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Relationships among the leaf area index, moisture availability, and spectral reflectance in an upland rice field
Agricultural Water Management 69 (2004) 83–100
Relationships among the leaf area index, moisture availability, and spectral reflectance in an upland rice field Reiji Kimura a,∗ , Shuhei Okada b , Hiroyuki Miura a , Makio Kamichika a a
Arid Land Research Center, Tottori University, Hamasaka 1390, Tottori 680-0001, Japan b Vision Tech Inc., Umezono 2, Tsukuba 305-0045, Japan Accepted 27 April 2004
Abstract The leaf area index (LAI) and ratio of actual evapotranspiration to potential evaporation (ma ) were estimated using spectral reflectance. Two indices, a vegetation index for LAI (VILAI) and chlorophyll concentration (VICC), were calculated using wavelengths of 550, 680, 800, and 980 nm. These indices were validated using data collected by upland rice field measurements. Indices, such as NIR/Red, NIR/Green, normalized differential vegetation index (NDVI), green normalized differential vegetation index (GNDVI), modified chlorophyll absorption in the reflectance index (MCARI), transformed chlorophyll absorption in the reflectance index (TCARI), soil-adjusted vegetation index (SAVI), optimized SAVI (OSAVI), modified SAVI (MSAVI), and VILAI, developed in this study, were statistically effective in estimating LAI. The correlation between ma and VICC was higher than that of other indices accounting for coefficients of determination (R2 ) = 0.97. This indicates that an index with four visible and near-infrared wavelengths expresses ma better than the traditional two or three wavelength indices. VICC can be considered to relate to ma through the chlorophyll concentration becoming low, reducing the photosynthetic ability and decreasing evapotranspiration. © 2004 Elsevier B.V. All rights reserved. Keywords: Chlorophyll concentration; Evapotranspiration; Potential evaporation; Vegetation index
1. Introduction The spectral reflectance for a vegetated surface is unique compared with a water surface or bare soil surface because the spectral reflectance in the visible region (400–700 nm) is ∗
Corresponding author. Tel.: +81 857 21 7031; fax: +81 857 29 6199. E-mail address: [email protected] (R. Kimura). 0378-3774/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2004.04.009
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low, that near the infrared region (700–1000 nm) is high, and that in the middle infrared region (1000–2400 nm) varies with the leaf water content (Rock et al., 1986). Many studies relating leaf area index (LAI) and chlorophyll concentration to spectral reflectance have been conducted (e.g., Gitelson and Merzlyak, 1994, 1996; Blackburn, 1998, 1999; Jago et al., 1999; Datt, 1998; Daughtry et al., 2000; Haboudane et al., 2002; Sims and Gamon, 2002). Most vegetation indices are broadly grouped into three categories (Rondeaux et al., 1996; Daughtry et al., 2000). The first category has intrinsic indices, such as the ratio of the wavelengths for near infrared to the visible (NIR/Red and NIR/Green), the normalized differential vegetation index (NDVI), chlorophyll absorption in the reflectance index (CARI; Kim, 1994), modified CARI (MCARI; Daughtry et al., 2000), and transformed CARI (TCARI; Haboudane et al., 2002). The second category has soil-line related indices, such as the perpendicular vegetation index (PVI; Richardson and Wiegand, 1977), soil-adjusted vegetation index (SAVI; Huete, 1988), optimized SAVI (OSAVI; Rondeaux et al., 1996), and modified SAVI (MSAVI; Qi et al., 1994). The third category has atmospherically adjusted indices (Rondeaux et al., 1996). The red-edge-position chlorophyll concentration relationship has been developed (e.g., Jago et al., 1999; Sims and Gamon, 2002) as another index. The red-edge represents the effective boundary between strong absorption of red radiation by chlorophyll and increased multiple scattering of radiation in near-infrared wavelengths. The intrinsic indices are often sensitive to soil background reflectance and are often difficult to interpret at low leaf-area indices (Rondeaux et al., 1996; Daughtry et al., 2000). Soil-line vegetation indices that remove the effect of soil background, such as PVI, SAVI, OSAVI, and MSAVI, have been developed as studies have progressed. The PVI is the perpendicular distance from the soil line to the vegetation point of red- versus near-infrared and is superior to the intrinsic indices since it removes the effect of the soil background. However, PVI is still significantly affected by the soil (Huete, 1988; Rondeaux et al., 1996). SAVI, OSAVI, and MSAVI were developed to reduce the soil impact. These vegetation indices using red, green, and near-infrared wavelengths relate directly to the green biomass per unit land area. These indices are thus affected in part by a changing LAI and chlorophyll concentration. For example, the indices for vegetation with yellow leaves differ from those for green leaves even with the same LAI value due to a difference in chlorophyll concentration. In fact, variations in background reflectance and LAI have confounded the detection of relatively subtle differences in canopy reflectance due to changes in the leaf chlorophyll concentration (Daughtry et al., 2000; Haboudane et al., 2002). Those studies indicated that the slope of MCARI versus OSAVI, NIR/Green versus NIR/Red, and the ratio TCARI/OSAVI correlate well with the chlorophyll content independent of LAI. In addition, Ito et al. (1996) reported that wavelengths of 550 and 980 nm are not influenced much by differing chlorophyll concentrations but vary uniformly with vegetation cover. The vegetation vigor index (VVI) was introduced to evaluate the chlorophyll concentration. VVI is calculated using the concept of PVI and wavelengths of 550, 680, 800, and 980 nm. There is a close linear relationship between the leaf chlorophyll content and leaf nitrogen content (Evans, 1983; Yoder and Pettigrew-Crosby, 1995) and between the nitrogen content and the net photosynthetic rate (Evans, 1989; Korner, 1989; Sage and Pearcy, 1987). It is also well-known that the transpiration rate corresponds well to the photosynthetic rate though the diffusion velocities for CO2 and H2 O to the surrounding environment differ (Aho et al.,
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1979). Therefore, a decrease in the chlorophyll concentration can cause a decrease in the photosynthetic capacity and, as a result, influences the evapotranspiration rate. It has been reported that vegetation indices, which include near-infrared to visible wavelengths and NDVI, tend to correspond linearly or curvilinearly with photosynthetic capacity, transpiration, and stomatal conductance (Sellers, 1985, 1987; Tucker and Sellers, 1986; Choudhury, 1987; Running and Nemani, 1988; Bartlett et al., 1990; Myneni et al., 1992; Sellers et al., 1992; Verma et al., 1993; Gamon et al., 1995; Carter, 1998). However, the relationship between the vegetation indices and moisture availability (ma = actual evapotranspiration ET/potential evaporation Ep ) is not clear. This study examines a method for estimating LAI and ma using remote sensing spectral reflectance data by comparing the estimated result with previously and newly developed indices. ma is used because the effects of a variety of physiological activities on evapotranspiration can be detected by normalization (Black, 1979). The green leaf area index is often used as GLAI or LAI. The leaf area index, including yellow and dead leaves, is defined as LAI in this study.
