Aust. J. Bot., 1999, 47, 909–923
Remote Sensing of Water Content in Eucalyptus Leaves
Bisun Datt
School of Geography, The University of New South Wales, Kensington, NSW 2052, Australia;
email: [email protected]
Abstract
The spectral reflectance of leaves from several Eucalyptus species was measured over the 400–2500 nm
wavelengths with a laboratory spectroradiometer. The relationship of reflectance with the gravimetric
water content and equivalent water thickness (EWT) of the leaves was analysed. The results showed that
EWT was strongly correlated with reflectance in several wavelength regions. No significant correlations
could be obtained between reflectance and gravimetric water content. It was also possible to confirm
theoretically that reflectance changes of leaves could be directly linked to changes in EWT but not to
changes in gravimetric water content. Several existing reflectance indices were evaluated for estimation of
leaf water content and some new indices were developed and tested. Two semi-empirical indices
developed in this study, (R850 - R2218)/(R850 - R1928) and (R850 - R1788)/(R850 - R1928), were found to show
significantly stronger correlations with EWT than all other indices tested. It was also shown that these new
indices were least sensitive to the effects of radiation scatter. The indices (R850 - R2218)/(R850 - R1928) and
(R850 - R1788)/(R850 - R1928) are therefore proposed as two new indices for the remote estimation of
vegetation water content.
Introduction
The detection of plant water status is important for monitoring the physiological status of
plants, and the assessment of drought and fire risk in natural plant communities, and in the
irrigation scheduling of crops (Peñuelas et al. 1993, 1996). Although field sampling of single
leaves and shoots provides the most accurate assessment of plant water status, such methods
are not feasible when estimates are required for large areas of vegetation. Remote sensing
techniques offer the alternative of a non-destructive and instantaneous method of assessing
the water status of vegetation over large spatial scales.
Water strongly absorbs radiant energy throughout the mid-infrared (MIR) region
(1300–2500 nm) of the electromagnetic spectrum, with strong absorption bands centred on
1450, 1940 and 2500 nm; there are also two weak absorption bands located in the nearinfrared (NIR) region (750–1300 nm) near 970 and 1200 nm (Gates et al. 1965; Knipling
1970; Woolley 1971). When radiation corresponding to the wavelengths of the water absorption
bands is incident upon green vegetation, the reflectance is reduced to a varying extent,
depending on the tissue water content (Thomas et al. 1971; Tucker 1980). Therefore, the
measurement of radiation reflected by leaves and canopies provides a basis for estimating
leaf and canopy water contents.
The potential for utilising leaf and canopy reflectance for measuring plant water status has
been the subject of much research. Laboratory investigations on single leaves have shown
that the reflectance at the major water absorption bands near 1450, 1940 and 2500 nm is
highly sensitive to water content (e.g. Knipling 1970; Thomas et al. 1971). However, because
of the strong absorption by water, these bands become saturated at high water contents
present in whole plants and optically thick canopies. Furthermore, strong absorption by
atmospheric water vapour in these spectral regions makes them unsuitable for aircraft- and
satellite-based remote sensing (Tucker 1980; Holben et al. 1983; Goetz and Boardman 1995).
But the regions of intermediate absorption by leaf water in the MIR wavelengths near 1650
and 2200 nm and the weak absorption bands in the NIR region near 970 and 1200 nm have
© CSIRO 1999
10.1071/BT98042
0067-1924/99/060909
910
B. Datt
been shown to be more suitable for remote sensing of plant water status (Carlson 1971;
Tucker 1980; Ripple 1985; Bowman 1989; Peñuelas et al. 1993, 1996, 1997; Goetz and
Boardman 1995; Gao 1996).
