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Spectral response of a plant canopy with different soil backgrounds
REMOTE SENSING OF ENVIRONMENT 17:37-53 (1985)
37
Spectral Response of a Plant Canopy with Different Soil Backgrounds
A. R. HUETE Department of Soils, Water and Engineering, University of Arizona, Tucson, AZ 85721
R. D. JACKSON U. S. Water Conservation Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Phoenix, AZ 85040
D. F. POST Department of Soils, Water and Engineering, University of Arizona, Tucson, AZ 85721
The spectral behavior of a cotton canopy with four soft types alternately inserted underneath was examined at various levels of vegetation density. Measured composite spectra, representing various mixtures of vegetation with different soil backgrounds, were compared with existing measures of greenness, including the NIR-red band ratios, the perpendicular vegetation index (PVI), and the greenness vegetation index (GVI). Observed spectral patterns involving constant vegetation amounts with different soil backgrounds could not be explained nor predicted by either the ratio or the orthogonal greenness measures. All greenness measures were found to be strongly dependent on soil brightness. Furthermore, soil-induced greenness changes became greater with increasing amounts of vegetation up to 60% green cover. The results presented suggests that soil and plant spectra interactively mix in a nonadditive, partly correlated manner to produce composite canopy spectra.
Introduction A major application of remote sensing involves the characterization of agricultural, range, and forest vegetation canopies through the measurement of multispectral response data. Spectral data collected over vegetative targets, however, often represent a complex mixture of individual plant, shadow, and soil background spectral contributions (Colwell, 1974; Kollenkark et al., 1982). Various vegetation indices have been developed in order to enhance the spectral contribution from green vegetation while minimizing those from soil background, sun angle, senesced vegetation, and atmosphere (Tucker, 1979; Colwell, 1974; ©Elsevier Science Publishing Co., Inc., 1985 52 Vanderbilt Ave., New York, NY 10017
Kauth and Thomas, 1976; Richardson and Wiegand, 1977; Ashley and Rea, 1975; Wiegand and Richardson, 1982). Vegetation indices or greenness measures developed thus far can be classified into two broad categories; the ratio indices and orthogonal indices. The nearinfrared to red ratio (NIR/red), normalized difference (NIR - red)/(NIR + red), and the transformed normalized difference ((NIR - r e d ) / ( N I R + red) + 0.5) 1/2 are the most common of the ratio transformations used as vegetation measures. They involve ratioing a linear combination of the NIR and red bands by another linear set of the same bands. In two-dimensional, NIR-red space, these indices are graphically displayed by lines 00344257/85/$3.30
38
of increasing slopes diverging out from the origin. Such indices have been found effective in normalizing soil background spectral variations (Colwell, 1974) and variations in irradiance conditions (Tucker, 1979). The second broad category of greenness indicators are the orthogonal transformations which include the two-dimensional perpendicular vegetation index (PVI) of Richardson and Wiegand (1977) and the four-dimensional green vegetation index (GVI) of Kauth and Thomas (1976). The soil line index (SLI) of Wiegand and Richardson (1982) and the n-dimensional GVI (Jackson, 1983; Crist, 1983; Crist and Cicone, 1984) also qualify as orthogonal indices. The orthogonal indices are distinct from the ratio indices in that lines of equal greenness do not converge at the origin, but instead remain parallel to a predefined, principal axis of soil spectral variation termed the "soil line." A greenness vector, orthogonal to the soil line, is computed to maximally include green vegetation signals while holding soil background constant. The projection of composite spectra onto this vector is subsequently used as the measure of greenness. The soil line concept has become widely accepted as a method of normalizing soil backgrotmd effects for vegetation discrimination (Miller et al., 1984; Wiegand and Richardson, 1982; Crist, 1983; Jackson, 1983). The performance of a vegetation index can be judged by the magnitude of soilinduced greenness changes caused by changes in soil background for constant vegetation amounts. Huete et al. (1984), in a study of wet and dry reflectance behavior of a wide spectral range of soil types, found significant projection of bare soil spectral deviations onto a four-band GVI. The GVI was found to be sensitive
A. R. HUETE ET AL.
to both soil type and soil moisture condition. Jackson et al. (1983) found the PVI and GVI to be sensitive to soil moisture condition on a uniform soil type. Colwell (1973) showed that dark soil canopies had the highest N I R / r e d ratios at low to intermediate percent oat covers. Malila and Gleason (1977), using simulated wheat reflectance data, found the highest GVI results to occur with light soil backgrounds at intermediate to high vegetation densities. Recently, Elvidge and Lyon (1984) examined the effectiveness of several vegetation indices in arid regions where soil variability is high. They additively mixed vegetation spectra with a series of background substrates and reported a soil brightness effect whereby darker soil substrates resulted in higher greenness values under constant vegetation amounts. This soil influence affected the ratio indices and had no effect on the PVI. There is a lack of detailed analyses concerning the limitations of these vegetation indices in their application to greenness assessment, characterization, and monitoring (Tucker, 1979). For the most part, quantitative information regarding the performance of the various greenness measures have been collected over uniform soil backgrounds with ground-based radiometers. Multispectral data collected from space- or air-borne sensors, on the other hand, will quite often include different soil types under several soil moisture conditions. In this paper we present ground-based spectral data obtained over a developing plant canopy with different soil backgrounds alternately placed underneath, thus physically guaranteeing a constant greenness amount during each experimental ran. This study allowed us to observe soil background-plant canopy
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE
spectral interactions at constant vegetation density levels while varying soil types and soil moisture conditions. The purpose of this research was to analyze soil background effects on composite plant canopy spectra and relate such influences to those predicted b y the orthogonal and ratiobased greenness indices.
Experimental Procedure Cotton ( Gossypium hirsutum L. var DPL-70) was planted in early June 1983 in 0.5-m rows oriented in a north-south direction on Avondale loam soil in Phoenix, AZ. Plant density averaged 30 p l a n t s / m e. A 6-m 2 site within the plot was selected as the target site on which a support frame was constructed to allow soil-filled trays to be readily slid into position. When the cotton plants were approximately 0.15 m tall, the support frame was adjusted to fit snugly against the stem on each side of five cotton rows. Forty soil trays, 3 cm deep, were constructed to fit against the support frame (2 × 0.5 m). Four soil types with varying spectral properties were used to fill the soil trays using 10 trays/soil. Five soil trays inserted onto the target site provided the soil background for the two middle cotton rows. For each soil type, five of the trays were kept dry at all times, while the other set of five trays were moistened during wet soil background experimental runs. Spectral readings were made using a Barnes 1multi-modular radiometer (MMR), which measured radiant flux simultan e o u s l y in s e v e n s p e c t r a l b a n d s I Trade names and company names, when included, are for the convenience of the reader and do not imply preferential endorsement of a particular product or company over others by the University of Arizona or the U. S. Department of Agriculture.
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(0.45-0.52, 0.52-0.60, 0.63-0.69, 0.760.90, 1.15-1.30, 1.55-1.75, and 2.08-2.30 /xm), with a 15 ° field of view. These wavebands are numbered 1-7, respectively. The radiometer was mounted on a rotatable boom at a height of 2.6 m over the target site. On cloudless days, measurements were made from 1030 to 1130 MST over the same canopy cover with four different groups of five soil trays alternately placed underneath. Each measured response represented an area of about 0.5 m in diameter. For each of the soil background types, four measurements were made directly over the two middle rows as well as directly over the adjacent, between-row sites. These four measurements were averaged to represent the composite spectra of that canopy. For each soil background, triplicate readings of the four measurements were made, preceded b y and followed by a spectral response reading from a 1 . 2 × 1 . 2 m barium sulfate (BaSO4) panel mounted above the canopy. This sequence of measurements required approximately 2 min each to complete. This procedure was repeated for the four soil types, after which the soils were measured again in reverse order, such that the first group of soil trays were also the last group to be used. This was carried out in order to monitor sun angle effects within the 1 h period and for adjustment of all measurements to a constant time (1100 h). Reflectance was calculated by ratioing the average of four measurements to the response from the BaSO 4 panel, the reflectance of which was adjusted for sun angle effects. The root-mean-square uncertainty from the triplicate measurements on each set of soils was fairly constant and averaged + 0.12% reflectance. Consequently, all 12 measurements from the triplicate runs were averaged to represent the composite
40
A. R. H U E T E E T AL. TABLE 1
M e a n C a n o p y C h a r a c t e r i s t i c s for C o t t o n f r o m Bare Soil to Full C o v e r
DAY OF YEAR
CREEN COVER (%)
CANOPy HEIGHT (Clll)
170 196 198 208 215 217 ~'
0 20 25 40 55 60
0 15 22 28 36 39
223 228
75 90
47 .