2. Vegetation indices 2.1. Previously defined indices Spectral vegetation indices, using the characteristic shape of the spectrum, are estimated by combining the low reflectance in the visible part with the high reflectance in the near-infrared part. The combination may be in the form of a ratio, a slope, or some other formulation. Indices can be broadly separated into three categories (Rondeaux et al., 1996; Daughtry et al., 2000): (1) intrinsic indices, (2) soil-line related indices, and (3) atmospherically adjusted indices. We focus on examples of intrinsic and soil-line vegetation indices only in this study. Representative intrinsic indices are described as follows. The simple ratio given by NIR r800 = , (1) Red r680 and r800 NIR = Green r550
(2)
is the most common. Here, r800 , r680 , and r550 are the reflectance ratios for 800, 680, and 550 nm. The intrinsic indices include the normalized differential vegetation index and green NDVI, given by r800 − r680 NDVI = , (3) r800 + r680 and Green NDVI =
r800 − r550 . r800 + r550
(4)
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The modified chlorophyll absorption in the reflectance index (Daughtry et al., 2000) and transformed CARI (Haboudane et al., 2002) are also included in this group, and are given by r700 , (5) MCARI = (r700 − r670 ) − 0.2(r700 − r550 ) r670 and
r700 . TCARI = 3 (r700 − r670 ) − 0.2(r700 − r550 ) r670
(6)
Here, r700 and r670 are the reflectance ratios for 700 and 670 nm. Rondeaux et al. (1996) indicated that these intrinsic indices are extremely sensitive to the effects of soil background. Soil-line vegetation indices that remove the effect of soil background include the perpendicular vegetation index (Richardson and Wiegand, 1977), soil adjusted vegetation index (Huete, 1988), optimized SAVI (Rondeaux et al., 1996), and modified SAVI (Qi et al., 1994). The relationship between the near-infrared and visible reflectance from bare soil is generally linear i.e., the soil line (Richardson and Wiegand, 1977); several vegetation indices have been developed using the coefficients of this relationship. PVI expresses the distance between the red and near-infrared red reflectance and the soil line. Fig. 1 diagramatically illustrates the principle of PVI. It was superior to NDVI at low vegetation densities. How-
Fig. 1. A diagram that illustrates the principle of PVI. Points A and B represent bare soil data for dry and wet conditions. Points C and D are representative of data taken over a vegetated field with low and high plant cover. The PVI is the perpendicular distance from the soil line to the point. The soil line is represented by y = ax + b.
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ever, PVI is still significantly affected by the soil (Huete, 1988; Rondeaux et al., 1996). Significant improvements were found with SAVI, defined as r800 − r680 . (7) SAVI = (1 + L) r800 + r680 + L Here, the constant L = 0.5 has been adjusted to account for first-order soil background variation (The principle of L value is described in detail by Huete (1988) and Qi et al. (1994)). Rondeaux et al. (1996) demonstrated that L is critical in the minimization of soil reflectance effects and proposed OSAVI, given by r800 − r680 OSAVI = (1 + 0.16) . (8) r800 + r680 + 0.16 The L value in Eq. (7) has been recently redefined and MSAVI has been defined as r800 − r680 MSAVI = (1 + L) , r800 + r680 + L
(9)
where L = 1 − 2a × NDVI × WDVI,
(10)
WDVI = r800 − (a × r680 ).
(11)
and
Here, a is the slope of the soil line determined by a linear regression of the reflectance ratios for 680 and 800 nm, taken over bare soil when the soil water conditions were dry to wet (see Fig. 1), and WDVI is the weighted differential vegetation index (Clevers and Verhoef, 1993). L becomes smaller as the vegetation becomes more dense i.e., L varied with the canopy cover from 0 (very dense) to 1 (very sparse). 2.2. Indices developed in this research Ito et al. (1996) reported that wavelengths of 550 and 980 nm are not significantly influenced by differing chlorophyll concentrations but vary uniformly with vegetation cover. They introduced the vegetation cover index (VCI) (Vegetation Vigor Index) to evaluate the vegetation cover (chlorophyll concentration). These indices are given by VCI = [(Xi550 − Xs )2 + (Yi980 − Ys )2 ]0.5 ,
(12)
with Xs = Ys =
a2 × Yi980 + Xi550 − a2 b2 , a22 + 1 a22 · Yi980 + a2 · Xi550 + b2 a22 + 1
,
(13)
(14)
and VVI =
PVI , VCI
(15)
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with PVI = [(Xi680 − Xs )2 + (Yi800 − Ys )2 ]0.5 ,
(16)
where Xs =
a × Yi800 + Xi680 − ab , a2 + 1
(17)
Ys =
a2 × Yi800 + a × Xi680 + b . a2 + 1
(18)
and
Here, Xi550 , Xi680 , Yi800 , and Yi980 are the respective reflectance ratios for 550, 680, 800, and 980 nm over vegetated ground with a bare soil surface background. The coefficients a and b are obtained by a linear regression of the reflectance ratios for 680 and 800 nm, while coefficients a2 and b2 are reached by a linear regression of the reflectance ratios for 550 and 980 nm taken over bare soil when the soil–water conditions were dry to wet (Fig. 1). VVI is an index for extracting only a change in the chlorophyll concentration by dividing PVI, which is influenced by both a change in the vegetation cover rate and the chlorophyll concentration, by VCI. However, PVI was significantly affected by the soil background, as indicated in Section 2.1. Therefore, it is possible that the soil background also influences VCI and VVI using the concept of PVI. We propose a vegetation index for LAI (VILAI) and chlorophyll concentration (VICC) in this study that adopts the concepts of MSAVI, VCI, and VVI. The respective indices can be expressed by VILAI = (1 + L2 )
r980 − r550 , r980 + r550 + L2
(19)
with L2 = 1 − 2a2 × NDVI2 × WDVI2 , NDVI2 =
r980 − r550 , r980 + r550
WDVI2 = r980 − (a2 × r550 ),
(20) (21) (22)
and VICC =
MSAVI . VILAI
(23)
3. Experimental method The field experiment was conducted from 21 June to 27 September 2001 in a field of upland rice (50 m × 60 m) located at the Arid Land Research Center, Tottori University in Tottori (35◦ 2 07.4 N, 134◦ 12 37.1 E, 10 m above sea level). The soil texture was sand (sand 96%, silt 1%, and clay 3%). The saturated volumetric water content and the field