While many experiments have shown that reflectance in the MIR wavelengths is directly
related to the variations in water content of plant leaves and canopies (Ripple 1986; Bowman
1989), results are inconclusive as to whether there is any direct link between spectral
measurements and the physiological variables of water status. Water content is a measure of
the absolute amount of water contained in leaves and is usually expressed as the gravimetric
water content (mass of water per unit fresh leaf mass) or the equivalent water thickness
(EWT) which is the volume of water per unit leaf area. The physiological variables which
characterise leaf water status include relative water content (RWC), water potential,
components of water potential (turgor pressure and osmotic potential), stomatal conductance,
or transpiration and photosynthetic rate (Peñuelas et al. 1993; Verdebout et al. 1994). Relative
water content is the volume of leaf water expressed as a fraction of the water volume for the
leaf at full turgidity. A number of studies have found that the reflectance of leaves and
canopies varies with several of these physiological variables, in a manner similar to the
variation of reflectance with water content (Ripple 1986; Bowman 1989; Carter 1991; Cibula
et al. 1992). By using the broad spectral bands corresponding to the Landsat TM satellite
channels 4 (760–900 nm) and 5 (1550–1750 nm), Hunt et al. (1987), Hunt and Rock (1989),
and Hunt (1991) developed and tested a liquid water content index for remote sensing of leaf
RWC. The use of RWC for leaf water estimation is not self-contained, since in calculating
RWC turgid and dry leaf reflectance data are required (Holben et al. 1983). Several studies
have also indicated that it may not be feasible to detect leaf water status change within a
biologically meaningful range as the relatively small changes in leaf water content associated
with large changes in turgor pressure, stomatal conductance or photosynthetic rates may not be
detectable from reflectance measurements (Bowman 1989; Hunt and Rock 1989; Pierce et al.
1990). Thus any reported correlations between leaf physiological variables and reflectance
are probably due to the covariance of these variables with water content (Ripple 1986).
A major problem in relating spectral reflectance to water content is caused by variations in
leaf structure. Differences in leaf surface, internal structure and thickness cause changes in
the scattering properties of leaves, thus producing reflectance differences that are unrelated to
water content. For a data set comprising several plant species, Danson et al. (1992) found that
leaf structure differences had an important effect on the reflectance/water content
relationships. They showed that the first derivative of the reflectance spectrum at selected
wavelengths was insensitive to the leaf structure effects. At the canopy level several additional
factors confound the relationship between reflectance and water content.
Although the reflectance of leaves increases with dehydration at all wavelengths over the
400–2500 nm range, as shown by laboratory studies of dehydrating leaves (Knipling 1970;
Gausman 1974; Carter 1991; Goetz and Boardman 1995; Aldakheel and Danson 1997), some
experiments conducted on whole plant canopies have shown that reflectance actually decreases
with decreasing water content (Holben et al. 1983; Jackson and Ezra 1985; Collier 1989). This
indicates that in field canopies, factors such as changes in canopy geometry and leaf area index
(LAI), and soil and background reflectance can have a greater effect on reflectance than the
physiological and anatomical changes in leaves caused by water stress (Collier 1989).
Several reflectance indices currently exist in the literature for the estimation of a range of
plant water status variables, but they have been developed and tested only on a few species of
plants from the northern hemisphere. It is important therefore that such techniques are evaluated
on a range of plant species from different geographical regions. With the increased availability
and use of high spectral resolution (hyperspectral) data in remote sensing, there is a need to
develop new and more accurate techniques for the remote estimation of plant water content.
This paper describes a laboratory study that examined how well variations in spectral
reflectance of Eucalyptus leaves related to their water content. The specific aims were to
Remote Sensing of Water Content in Eucalyptus Leaves
911
compare the relationship of EWT and gravimetric water content with reflectance, to evaluate
the effectiveness of several reflectance indices for estimating leaf water content, and to
develop and test new indices that are independent of leaf structural effects.
Materials and Methods
Study Sites, Species and Sample Collection
Sampling was done at two study sites located in New South Wales; Lane Cove National Park
(33°488S, 151°108E, 11 km north-west of Sydney) and Nullica State Forest (37°S, 149°458E, 20 km
north-west of Eden township). The sampling scheme was designed to cover a wide variation in leaf type
and water content. A total of 21 Eucalyptus species were selected, of which 17 were from the Nullica site
and seven from the Lane Cove site, with three species common to both sites. Leaf samples were obtained
by cutting small branchlets from sunlit parts of the canopy by using a long pruner. The leaves were
immediately clipped from the branchlets and divided into two samples: young leaves (leaves from the
upper half of each twig) and mature leaves (leaves from the lower half of each twig). Immediately after
clipping, three leaves were selected at random from each sample and sealed into preweighed plastic tubes
for determination of water content. The rest of each sample was sealed in plastic bags for reflectance
measurements. To maintain the freshness of the leaves, all samples were immediately stored over ice in a
portable refrigeration unit. All reflectance measurements were completed within 3–4 h of picking the
leaves. For the 17 species at the Nullica site, sampling was done twice, during November 1996 and June
1997, and for the seven species at the Lane Cove site, sampling was repeated on the same trees once
every month from October 1996 to July 1997. This produced a total of 208 samples for the data set.