235 242
95 100
69 83
VERTICAL LEAF NUMBER
LE,~" AREA I N D E X (II12/Ill 2 )
0 2 2 2 3 :3 .
-.
DRY HIOMASS (kg/m 2)
0 0.5 0.7 1.0 1.5 2.~ .
4 5
.
. 3:3 3.6
WET BIOMASS (kg/in ~)
0 0.03 0.06 O. IO 0. l-t
0 0.15 0.30 0.50 0.60 __
().25
1.40
0 3O (}3:5
1.70 1.80
.
" F i e l d fertilized a f t e r readings.
canopy spectra for a given soil background. Percent green canopy covers, coinciding with experimental runs, were derived from 35 mm slides taken with a camera attached to the radiometer. Green cover was estimated to about 5% by projecting a slide onto a dot grid and counting the dots that fell onto both sunlit and shaded plant surfaces. Leaf area index and wet and dry biomass were estimated with two plants of similar height and width selected from outside the plot (Table 1). The four soils included a 1) dark, organic-rich Cloversprings loam (fine-silty, mixed Cumulic Cryoboroll), typical of m a n y agricultural softs found in the Midwest and Great Plains, 2) a brown Avondale loam (fine-loamy, mixed, hyperthermic Typic Torrifluvent), characteristic of many low organic matter agricultural soils; 3) a high iron, red Whitehouse-B sandy clay loam (fine, mixed, thermic Ustollic Haplargid), common in Arizona rangeland soils, but also similar in spectral properties to many forested and tropical soils, and 4) a bright, yellowish-brown Superstition sand (sandy, mixed, hyperthennic Typic Calciorthid), typical of many alluvial or aeolian soils. The physi-
cal characteristics of the four softs are given in Table 2. All four dry soil backgrounds were used during each experimental run except at 100% green cover where only the dark Cloversprings loam and bright Superstition sand were used. The four wet soils were measured at green cover levels of 0%, 40%, 60%, and 75%. The wet Cloversprings loam was also used under a 20% green cover.
Results The relationship between soil and plant composite red (band 3) reflectance and percent green cover for one wet soil and four dry soil backgrounds are illustrated in Fig. 1. Significant decreases in red reflectance with increasing vegetation amounts, from low to intermediate green covers, occurred over the dry Superstition, Avondale, and Whitehouse-B soil types. On the other hand, measured red reflectance remained nearly constant over the wet Cloversprings, regardless of vegetation cover. Composite red reflectance values converged to a constant value at 90% green cover, after which red reflectance increased slightly and remained independent of soil background. The influence of soil background and green cover
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE TABLE 2
Physical Characteristics of the Four Soil Background Types
Sore SzRms Cloversprings Whitehouse-B
TEX'TLrRAL
OBGA.NIC MATTEa
CLASS
(%)
(%)
(%)
DRY
MOIST
5.7 1.5
1.8 2.5 0.8 0.2
0 0 5 3
10YR3/2 2.5YR4/6 7.5YR5/4 10YR7/4
10YR2/1 2.5YR3/6 7.5YR4/4 10YR5/4
loam sandy clay loam loam sand
Avondale Superstition
0.2
SO
One can visualize and compare the observed spectral behavior of soil-vegetation mixtures with various NIR-red band vegetation indices by mapping the raw spectral data in two-dimensional NIR-red space (Fig. 3). Each solid line represents the distribution of composite spectra taken at a constant vegetation density level, but with varying soil backgrounds. Since only soil backgrounds vary, each line may be
70
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on measured near-infrared reflectance is plotted in Fig. 2. With increasing vegetation development, a fairly linear increase in reflectance was observed from bare soil to 90% green cover over each soil type. This was followed by a steep increase in NIR reflectance beyond 90% green cover, which was attributable to rapidly accumulating green biomass with only gradual lateral percent cover increases. Even at 100% green cover, NIR reflectance measurements were consistently higher over the bright Superstition sand, which was possibly due to NIR penetration of the fully covered canopy.
2w
41
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, 40
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Relationship b e t w e e n composite red reflectance and green c a n o p y cover for one wet and four dry soil backgrounds: (12) Superstition sand (dry); ( ~ ) Avondale loam (dry); (A) Whitehouse-B SCL (dry); (O) Cloversprings l o a m (dry); (@) Cloversprings loam (wet).
0
,
,
,
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NO GREEN
,
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, 80
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FIGURE 2. Relationship b e t w e e n composite nearinfrared reflectance and green canopy cover for four dry soil backgrounds: (O) Superstition sand (dry); (<~) Avondale loam (dry); (zx) Whitehouse-B SCL (dry); ((3) Cloversprings loam (dry).
42
.,\. R. H U E T E E T AL.
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F I C U R E 3. Distrfl)ution of composite spectra ill two dimensional Nil{ red space a~ a [traction of percent canopy cover. Lines of equal N I R / r e d ratios tire indicated I)y the dotted lines.
considered a "greenness line," representing a constant amount of vegetation. The greenness line with the least slope and closest to the main diagonal is the bare soil line characterizing the spectral behavior of nonvegetated plots. With increasing amounts of vegetation, the
greenness lines behave in the following manner: 1) shift upward in the positive NIR and negative red direction, 2) decrease in vector length, and 3) increase in slope. Table 3 summarizes the geometrical properties of each greenness line. The shift upward offsets the slope increases
43
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE TABLE 3 Geometric Properties of Soil-Varying Greenness Lines at Various Vegetation Density Levels in NIR-Red Waveband Space
GREEN COVER (%)
SLOPE
0 20 25 40 55 60 75 90 95 100
1.06 1.24 1.30 1.54 2.01 2.50 3.93 15.26 2.19 55.33
INTERCEPT 2.55 10.57 13.11 18.56 25.32 24.01 29.91 0.58 0.53 - 1.47
resulting in higher NIR intercepts with greater vegetation densities. The decrease in the vector length of each line approaches a point, representative of zerosoil influence. As the vector lengths approach zero, correlation coefficients become unreliable. The greenness lines predicted by the N I R / r e d vegetation index are shown with dotted lines in Fig. 3. The plotted index values simulate a logarithmic scale as the index scale becomes compressed at higher values. Since bare soils possess low ratios, large variations in soil spectral behavior resulted in low N I R / r e d ratio deviations while, at higher N I R / r e d ratios, slight deviations in composite spectra resulted in large ratio differences. Figure 3 demonstrates how observed greenness lines of constant vegetation intersect and span several N I R / r e d predicted greenness lines. The N I R / r e d ratio lines only approximated the observed greenness lines at 0% cover and green covers greater than 90%. A plot of the N I R / r e d ratios as a function of soil background reflectance for various vegetation density levels is shown in Fig. 4. Each line represents a greenness line of constant vegetation density with
LENGTH 39.56 30.12 27.83 22.40 15.57 15.31 9.40 5.21 2.12 1.66
NO. OF PoiN'rs
COBRELATION COEFFICIENT(r)
8 5 4 8 4 8 8 4 4 2
0.997 0.998 0.999 0.997 0.999 0.997 0.994 0.977 0.134 --
varying soil backgrounds. The x-axis is a relative measure of bare soft brightness in the NIR and is used only to spread the soils out with respect to soil type and moisture condition. Such a plot shows the correlation between a vegetation index and soil background and allows one to assess if there exists a soil brightness effect in the greenness measure. The boundary conditions for the N I R / red ratio were satisfied in that, at both 0% green cover and 90% and above green cover, greenness was relatively independent of soil background. At vegetation covers between 20% and 75%, however, greenness became strongly dependent upon soil background, and a definite brightness effect emerged. As soil background became less bright, the N I R / r e d ratio increased curvilinearly with the sharpest rise occurring at the darker soil end. The N I R / r e d ratio nearly doubled from the dry Superstition sand to the wet Cloversprings loam at vegetation density levels between 20% and 60% green cover. A 20%-green cover over the wet Cloversprings loam possessed the same N I R / r e d ratio as a 40%-green cover over the wet Avondale loam or a 55%-green cover over the dry Superstition sand. Even with the
44
,\. R. HUETE ET AL
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FI(,URE 4. Relationship betweu NIR/Red ratios and bare soil NIR reflevtatlct~ [,)r various vegetation density levels: ([3, II) Superstition sand; ( ~ , 0 ) Avoudale loam; (A, A) Whitehouse-B; (Q, O) Cloversprings loam. Dry and wet soils are represented by opezl a11d solid symbols, respet'tively.