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capacity were 0.43 and 0.085 m3 m−3 . Upland rice (Oryza sativaL.) was planted in rows alternately spaced at 0.45 and 0.1 m, oriented in a southeast–northwest direction. There was little precipitation during the growing period, so sprinkler irrigation was used for adequate water management. The soil water content during this period was therefore sufficiently wet for plant growth. Fig. 2 depicts the growth stages (day of year (DOY) = 164 for germination; DOY = 217 for differentiation; DOY = 234 for heading; and DOY = 283 for maturity), the seasonal changes of the plant height h, and LAI. LAI, including yellow leaves, was measured by an automatic area scanner for six quadrates (1 m × 1 m), for which the mean value was calculated and subsequently used. Plant height reached a maximum of 1 m on September 7 (DOY = 250) and decreased thereafter, and LAI increased from germination to 3.5 on August 20 (DOY = 232) and decreased thereafter. The spectral irradiance was sampled every 1 nm from 350 to 2500 nm (Analytical Spectral Device Inc., FieldSpec FR). The head of the sensor was installed about two meters above the canopy and observed a canopy range with a diameter of one meter. The same observation was conducted from four meters above the canopy. The spectral reflectance for four meters did not differ from that for two meters. Therefore, an observational height of two meters above the canopy was considered to be appropriate for this study. The spectral data were sampled three times and the averaged data were used in the analysis. We used 1 nm wide samples only in all calculations. The spectral reflectance was calculated as the reflectance based on a standard white board (Spectralon, Labsphere, North Sutton, USA) being 100%. Spectralon reflectance material is a perfectly diffuse reflecting material that is ideal for applications ranging from the UV–vis to the near-infrared to mid-infrared wavelength regions. Evapotranspiration was estimated by the Bowen ratio method. The observation elements were solar radiation, reflected solar radiation, downward longwave radiation, upward longwave radiation (EKO; MR-40), soil temperature (at depths of 0–6, 8, 10, 15, 25, 35, 50, and 70 cm) (thermocouple), soil water (at depths of 5, 15, 25, 35, 50, and 70 cm) (Delta-T Devices; ML2), wind speed (Young; Wind sentry anemometer 03101-5), and air temperature and vapor pressure (ventilated psychrometer using a thermocouple) on four elevations. Elevations of the cup anemometers and ventilated psychrometers were adjusted during the growing season. The lowest cup anemometer and ventilated psychrometer were installed 0.5 m above the canopy. The distances between the elevations of each cup anemometer and ventilated psychrometer were 0.5 and 0.15 m. The wind speed, air temperature, and vapor pressure observed on four elevations are represented by a smooth curve (wind, temperature, and vapor pressure profiles) considering the atmospheric stability, and two points on the curve are used to estimate sensible heat flux and latent heat flux by the Bowen ratio method. The estimation result is the same when any two points on the curve are used. The seasonal change of evapotranspiration ET is indicated in Fig. 2. Ep (mm day−1 ) was calculated as the potential evaporation defined by Kondo and Xu (1997a, b). Ep is the evaporation expected from an imaginary surface with a continuously saturated state of soil water content and a roughness length for momentum of 0.005 m, a roughness length for sensible heat flux of 0.0003 m, an albedo of 0.06 (water surface), and a surface emissivity of 0.98. A ground surface that satisfies these conditions can be imagined as a vast field with a wet, rough, and black surface or a newly planted paddy field with dripping wet leaves. The dataset used in the calculation includes the daily mean solar radiation, daily mean air temperature, daily mean vapor pressure, and daily mean wind
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Fig. 2. Seasonal variations of the leaf area index, plant height, evapotranspiration, potential evaporation, and moisture availability. The top panel represents the growth stage.
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speed. Data of the top elevation (air temperature, vapor pressure, and wind speed) were used for calculation. The daily mean values of the surface temperature, sensible heat flux, and latent heat flux can be calculated from the daily mean energy balance, assuming that the daily mean value of the soil heat flux is zero i.e., R↓ − εσTs 4 − H − lE = 0,
(24)
where R↓ = (1 − ref)S ↓ + εL↓ ,
(25)
H = cp ρCH U(Ts − T),
(26)
lE = lρCH U{qsat (Ts ) − q},
(27)
and CH =
k2 . ln[z/z0 ] · ln[z/zT ]
(28)
Here, ref is the surface albedo, S↓ the daily mean solar radiation (W m−2 ), and ε the surface emissivity. L↓ is the daily mean downward long-wave radiation (W m−2 ), H is the daily mean sensible heat flux (W m−2 ), cp is the specific heat of the air (J kg−1 K−1 ), and ρ is the density of the air (kg m−3 ). CH is the bulk transfer coefficient and U is the daily mean wind speed (m s−1 ). Ts is the daily mean surface temperature (K), T is the daily mean air temperature (K), lE is the daily mean latent heat flux (W m−2 ), l is the latent heat of vaporization (J kg−1 ), qsat (Ts ) is the daily mean specific humidity at saturation at Ts (kg kg−1 ), and q is the daily mean specific humidity (kg kg−1 ). k is the Von Karman
Fig. 3. Effect of the growth stage on the spectral reflectance vs. wavelength.
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Fig. 4. Relations between spectral indices and the leaf area index. The symbols indicate the observed values and the curves are the lines of best fit.
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Fig. 4. (Continued ).
constant (=0.4), z is the reference height for wind speed, z0 is the roughness length for momentum (=0.005 m), and zT is the roughness length for sensible heat flux (=0.0003 m). The evaporation rate under these conditions is considered to be the potential evaporation Ep . ET/Ep (=ma ) under rainfall conditions may have large values exceeding 1.0 by the interception, so these data were not used in the present analysis. The seasonal change of Ep is presented in Fig. 2.
4. Results and discussion Fig. 3 illustrates the variations in the spectral reflectance for wet soil and vegetated ground with a bare soil surface background. The lines denote the spectral reflectance for the growth stages of germination, differentiation, heading, and maturity. The shape of the spectral reflectance in germination closely corresponds to that of the soil. However, this changes with the growth stage.
