Responses of Spectral Indices to Variations inVegetation Cover and Soil
Background
Stella W. Todd and
Roger M. Hoffer
Abstract
The primary objective of this
study was to evaluate the effects of variations in soil texture and moisture
upon the green vegetation index (GVI)and the normalized difference vegetation
index (NDVI) for targets with specific vegetation cover amounts and varying
soil backgrounds. The second objective was to understand the difference in
information provided between NDVI and GVI relative to estimating vegetation cover.
The third objective was to investigate the information contained within the
wetnesdbrigh tness plane in relation to soil background characteristics and
variations in percent canopy cover. Brightness and wetness were estimated using
the Tasseled Cap brightness index (BI) and wetness index (wI]. A simple
two-component model of soil and green vegetation reflectance was used to
simulate the effects of three soil texture types (sand, silt, and clay] and two
soil moisture classes on greenness, brightness, and wetness values. The results
indicated that, for the same vegetation percent cover class, targets with more
moist soil backgrounds displayed higher NDVI values than did targets with more
dry soil backgrounds. In contrast, GVI values were much less influenced by soil
background variation. WI values increased as green vegetation cover increased
for all soil backgrounds.bThe largest increase was for dry soil backgrounds. BI
values either increased or decreased as green vegetation cover increased,
depending on soil background brightness. BI and WI provided complimentary
spectral information.
Introduction
One of the most promising
applications of remote sensing imagery is for the estimation of aboveground
plant biomass and/or plant cover across multiple geographic scales. For global
or continental scales, the Advanced Very High Resolution Radiometer (AVHRR) is
currently the only realistic option for biomass estimation. Its large spatial
extent, daily temporal coverage, and low cost facilitate seasonal and yearly
estimates of biomass production. Limitations include a relatively large spatial
resolution (1 km) and few spectral reflectance channels (one visible and one
near-infrared). Biomass across large geographic areas has primarily been
estimated using the normalized difference vegetation index (NDVI), which is based
on the ratios of red and near-infrared channels (Rouseet al., 1973). The NDVI
formulation is identical for AVHRR, the Multispectral Scanner ( ~ s s ) , and
the Thematic Mapper (TM) satellite sensors, with the only variation being the
width of visible and near-infrared wavebands.
For small geographic regions,
sensors other than AVHRR can be employed for biomass estimation, which overcome
some of AVHRR'S spatial and spectral resolution limitations. The M ~ S with its
smaller spatial resolution (80 m) and increased spectral reflectance resolution
(two visible and two near-infrared) expanded the capacity of satellite sensors
to
characterize physical scene
reflectance characteristics. Vegetation and soil indices utilizing four
spectral channels were now possible. The four-band Tasseled Cap orthogonal
linear transformation, developed by Kauth and Thomas (1976),produced a plane of
data in which the primary axis aligned with soil reflectance variation
(brightness) and the secondary axis aligned with green vegetation (greenness]
variation. Virtually all of the soil variation observed is arranged in a longcigar-shaped
cloud centered along the brightness axis for M s s data.
The TM satellite further increased
the spatial (30 m) and spectral resolution (three visible, one near-infrared,
and two mid-infrared) for characterizing physical object reflectances. The
mid-inhared TM bands are coincident with the water absorption regions of the
spectrum (Hoffer, 1978). Mid-infrared band reflectances are incorporated into
the Tasseled Cap dimension of total scene reflectance (brightness). They are also
contrasted with the near-infrared bands to form the dimension of scene moisture
(wetness).In TM space, soil variation is represented by both the brightness and
wetness axes, creating a two-dimensional plane (Crist, 1983; Crist and Cicone, 1984).
The wetness axis is sensitive to both
soil moisture and vegetation moisture (Crist and Cicone, 1984).
We were interested in determining
which spectral indices would be useful for characterizing variation in green
vegetation biomass across small regions with heterogenous soil background
characteristics. We therefore focused on TM indices for this study. One of the
major problems in determining the quantity of green vegetation using satellite
sensors is that the spatial resolution of the sensors is generally larger than the
vegetation objects. This is true for TM as well as for MSS and AVHRR.
Therefore, pixel measurements represent an integration of subpixel reflectance
components of soil, vegetation, the reflectance interaction between soil and
vegetation, and shadows, all modified by atmosphere (Richardson and Weigand,
1990; Jasinski and Eagleson, 1989).
Several research results have
been reported concerning the effects of soil background on ratio-based and
orthogonal vegetation index values (Elvidge and Lyon, 1985; Gardner and Blad,
1986; Huete and Jackson, 1987; Huete et al., 1984; Huete et al., 1985; Tueller,
1987). Previous studies showed that backgrounds containing dark colored soils
and other low reflecting soils displayed higher TM ratio-based vegetation index
values ( N D ~ ) than did light colored soils or other high reflecting soils,
given the same vegetation cover (Elvidge and Lyon, 1985; Huete et al., 1985;
Huete and Jackson, 1987;Huete and Jackson, 1988; Heilman and Boyd, 1986).