Reflectance Measurements
Leaf reflectance measurements were carried out in a laboratory by using the Geophysical Environment
Research Infrared Intelligent Spectroradiometer (GER IRIS Mark IV). The IRIS is a dual-beam
spectroradiometer which measures the radiance from a reference standard (spectralon) and a target
sample simultaneously over the 400–2500-nm range. Therefore all reflectance measurements were
reflectance relative to spectralon. The radiation in the visible and near-infrared wavelengths from 400 to
1100 nm is recorded by silicon detectors at a spectral bandwidth of 2 nm, and a lead sulfide detector
records radiation from 1100 to 2500 nm at a spectral bandwidth of 4 nm. A 500-W quartz halogen lamp
was used as the light source to illuminate the target and reference.
Leaves were arranged into a 15 ´ 15-cm stack, six layers thick. The leaf stack and the spectralon
reference panel were placed on a target platform, about 70 cm from the IRIS lens. Leaf stacks rather
than single leaf layers were used so as to obtain the infinite reflectance. The infinite reflectance is the
maximum reflectance obtained from an optically thick medium. In the case of leaves this is achieved by
adding successive leaf layers to a pile until there is no further increase in reflectance. For this study six
leaf layers gave the maximum near-infrared reflectance. For each sample, five reflectance measurements
were taken and averaged. After reflectance measurements the data were downloaded from the IRIS onto
a computer using the PCS software developed by the GER company (New York) and CSIRO (Sydney).
Determination of Water Content
The fresh leaf mass and leaf area were determined for the leaf samples. The three leaves from each
sample were weighed together to obtain the total leaf mass. Leaf area was measured separately for each
of the three leaves by using a LICOR LI-3000 leaf-area meter. The leaf-area meter gave readings in cm 2
with a 0.01-cm2 resolution and ± 2% accuracy. The area of each leaf was measured three times and
averaged. The average leaf areas for the three leaves were added to obtain the total leaf area of each
sample. Leaf thickness of the samples was measured with a vernier scale to 0.001 of a millimetre.
The leaves were dried to a constant mass in an oven at a temperature of 70°C. The gravimetric water
content (GWC) of the samples was determined on fresh and dry leaf mass basis as follows:
GWCF = (FM - DM)/(FM) and
(1)
GWCD = (FM - DM)/(DM),
(2)
where GWCF is the gravimetric water content (grams water/gram fresh leaf mass), GWCD is the
gravimetric water content (grams water/gram dry leaf mass), FM is the fresh leaf mass (g), and DM is
the oven dry leaf mass (g).
912
B. Datt
The equivalent water thickness of the samples was calculated as the volume of water per unit leaf
area (cm):
EWT = (FM - DM)/(rw ´ leaf area),
(3)
where rw is a physical constant representing the density of pure water (1 g cm–3).
The dry matter content (g cm–2) of the samples was obtained as the specific leaf weight, SLW:
SLW = DM/leaf area.
(4)
Data Analysis
All measured spectra were converted to percentage reflectance and linearised to 2 nm wavelength
resolution by using the PCS software. This produced spectra consisting of 1051 bands over the
400–2500-nm range. The XSpectra software, developed by CSIRO (Sydney), was used for analysis of
reflectance data. Regression and correlation analyses were the main statistical procedures used to
analyse the relationship between leaf-water content variables and reflectance data.
Results and Discussion
The summary statistics for leaf water content are given in Table 1. The leaves sampled
represented a wide variety of water contents and leaf thicknesses. The correlations among the
measured variables are given in Table 2. The strong positive correlation of leaf thickness with
EWT (r = 0.76) and SLW (r = 0.82) was as expected, since thicker leaves contain more water
and dry matter per unit leaf area. However, the small but significant (P < 0.001) negative
correlation of leaf thickness with GWCF (r = -0.32) and GWCD (r = -0.31) indicates that the
gravimetric water content or the ‘water concentration’ actually decreased with leaf thickness.