relatively flat bare soil line, the N I R / r e d ratio of a bare, wet Cloversprings loam was nearly equivalent to that of a 20% green cover canopy over the dry Superstition sand. The differences in N I R / r e d ratios, caused by various soil backgrounds, increased in magnitude with in-
creasing vegetation amounts up to 60% green cover. Thus, the influence of soil background on this greenness measure was greater at higher vegetation densities (50-60% cover) than at lower vegetation densities where the relative fraction of soil showing was highest.
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE
45
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BARE SOIL NEAR-IR REFLEETANCE FIGURE 5. Relationship between the normalized difference and bare soil NIR reflectance for various vegetation density levels. See Fig. 4 caption for symbols.
Figures 5 and 6 show the normalized difference (ND) and transformed normalized difference (TND) substituted for the N I R / r e d ratio as the greenness indicator. The ND and TND are similar to the N I R / r e d ratio in that all lines of equal greenness converge at the origin in NIR-red space. However, the slopes of
the lines radiating out from the origin are transformed so that spectral deviations closer to the main diagonal are enhanced. As shown in Figs. 5 and 6, greenness lines representing low vegetation covers are now spread further apart from each other while greenness lines at higher vegetation densities are more compressed than those
46
A R. H U E T E ET AL.
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FIGURE 6. Relationship between the transformed nonn~ized difference and })are soil NIR reflectance for various vegetation density levels. See Fig, 4 caption for symbols.
in the N I R / r e d ratio plot (Fig. 4). The enhancement at lower vegetation densities, however, created greater bare soil greenness variations and the bare soil line was no longer independent of soil background. A significant brightness effect still resulted in higher greenness values with darker soil backgrounds; however, the
slopes of the ND and TND greenness lines were lower than those from the N I R / r e d ratio lines. Nevertheless, all three indices produced a greenness value for a 20%-green cover on the wet Cloversprings loam that was equivalent to that of a 55%-green cover on the dry Superstition sand.
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE
In contrast to the ratio-based greenness indices, the concept behind the PVI is quite different despite the common use of the NIR and red bands. In the PVI, the perpendicular distance of a vegetated spectral point to the soil line is the measure of greenness. Consequently, for this concept to remain valid, greenness lines of constant vegetation amounts must remain parallel to the bare soil line, and thus possess constant slopes in NIR-Red
47
space. However, as demonstrated in Fig. 3 and Table 3, the actual observed greenness lines increased in slope with higher vegetation amounts and did not parallel the bare soil line. Figure 7 demonstrates the nonparallel nature of the PVI greenness lines plotted against 2-space brightness. Bright soils possessed the highest greenness values for equal vegetation amounts, and the difference in greenness values from dark to bright soil back-
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INDEX
FIGURE 7. Relationship between the perpendicular vegetation index and the soil line index for various vegetation density levels. See Fig. 4 caption for symbols.
48
grounds became greater with increasing vegetation densities up to 60% green cover.
The overall magnitude of soil background influence on greenness appeared to be less with the PVI in comparison with the ratio indices. At worst, a 40% green cover on the dry Superstition sand had a PVI nearly the same as that of a 60% green cover on the wet Cloversprings loam. The PVI greenness measure restilted in a well-behaved bare soil line, uncorrelated with greenness, and there was a distinct contrast between the bare soil line and the 20% greenness line. The combined soil spectral and brightness effects were minimal at low vegetation densities, and thus the PVI may be particularly useful in normalizing soil background influences at vegetation levels below 30%. The previous analyses of vegetation indices only utilized N I R - r e d band combinations which were easily depicted and compared in two-dimensional space. The GVI extends this analysis into n-space, where n equals the number of bands incorporated into a partiodar vegetation index. Geometrically, the GVI behaves in a similar manner as the PVI. A soil line extending through an axis of maximum soil spectral variation is first derived, and greenness is made orthogonal to this line in a direction of maximum vegetation density. The GVI thus requires that individual greenness lines, of constant vegetation amounts, be parallel to the bare soil line. This means that unit vector coefficients of each greenness line must be identical and their slopes in all possible two-band combinations must remain invariant. Using all seven MMR-bands, Fig. 8 shows the relationship of the GVI, at dif-
,\. R. HUETE ET AL.
ferent vegetation amounts, with changes in soil background. In comparison with the PVI, the dynamic range of greenness increased from 40 units to 55 units; however, the noise presented by bare soil deviations increased more drastically. There was no longer a clear contrast between the greenness values of bare soil and 20% green cover. A brightness effect, similar to that encountered with the PVI, was evident and increased with higher vegetation densities. The brightness effect was similar in magnitude to the PVI such that a 60%-green cover on the wet Cloversprings loam resembled a 40%green cover on the dry Superstition sand. However, it is clear that the added problems due to soil variations in greenness diminished the usefulness of this sevenband GVI in relation to the two-band PVI. This does not necessarily imply that the GVI becomes less useful with the use of more bands in linear combination. However, it should be realized that the inclusion of certain bands in the GVI may enhance nonvegetative spectral components more than the spectra from the vegetated canopy. Discussion
Remote sensing of agricultural, forest, and rangelands frequently involves the measurement o f t w o o r m o r e components (vegetation, soil, atmosphere, etc.) in the presence of each other. The development and usefulness of vegetation indices is thus dependent upon the degree to which the spectral contribution of nonvegetation c o m p o n e n t s call lye isolated from measured response data. Our results indicated that the soil background influences vegetation spectral response in two ways. Soil brightness influences involved green-
49
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE
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FIGURE 8. Relationship between a seven-band green vegetation index and seven-band soil brightness for various vegetation density levels, See Fig. 4 caption for syanbols.
ness lines which were nonparallel with each other causing a particular measure of greenness to be correlated with soil background. As shown in Figs. 4-8, not only were all tested greenness measures d e p e n d e n t on soil background, but the soil brightness effect from the ratio indices was completely opposite that from the orthogonal indices. In the NIR/red, ND, and T N D vegetation indices, greenness values decreased with increasing soil
brightness under a constant amount of vegetation. When the PVI and GVI were used, greenness increased with increasing soil brightness under equivalent vegetation amounts. The second soil background influence on greenness assessment involved those soil spectral differences, aside from brightness, that cause deviations in spectra away from a greenness line. Such influences were most pronounced in the seven-band GVI and
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appear to be dependent on specific soil types. Thus, greenness is also sensitive to soil type within a given brightness range. The vegetation indices explored in this study attempt to remove soil background influences on measured canopy spectral response through simple ratios or orthogonal rotations. Both types of transformations assume that, once bare soil spectral variance is normalized, then soil influences can only decrease with vegetation development since the relative fraction of bare soil exposed is reduced. Consequently, soil background influences on canopy reflectance would approach a maximum level at low vegetation densities. Based on the data presented here, both the N I R / r e d ratio and PVI greenness measures successfully normalized bare soil spectral behavior to either a constant ratio or a one-dimensional soil line. Regardless of how bare soil spectral behavior was normalized, soil brightness influences became greater with increased vegetation densities up to 60% green covers. Spectra from a 75% green canopy cover were more sensitive to soil-background influences than spectra from either a 0% or a 20% green cover when the PVI and N I R / r e d ratio were used as greenness measures. Whereas soil brightness influences became greater with increasing canopy cover, soil spectral influences or deviations within a greenness line generally decreased with canopy development as a result of the decreasing amount of exposed soil background. In general, all the vegetation indices produced a series of nonparallel greenness lines such that each greenness measure became independent of soil background only over a limited range of vegetation density levels. The orthogonal indices exhibited minimal brightness effects at low
,\. R HUETE ET AL.