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Fig. 4 presents the relationship between each index and LAI. We found that each index tended to increase with increases in LAI; this result was obtained by many studies (e.g., Daughtry et al., 2000; McGwire et al., 2000). The coefficients of determination (R2 ) among LAI, NIR/Red, NIR/Green, NDVI, GNDVI (Green NDVI), SAVI, OSAVI, and MSAVI were high, R2 > 0.96, while that of MCARI and TCARI exceeded 0.92. The R2 of PVI and VCI were 0.94 and 0.95 (not shown in Figure). The relationship between LAI and VILAI, which was developed in this study, is also presented in Fig. 4. Good correspondence can be seen in this and in other indices. These results indicate that these indices are statistically effective for estimating LAI. Fig. 5 illustrates a VICC and LAI range. The data labels in the figure correspond to the DOY. The circles denote VICC from germination (DOY = 164) to heading (DOY = 234), and the triangles represent heading to maturity (DOY = 283). This figure indicates that VICC tends to increase with rice growth and corresponds to the growth stage. VICC increased drastically from germination to elongation (DOY = 204), and had a gentle slope to heading. However, the relationship between VICC and LAI from heading to maturity (triangles) is different. The spectral index represents the variation of chlorophyll concentration and depends on whether the leaf is green or yellow, even with the same LAI before and after heading, as seen in Fig. 5. For example, measurements made on DOY = 207 and 270 exhibit similar LAI (approximately 1.0), though there are vast changes in the value of the spectral index. However, the indices in Fig. 4 could not detect this change in leaf color. Fig. 6 depicts the relationship among ma , NIR/Red, NIR/Green, NDVI, GNDVI, MCARI, TCARI, SAVI, OSAVI, MSAVI, and VILAI. Daughtry et al. (2000) and Haboudane et al. (2002) indicated that the slope of NIR/Green versus NIR/Red and the ratio TCARI/OSAVI correlated well with the chlorophyll content independent of LAI. However, the NIR/Green
Fig. 5. Relations between VICC and the leaf area index. The labels in the figure represent the DOY. There is a total of 12 observations from the 11 measurements, since DOY = 232 was used in both the germination to heading and heading to maturity equations.
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Fig. 6. Relations between spectral indices and ma . The symbols indicate the observed values and the lines are the best fit.
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Fig. 6. (Continued ).
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Fig. 7. Relationships among ma , MSAVI, and VICC. The labels in the figure represent the DOY. S.D. indicates the standard deviation.
versus NIR/Red combination was not implemented for predictive purposes (Haboudane et al., 2002). We substituted the ratio (NIR/Red)/(NIR/Green) for this combination in this study, as in TCARI/OSAVI. These indices are also examined in Fig. 6. The temporal extent was from DOY = 195–260, and hence, only 7 of the 11 observations in Fig. 5 are shown. The correlation among ma , SAVI, OSAVI, MSAVI, and (NIR/Red)/(NIR/Green) were stronger than those of other indices, accounting for R2 > 0.7. The R2 of PVI and VVI were 0.86 and 0.85 (not shown). Fig. 7 illustrates the relationship among ma , MSAVI, and VICC. The data labels in the figure correspond to the DOY. The correlation between ma and VICC was stronger than that of other indices, accounting for R2 = 0.97. The figure demonstrates that the accuracy of estimating evapotranspiration after the growth stage (DOY = 232, 243, 248, and 260) was improved by using VICC. The result of Figs. 6 and 7 indicates that an index with four visible and near-infrared wavelengths expresses ma better than the traditional twoor three-wavelength indices, except for TCARI/OSAVI using wavelengths of 550, 670–680, 700, and 800 nm. It has been reported over the last several decades that ma depends strongly on GLAI when the soil is sufficiently wet (e.g., Kristensen, 1974). However, ma is independent of GLAI (staying nearly constant) or gradually increases when GLAI is above a certain value (e.g., Kristensen, 1974; Maruyama et al., 2004). This relationship resembles that in Fig. 5 i.e., VICC increased when LAI < 1.0 and had a gentle slope when LAI > 1.0. This explains the linear relationship between VICC and ma . We can assume that VICC represents the chlorophyll concentration, as does the index defined by Ito et al. (1996). The value of R2 between VVI and VICC was high, R2 = 0.96. However, the actual chlorophyll concentration
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was not measured in this study. It will be necessary in the future to examine VICC in detail by comparing VICC and the chlorophyll concentration. If VICC is related to the chlorophyll concentration, then we can assume that VICC is related to ma through a process in which the chlorophyll concentration becomes low, the photosynthetic ability is reduced, and evapotranspiration then decreases. It will also be necessary in the future to examine whether VICC corresponds to the diurnal change of evapotranspiration. Those results may lead us to consider a method of combining the spectral reflectance with radiation temperature to estimate the diurnal change of evapotranspiration (e.g., Moran et al., 1994; Inoue and Moran, 1997; Bastiaanssen et al., 1998; Boegh et al., 1999; Sandholt et al., 2002), since it is directly related to thermal remotely sensed data, not to the reflective wavelengths we used.
5. Conclusions This study presents a method for estimating LAI and ma with spectral reflectance. A synopsis follows. (1) Vegetation indices for LAI and chlorophyll concentration were developed using wavelengths for 550, 680, 800, and 980 nm. (2) The coefficients of determination among LAI, NIR/Red, NIR/Green, NDVI, GNDVI, MCARI, TCARI, SAVI, OSAVI, and MSAVI were high, R2 > 0.92, and good correspondence was also observed in VILAI. These results indicate that these indices are statistically effective for estimating LAI. (3) The correlation between VICC and ma (R2 = 0.97) was stronger than that of other indices. The estimation accuracy of evapotranspiration after the growth stage was improved in VICC. An index with four visible and near-infrared wavelengths evidently expresses ma better than the traditional two- or three-wavelength indices.