Soil reflectance properties
depend on numerous soil characteristics. Field soil reflectance is reduced,
particularly in the visible portion of the spectrum, when organic matter, iron
oxides, or moisture content is high (Hoffer, 19781. The near-infrared and
mid-infrared regions of the spectrum are also affected. Soil has an easily
distinguishable characteristic reflectance pattern in the visible,
near-infrared and mid-infrared wavelengths. Soil reflectance patterns are
generally linear with increasing reflectance as wavelengths increase from
visible to mid-infrared.
The characteristic soil
reflectance pattern is easily distinguishable from green vegetation
(Bartolucci, 1977).Green vegetation reflectance is low for the visible bands
(particularly red), with a sharp increase in reflectance in the near-infrared portion
of the spectrum. Reflectance is also low in the midinfrared regions associated with
water absorption. Physical vegetation properties vary with plant species,
environmental stress, and phenology (Hoffer, 1978). Pigmentation and moisture
content change as a plant senesces. A loss of chlorophyll pigmentation produces
higher visible reflectance, particularly in the red region of the spectrum
(Hoffer, 1978). Plant drying also increases visible and mid-inbared
reflectance.
Some of the soil induced effects
on vegetation indices have been attributed to additional NIR irradiance
underneath and in-between canopies due to NIR scattering and transmission
properties of the canopy, with intermediate canopies displaying the largest
effect (Huete, 1988). Canopy scattering is small with low vegetation cover
while the soil signal is small with high vegetation cover. Soil reflects some
of the scattered and transmitted NIR flux back toward the sensor,depending upon
the soil's reflectance properties.
NIR absorbtance, transmittance,
scattering, and reflectance can be modeled based on the physical properties of plant
canopies and soil (Suits, 1972; Verhoeff and Bunnik,1981; Verhoef, 1984;
Verhoef, 1985). These models were developed for homogeneous plant canopies
assuming fixed [Suits modell or arbitrarilv [SAIL model1 inclined leaves (suits,
1972;'verhoeff and ~ u n n i k , l98i). In a comparative study, the Suits and
SAIL models were poorly to moderately correlated, respectively, with observed
reflectance measurements for homogeneous crops (Badhwar et al., 1985). Modeling
heterogeneous canopies would require even greater complexity than homogeneous
canopies.
Another approach to modeling
canopy reflectance conceptualizes clumps of vegetation as three-dimensional
geometric elements against a flat soil background (Jasinskiand Eagleson,
1989).This type of model assumes that soil and vegetation are independent
contributors to spectral reflectance. In addition, the effects of canopy
shadows can be modeled based on plant size, shape, and geometric distribution.
The simplest case is at the nadir view angle where the shadow component drops out.
The geometric model can be applied to landscapes using statistical plant distribution
patterns (Li and Strahler, 1985).
Previous studies have described
methods to minimize soil background effects given a priori knowledge about
canopy cover (Huete, 1988) or soil background characteristics (Jackson, 1983).
Canopy cover information is often unknown. Realistically, real-time soil
background reflectance information is not available. Soil moisture conditions
are dynamic and spatially variable even within a small region. Therefore, vegetation
indices which are insensitive to differing soil backgrounds are desirable for
determining vegetation biomass and/or vegetation cover on small fields or
across regions.
While a study by Huete et al.
(1985) provided some insights into the limitations of NDVI and GVI for
estimating vegetation cover, it focused only on soil brightness variation.
Within TM data, soil reflectance
characteristics are distributed in a plane defined by wetness as well as
brightness. Therefore, normalization to minimize soil related influences on
vegetation indices should be based on the more expanded soil plane information
(Huete and Tucker, 1991). Understanding the relationship of both soil type and
moisture content with brightness and wetness dimensions and their interaction
with greenness values for pixels containing both vegetation and soil components
should precede the development of soil normalization applications. This study
will investigate wetness as well as brightness characteristics in relation to
vegetation index responses to variations in green vegetation canopy cover as well
as variations in soil type and moisture content, using a simple two component
model of soil and vegetation.
Objectives
The first objective of this study
was to evaluate the effects of variations in soil texture and moisture upon the
green vegetation index (GVI) and the normalized difference vegetation index
(NDVI) for targets with specific vegetation cover amounts and varying soil
backgrounds. The second objective was to understand the difference in
information provided between NDVI and GVI relative to estimating vegetation
cover. The third objective was to investigate the information contained within
the wetness and brightness axes in relation to soil background characteristics
and variations in percent canopy cover.
Methods
A composite reflectance (soil and green
vegetation) was estimated using a simple two-component model, assuming that observations
were from nadir, the sun was near zenith, and vegetation and soil components
contributed proportionately to the total reflectance. Vegetation and soil
components were modeled as linear, non-interacting mixtures. Variation in soil tvDe
and moisture content ~roduced variations in soil reflecd 1 tance properties.
Vegetation reflectance properties were those of green healthy vegetation only.
Variations in reflectances due to vegetation senescence, soil color or organic
matter.