vegetation covers. The ND and TND produced soil-independent greenness lines at 75% and above green covers. In general, the observed soil-vegetation spectra were best approximated by the PVI at low vegetation densities, while, at higher densities, the N I R / r e d ratio was the most useftd index. At intermediate vegetation levels, neither greenness measure adequately described the observed behavior of canopy spectra. The nonparallel nature of the individual greenness lines suggest that soil and vegetation spectral behavior are somehow correlated and dependent upon each other. Soil background spectral response may be dependent upon the transmittance or scattering properties of the overlying canopy. Lillesaeter (1982) showed how light and dark instrument backgrounds affect leaf spectral reflectance measured spectrophotometrically. Single l e g NIR reflectance was most highly dependent on instnmlent background while the red reflectance of a single leaf was nearly independent of background. Allen and Richardson (1968) demonstrated how" NIR radiation can be transmitted through eight layers of leaves. Jackson et al. (1980) similarly reported higher NIR radiation penetration through plant canopies and showed how sunlit and shaded soil backgrounds might affect measured greenness. Transmission of NIR radiation without a corresponding penetration of red light through a plant canopy may account for the brightness effect observed in greenness with the orthogonal indices. As soil background becomes brighter, much more of the vegetationtransmitted NIR radiation is reflected upward toward the sensor in relation to the nontransmitted red light. Due to the highly selective absorption of red radia-
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE
tion by leaf chlorophyll pigments, one would expect composite red reflectance to become independent of soil background much earlier in canopy development. At 100% green cover, the average number of leaf layers in our canopy ranged from 5, over the rows, to 2.5 between rows. Thus, NIR radiation was able to penetrate through five leaf layers between the rows, thereby enabling the sensor to detect soil background influences. The orthogonal greenness indices assume simple, additive mixing of fractional amounts of soil and vegetation spectra. Consequently, differences in spectral transmission of radiant energy are ignored. In theory, one would expect the slope of a greenness line (A NIR/A red) to increase in relation to the bare soil line because of NIR radiation penetration and reflectance from brighter soils. In other words, the greenness lines would not remain parallel to the soil line, and the slopes of the greenness lines would increase with higher vegetation amounts due to a greater extent of canopy-transmitted, soil-reflected NIR spectra. This trend would continue until a canopy is nearly closed and NIR transmission is diminished by the increasing biomass from vertical canopy development. The N I R / r e d ratios predicts such increases in greenness slopes and NIR radiant flux penetration. However, the predicted changes in slope overcompensate the observed changes, and an opposite soil brightness behavior emerged, namely, darker soil backgrounds produced higher greenness measures. The apparent problem with the N I R / r e d ratio is that it does not account for fractional increases in vegetation cover as all greenness lines approach the origin in NIR-red
51
space. Such direct additive increases from the vegetation itself should result in a shifting of a greenness line to a higher NIR intercept. Thus, if a green canopy were growing over a soil that resembled a blackbody in spectral behavior (zero brightness), then, with increasing vegetation densities, the measured NIR reflectance would climb up the NIR axis. The N I R / r e d ratio has lines of equal greenness that all converge at the origin and thus predict a constant NIR reflectance of zero for any vegetation amount growing over a black soft.
Conclusions Soil background influences were found to seriously hamper the assessment and characterization of vegetated canopy covers. Both a soil brightness and a soil spectral effect were found to influence greenness measures, not only at low vegetation densities but also at canopy covers approaching 75%. Soil brightness influences were of greatest concern, because even under conditions of complete bare soil spectral normalization, soil influences became greater with increased vegetation canopy covers. Consequently, the normalization of soil background to a constant ratio or a perfect one-dimensional soil line only removed bare soil spectral influences and not the greater soil brightness influence. Furthermore, the soft brightness influence of the ratio indices behaved oppositely to that of the orthogonal indices. It thus becomes apparent that soil and vegetation spectra interactively mix to produce composite spectra and the normalization of bare soil spectral behavior is only a first step in removing soil background influences from composite canopy spectra. The removal and under-
52
k.R. HUETEET AL.
standing of all soft background influences would greatly enhance the study of the vegetated canopy component.
Jackson, R. D. (1983), Spectral indices in n-space, Remote Sens. Environ. 13:409421.
References
Jackson, R. D., Pinter, P. J. Jr., Reginato, R. J., and Idso, S. B. (1980), Hand-held radiometry, Agricultural Reviews and Manuals W-19, U. S. Dept. of Agriculture, Science and Education Admin., Oakland, CA, 66 pp.
Allen, W. A., and Richardson, A. J. (1968), Interaction of light with a plant canopy, J. Opt. Soc. Am. 58:1023 1028. Ashley, M. D., and Rea, J. (1975), Seasonal vegetation differences from ERTS imagery, Photogramm. Eng. Renugte Sens. 41:713719. Crist, E. P. (1983), The thematic mapper tasseled cap--a preliminary formulation, Proc. Symp. on Machine Processing of Remotely Sensed Data, LARS, Purdue University, West Lafayette, IN, pp. 357-364. Crist, E. P., and Cicone, R. C. (1984), Application of the tasseled cap concept to simulated thematic mapper data, Photogramm. Eng. Remote Sens. 50:343-352. Colwell, J. E. (1973), Bidirectional spectral reflectance of grass canopies for determination of above ground standing biomass, PhD dissertation, Univ. of Michigan, Ann Arbor. Colwell, J. E. (1974), Vegetation canopy reflectance, Renugte Sens. Environ. 3:17518:3. Elvidge, C. D., and Lyon, R. J. P, (1984), Influence of substrate albedo on vegetation indices employing red and near-infrared, Seminar on Remote Sensing for Geological Mapping, IUGS-Unesco Programme on Geological Applications of Remote Sensing, France, p. 11. Huete, A. R., Post, D. F., and Jackson, R. D. (1984), Soil spectral effects on 4-space vegetation discrimination, Remote Sens. Environ. 15:155-165.
Jackson, R. D., Slater, P. N., and Pinter, P. J. Jr. (1983), Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres, Remote Sen,s'. Environ. 13:187208. Kauth, R. J., and Thomas, G. S. (1976), The tasseled cap--a graphic description of the spectral-temporal development of agrictdtural crops as seen by Landsat, Proc. Symp. on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, IN, pp. 41 -51. Kollenkark, J. C., Daughtry, C. S. T., Bauer, M. E., and Housley, T. L. (1982), Effects of cultural practices on agronomic and reflectance characteristics of soybean canopies, Agron. J. 74:751 758. Lillesaeter, O. (1982), Spectral reflectance of partly transmitting leaves: laboratory measurements and mathematical modeling, Renu)te Sens. Environ. 12:247 254. Malila, W. A., and Gleason, J. M. (1977), Investigations of spectral separability of sinall grains, early season wheat detection, and mtdticrop inventory planning, ERIM Report 122700-34-F, Environmental Research Institute of Michigan, Ann Arbor, MI. Miller, G. P., Fuchs, M., Hall, M. J., Asrar, G., Kanemasu, E. T., and Johnson, D. E. (1984), Analysis of seasonal multispectral reflectances of small grains, Remote Sens. Environ. 14:153-167.
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE
Richardson, A. J., and Wiegand, C. L. (1977), Distinguishing vegetation from soil backgrotmd information, Photogramm. Eng. Remote Sens. 43:1541-1552. Tucker, C. J. (1979), Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ. 8:127150.
53
Wiegand, C. L., and Richardson, A. J. (1982), Comparisons among a new soil index and other two- and four-dimensional vegetation indices, Proc. 48th Annual Meeting, American Society of Photogrammetry, pp. 211227. Received 23 April 1984; revised 20 1une 1984.