Similar relationships existed between the dry matter content and water content of the leaves;
SLW showed strong negative correlations with GWCF (r = -0.68) and GWCD (r = -0.64) but
was positively correlated with EWT (r = 0.67). These observations reflect the sclerophyllic
nature of Eucalyptus leaves where the increase in dry matter content (cell density) with leaf
development is accompanied by a proportionately smaller increase in water content. The near
zero correlation of EWT with GWCF (r = -0.03) and GWCD (r = 0.09) shows that the water
Table 1. Summary statistics for leaf water content and water concentration (n = 208)
GWCF, gravimetric water content on fresh leaf mass basis; GWCD, gravimetric water content
on dry leaf mass basis; EWT, equivalent water thickness; SLW, specific leaf weight
GWCF (g g–1)
GWCD (g g–1)
EWT (cm)
SLW (g cm–2)
Leaf thickness (mm)
Mean
Range
Standard
deviation
Coefficient of
variation
0.508
1.053
0.017
0.017
0.355
0.408–0.717
0.704–2.215
0.009–0.025
0.008–0.031
0.230–0.580
0.054
0.251
0.003
0.005
0.060
0.107
0.239
0.188
0.273
0.168
Table 2. Intercorrelation among variables in leaf data set (n = 208)
For definitions of GWCF, GWCD, EWT and SLW see Table 1
GWCF
GWCD
EWT
SLW
Leaf thickness
GWCF
GWCD
EWT
-0.32
-0.31
0.76
0.82
0.90
-0.03
-0.68
0.09
-0.64
0.67
Remote Sensing of Water Content in Eucalyptus Leaves
913
thickness was unrelated to the gravimetric water content for the leaves studied. This provides
for a more meaningful comparison of the relationships between spectral reflectance and these
two measures of water content.
The reflectance spectra of leaf samples from all species showed spectral features similar to
that of most green vegetation. There were large variations in the reflectance amplitude across
several wavelength regions, resulting from the wide variation in leaf structure, pigmentation
and water contents in the data set. The mean, minimum, and maximum reflectance spectra are
shown in Fig. 1.
1
Max.
0.9
0.8
Mean
0.7
Reflectance
0.6
0.5
Min.
0.4
0.3
0.2
0.1
0
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
Wavelength (nm)
Fig. 1.
Mean, maximum and minimum reflectance curves from 208 Eucalyptus leaf samples.
Gravimetric Water Content, Equivalent Water Thickness and Reflectance
To determine how the relationship between GWCF, GWCD, EWT and reflectance changed
with wavelength, the linear correlation coefficient, r, was calculated for all wavelengths in the
range 400–2500 nm and plotted as correlograms. The correlograms are shown in Fig. 2 where
the wavelength regions of statistically significant correlation (P < 0.001) are indicated by values
of r greater than the critical value of +0.23 or less than -0.23. The results show that there was
almost no significant correlation between reflectance and GWCF and GWCD (Fig. 2a, b),
except for small but significant positive correlations in the NIR region (700–1300 nm for
GWCF and 700–1080 nm for GWCD). However, EWT was strongly correlated with
914
B. Datt
0.8
(a )
Correlation coefficient
0.6
0.4
0.2
0
-0.2
600
800
1000
1200
1400
1600
1800
2000
2200
2400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
-0.4
-0.6
-0.8
0.8
(b )
Correlation coefficient
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
0.8
(c )
Correlation coefficient
0.6
0.4
0.2
0
-0.2
982
-0.4
-0.6
1188
2218
-0.8
1788
Wavelength (nm)
Fig. 2. Correlation between reflectance and (a) GWCF, (b) GWCD and (c) EWT, where GWCF
and GWCD are the gravimetric water contents (g g–1) expressed on fresh and dry leaf mass basis,
respectively; EWT is the equivalent water thickness (cm). The critical values of correlation
coefficient (P < 0.001) are indicated with the horizontal lines at r = +0.23 and r = -0.23.