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Rondeaux, G., Steven, M., Baret, F., 1996. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 55, 95–107. Running, S.W., Nemani, R.R., 1988. Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates. Remote Sens. Environ. 24, 347–367. Sage, R.F., Pearcy, R.W., 1987. The nitrogen use efficiency of C3 and C4 plants. II. Leaf nitrogen effects on the gas exchange characteristics of Chenopodium album (L.) and Amaranthus retroflexus (L.). Plant Physiol. 84, 959–963. Sandholt, I., Rasmussen, K., Andersen, J., 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sens. Environ. 79, 213–224. Sellers, P.J., 1985. Canopy reflectance photosynthesis and transpiration. Int. J. Remote Sens. 6, 1335–1372. Sellers, P.J., 1987. Canopy reflectance, photosynthesis, and transpiration. II. The role of biophysics in the linearity of their interdependence. Remote Sens. Environ. 21, 143–183. Sellers, P.J., Berry, J.A., Collatz, G.J., Field, C.B., Hall, F.G., 1992. Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sens. Environ. 42, 187–216. Sims, D.A., Gamon, J.A., 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81, 337–354. Tucker, C.J., Sellers, P.J., 1986. Satellite remote sensing of primary production. Int. J. Remote Sens. 7, 1395–1416. Verma, S.B., Sellers, P.J., Walthall, C.L., Hall, F.G., Kim, J., Goetz, S.J., 1993. Photosynthesis and stomatal conductance related to reflectance on the canopy scale. Remote Sens. Environ. 44, 103–116. Yoder, B.J., Pettigrew-Crosby, R.E., 1995. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote Sens. Environ. 53, 199–211.
Relationships among the leaf area index, moisture availability, and spectral reflectance in an upland rice field Reiji Kimura a,∗ , Shuhei Okada b , Hiroyuki Miura a , Makio Kamichika a a
Arid Land Research Center, Tottori University, Hamasaka 1390, Tottori 680-0001, Japan b Vision Tech Inc., Umezono 2, Tsukuba 305-0045, Japan Accepted 27 April 2004
Abstract The leaf area index (LAI) and ratio of actual evapotranspiration to potential evaporation (ma ) were estimated using spectral reflectance. Two indices, a vegetation index for LAI (VILAI) and chlorophyll concentration (VICC), were calculated using wavelengths of 550, 680, 800, and 980 nm. These indices were validated using data collected by upland rice field measurements. Indices, such as NIR/Red, NIR/Green, normalized differential vegetation index (NDVI), green normalized differential vegetation index (GNDVI), modified chlorophyll absorption in the reflectance index (MCARI), transformed chlorophyll absorption in the reflectance index (TCARI), soil-adjusted vegetation index (SAVI), optimized SAVI (OSAVI), modified SAVI (MSAVI), and VILAI, developed in this study, were statistically effective in estimating LAI. The correlation between ma and VICC was higher than that of other indices accounting for coefficients of determination (R2 ) = 0.97. This indicates that an index with four visible and near-infrared wavelengths expresses ma better than the traditional two or three wavelength indices. VICC can be considered to relate to ma through the chlorophyll concentration becoming low, reducing the photosynthetic ability and decreasing evapotranspiration. © 2004 Elsevier B.V. All rights reserved. Keywords: Chlorophyll concentration; Evapotranspiration; Potential evaporation; Vegetation index
1. Introduction The spectral reflectance for a vegetated surface is unique compared with a water surface or bare soil surface because the spectral reflectance in the visible region (400–700 nm) is ∗
Corresponding author. Tel.: +81 857 21 7031; fax: +81 857 29 6199. E-mail address: [email protected] (R. Kimura). 0378-3774/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2004.04.009
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low, that near the infrared region (700–1000 nm) is high, and that in the middle infrared region (1000–2400 nm) varies with the leaf water content (Rock et al., 1986). Many studies relating leaf area index (LAI) and chlorophyll concentration to spectral reflectance have been conducted (e.g., Gitelson and Merzlyak, 1994, 1996; Blackburn, 1998, 1999; Jago et al., 1999; Datt, 1998; Daughtry et al., 2000; Haboudane et al., 2002; Sims and Gamon, 2002). Most vegetation indices are broadly grouped into three categories (Rondeaux et al., 1996; Daughtry et al., 2000). The first category has intrinsic indices, such as the ratio of the wavelengths for near infrared to the visible (NIR/Red and NIR/Green), the normalized differential vegetation index (NDVI), chlorophyll absorption in the reflectance index (CARI; Kim, 1994), modified CARI (MCARI; Daughtry et al., 2000), and transformed CARI (TCARI; Haboudane et al., 2002). The second category has soil-line related indices, such as the perpendicular vegetation index (PVI; Richardson and Wiegand, 1977), soil-adjusted vegetation index (SAVI; Huete, 1988), optimized SAVI (OSAVI; Rondeaux et al., 1996), and modified SAVI (MSAVI; Qi et al., 1994). The third category has atmospherically adjusted indices (Rondeaux et al., 1996). The red-edge-position chlorophyll concentration relationship has been developed (e.g., Jago et al., 1999; Sims and Gamon, 2002) as another index. The red-edge represents the effective boundary between strong absorption of red radiation by chlorophyll and increased multiple scattering of radiation in near-infrared wavelengths. The intrinsic indices are often sensitive to soil background reflectance and are often difficult to interpret at low leaf-area indices (Rondeaux et al., 1996; Daughtry et al., 2000). Soil-line vegetation indices that remove the effect of soil background, such as PVI, SAVI, OSAVI, and MSAVI, have been developed as studies have progressed. The PVI is the perpendicular distance from the soil line to the vegetation point of red- versus near-infrared and is superior to the intrinsic indices since it removes the effect of the soil background. However, PVI is still significantly affected by the soil (Huete, 1988; Rondeaux et al., 1996). SAVI, OSAVI, and MSAVI were developed to reduce the soil impact. These vegetation indices using red, green, and near-infrared wavelengths relate directly to the green biomass per unit land area. These indices are thus affected in part by a changing LAI and chlorophyll concentration. For example, the indices for vegetation with yellow leaves differ from those for green leaves even with the same LAI value due to a difference in chlorophyll concentration. In fact, variations in background reflectance and LAI have confounded the detection of relatively subtle differences in canopy reflectance due to changes in the leaf chlorophyll concentration (Daughtry et al., 2000; Haboudane et al., 2002). Those studies indicated that the slope of MCARI versus OSAVI, NIR/Green versus NIR/Red, and the ratio TCARI/OSAVI correlate well with the chlorophyll content independent of LAI. In addition, Ito et al. (1996) reported that wavelengths of 550 and 980 nm are not influenced much by differing chlorophyll concentrations but vary uniformly with vegetation cover. The vegetation vigor index (VVI) was introduced to evaluate the chlorophyll concentration. VVI is calculated using the concept of PVI and wavelengths of 550, 680, 800, and 980 nm. There is a close linear relationship between the leaf chlorophyll content and leaf nitrogen content (Evans, 1983; Yoder and Pettigrew-Crosby, 1995) and between the nitrogen content and the net photosynthetic rate (Evans, 1989; Korner, 1989; Sage and Pearcy, 1987). It is also well-known that the transpiration rate corresponds well to the photosynthetic rate though the diffusion velocities for CO2 and H2 O to the surrounding environment differ (Aho et al.,
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1979). Therefore, a decrease in the chlorophyll concentration can cause a decrease in the photosynthetic capacity and, as a result, influences the evapotranspiration rate. It has been reported that vegetation indices, which include near-infrared to visible wavelengths and NDVI, tend to correspond linearly or curvilinearly with photosynthetic capacity, transpiration, and stomatal conductance (Sellers, 1985, 1987; Tucker and Sellers, 1986; Choudhury, 1987; Running and Nemani, 1988; Bartlett et al., 1990; Myneni et al., 1992; Sellers et al., 1992; Verma et al., 1993; Gamon et al., 1995; Carter, 1998). However, the relationship between the vegetation indices and moisture availability (ma = actual evapotranspiration ET/potential evaporation Ep ) is not clear. This study examines a method for estimating LAI and ma using remote sensing spectral reflectance data by comparing the estimated result with previously and newly developed indices. ma is used because the effects of a variety of physiological activities on evapotranspiration can be detected by normalization (Black, 1979). The green leaf area index is often used as GLAI or LAI. The leaf area index, including yellow and dead leaves, is defined as LAI in this study.