37
Spectral Response of a Plant Canopy with Different Soil Backgrounds
A. R. HUETE Department of Soils, Water and Engineering, University of Arizona, Tucson, AZ 85721
R. D. JACKSON U. S. Water Conservation Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Phoenix, AZ 85040
D. F. POST Department of Soils, Water and Engineering, University of Arizona, Tucson, AZ 85721
The spectral behavior of a cotton canopy with four soft types alternately inserted underneath was examined at various levels of vegetation density. Measured composite spectra, representing various mixtures of vegetation with different soil backgrounds, were compared with existing measures of greenness, including the NIR-red band ratios, the perpendicular vegetation index (PVI), and the greenness vegetation index (GVI). Observed spectral patterns involving constant vegetation amounts with different soil backgrounds could not be explained nor predicted by either the ratio or the orthogonal greenness measures. All greenness measures were found to be strongly dependent on soil brightness. Furthermore, soil-induced greenness changes became greater with increasing amounts of vegetation up to 60% green cover. The results presented suggests that soil and plant spectra interactively mix in a nonadditive, partly correlated manner to produce composite canopy spectra.
Introduction A major application of remote sensing involves the characterization of agricultural, range, and forest vegetation canopies through the measurement of multispectral response data. Spectral data collected over vegetative targets, however, often represent a complex mixture of individual plant, shadow, and soil background spectral contributions (Colwell, 1974; Kollenkark et al., 1982). Various vegetation indices have been developed in order to enhance the spectral contribution from green vegetation while minimizing those from soil background, sun angle, senesced vegetation, and atmosphere (Tucker, 1979; Colwell, 1974; ©Elsevier Science Publishing Co., Inc., 1985 52 Vanderbilt Ave., New York, NY 10017
Kauth and Thomas, 1976; Richardson and Wiegand, 1977; Ashley and Rea, 1975; Wiegand and Richardson, 1982). Vegetation indices or greenness measures developed thus far can be classified into two broad categories; the ratio indices and orthogonal indices. The nearinfrared to red ratio (NIR/red), normalized difference (NIR - red)/(NIR + red), and the transformed normalized difference ((NIR - r e d ) / ( N I R + red) + 0.5) 1/2 are the most common of the ratio transformations used as vegetation measures. They involve ratioing a linear combination of the NIR and red bands by another linear set of the same bands. In two-dimensional, NIR-red space, these indices are graphically displayed by lines 00344257/85/$3.30
38
of increasing slopes diverging out from the origin. Such indices have been found effective in normalizing soil background spectral variations (Colwell, 1974) and variations in irradiance conditions (Tucker, 1979). The second broad category of greenness indicators are the orthogonal transformations which include the two-dimensional perpendicular vegetation index (PVI) of Richardson and Wiegand (1977) and the four-dimensional green vegetation index (GVI) of Kauth and Thomas (1976). The soil line index (SLI) of Wiegand and Richardson (1982) and the n-dimensional GVI (Jackson, 1983; Crist, 1983; Crist and Cicone, 1984) also qualify as orthogonal indices. The orthogonal indices are distinct from the ratio indices in that lines of equal greenness do not converge at the origin, but instead remain parallel to a predefined, principal axis of soil spectral variation termed the "soil line." A greenness vector, orthogonal to the soil line, is computed to maximally include green vegetation signals while holding soil background constant. The projection of composite spectra onto this vector is subsequently used as the measure of greenness. The soil line concept has become widely accepted as a method of normalizing soil backgrotmd effects for vegetation discrimination (Miller et al., 1984; Wiegand and Richardson, 1982; Crist, 1983; Jackson, 1983). The performance of a vegetation index can be judged by the magnitude of soilinduced greenness changes caused by changes in soil background for constant vegetation amounts. Huete et al. (1984), in a study of wet and dry reflectance behavior of a wide spectral range of soil types, found significant projection of bare soil spectral deviations onto a four-band GVI. The GVI was found to be sensitive
A. R. HUETE ET AL.
to both soil type and soil moisture condition. Jackson et al. (1983) found the PVI and GVI to be sensitive to soil moisture condition on a uniform soil type. Colwell (1973) showed that dark soil canopies had the highest N I R / r e d ratios at low to intermediate percent oat covers. Malila and Gleason (1977), using simulated wheat reflectance data, found the highest GVI results to occur with light soil backgrounds at intermediate to high vegetation densities. Recently, Elvidge and Lyon (1984) examined the effectiveness of several vegetation indices in arid regions where soil variability is high. They additively mixed vegetation spectra with a series of background substrates and reported a soil brightness effect whereby darker soil substrates resulted in higher greenness values under constant vegetation amounts. This soil influence affected the ratio indices and had no effect on the PVI. There is a lack of detailed analyses concerning the limitations of these vegetation indices in their application to greenness assessment, characterization, and monitoring (Tucker, 1979). For the most part, quantitative information regarding the performance of the various greenness measures have been collected over uniform soil backgrounds with ground-based radiometers. Multispectral data collected from space- or air-borne sensors, on the other hand, will quite often include different soil types under several soil moisture conditions. In this paper we present ground-based spectral data obtained over a developing plant canopy with different soil backgrounds alternately placed underneath, thus physically guaranteeing a constant greenness amount during each experimental ran. This study allowed us to observe soil background-plant canopy
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE
spectral interactions at constant vegetation density levels while varying soil types and soil moisture conditions. The purpose of this research was to analyze soil background effects on composite plant canopy spectra and relate such influences to those predicted b y the orthogonal and ratiobased greenness indices.
Experimental Procedure Cotton ( Gossypium hirsutum L. var DPL-70) was planted in early June 1983 in 0.5-m rows oriented in a north-south direction on Avondale loam soil in Phoenix, AZ. Plant density averaged 30 p l a n t s / m e. A 6-m 2 site within the plot was selected as the target site on which a support frame was constructed to allow soil-filled trays to be readily slid into position. When the cotton plants were approximately 0.15 m tall, the support frame was adjusted to fit snugly against the stem on each side of five cotton rows. Forty soil trays, 3 cm deep, were constructed to fit against the support frame (2 × 0.5 m). Four soil types with varying spectral properties were used to fill the soil trays using 10 trays/soil. Five soil trays inserted onto the target site provided the soil background for the two middle cotton rows. For each soil type, five of the trays were kept dry at all times, while the other set of five trays were moistened during wet soil background experimental runs. Spectral readings were made using a Barnes 1multi-modular radiometer (MMR), which measured radiant flux simultan e o u s l y in s e v e n s p e c t r a l b a n d s I Trade names and company names, when included, are for the convenience of the reader and do not imply preferential endorsement of a particular product or company over others by the University of Arizona or the U. S. Department of Agriculture.
39
(0.45-0.52, 0.52-0.60, 0.63-0.69, 0.760.90, 1.15-1.30, 1.55-1.75, and 2.08-2.30 /xm), with a 15 ° field of view. These wavebands are numbered 1-7, respectively. The radiometer was mounted on a rotatable boom at a height of 2.6 m over the target site. On cloudless days, measurements were made from 1030 to 1130 MST over the same canopy cover with four different groups of five soil trays alternately placed underneath. Each measured response represented an area of about 0.5 m in diameter. For each of the soil background types, four measurements were made directly over the two middle rows as well as directly over the adjacent, between-row sites. These four measurements were averaged to represent the composite spectra of that canopy. For each soil background, triplicate readings of the four measurements were made, preceded b y and followed by a spectral response reading from a 1 . 2 × 1 . 2 m barium sulfate (BaSO4) panel mounted above the canopy. This sequence of measurements required approximately 2 min each to complete. This procedure was repeated for the four soil types, after which the soils were measured again in reverse order, such that the first group of soil trays were also the last group to be used. This was carried out in order to monitor sun angle effects within the 1 h period and for adjustment of all measurements to a constant time (1100 h). Reflectance was calculated by ratioing the average of four measurements to the response from the BaSO 4 panel, the reflectance of which was adjusted for sun angle effects. The root-mean-square uncertainty from the triplicate measurements on each set of soils was fairly constant and averaged + 0.12% reflectance. Consequently, all 12 measurements from the triplicate runs were averaged to represent the composite
40
A. R. H U E T E E T AL. TABLE 1
M e a n C a n o p y C h a r a c t e r i s t i c s for C o t t o n f r o m Bare Soil to Full C o v e r
DAY OF YEAR
CREEN COVER (%)
CANOPy HEIGHT (Clll)
170 196 198 208 215 217 ~'
0 20 25 40 55 60
0 15 22 28 36 39
223 228
75 90
47 .