Remote Sensing of Water Content in Eucalyptus Leaves
915
reflectance over several wavelength regions (Fig. 2c). There was a statistically significant
negative correlation between EWT and reflectance throughout most of the 1120–1870- and
1980–2440-nm wavelength regions. The regions of no significant correlation between EWT
and reflectance (1870–1980 and 2440–2500-nm) corresponded to the wavelengths of
maximum absorption by water. It is therefore suggested that (1) there is no correlation
between reflectance in the 400–2500 nm range and the gravimetric water content (GWCF and
GWCD) of leaves and (2) reflectance is strongly correlated with EWT of leaves throughout
most of the MIR wavelengths, except for the regions of near total absorption by water.
These observations can be explained theoretically by examining the roles of leaf structure
and water content in determining the magnitude of reflectance. The reflectance properties of
leaves are controlled by the absorption and scattering processes which occur within the leaf.
Scattering is caused mainly by the refractive index differences between cell walls and air
spaces inside the leaf (Woolley 1971). In the absence of any absorption medium, the
‘background’ reflectance spectrum of a leaf would be determined entirely by the process of
scattering. The absorption effects of leaf biochemicals (e.g. photosynthetic pigments in the
visible wavelengths, water in the NIR and MIR wavelengths) are superimposed upon this
background spectrum. Since scattering increases the path length of radiation inside the leaf
and it is this passage through the various materials that causes absorption, the absorption
process can be described by a mean path length l (cm) and absorption coefficient k (cm-1)
(Clark and Roush 1984):
R = e-kl,
(5)
where R is the reflectance. Equation 5 is easily transformed to obtain the apparent absorbance, A:
A = -ln (R) = kl.
(6)
The absorption of radiation by leaf water at any wavelength can be calculated from equation 6
by using the absorption coefficient of water, k, at that wavelength and the leaf water content
expressed in pathlength units (cm). The EWT which represents the leaf water content as the
hypothetical thickness of a single layer of water over the leaf surface (i.e. volume of water per
unit leaf area), can be substituted in place of l in Equations 5 and 6 since it is a more direct
representation of l than GWC. In other words, EWT represents the thickness of the absorbing
medium (water) in the path of radiation and is an absolute measure of water content that is
independent of the dry matter content of leaves. Gravimetric water content is the ratio of leaf
water mass to total leaf mass, and is therefore affected by variations in the dry matter content
of the leaves. Gravimetric water content is strictly a measure of water concentration within the
leaf tissue rather than the absolute volume or thickness of water.
The relationship of EWT and GWC with reflectance can be further illustrated by examining
how these variables change with the leaf area index (LAI). The LAI is the number of leaf
layers present in a plant canopy. When several identical leaves are piled on top of each other,
their individual EWT values add up to produce the total EWT of the pile. The change in
reflectance with LAI will therefore relate to the corresponding change in EWT, especially in
the wavelengths of intermediate to low absorption by water. But the GWC remains invariant
with LAI because the ratio of total water mass to total leaf mass for a pile of identical leaves
will be the same regardless of the number of leaf layers. This is analogous to filling a jar with
pure water, where the depth increases as more water is added but the concentration of water
remains the same. Thus the increased absorption by leaf water at higher LAI values
corresponds with the total EWT and not GWC.
The foregoing analysis and theoretical considerations clearly show that EWT is directly
related to reflectance. The rest of the analysis will focus on the relationship between EWT
and reflectance.
916
B. Datt
Relationship between Existing Indices and EWT
The following spectral indices were calculated from the reflectance data and regressed
against EWT.
Moisture Stress Index (MSI)
The MSI was originally derived as the ratio of the broad wavelength Landsat TM satellite
bands 5 to 4 (1550–1750 and 760–900 nm) (Hunt and Rock 1989). In the present study a
narrow band MSI was calculated using single wavelength reflectances in these two regions:
MSI = R1650/R820,
(7)
where R represents the reflectance at the indicated wavelengths.
Ratio of Thematic Mapper Band 5 to Band 7 (TM5/TM7)
The TM5/TM7 index is the ratio of Landsat TM bands 5 and 7 (1550–1750 and 2080–2350 nm)
(Elvidge and Lyon 1985), and was calculated here by using single wavelength reflectances
situated in these bands:
TM5/TM7 = R1650/R2218.
(8)
Water Index (WI)
The WI was calculated according to Peñuelas et al. (1997):
WI = R900/R970.
(9)
Normalised Difference Water Index (NDWI)
The NDWI was calculated according to Gao (1996):
NDWI = (R860 - R1240)/(R860 + R1240).