2. Vegetation indices 2.1. Previously defined indices Spectral vegetation indices, using the characteristic shape of the spectrum, are estimated by combining the low reflectance in the visible part with the high reflectance in the near-infrared part. The combination may be in the form of a ratio, a slope, or some other formulation. Indices can be broadly separated into three categories (Rondeaux et al., 1996; Daughtry et al., 2000): (1) intrinsic indices, (2) soil-line related indices, and (3) atmospherically adjusted indices. We focus on examples of intrinsic and soil-line vegetation indices only in this study. Representative intrinsic indices are described as follows. The simple ratio given by NIR r800 = , (1) Red r680 and r800 NIR = Green r550
(2)
is the most common. Here, r800 , r680 , and r550 are the reflectance ratios for 800, 680, and 550 nm. The intrinsic indices include the normalized differential vegetation index and green NDVI, given by r800 − r680 NDVI = , (3) r800 + r680 and Green NDVI =
r800 − r550 . r800 + r550
(4)
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The modified chlorophyll absorption in the reflectance index (Daughtry et al., 2000) and transformed CARI (Haboudane et al., 2002) are also included in this group, and are given by r700 , (5) MCARI = (r700 − r670 ) − 0.2(r700 − r550 ) r670 and
r700 . TCARI = 3 (r700 − r670 ) − 0.2(r700 − r550 ) r670
(6)
Here, r700 and r670 are the reflectance ratios for 700 and 670 nm. Rondeaux et al. (1996) indicated that these intrinsic indices are extremely sensitive to the effects of soil background. Soil-line vegetation indices that remove the effect of soil background include the perpendicular vegetation index (Richardson and Wiegand, 1977), soil adjusted vegetation index (Huete, 1988), optimized SAVI (Rondeaux et al., 1996), and modified SAVI (Qi et al., 1994). The relationship between the near-infrared and visible reflectance from bare soil is generally linear i.e., the soil line (Richardson and Wiegand, 1977); several vegetation indices have been developed using the coefficients of this relationship. PVI expresses the distance between the red and near-infrared red reflectance and the soil line. Fig. 1 diagramatically illustrates the principle of PVI. It was superior to NDVI at low vegetation densities. How-
Fig. 1. A diagram that illustrates the principle of PVI. Points A and B represent bare soil data for dry and wet conditions. Points C and D are representative of data taken over a vegetated field with low and high plant cover. The PVI is the perpendicular distance from the soil line to the point. The soil line is represented by y = ax + b.
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ever, PVI is still significantly affected by the soil (Huete, 1988; Rondeaux et al., 1996). Significant improvements were found with SAVI, defined as r800 − r680 . (7) SAVI = (1 + L) r800 + r680 + L Here, the constant L = 0.5 has been adjusted to account for first-order soil background variation (The principle of L value is described in detail by Huete (1988) and Qi et al. (1994)). Rondeaux et al. (1996) demonstrated that L is critical in the minimization of soil reflectance effects and proposed OSAVI, given by r800 − r680 OSAVI = (1 + 0.16) . (8) r800 + r680 + 0.16 The L value in Eq. (7) has been recently redefined and MSAVI has been defined as r800 − r680 MSAVI = (1 + L) , r800 + r680 + L
(9)
where L = 1 − 2a × NDVI × WDVI,
(10)
WDVI = r800 − (a × r680 ).
(11)
and
Here, a is the slope of the soil line determined by a linear regression of the reflectance ratios for 680 and 800 nm, taken over bare soil when the soil water conditions were dry to wet (see Fig. 1), and WDVI is the weighted differential vegetation index (Clevers and Verhoef, 1993). L becomes smaller as the vegetation becomes more dense i.e., L varied with the canopy cover from 0 (very dense) to 1 (very sparse). 2.2. Indices developed in this research Ito et al. (1996) reported that wavelengths of 550 and 980 nm are not significantly influenced by differing chlorophyll concentrations but vary uniformly with vegetation cover. They introduced the vegetation cover index (VCI) (Vegetation Vigor Index) to evaluate the vegetation cover (chlorophyll concentration). These indices are given by VCI = [(Xi550 − Xs )2 + (Yi980 − Ys )2 ]0.5 ,
(12)
with Xs = Ys =
a2 × Yi980 + Xi550 − a2 b2 , a22 + 1 a22 · Yi980 + a2 · Xi550 + b2 a22 + 1
,
(13)
(14)
and VVI =
PVI , VCI
(15)
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with PVI = [(Xi680 − Xs )2 + (Yi800 − Ys )2 ]0.5 ,
(16)
where Xs =
a × Yi800 + Xi680 − ab , a2 + 1
(17)
Ys =
a2 × Yi800 + a × Xi680 + b . a2 + 1
(18)
and
Here, Xi550 , Xi680 , Yi800 , and Yi980 are the respective reflectance ratios for 550, 680, 800, and 980 nm over vegetated ground with a bare soil surface background. The coefficients a and b are obtained by a linear regression of the reflectance ratios for 680 and 800 nm, while coefficients a2 and b2 are reached by a linear regression of the reflectance ratios for 550 and 980 nm taken over bare soil when the soil–water conditions were dry to wet (Fig. 1). VVI is an index for extracting only a change in the chlorophyll concentration by dividing PVI, which is influenced by both a change in the vegetation cover rate and the chlorophyll concentration, by VCI. However, PVI was significantly affected by the soil background, as indicated in Section 2.1. Therefore, it is possible that the soil background also influences VCI and VVI using the concept of PVI. We propose a vegetation index for LAI (VILAI) and chlorophyll concentration (VICC) in this study that adopts the concepts of MSAVI, VCI, and VVI. The respective indices can be expressed by VILAI = (1 + L2 )
r980 − r550 , r980 + r550 + L2
(19)
with L2 = 1 − 2a2 × NDVI2 × WDVI2 , NDVI2 =
r980 − r550 , r980 + r550
WDVI2 = r980 − (a2 × r550 ),
(20) (21) (22)
and VICC =
MSAVI . VILAI
(23)