235 242
95 100
69 83
VERTICAL LEAF NUMBER
LE,~" AREA I N D E X (II12/Ill 2 )
0 2 2 2 3 :3 .
-.
DRY HIOMASS (kg/m 2)
0 0.5 0.7 1.0 1.5 2.~ .
4 5
.
. 3:3 3.6
WET BIOMASS (kg/in ~)
0 0.03 0.06 O. IO 0. l-t
0 0.15 0.30 0.50 0.60 __
().25
1.40
0 3O (}3:5
1.70 1.80
.
" F i e l d fertilized a f t e r readings.
canopy spectra for a given soil background. Percent green canopy covers, coinciding with experimental runs, were derived from 35 mm slides taken with a camera attached to the radiometer. Green cover was estimated to about 5% by projecting a slide onto a dot grid and counting the dots that fell onto both sunlit and shaded plant surfaces. Leaf area index and wet and dry biomass were estimated with two plants of similar height and width selected from outside the plot (Table 1). The four soils included a 1) dark, organic-rich Cloversprings loam (fine-silty, mixed Cumulic Cryoboroll), typical of m a n y agricultural softs found in the Midwest and Great Plains, 2) a brown Avondale loam (fine-loamy, mixed, hyperthermic Typic Torrifluvent), characteristic of many low organic matter agricultural soils; 3) a high iron, red Whitehouse-B sandy clay loam (fine, mixed, thermic Ustollic Haplargid), common in Arizona rangeland soils, but also similar in spectral properties to many forested and tropical soils, and 4) a bright, yellowish-brown Superstition sand (sandy, mixed, hyperthennic Typic Calciorthid), typical of many alluvial or aeolian soils. The physi-
cal characteristics of the four softs are given in Table 2. All four dry soil backgrounds were used during each experimental run except at 100% green cover where only the dark Cloversprings loam and bright Superstition sand were used. The four wet soils were measured at green cover levels of 0%, 40%, 60%, and 75%. The wet Cloversprings loam was also used under a 20% green cover.
Results The relationship between soil and plant composite red (band 3) reflectance and percent green cover for one wet soil and four dry soil backgrounds are illustrated in Fig. 1. Significant decreases in red reflectance with increasing vegetation amounts, from low to intermediate green covers, occurred over the dry Superstition, Avondale, and Whitehouse-B soil types. On the other hand, measured red reflectance remained nearly constant over the wet Cloversprings, regardless of vegetation cover. Composite red reflectance values converged to a constant value at 90% green cover, after which red reflectance increased slightly and remained independent of soil background. The influence of soil background and green cover
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE TABLE 2
Physical Characteristics of the Four Soil Background Types
Sore SzRms Cloversprings Whitehouse-B
TEX'TLrRAL
OBGA.NIC MATTEa
CLASS
(%)
(%)
(%)
DRY
MOIST
5.7 1.5
1.8 2.5 0.8 0.2
0 0 5 3
10YR3/2 2.5YR4/6 7.5YR5/4 10YR7/4
10YR2/1 2.5YR3/6 7.5YR4/4 10YR5/4
loam sandy clay loam loam sand
Avondale Superstition
0.2
SO
One can visualize and compare the observed spectral behavior of soil-vegetation mixtures with various NIR-red band vegetation indices by mapping the raw spectral data in two-dimensional NIR-red space (Fig. 3). Each solid line represents the distribution of composite spectra taken at a constant vegetation density level, but with varying soil backgrounds. Since only soil backgrounds vary, each line may be
70
60
I
v
80
O\iO
Ld V Z
v
V w .J b_ W rs
MUNSELLCOLOR
on
m f~ Z
Ld k.) Z
CaCO3
Z v 2~ 121 Z qZ
r~ v
00
EXTRACTABLE IRON
0.9
on measured near-infrared reflectance is plotted in Fig. 2. With increasing vegetation development, a fairly linear increase in reflectance was observed from bare soil to 90% green cover over each soil type. This was followed by a steep increase in NIR reflectance beyond 90% green cover, which was attributable to rapidly accumulating green biomass with only gradual lateral percent cover increases. Even at 100% green cover, NIR reflectance measurements were consistently higher over the bright Superstition sand, which was possibly due to NIR penetration of the fully covered canopy.
2w
41
[~,..
°\
V Ld .d b_ W r~
T
01 0
,
, 20
,
GREEN
FIGURE 1.
, 40
,
~ 80
(OVER
,
, 80
,
,°t/ 0!
100
(~)
Relationship b e t w e e n composite red reflectance and green c a n o p y cover for one wet and four dry soil backgrounds: (12) Superstition sand (dry); ( ~ ) Avondale loam (dry); (A) Whitehouse-B SCL (dry); (O) Cloversprings l o a m (dry); (@) Cloversprings loam (wet).
0
,
,
,
20
,
,
NO GREEN
,
,
80 COVER
, 80
, tO0
(~)
FIGURE 2. Relationship b e t w e e n composite nearinfrared reflectance and green canopy cover for four dry soil backgrounds: (O) Superstition sand (dry); (<~) Avondale loam (dry); (zx) Whitehouse-B SCL (dry); ((3) Cloversprings loam (dry).
42
.,\. R. H U E T E E T AL.
70 tOl~l
.
.
.
.
•
•
;u
•
•
-
•
60 r,,, M.,,,
SO W
Z kJ W _J b_ W nl
qO
90
n,, I,--t
I rl,"
20
10
eo
.%
•
!
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!
10
!
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!
30
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F I C U R E 3. Distrfl)ution of composite spectra ill two dimensional Nil{ red space a~ a [traction of percent canopy cover. Lines of equal N I R / r e d ratios tire indicated I)y the dotted lines.
considered a "greenness line," representing a constant amount of vegetation. The greenness line with the least slope and closest to the main diagonal is the bare soil line characterizing the spectral behavior of nonvegetated plots. With increasing amounts of vegetation, the
greenness lines behave in the following manner: 1) shift upward in the positive NIR and negative red direction, 2) decrease in vector length, and 3) increase in slope. Table 3 summarizes the geometrical properties of each greenness line. The shift upward offsets the slope increases
43
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE TABLE 3 Geometric Properties of Soil-Varying Greenness Lines at Various Vegetation Density Levels in NIR-Red Waveband Space
GREEN COVER (%)
SLOPE
0 20 25 40 55 60 75 90 95 100
1.06 1.24 1.30 1.54 2.01 2.50 3.93 15.26 2.19 55.33
INTERCEPT 2.55 10.57 13.11 18.56 25.32 24.01 29.91 0.58 0.53 - 1.47
resulting in higher NIR intercepts with greater vegetation densities. The decrease in the vector length of each line approaches a point, representative of zerosoil influence. As the vector lengths approach zero, correlation coefficients become unreliable. The greenness lines predicted by the N I R / r e d vegetation index are shown with dotted lines in Fig. 3. The plotted index values simulate a logarithmic scale as the index scale becomes compressed at higher values. Since bare soils possess low ratios, large variations in soil spectral behavior resulted in low N I R / r e d ratio deviations while, at higher N I R / r e d ratios, slight deviations in composite spectra resulted in large ratio differences. Figure 3 demonstrates how observed greenness lines of constant vegetation intersect and span several N I R / r e d predicted greenness lines. The N I R / r e d ratio lines only approximated the observed greenness lines at 0% cover and green covers greater than 90%. A plot of the N I R / r e d ratios as a function of soil background reflectance for various vegetation density levels is shown in Fig. 4. Each line represents a greenness line of constant vegetation density with
LENGTH 39.56 30.12 27.83 22.40 15.57 15.31 9.40 5.21 2.12 1.66
NO. OF PoiN'rs
COBRELATION COEFFICIENT(r)
8 5 4 8 4 8 8 4 4 2
0.997 0.998 0.999 0.997 0.999 0.997 0.994 0.977 0.134 --
varying soil backgrounds. The x-axis is a relative measure of bare soft brightness in the NIR and is used only to spread the soils out with respect to soil type and moisture condition. Such a plot shows the correlation between a vegetation index and soil background and allows one to assess if there exists a soil brightness effect in the greenness measure. The boundary conditions for the N I R / red ratio were satisfied in that, at both 0% green cover and 90% and above green cover, greenness was relatively independent of soil background. At vegetation covers between 20% and 75%, however, greenness became strongly dependent upon soil background, and a definite brightness effect emerged. As soil background became less bright, the N I R / r e d ratio increased curvilinearly with the sharpest rise occurring at the darker soil end. The N I R / r e d ratio nearly doubled from the dry Superstition sand to the wet Cloversprings loam at vegetation density levels between 20% and 60% green cover. A 20%-green cover over the wet Cloversprings loam possessed the same N I R / r e d ratio as a 40%-green cover over the wet Avondale loam or a 55%-green cover over the dry Superstition sand. Even with the
44
,\. R. HUETE ET AL
IB
16 9o~ o - - - - - - ~ - - - - ~ r - ~
"
°
14 0 I,--I
I-
12
c7 bJ n"
iO
n"
8
I
12:
6
4
eoJi k
~
$~A~.