(10)
Normalised Difference Vegetation Index (NDVI)
The NDVI was calculated according to Rouse et al. (1973):
NDVI = (R800 - R680)/(R800 + R680).
(11)
Although the NDVI is not an index of vegetation water content, it is the most common of all
vegetation indices and was used here merely for comparison with the other indices.
The regressions for the existing vegetation indices with EWT are shown in Fig. 3. The
relationships were linear for all indices. The correlation coefficient for each index is also
shown on the graphs. As expected, NDVI did not show any significant correlation with EWT.
Of the other indices, MSI was the most sensitive to EWT (r = -0.67) and TM5/TM7 showed
the lowest but a significant correlation. Water index and NDWI both showed moderate
sensitivity to EWT.
Single-waveband Reflectance Indices
The reflectances at the wavelengths corresponding to the correlation maxima in Fig. 2c
were plotted against EWT (Fig. 4). The relationship between reflectance and EWT at these
wavelengths was a non-linear one of the form y = ax-b. R1788 and R2218 showed the highest
sensitivity to variations in EWT. In these two spectral regions the absorptivity of water is
intermediate and reflectance remains sensitive over a larger range of water contents than in the
zones of maximum absorption near 1930 and 2500 nm. The reflectances at 982 and 1188 nm
showed weak but significant correlations with EWT. The weak absorptance features of water
in these two NIR wavelengths are located in a high reflectance region of the spectrum, where
Remote Sensing of Water Content in Eucalyptus Leaves
917
2.5
r = 0.37
TM5/TM7
Vegetationindex
index
Vegetation
2
1.5
r = 0.53
1
WI
r = –0.13
NDVI
MSI
0.5
r = –0.67
r = 0.51
0
0.006
NDWI
0.01
0.014
0.018
0.022
0.026
EWT (cm)
Fig. 3. Linear regression of existing reflectance indices with EWT. The correlation coefficients
(r) are indicated next to the regression lines.
shifts in the overall reflectance level associated with the differential scattering effects of leaf
structure are much greater than the subtle variations in reflectance due to water absorption.
Construction of New Indices
The spectral indices considered so far are mainly empirical and therefore lack a physical
basis. These indices may suffer from calibration problems when applied in different
situations. Therefore a semi-empirical approach, based on theoretical considerations of
radiation scatter and absorption in plant leaves was taken to develop a new index for the
remote estimation of water content.
When radiant energy strikes a leaf, part of it is reflected by the leaf surface and the rest
enters the leaf where it is scattered by the mesophyll structure. Part of the internally scattered
radiation is reflected back out of the surface of incidence and the rest is transmitted through
the leaf. The internally scattered radiation is also absorbed at specific wavelengths by the
various leaf biochemicals. Baret et al. (1988) showed that leaf reflectance could be
approximated by the following semi-empirical model:
R = Rs+ S exp(-SkiCi),
(12)
918
B. Datt
1
0.9
0.8
R982
r = –0.25
R1188
0.7
r = –0.56
Reflectance
0.6
0.5
0.4
R1788
0.3
r = –0.79
0.2
R2218
r = –0.75
0.1
0
0.006
0.01
0.014
0.018
0.022
0.026
EWT (cm)
Fig. 4. Relationship between EWT and reflectance at 982, 1188, 1788 and 2218 nm wavelengths.
The relationships were best described by power curves of the form y = ax-b. The correlation coefficients
(r) are indicated on the graphs.
where Rs is the reflectance from the leaf surface, and S is the reflectance from the leaf interior
when there is no absorption (SkiCi = 0). Rs is almost wavelength independent as it results from
simple scattering on the leaf surface, while S might depend on wavelength (Peñuelas et al. 1995).
The total absorption at any wavelength by the leaf biochemicals is given by exp(-SkiCi), where
ki and Ci are the specific absorption coefficient and content of leaf biochemical i, respectively.
Equation 12 shows that the relationship between reflectance, R, and biochemical content,
Ci, at any wavelength may change from one leaf to another according to leaf surface and
internal structure. These two factors cause an additive offset, Rs, and a multiplicative effect,
S, to the reflectance spectrum. The derivation of an analytical relationship between leaf
reflectance and chemical content from equation 12 therefore requires the elimination of these
scattering effects of leaf structure. Simple ratio indices such as MSI and WI provide a firstorder approximation of water content, only if the leaf surface reflectance, Rs, is negligible.
