Remote Sensing of Water Content in Eucalyptus Leaves

Transcription

1 Aust. J. Bot., 1999, 47, Remote Sensing of Water Content in Eucalyptus Leaves Bisun Datt School of Geography, The University of New South Wales, Kensington, NSW 2052, Australia; Abstract The spectral reflectance of leaves from several Eucalyptus species was measured over the 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, (R R 2218 )/(R R 1928 ) and (R R 1788 )/(R R 1928 ), 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 (R R 2218 )/(R R 1928 ) and (R R 1788 )/(R R 1928 ) 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 ( 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 ( 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 /99/060909

2 910 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 ( nm) and 5 ( 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 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

3 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 48 S, E, 11 km north-west of Sydney) and Nullica State Forest (37 S, E, 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 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 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 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-cm 2 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 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: GWC F = (FM - DM)/(FM) and (1) GWC D = (FM - DM)/(DM), (2) where GWC F is the gravimetric water content (grams water/gram fresh leaf mass), GWC D 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).

4 912 B. Datt The equivalent water thickness of the samples was calculated as the volume of water per unit leaf area (cm): EWT = (FM - DM)/( w leaf area), (3) where w 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 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 GWC F (r = -0.32) and GWC D (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 GWC F (r = -0.68) and GWC D (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 GWC F (r = -0.03) and GWC D (r = 0.09) shows that the water Table 1. Summary statistics for leaf water content and water concentration (n = 208) GWC F, gravimetric water content on fresh leaf mass basis; GWC D, gravimetric water content on dry leaf mass basis; EWT, equivalent water thickness; SLW, specific leaf weight Mean Range Standard Coefficient of deviation variation GWC F (g g 1 ) GWC D (g g 1 ) EWT (cm) SLW (g cm 2 ) Leaf thickness (mm) Table 2. Intercorrelation among variables in leaf data set (n = 208) For definitions of GWC F, GWC D, EWT and SLW see Table 1 Leaf thickness GWC F GWC D EWT GWC F GWC D EWT SLW

5 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 Max Mean Reflectance Min 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 GWC F, GWC D, EWT and reflectance changed with wavelength, the linear correlation coefficient, r, was calculated for all wavelengths in the range 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 or less than The results show that there was almost no significant correlation between reflectance and GWC F and GWC D (Fig. 2a, b), except for small but significant positive correlations in the NIR region ( nm for GWC F and nm for GWC D ). However, EWT was strongly correlated with

6 914 B. Datt (a ) Correlation coefficient (b ) Correlation coefficient (c ) Correlation coefficient Wavelength (nm) Fig. 2. Correlation between reflectance and (a) GWC F, (b) GWC D and (c) EWT, where GWC F and GWC D 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 = and r =

7 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 and nm wavelength regions. The regions of no significant correlation between EWT and reflectance ( and nm) corresponded to the wavelengths of maximum absorption by water. It is therefore suggested that (1) there is no correlation between reflectance in the nm range and the gravimetric water content (GWC F and GWC D ) 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.

8 916 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 ( and nm) (Hunt and Rock 1989). In the present study a narrow band MSI was calculated using single wavelength reflectances in these two regions: MSI = R 1650 /R 820, (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 ( and nm) (Elvidge and Lyon 1985), and was calculated here by using single wavelength reflectances situated in these bands: TM5/TM7 = R 1650 /R (8) Water Index (WI) The WI was calculated according to Peñuelas et al. (1997): WI = R 900 /R 970. (9) Normalised Difference Water Index (NDWI) The NDWI was calculated according to Gao (1996): NDWI = (R R 1240 )/(R R 1240 ). (10) Normalised Difference Vegetation Index (NDVI) The NDVI was calculated according to Rouse et al. (1973): NDVI = (R R 680 )/(R R 680 ). (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. R 1788 and R 2218 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

9 Remote Sensing of Water Content in Eucalyptus Leaves r = 0.37 TM5/TM7 2 Vegetation index index WI r = 0.53 NDVI r = MSI r = 0.67 r = NDWI 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 = R s + S exp(- k i C i ), (12)

10 918 B. Datt R 982 R 1188 r = r = 0.56 Reflectance R R 2218 r = 0.79 r = 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 R s is the reflectance from the leaf surface, and S is the reflectance from the leaf interior when there is no absorption ( k i C i = 0). R s 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(- k i C i ), where k i and C i are the specific absorption coefficient and content of leaf biochemical i, respectively. Equation 12 shows that the relationship between reflectance, R, and biochemical content, C i, at any wavelength may change from one leaf to another according to leaf surface and internal structure. These two factors cause an additive offset, R s, 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, R s, is negligible. Likewise, the first derivative transformations of reflectance spectra eliminate the additive R s 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.

11 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 ( k i C i = 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 ( k i C i = maximum). Allen et al. (1969, 1970) have shown that the absorption spectra of plant leaves over the 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: R 850 = R s + S, (13) R 1928 = R s + S exp(-k w(1928) EWT), (14) R 1788 = R s + S exp(-k w(1788) EWT) and (15) R 2218 = R s + S exp(-k w(2218) EWT), (16) where k w 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 (R R 1788 )/(R R 1928 ) = [1 - exp(-k w(1788) EWT)]/[1 - exp(-k w(1928) EWT)]. (17) Similarly, by using Equation 16 in place of Equation 15 in the above step we obtain: (R R 2218 )/(R R 1928 ) = [1 - exp(-k w(2218) EWT)]/[1 - exp(-k w(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 (R R 1788 )/(R R 1928 ) and (R R 2218 )/(R R 1928 ) 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 (R R 1788 )/(R R 1928 ) and r = 0.78 for (R R 2218 )/(R R 1928 )) 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 (R R 1788 )/ (R R 1928 ) and (R R 2218 )/(R R 1928 ), 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: R degraded = ar original + b, (19)

12 920 B. Datt (R 850 R 2218 )/(R 850 R 1928 ) r = r = (R 850 R 1788 )/(R 850 R 1928 ) Vegetation index EWT (cm) Fig. 5. Regression between EWT and the indices (R R 2218 )/(R R 1928 ) and (R R 1788 )/ (R R 1928 ). The best-fit lines were of the linear form y = ax + b. The correlation coefficients (r) are shown on the graphs. where R original 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 = 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 = 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 (R 1788 and R 2218 ) are weaker in the degraded spectra, but the correlations for the two new indices ((R R 1788 )/(R R 1928 ) and (R R 2218 )/(R R 1928 )) 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.

13 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 Correlation coefficient (r) Normal spectra Degraded spectra (R R 2218 )/(R R 1928 ) (R R 1788 )/(R R 1928 ) R R MSI WI NDWI TM5/TM From the linear regression of (R R 1788 )/(R R 1928 ) and (R R 2218 )/(R R 1928 ) with EWT, the following algorithm equations were developed for the remote estimation of water content: EWT (cm) = [(R R 1788 )/(R R 1928 )] and (20) EWT (cm) = [(R R 2218 )/(R R 1928 )] (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, (R R 2218 )/ (R R 1928 ) and (R R 1788 )/(R R 1928 ), 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.

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