2
I
0
10
I
20
B RE SOIL N E A R - I R
I
30
REFLECTANCE
q0
Cm)
FI(,URE 4. Relationship betweu NIR/Red ratios and bare soil NIR reflevtatlct~ [,)r various vegetation density levels: ([3, II) Superstition sand; ( ~ , 0 ) Avoudale loam; (A, A) Whitehouse-B; (Q, O) Cloversprings loam. Dry and wet soils are represented by opezl a11d solid symbols, respet'tively.
relatively flat bare soil line, the N I R / r e d ratio of a bare, wet Cloversprings loam was nearly equivalent to that of a 20% green cover canopy over the dry Superstition sand. The differences in N I R / r e d ratios, caused by various soil backgrounds, increased in magnitude with in-
creasing vegetation amounts up to 60% green cover. Thus, the influence of soil background on this greenness measure was greater at higher vegetation densities (50-60% cover) than at lower vegetation densities where the relative fraction of soil showing was highest.
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE
45
1.0
7r;~tl ~
--
v
0.8
0.6 I----i
0. H
0.2
0°0
i
0
10
20
B0
q0
BARE SOIL NEAR-IR REFLEETANCE FIGURE 5. Relationship between the normalized difference and bare soil NIR reflectance for various vegetation density levels. See Fig. 4 caption for symbols.
Figures 5 and 6 show the normalized difference (ND) and transformed normalized difference (TND) substituted for the N I R / r e d ratio as the greenness indicator. The ND and TND are similar to the N I R / r e d ratio in that all lines of equal greenness converge at the origin in NIR-red space. However, the slopes of
the lines radiating out from the origin are transformed so that spectral deviations closer to the main diagonal are enhanced. As shown in Figs. 5 and 6, greenness lines representing low vegetation covers are now spread further apart from each other while greenness lines at higher vegetation densities are more compressed than those
46
A R. H U E T E ET AL.
t.2
2~ I--I
t-~
1.0
0 Z dZi I.I E: rr 0 i, gO Z
0.8
0.6
0
,
,
i0
20
-
90
BARE SOIL N E A R - I R R E F L E C T A N C E
qO
($)
FIGURE 6. Relationship between the transformed nonn~ized difference and })are soil NIR reflectance for various vegetation density levels. See Fig, 4 caption for symbols.
in the N I R / r e d ratio plot (Fig. 4). The enhancement at lower vegetation densities, however, created greater bare soil greenness variations and the bare soil line was no longer independent of soil background. A significant brightness effect still resulted in higher greenness values with darker soil backgrounds; however, the
slopes of the ND and TND greenness lines were lower than those from the N I R / r e d ratio lines. Nevertheless, all three indices produced a greenness value for a 20%-green cover on the wet Cloversprings loam that was equivalent to that of a 55%-green cover on the dry Superstition sand.
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE
In contrast to the ratio-based greenness indices, the concept behind the PVI is quite different despite the common use of the NIR and red bands. In the PVI, the perpendicular distance of a vegetated spectral point to the soil line is the measure of greenness. Consequently, for this concept to remain valid, greenness lines of constant vegetation amounts must remain parallel to the bare soil line, and thus possess constant slopes in NIR-Red
47
space. However, as demonstrated in Fig. 3 and Table 3, the actual observed greenness lines increased in slope with higher vegetation amounts and did not parallel the bare soil line. Figure 7 demonstrates the nonparallel nature of the PVI greenness lines plotted against 2-space brightness. Bright soils possessed the highest greenness values for equal vegetation amounts, and the difference in greenness values from dark to bright soil back-
SO X W Z
qO
Z 0
90
W k.m W n,,
20
tO
Z W 13_ n" i,i 13_
0
-iO
0
0
. iO
.
20
SOIL
.
. 90
LINE
.
qO
SO
60
INDEX
FIGURE 7. Relationship between the perpendicular vegetation index and the soil line index for various vegetation density levels. See Fig. 4 caption for symbols.
48
grounds became greater with increasing vegetation densities up to 60% green cover.
The overall magnitude of soil background influence on greenness appeared to be less with the PVI in comparison with the ratio indices. At worst, a 40% green cover on the dry Superstition sand had a PVI nearly the same as that of a 60% green cover on the wet Cloversprings loam. The PVI greenness measure restilted in a well-behaved bare soil line, uncorrelated with greenness, and there was a distinct contrast between the bare soil line and the 20% greenness line. The combined soil spectral and brightness effects were minimal at low vegetation densities, and thus the PVI may be particularly useful in normalizing soil background influences at vegetation levels below 30%. The previous analyses of vegetation indices only utilized N I R - r e d band combinations which were easily depicted and compared in two-dimensional space. The GVI extends this analysis into n-space, where n equals the number of bands incorporated into a partiodar vegetation index. Geometrically, the GVI behaves in a similar manner as the PVI. A soil line extending through an axis of maximum soil spectral variation is first derived, and greenness is made orthogonal to this line in a direction of maximum vegetation density. The GVI thus requires that individual greenness lines, of constant vegetation amounts, be parallel to the bare soil line. This means that unit vector coefficients of each greenness line must be identical and their slopes in all possible two-band combinations must remain invariant. Using all seven MMR-bands, Fig. 8 shows the relationship of the GVI, at dif-
,\. R. HUETE ET AL.
ferent vegetation amounts, with changes in soil background. In comparison with the PVI, the dynamic range of greenness increased from 40 units to 55 units; however, the noise presented by bare soil deviations increased more drastically. There was no longer a clear contrast between the greenness values of bare soil and 20% green cover. A brightness effect, similar to that encountered with the PVI, was evident and increased with higher vegetation densities. The brightness effect was similar in magnitude to the PVI such that a 60%-green cover on the wet Cloversprings loam resembled a 40%green cover on the dry Superstition sand. However, it is clear that the added problems due to soil variations in greenness diminished the usefulness of this sevenband GVI in relation to the two-band PVI. This does not necessarily imply that the GVI becomes less useful with the use of more bands in linear combination. However, it should be realized that the inclusion of certain bands in the GVI may enhance nonvegetative spectral components more than the spectra from the vegetated canopy. Discussion
Remote sensing of agricultural, forest, and rangelands frequently involves the measurement o f t w o o r m o r e components (vegetation, soil, atmosphere, etc.) in the presence of each other. The development and usefulness of vegetation indices is thus dependent upon the degree to which the spectral contribution of nonvegetation c o m p o n e n t s call lye isolated from measured response data. Our results indicated that the soil background influences vegetation spectral response in two ways. Soil brightness influences involved green-
49
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE
60
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-i0
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7-BRND SOIL BRIGHTNESS
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FIGURE 8. Relationship between a seven-band green vegetation index and seven-band soil brightness for various vegetation density levels, See Fig. 4 caption for syanbols.