Likewise, the first derivative transformations of reflectance spectra eliminate the additive Rs
term but are affected by slope variations in the spectrum due to the multiplicative effect, S.
A better reflectance index, which completely removes the structural effects can be formulated
by taking the ratio of reflectance differences between a reference band and two other bands.
Remote Sensing of Water Content in Eucalyptus Leaves
919
A similar approach was used by Peñuelas et al. (1995) to develop an index for assessing the
chlorophyll/carotenoid pigment ratio in leaves.
The wavelengths for the development of scatter-insensitive water indices were chosen
from Fig. 2c, 1788 and 2218 nm were selected as the sensitive bands, 850 nm was chosen as
the NIR reference band, and 1928 nm was selected as a second reference band. At 1788 and
2218 nm reflectance is determined by leaf scattering properties and water absorption. At 850
nm, reflectance is mainly a function of scattering by leaf surface and internal structure, as there
is no absorption by water or any other biochemicals in this region (SkiCi = 0). Absorption by
water is at a maximum at 1928 nm so that reflectance in this region is largely a function of leaf
surface scattering (SkiCi = maximum). Allen et al. (1969, 1970) have shown that the
absorption spectra of plant leaves over the 1400–2500 nm region were statistically similar to
the absorption spectra of liquid water. The spectral response of the MIR wavelengths to
changes in leaf water content are directly related to differences in the absorption coefficients
of liquid water (Danson et al. 1992). Therefore the absorption coefficients of other leaf
biochemicals in the MIR region are assumed negligible in relation to the absorption
coefficients of water. Equation 12 is written as follows for these wavelengths:
R850 = Rs + S,
R1928 = Rs + S exp(-kw(1928)EWT),
(13)
(14)
R1788 = Rs + S exp(-kw(1788)EWT) and
(15)
R2218 = Rs + S exp(-kw(2218)EWT),
(16)
where kw is the absorption coefficient (cm-1) of water at the indicated wavelengths and EWT
is the water content (cm).
Taking the difference between Equations 13 and 15, and Equations 13 and 14, and
dividing the results gives
(R850 - R1788)/(R850 - R1928) = [1 - exp(-kw(1788)EWT)]/[1 - exp(-kw(1928)EWT)].
(17)
Similarly, by using Equation 16 in place of Equation 15 in the above step we obtain:
(R850 - R2218)/(R850 - R1928) = [1 - exp(-kw(2218)EWT)]/[1 - exp(-kw(1928)EWT)].
(18)
Equations 17 and 18 are now functions of water absorption only, and are independent of
the additive and multiplicative effects of leaf structure. The regressions between EWT and
the new indices (R850 - R1788)/(R850 - R1928) and (R850 - R2218)/(R850 - R1928) are shown in
Fig. 5. Both these indices showed a direct linear relationship of the form y = ax + b with
EWT, and the correlation coefficients (r = 0.76 for (R850 - R1788)/(R850 - R1928) and r = 0.78
for (R850 - R2218)/(R850 - R1928)) were higher than those obtained with the existing vegetation
indices from Fig. 3.
Evaluation of the Different Indices
Because of the nature of their derivation, the two difference indices are also unaffected by
changes in absolute reflectance caused by extraneous factors. Differences in measurement
conditions (such as sample geometry and illumination angles), which are common when
reflectance measurements are made on different dates, at different sites or by different
workers, can cause changes in absolute reflectance levels that are unrelated to the leaf
structural and biochemical parameters. These effects can cause calibration errors when single
wavelength or simple ratio indices are used. To demonstrate the effectiveness of (R850 - R1788)/
(R850 - R1928) and (R850 - R2218)/(R850 - R1928), all spectra were ‘degraded’ by increasing the
scatter variation among the samples, and all reflectance indices were recalculated and
regressed against EWT. The spectra were degraded by applying the following transformation
across all wavelengths in each spectrum:
(19)
Rdegraded = aRoriginal + b,
920
B. Datt
1
(R850–R2218)/(R850–R1928)
r = 0.78
0.9
0.8
Vegetation index
0.7
r = 0.76
(R850–R1788)/(R850–R1928)
0.6
0.5
0.4
0.3
0.2
0.1
0
0.002
0.006
0.01
0.014
0.018
0.022
0.026
EWT (cm)
Fig. 5. Regression between EWT and the indices (R850 - R2218)/(R850 - R1928) and (R850 - R1788)/
(R850 - R1928). The best-fit lines were of the linear form y = ax + b. The correlation coefficients (r) are
shown on the graphs.