3. Experimental method The field experiment was conducted from 21 June to 27 September 2001 in a field of upland rice (50 m × 60 m) located at the Arid Land Research Center, Tottori University in Tottori (35◦ 2 07.4 N, 134◦ 12 37.1 E, 10 m above sea level). The soil texture was sand (sand 96%, silt 1%, and clay 3%). The saturated volumetric water content and the field
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capacity were 0.43 and 0.085 m3 m−3 . Upland rice (Oryza sativaL.) was planted in rows alternately spaced at 0.45 and 0.1 m, oriented in a southeast–northwest direction. There was little precipitation during the growing period, so sprinkler irrigation was used for adequate water management. The soil water content during this period was therefore sufficiently wet for plant growth. Fig. 2 depicts the growth stages (day of year (DOY) = 164 for germination; DOY = 217 for differentiation; DOY = 234 for heading; and DOY = 283 for maturity), the seasonal changes of the plant height h, and LAI. LAI, including yellow leaves, was measured by an automatic area scanner for six quadrates (1 m × 1 m), for which the mean value was calculated and subsequently used. Plant height reached a maximum of 1 m on September 7 (DOY = 250) and decreased thereafter, and LAI increased from germination to 3.5 on August 20 (DOY = 232) and decreased thereafter. The spectral irradiance was sampled every 1 nm from 350 to 2500 nm (Analytical Spectral Device Inc., FieldSpec FR). The head of the sensor was installed about two meters above the canopy and observed a canopy range with a diameter of one meter. The same observation was conducted from four meters above the canopy. The spectral reflectance for four meters did not differ from that for two meters. Therefore, an observational height of two meters above the canopy was considered to be appropriate for this study. The spectral data were sampled three times and the averaged data were used in the analysis. We used 1 nm wide samples only in all calculations. The spectral reflectance was calculated as the reflectance based on a standard white board (Spectralon, Labsphere, North Sutton, USA) being 100%. Spectralon reflectance material is a perfectly diffuse reflecting material that is ideal for applications ranging from the UV–vis to the near-infrared to mid-infrared wavelength regions. Evapotranspiration was estimated by the Bowen ratio method. The observation elements were solar radiation, reflected solar radiation, downward longwave radiation, upward longwave radiation (EKO; MR-40), soil temperature (at depths of 0–6, 8, 10, 15, 25, 35, 50, and 70 cm) (thermocouple), soil water (at depths of 5, 15, 25, 35, 50, and 70 cm) (Delta-T Devices; ML2), wind speed (Young; Wind sentry anemometer 03101-5), and air temperature and vapor pressure (ventilated psychrometer using a thermocouple) on four elevations. Elevations of the cup anemometers and ventilated psychrometers were adjusted during the growing season. The lowest cup anemometer and ventilated psychrometer were installed 0.5 m above the canopy. The distances between the elevations of each cup anemometer and ventilated psychrometer were 0.5 and 0.15 m. The wind speed, air temperature, and vapor pressure observed on four elevations are represented by a smooth curve (wind, temperature, and vapor pressure profiles) considering the atmospheric stability, and two points on the curve are used to estimate sensible heat flux and latent heat flux by the Bowen ratio method. The estimation result is the same when any two points on the curve are used. The seasonal change of evapotranspiration ET is indicated in Fig. 2. Ep (mm day−1 ) was calculated as the potential evaporation defined by Kondo and Xu (1997a, b). Ep is the evaporation expected from an imaginary surface with a continuously saturated state of soil water content and a roughness length for momentum of 0.005 m, a roughness length for sensible heat flux of 0.0003 m, an albedo of 0.06 (water surface), and a surface emissivity of 0.98. A ground surface that satisfies these conditions can be imagined as a vast field with a wet, rough, and black surface or a newly planted paddy field with dripping wet leaves. The dataset used in the calculation includes the daily mean solar radiation, daily mean air temperature, daily mean vapor pressure, and daily mean wind
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Fig. 2. Seasonal variations of the leaf area index, plant height, evapotranspiration, potential evaporation, and moisture availability. The top panel represents the growth stage.
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speed. Data of the top elevation (air temperature, vapor pressure, and wind speed) were used for calculation. The daily mean values of the surface temperature, sensible heat flux, and latent heat flux can be calculated from the daily mean energy balance, assuming that the daily mean value of the soil heat flux is zero i.e., R↓ − εσTs 4 − H − lE = 0,
(24)
where R↓ = (1 − ref)S ↓ + εL↓ ,
(25)
H = cp ρCH U(Ts − T),
(26)
lE = lρCH U{qsat (Ts ) − q},
(27)
and CH =
k2 . ln[z/z0 ] · ln[z/zT ]
(28)
Here, ref is the surface albedo, S↓ the daily mean solar radiation (W m−2 ), and ε the surface emissivity. L↓ is the daily mean downward long-wave radiation (W m−2 ), H is the daily mean sensible heat flux (W m−2 ), cp is the specific heat of the air (J kg−1 K−1 ), and ρ is the density of the air (kg m−3 ). CH is the bulk transfer coefficient and U is the daily mean wind speed (m s−1 ). Ts is the daily mean surface temperature (K), T is the daily mean air temperature (K), lE is the daily mean latent heat flux (W m−2 ), l is the latent heat of vaporization (J kg−1 ), qsat (Ts ) is the daily mean specific humidity at saturation at Ts (kg kg−1 ), and q is the daily mean specific humidity (kg kg−1 ). k is the Von Karman
Fig. 3. Effect of the growth stage on the spectral reflectance vs. wavelength.
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Fig. 4. Relations between spectral indices and the leaf area index. The symbols indicate the observed values and the curves are the lines of best fit.
