
Serrano L, Gamon JA, Berry J (1997) Estimation of leaf area with an
integrating sphere. Tree Physiol 17:571–576.
Stimson HC, Breshears DD, Ustin SL, Kefauver SC (2005) Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus
edulis and Juniperus monosperma. Remote Sens Environ 96:108–118.
Taylor AH (1920) The measurement of diffuse reflection factors and a
new absolute reflectometer. J Opt Soc Am 4:9–23.
Woolley JT (1971) Reflectance and transmittance of light by leaves. Plant
Physiol 47:656–662.
Yáñez-Rausell L, Malenovský Z, Clevers JGPW, Schaepman ME (2014)
Minimizing measurement uncertainties of coniferous needle-leaf optical properties, part II: experimental setup and error analysis. IEEE J Sel
Top Appl 7:406–420.
Zarco-Tejada PJ, Berjón A, López-Lozano R, Miller JR, Martín P, Cachorro
V, González MR, de Frutos A (2005) Assessing vineyard condition with
hyperspectral indices: leaf and canopy reflectance simulation in a rowstructured discontinuous canopy. Remote Sens Environ 99:271–287.
where AT is the total absorption of the leaf sample and aS is the
total leaf surface area. The incoming radiant flux Q is now being
absorbed by the leaf sample, sphere walls, ports, detector and
white thread. Finally, we remove the sample, spray it with a black
paint of known absorption (ABLACK) and redispose the same leaf
sample inside the sphere hanging from the white thread; the
photon flux density measured under these conditions is
addressed as black (IB). This situation can be described as:
Q = IW AW aW
(A1)
where AW is the cumulative absorption of the internal sphere
walls, ports, detector and white thread, and aW is the total area
of all these surfaces. We conduct a second measurement of
the internal photon flux density with the integrating sphere
­containing a leaf sample hanging in the white thread; this is the
sample measurement (IS). This situation can be described as:
Q = IS( AW aW + AT aS ) (A2)
(A3)
and by separating AWaW in Eq. (A1), substituting in Eqs (A2) and
(A3), we obtain:
Q

Q = IS 
+ AT aS 
 IW

(A4)
and
Q

Q = IB  + ABLACK aB 
 IW

(A5)
Next, by arranging Eqs (A4) and (A5), separating Q and equalling we obtain:
IS AT aS
I A
a
= B BLACK B
1 − (IS /IW ) 1 − (IB /IW ) (A6)
from where AT can be obtained as:
Appendix
Derivation of Eq. (4) and estimation of total leaf absorption (AT)
We define Q as the radiant flux entering the integrating sphere.
We conduct first a measurement of the internal photon flux density with the integrating sphere containing only a white cotton
thread transversally disposed; this is the white measurement
(IW). This situation can be described as:
Q = IB( AW aW + ABLACK aB ) IB ABLACKaB [1 − (IS /IW )] IB ABLACK[(IW − IS )/IW ] aB
=
[(IW − IB )/IW ]ISaS
[1 − (IB /IW )]ISaS
(I − I )I A
a
(A7)
= W S B BLACK B
(IW − IB )ISaS
AT =
And assuming that the total leaf surface area is not affected by
the painting treatment, so that:
aB = aS (A8)
We can express AT as:
AT =
(IW − IS )IB ABLACK
(IW − IB )IS
(A9)
Note, however, that if aB is larger than aS, then AT (real) will be
larger than AT (estimated), following the same proportion.
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