clear-sky conditions by using high resolution full range PSR-
3500 (Portable Spectroradiometer, Spectral Revolution, Inc.,
Lawrence, Massachusetts). The wavelength range of PSR-3500
is 350-2500 nm with a resolution of 3.5 nm in the 350-1000
nm range, 10 nm in the 1000-1900 nm range, and 7 nm in
the 1900-2500 nm range. The 1.2 m long fiber optic with 25°
circular field of view (
FOV
) was attached to the device and
pointed to 1 m above the canopy without shading in nadir
orientation holding with pistol grip, which has a low reflec-
tance and impact resistance ABS thermoplastic handle. This
resulted in a
FOV
with radius of 0.2 m and an area of about
0.13 m
2
. Care was taken to ensure that the
FOV
of the spec-
trometer covers the grapevine canopy, excluding background
effects (e.g., soil). A reference spectrum was taken from a 99
percent reflectance Spectralon calibration panel (Labsphere,
Inc., North Sutton, New Hampshire) before target measure-
ment and repeated for every other five measurements to re-
adjust the base line to account for any change in illumination.
Following the canopy reflectance factor measurement,
PSR-3500 was equipped with a specifically developed leaf
clip with a bifurcated fiber-optic connected to both the device
and 5 watt tungsten halogen lamp light source. The leaf clip
is made from the same material as the reference panel and
enables us to take leaf reflectance factor (
HCRF
) with both spec-
trally white and black backgrounds. The white background is
used to take reference spectra. The third leaf back from the top
of the growing vine was held with the leaf clip, and the mea-
surements were taken with a black background after taking the
reference spectrum as the purpose of the measurement was to
obtain the pure adaxial reflectance factor (first surface spec-
trum). To effectively detect the spectral differences between
the two grapevine species, the measurement took place at two
different growth stages with a ten-day interval in average.
In both cases, PSR-3500 was configured to average au-
tomatically 40 spectra per sampling, and the raw spectra
bandwidth was interpolated to 1 nm. This resulted in 2,151
individual spectral bands. The spectral bands 1887-1978 and
2418-2500 were excluded from the canopy reflectance due
to strong atmospheric water absorption. Getac
®
PS336 PDA
preloaded with DARWin software (Compact V.1.2.4903, Spec-
tral Revolution, Inc., Lawrence, Massachusetts) was used to
manipulate the device and assisted for quick data collection.
Spectral Separability Analysis
We investigated the spectral separability of
V. riparia
and
V.
rupestris
,
and of genotypes within each species at the leaf
and canopy levels. The collected reflectance factor spectra in
different growing stages and conditions were averaged and an
independent t-test was applied as our intention was band-
by-band spectral comparison. Averaging of spectral data may
also help us identify those consistent spectral features, which
are not affected by the abiotic environment or growing stage
and unique to the specific species or genotypes. p-values
were calculated to determine the statistical significance of the
spectral separability (Zar, 1996). The vegetation reflectance
factor spectra, regardless of the canopy or leaf levels, indicate
a certain degree of absorption characteristics from 350 to 2500
nm due to the presence of pigments, water, and dry matter.
However, the recorded data are always subject to contamina-
tion caused by scattering, viewing geometry, and changing
illumination. Derivative analysis has been a desirable tool to
suppress background noise, accentuate individual absorp-
tion features and resolve the over-lapping spectral features
(Butler and Hopkins, 1970; Tsai and Philpot, 1998; Sawut
et
al
., 2014). Therefore, we treated the reflectance factor spectra
with 1
st
-and 2
nd
- derivative processing. The derivative calcu-
lations were performed with Savitzky-Golay (
SG
) methods (2
degrees and 5 points) of GRAMS/AI software (V. 9.1, Thermo
Fisher Science, Inc, Waltham, Massachusetts). The variances
were pooled when inequality was detected in each species
and genotype grouping in order to achieve the best estimate of
the variances.
Fresh and Dry Leaf Weight Measurement
The leaves, used for leaf reflectance factor measurement, were
destructively collected and sent to the lab for fresh leaf weight
measurement using a balance. After weight measurement, the
samples contained in a paper envelope were oven-dried for
one hour at about 75°C. When there is no change in weight,
the dry leaf weight of the samples were determined.
Leaf Area Index Measurement
The
LAI
-2200 Plant Canopy Analyzer (LI-COR, Inc., Lincoln,
Nebraska) was used to make indirect measurements of
LAI
.
The
LAI
of each grapevine is estimated with eight measure-
ments of Plant Canopy Analyzer pointing at the four compass
directions and using a 45° view cap (four readings above the
plant, four readings below the plant). The sensor was located
at the base of the individual plant while below the canopy
measurement. Fieldwork was conducted at dusk or dawn
in order to minimize the effects from the scattering of direct
sunlight through the leaf canopy.
Leaf Bio- and Photochemical Properties
Chlorophyll index (
CI
), developed by Gitelson and Merzlyak
(1994), was calculated based on 1 nm interpolated leaf reflectance
factor from spectroradiometer data.
CI
was formulated as follows:
CI
R R
R R
= −
+
750
705
750
705
(1)
where
R
is the reflectance factor value and subscripts are wave-
lengths in nm. Then,
CI
was converted into total chlorophyll
content (total chl; µg cm
-2
) following Richardson
et al
. (2002).
Using the interpolated leaf reflectance factor spectra, we
calculated the photochemical reflective index (
PRI
) proposed
by Gamon
et al
. (1997) as:
PRI
R R
R R
= −
+
531
570
531
570
.
(2)
To avoid negative values for
PRI
, we scaled the
PRI
as Letts
et al. (2008):
sPRI
PRI
= +
1
2
.
(3)
The
PRI
is sensitive to changes in xanthophyll pigments,
which is a key indicator of photosynthetic light use efficiency;
therefore, the rate of carbon dioxide uptake by foliage per unit
energy absorbed.
Simulated Sensor-Specific Indices
No hyperspectral satellite data were available for our study
area during the experiment period. Simulation of satellite
spectra using the spectral response function provides insights
on the use of the methods at satellite levels. Since both
NASA
’s
Hyperion and planned
HyspIRI
sensors have 10 nm spectral
resolutions, we decided to use Hyperion spectral response
function for the simulation. Leaf level reflectance factor were
convolved using sensor-specific spectral response function to
simulate EO-1 Hyperion following Equation 4:
f d
( )
f d
( )
ρ
λ
ρ λ λ
λ
λ
λ
λ
λ
Hyperion
leaf
min
max
min
max
( )
=
( )
∫
∫
(4)
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
January 2016
53