where Roriginal is the original reflectance, a and b are arbitrary multiplicative and additive
constants. The first 52 spectra were transformed by setting a = 0.9 and b = -0.05 in Equation
19, the second 52 spectra with a = 0.7 and b = 0.04, the third 52 spectra with a = 0.8 and
b = -0.06, and the final 52 spectra with a = 0.6 and b = 0.05. The correlation coefficients
between EWT and the reflectance indices obtained from the degraded spectra and the original
spectra are compared in Table 3. The results show that the correlations for the existing
indices (MSI, WI, NDWI, TM5/TM7) and the single-wavelength indices (R1788 and R2218)
are weaker in the degraded spectra, but the correlations for the two new indices ((R850 R1788)/(R850 - R1928) and (R850 - R2218)/(R850 - R1928)) remained exactly the same in the
original and degraded spectra. This result demonstrates that single wavelength reflectance
indices, simple ratios, and normalised difference ratios are all affected by changes in the
absolute reflectance values and are therefore sensitive to calibration. The difference ratios
formulated in this study are shown to be insensitive to absolute calibration and therefore are
better indices for the estimation of EWT.
Remote Sensing of Water Content in Eucalyptus Leaves
921
Table 3. Correlations between reflectance indices and equivalent water
thickness (EWT) in normal and degraded spectra (n = 208)
MSI, moisture stress index; WI, water index; NDWI, normalised difference
water index; TM5/TM7, ratio of Thematic Mapper band 5 to band 7
Reflectance index
(R850 - R2218)/(R850 - R1928)
(R850 - R1788)/(R850 - R1928)
R2218
R1788
MSI
WI
NDWI
TM5/TM7
Correlation coefficient (r)
Normal spectra
Degraded spectra
0.78
0.76
-0.75
-0.79
-0.67
0.53
0.51
0.37
0.78
0.76
-0.30
-0.55
-0.44
0.47
0.46
0.00
From the linear regression of (R850 - R1788)/(R850 - R1928) and (R850 - R2218)/(R850 - R1928)
with EWT, the following algorithm equations were developed for the remote estimation of
water content:
EWT (cm) = 0.045 [(R850 - R1788)/(R850 - R1928)] - 0.014 and
(20)
EWT (cm) = 0.080 [(R850 - R2218)/(R850 - R1928)] - 0.052.
(21)
Either of Equations 20 or 21 can be used directly for the estimation of EWT in Eucalyptus
species. Since these indices have been derived by using a theoretical approach they can
possibly be applied to all types of green leaves. However, the calibration coefficients in the
equations would need to be determined for use with other species.
Conclusions
The analysis of reflectance and water content measurements for leaves from several
Eucalyptus species has revealed some new findings on the quantification of vegetation water
content by remote sensing. The results have shown that leaf reflectance is related to changes
in EWT but not to the gravimetric water content. Two new reflectance indices, (R850 - R2218)/
(R850 - R1928) and (R850 - R1788)/(R850 - R1928), were found to show the best correlations
with EWT and are proposed as potential vegetation indices for the remote estimation of EWT
in all types of plants. These new indices have been developed by using a semi-empirical
approach and have been shown to possess several advantages over other commonly used
empirical indices such as simple ratios or normalised band ratios. The results of this study
have shown that the natural variability in Eucalyptus leaf water content can be accurately
estimated by using hyperspectral reflectance data. However, the laboratory measurements
conducted on piles of leaves are representative of rather simplistic canopies. For remote
estimation of vegetation water content at the canopy or landscape levels, further experiments
involving airborne- or satellite-based hyperspectral measurements would be needed.
Acknowledgments
The author thanks the CSIRO Mineral Resources Laboratories, Sydney, for the loan of the
IRIS spectroradiometer.
922
B. Datt
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Manuscript received 18 May 1998, accepted 16 October 1998
http://www.publish.csiro.au/journals/ajb