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Fig. 4. (Continued ).
constant (=0.4), z is the reference height for wind speed, z0 is the roughness length for momentum (=0.005 m), and zT is the roughness length for sensible heat flux (=0.0003 m). The evaporation rate under these conditions is considered to be the potential evaporation Ep . ET/Ep (=ma ) under rainfall conditions may have large values exceeding 1.0 by the interception, so these data were not used in the present analysis. The seasonal change of Ep is presented in Fig. 2.
4. Results and discussion Fig. 3 illustrates the variations in the spectral reflectance for wet soil and vegetated ground with a bare soil surface background. The lines denote the spectral reflectance for the growth stages of germination, differentiation, heading, and maturity. The shape of the spectral reflectance in germination closely corresponds to that of the soil. However, this changes with the growth stage.
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Fig. 4 presents the relationship between each index and LAI. We found that each index tended to increase with increases in LAI; this result was obtained by many studies (e.g., Daughtry et al., 2000; McGwire et al., 2000). The coefficients of determination (R2 ) among LAI, NIR/Red, NIR/Green, NDVI, GNDVI (Green NDVI), SAVI, OSAVI, and MSAVI were high, R2 > 0.96, while that of MCARI and TCARI exceeded 0.92. The R2 of PVI and VCI were 0.94 and 0.95 (not shown in Figure). The relationship between LAI and VILAI, which was developed in this study, is also presented in Fig. 4. Good correspondence can be seen in this and in other indices. These results indicate that these indices are statistically effective for estimating LAI. Fig. 5 illustrates a VICC and LAI range. The data labels in the figure correspond to the DOY. The circles denote VICC from germination (DOY = 164) to heading (DOY = 234), and the triangles represent heading to maturity (DOY = 283). This figure indicates that VICC tends to increase with rice growth and corresponds to the growth stage. VICC increased drastically from germination to elongation (DOY = 204), and had a gentle slope to heading. However, the relationship between VICC and LAI from heading to maturity (triangles) is different. The spectral index represents the variation of chlorophyll concentration and depends on whether the leaf is green or yellow, even with the same LAI before and after heading, as seen in Fig. 5. For example, measurements made on DOY = 207 and 270 exhibit similar LAI (approximately 1.0), though there are vast changes in the value of the spectral index. However, the indices in Fig. 4 could not detect this change in leaf color. Fig. 6 depicts the relationship among ma , NIR/Red, NIR/Green, NDVI, GNDVI, MCARI, TCARI, SAVI, OSAVI, MSAVI, and VILAI. Daughtry et al. (2000) and Haboudane et al. (2002) indicated that the slope of NIR/Green versus NIR/Red and the ratio TCARI/OSAVI correlated well with the chlorophyll content independent of LAI. However, the NIR/Green
Fig. 5. Relations between VICC and the leaf area index. The labels in the figure represent the DOY. There is a total of 12 observations from the 11 measurements, since DOY = 232 was used in both the germination to heading and heading to maturity equations.
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Fig. 6. Relations between spectral indices and ma . The symbols indicate the observed values and the lines are the best fit.
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Fig. 6. (Continued ).
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Fig. 7. Relationships among ma , MSAVI, and VICC. The labels in the figure represent the DOY. S.D. indicates the standard deviation.
versus NIR/Red combination was not implemented for predictive purposes (Haboudane et al., 2002). We substituted the ratio (NIR/Red)/(NIR/Green) for this combination in this study, as in TCARI/OSAVI. These indices are also examined in Fig. 6. The temporal extent was from DOY = 195–260, and hence, only 7 of the 11 observations in Fig. 5 are shown. The correlation among ma , SAVI, OSAVI, MSAVI, and (NIR/Red)/(NIR/Green) were stronger than those of other indices, accounting for R2 > 0.7. The R2 of PVI and VVI were 0.86 and 0.85 (not shown). Fig. 7 illustrates the relationship among ma , MSAVI, and VICC. The data labels in the figure correspond to the DOY. The correlation between ma and VICC was stronger than that of other indices, accounting for R2 = 0.97. The figure demonstrates that the accuracy of estimating evapotranspiration after the growth stage (DOY = 232, 243, 248, and 260) was improved by using VICC. The result of Figs. 6 and 7 indicates that an index with four visible and near-infrared wavelengths expresses ma better than the traditional twoor three-wavelength indices, except for TCARI/OSAVI using wavelengths of 550, 670–680, 700, and 800 nm. It has been reported over the last several decades that ma depends strongly on GLAI when the soil is sufficiently wet (e.g., Kristensen, 1974). However, ma is independent of GLAI (staying nearly constant) or gradually increases when GLAI is above a certain value (e.g., Kristensen, 1974; Maruyama et al., 2004). This relationship resembles that in Fig. 5 i.e., VICC increased when LAI < 1.0 and had a gentle slope when LAI > 1.0. This explains the linear relationship between VICC and ma . We can assume that VICC represents the chlorophyll concentration, as does the index defined by Ito et al. (1996). The value of R2 between VVI and VICC was high, R2 = 0.96. However, the actual chlorophyll concentration
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was not measured in this study. It will be necessary in the future to examine VICC in detail by comparing VICC and the chlorophyll concentration. If VICC is related to the chlorophyll concentration, then we can assume that VICC is related to ma through a process in which the chlorophyll concentration becomes low, the photosynthetic ability is reduced, and evapotranspiration then decreases. It will also be necessary in the future to examine whether VICC corresponds to the diurnal change of evapotranspiration. Those results may lead us to consider a method of combining the spectral reflectance with radiation temperature to estimate the diurnal change of evapotranspiration (e.g., Moran et al., 1994; Inoue and Moran, 1997; Bastiaanssen et al., 1998; Boegh et al., 1999; Sandholt et al., 2002), since it is directly related to thermal remotely sensed data, not to the reflective wavelengths we used.
5. Conclusions This study presents a method for estimating LAI and ma with spectral reflectance. A synopsis follows. (1) Vegetation indices for LAI and chlorophyll concentration were developed using wavelengths for 550, 680, 800, and 980 nm. (2) The coefficients of determination among LAI, NIR/Red, NIR/Green, NDVI, GNDVI, MCARI, TCARI, SAVI, OSAVI, and MSAVI were high, R2 > 0.92, and good correspondence was also observed in VILAI. These results indicate that these indices are statistically effective for estimating LAI. (3) The correlation between VICC and ma (R2 = 0.97) was stronger than that of other indices. The estimation accuracy of evapotranspiration after the growth stage was improved in VICC. An index with four visible and near-infrared wavelengths evidently expresses ma better than the traditional two- or three-wavelength indices.
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