ness lines which were nonparallel with each other causing a particular measure of greenness to be correlated with soil background. As shown in Figs. 4-8, not only were all tested greenness measures d e p e n d e n t on soil background, but the soil brightness effect from the ratio indices was completely opposite that from the orthogonal indices. In the NIR/red, ND, and T N D vegetation indices, greenness values decreased with increasing soil
brightness under a constant amount of vegetation. When the PVI and GVI were used, greenness increased with increasing soil brightness under equivalent vegetation amounts. The second soil background influence on greenness assessment involved those soil spectral differences, aside from brightness, that cause deviations in spectra away from a greenness line. Such influences were most pronounced in the seven-band GVI and
50
appear to be dependent on specific soil types. Thus, greenness is also sensitive to soil type within a given brightness range. The vegetation indices explored in this study attempt to remove soil background influences on measured canopy spectral response through simple ratios or orthogonal rotations. Both types of transformations assume that, once bare soil spectral variance is normalized, then soil influences can only decrease with vegetation development since the relative fraction of bare soil exposed is reduced. Consequently, soil background influences on canopy reflectance would approach a maximum level at low vegetation densities. Based on the data presented here, both the N I R / r e d ratio and PVI greenness measures successfully normalized bare soil spectral behavior to either a constant ratio or a one-dimensional soil line. Regardless of how bare soil spectral behavior was normalized, soil brightness influences became greater with increased vegetation densities up to 60% green covers. Spectra from a 75% green canopy cover were more sensitive to soil-background influences than spectra from either a 0% or a 20% green cover when the PVI and N I R / r e d ratio were used as greenness measures. Whereas soil brightness influences became greater with increasing canopy cover, soil spectral influences or deviations within a greenness line generally decreased with canopy development as a result of the decreasing amount of exposed soil background. In general, all the vegetation indices produced a series of nonparallel greenness lines such that each greenness measure became independent of soil background only over a limited range of vegetation density levels. The orthogonal indices exhibited minimal brightness effects at low
,\. R HUETE ET AL.
vegetation covers. The ND and TND produced soil-independent greenness lines at 75% and above green covers. In general, the observed soil-vegetation spectra were best approximated by the PVI at low vegetation densities, while, at higher densities, the N I R / r e d ratio was the most useftd index. At intermediate vegetation levels, neither greenness measure adequately described the observed behavior of canopy spectra. The nonparallel nature of the individual greenness lines suggest that soil and vegetation spectral behavior are somehow correlated and dependent upon each other. Soil background spectral response may be dependent upon the transmittance or scattering properties of the overlying canopy. Lillesaeter (1982) showed how light and dark instrument backgrounds affect leaf spectral reflectance measured spectrophotometrically. Single l e g NIR reflectance was most highly dependent on instnmlent background while the red reflectance of a single leaf was nearly independent of background. Allen and Richardson (1968) demonstrated how" NIR radiation can be transmitted through eight layers of leaves. Jackson et al. (1980) similarly reported higher NIR radiation penetration through plant canopies and showed how sunlit and shaded soil backgrounds might affect measured greenness. Transmission of NIR radiation without a corresponding penetration of red light through a plant canopy may account for the brightness effect observed in greenness with the orthogonal indices. As soil background becomes brighter, much more of the vegetationtransmitted NIR radiation is reflected upward toward the sensor in relation to the nontransmitted red light. Due to the highly selective absorption of red radia-
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE
tion by leaf chlorophyll pigments, one would expect composite red reflectance to become independent of soil background much earlier in canopy development. At 100% green cover, the average number of leaf layers in our canopy ranged from 5, over the rows, to 2.5 between rows. Thus, NIR radiation was able to penetrate through five leaf layers between the rows, thereby enabling the sensor to detect soil background influences. The orthogonal greenness indices assume simple, additive mixing of fractional amounts of soil and vegetation spectra. Consequently, differences in spectral transmission of radiant energy are ignored. In theory, one would expect the slope of a greenness line (A NIR/A red) to increase in relation to the bare soil line because of NIR radiation penetration and reflectance from brighter soils. In other words, the greenness lines would not remain parallel to the soil line, and the slopes of the greenness lines would increase with higher vegetation amounts due to a greater extent of canopy-transmitted, soil-reflected NIR spectra. This trend would continue until a canopy is nearly closed and NIR transmission is diminished by the increasing biomass from vertical canopy development. The N I R / r e d ratios predicts such increases in greenness slopes and NIR radiant flux penetration. However, the predicted changes in slope overcompensate the observed changes, and an opposite soil brightness behavior emerged, namely, darker soil backgrounds produced higher greenness measures. The apparent problem with the N I R / r e d ratio is that it does not account for fractional increases in vegetation cover as all greenness lines approach the origin in NIR-red
51
space. Such direct additive increases from the vegetation itself should result in a shifting of a greenness line to a higher NIR intercept. Thus, if a green canopy were growing over a soil that resembled a blackbody in spectral behavior (zero brightness), then, with increasing vegetation densities, the measured NIR reflectance would climb up the NIR axis. The N I R / r e d ratio has lines of equal greenness that all converge at the origin and thus predict a constant NIR reflectance of zero for any vegetation amount growing over a black soft.
Conclusions Soil background influences were found to seriously hamper the assessment and characterization of vegetated canopy covers. Both a soil brightness and a soil spectral effect were found to influence greenness measures, not only at low vegetation densities but also at canopy covers approaching 75%. Soil brightness influences were of greatest concern, because even under conditions of complete bare soil spectral normalization, soil influences became greater with increased vegetation canopy covers. Consequently, the normalization of soil background to a constant ratio or a perfect one-dimensional soil line only removed bare soil spectral influences and not the greater soil brightness influence. Furthermore, the soft brightness influence of the ratio indices behaved oppositely to that of the orthogonal indices. It thus becomes apparent that soil and vegetation spectra interactively mix to produce composite spectra and the normalization of bare soil spectral behavior is only a first step in removing soil background influences from composite canopy spectra. The removal and under-
52
k.R. HUETEET AL.
standing of all soft background influences would greatly enhance the study of the vegetated canopy component.
Jackson, R. D. (1983), Spectral indices in n-space, Remote Sens. Environ. 13:409421.
References
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Jackson, R. D., Slater, P. N., and Pinter, P. J. Jr. (1983), Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres, Remote Sen,s'. Environ. 13:187208. Kauth, R. J., and Thomas, G. S. (1976), The tasseled cap--a graphic description of the spectral-temporal development of agrictdtural crops as seen by Landsat, Proc. Symp. on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, IN, pp. 41 -51. Kollenkark, J. C., Daughtry, C. S. T., Bauer, M. E., and Housley, T. L. (1982), Effects of cultural practices on agronomic and reflectance characteristics of soybean canopies, Agron. J. 74:751 758. Lillesaeter, O. (1982), Spectral reflectance of partly transmitting leaves: laboratory measurements and mathematical modeling, Renu)te Sens. Environ. 12:247 254. Malila, W. A., and Gleason, J. M. (1977), Investigations of spectral separability of sinall grains, early season wheat detection, and mtdticrop inventory planning, ERIM Report 122700-34-F, Environmental Research Institute of Michigan, Ann Arbor, MI. Miller, G. P., Fuchs, M., Hall, M. J., Asrar, G., Kanemasu, E. T., and Johnson, D. E. (1984), Analysis of seasonal multispectral reflectances of small grains, Remote Sens. Environ. 14:153-167.
SOIL BACKGROUND EFFECTS ON CANOPY REFLECTANCE
Richardson, A. J., and Wiegand, C. L. (1977), Distinguishing vegetation from soil backgrotmd information, Photogramm. Eng. Remote Sens. 43:1541-1552. Tucker, C. J. (1979), Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ. 8:127150.
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Wiegand, C. L., and Richardson, A. J. (1982), Comparisons among a new soil index and other two- and four-dimensional vegetation indices, Proc. 48th Annual Meeting, American Society of Photogrammetry, pp. 211227. Received 23 April 1984; revised 20 1une 1984.