a generalized soil-adjusted vegetation index

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A generalized soil-adjusted vegetation index

M.A. Gilabert *, J. González-Piqueras, F.J. Garcı́a-Haro, J. Meliá

Departament de Termodinà mica, Facultat de Fı́ sica, Universitat de Valè ncia, Dr. Moliner, 50, 46100-Burjassot, Valencia, Spain

Received 31 July 2001; received in revised form 27 March 2002; accepted 27 March 2002

Abstract

Operational monitoring of vegetative cover by remote sensing currently involves the utilisation of vegetation indices (VIs), most of them

being functions of the reflectance in red ( R) and near-infrared (NIR) spectral bands. A generalized soil-adjusted vegetation index (GESAVI),

theoretically based on a simple vegetation canopy model, is introduced. It is defined in terms of the soil line parameters ( A and B) as:GESAVI=(NIR BR A)/( R + Z ), where Z is related to the red reflectance at the cross point between the soil line and vegetation isolines. As Z is a soil adjustment coefficient, this new index can be considered as belonging to the SAVI family. In order to analyze the GESAVI

sensitivity to soil brightness and soil color, both high resolution reflectance data from two laboratory experiments and data obtained by

applying a radiosity model to simulate heterogeneous vegetation canopy scenes were used. VIs (including GESAVI, NDVI, PVI and SAVI

family indices) were computed and their correlation with LAI for the different soil backgrounds was analyzed. Results confirmed the lower

sensitivity of GESAVI to soil background in most of the cases, thus becoming a very efficient index. This good index performance results

from the fact that the isolines in the NIR- R plane are neither parallel to the soil line (as required by the PVI) nor convergent at the origin (as

required by the NDVI) but they converge somewhere between the origin and infinity in the region of negative values of both NIR and R. This

convergence point is not necessarily situated on the bisectrix, as required by other SAVI family indices.

D 2002 Published by Elsevier Science Inc.

1. Introduction

Vegetation indices (VIs) derived from satellite data are

one of the primary sources of information for operational

monitoring of the Earth’s vegetative cover. These VIs are

radiometric measures of the spatial and temporal patterns of

vegetation photosynthetic activity that are related to canopy

biophysical variables such as leaf area index (LAI), frac-

tional vegetation cover, biomass, etc. (Asrar, Kanemasu, &

Yoshida, 1985; Baret & Guyot, 1991; Gilabert, Gandı́a, &

Meliá, 1996; Richardson, Wiegand, Wajura, Dusek, &

Steiner, 1992). Most of them are called broadband VIs

because they are based on algebraic combinations of reflec-

tance in the red, R, and that in the near infrared, NIR, spectral

bands (Bannari, Morin, Bonn, & Huete, 1995; Baret, 1995;

Elvidge & Chen, 1995; LePrieur, Verstraete, & Pinty, 1994).

These algebraic combinations are designed to minimize the

effect of external influences such as solar irradiance changes

due to the atmospheric effect or variations in soil background

optical properties in the vegetation canopy spectral response.

The soil line concept is a key concept in understanding

the functionality of VIs. This is the linear relation between

the reflectances R and NIR that best fits the values measured

for bare soils with varying amount of moisture, roughness,

etc. The soil type is the main factor of variation of the soil

line, and hence a different soil line should be defined for

each soil type (Baret, Jacquemoud, & Hanocq, 1993).

However, a ‘‘global’’ soil line is often used when dealing

with large satellite scenes, being allowable in this case a

certain range of variation of the soil line parameters (i.e., the

coefficients in the linear relation between R and NIR).

Soil background is one source of variation that has

received much attention in recent years, and soil-adjusted

indices such as SAVI, TSAVI, and OSAVI have been

introduced to address this issue (see Table 1). These indices

attempt to minimize brightness-related soil effects by con-

sidering first-order soil vegetation interaction by means of a

soil-adjustment parameter ( L, X , and Y in the equations

shown in Table 1), which usually depends on the vegetation

amount and has to be empirically determined, although it

can also be measured or modeled (e.g., in MSAVI). In

particular, for the case of intermediate vegetation canopy

levels, their respective authors have suggested the values:

L = 0.5, X = 0.08, and Y = 0.16.

0034-4257/02/$ - see front matter D 2002 Published by Elsevier Science Inc.

PII: S 0 0 3 4 - 4 2 5 7 ( 0 2 ) 0 0 0 4 8 - 2

* Corresponding author. Tel.: +34-96-3983118; fax: +34-96-3983385.

E-mail address: [email protected] (M.A. Gilabert).

www.elsevier.com/locate/rse

Remote Sensing of Environment 82 (2002) 303–310


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In this work we aim at designing a new VI from a simple

canopy model that accounts for the contribution of soil and

vegetation to R and NIR, offering new insights into theexisting soil-adjustment factor. To analyze the performance

of this index, we conducted three experiments and com-

pared background effects for a variety of broadband VIs. All

these indices, including the new one, have been evaluated in

terms of their ability to accurately estimate LAI in the

presence of diverse backgrounds.

2. Theoretical basis of the GESAVI

2.1. Vegetation isolines and the SAVI-family indices

As mentioned above, the reflectances R and NIR of

bare soils distribute along the line NIR soil = A + BR. Due to

the presence of green vegetation, the measured values of

NIR increase while those of R decrease. Then, their

representation in the NIR- R plane yield points in a region

located in the upper left part of the soil line. This region is

called reflectance triangle and delimits the domain of

variability of NIR and R of a given vegetation canopy.

As an example, Fig. 1 exhibits the total spectral measure-

ments obtained in a laboratory experiment from a vegeta-

tion canopy for different LAI values and over different soil

backgrounds (these measurements are discussed later).

Lines connecting points corresponding to a similar vege-

tation amount over different soils are called vegetation

isolines. In particular, the isoline corresponding to absence

of vegetation is the soil line. In general, the vegetation

isolines can be represented rather accurately by straight

lines, but these lines are neither parallel nor convergent at

the origin. They can converge somewhere between theorigin and infinity, in the region of negative values of both

NIR and R, which seems to be a consequence of the

multiple scattering of the NIR radiation inside the vegeta-

tion canopy. But it can also occur that the isolines may not

converge at all. However, they all intersect with soil line,

at different locations. The distance between a given point

in the NIR- R plane and the soil line (measured either as

Euclidean distance or angular difference) is related to the

amount of vegetation. The slopes and intercepts of the

vegetation isolines in the R-NIR plane are related to the

optical properties of the canopy medium.

A VI performs satisfactorily when it predicts vegetation

isolines in agreement with the observations. Early indices

such as RVI and NDVI, the most widely used by far, predict

isolines convergent in the origin of the NIR- R plane. Other

indices such as PVI, which is based on the Euclidean

distance to the soil line, predict parallel isolines. Most

indices of the SAVI family (SAVI, TSAVI, and OSAVI)

predict that isolines converge in a point situated on the

bisectrix of the domain of negative values of NIR and R.

MSAVI, however, modeled the adjustment factor, recogniz-

ing that the locations do vary. As mentioned before, the

experimentally observed vegetation isolines do not match

those predicted by traditional indices, and they seem to

converge in a point situated in the domain of negative valuesof NIR and R. This point is not necessarily situated on the

bisectrix. Anyway, isolines predicted by SAVI family indi-

ces seem to better reproduce the experimental behaviour of

vegetation isolines, and this is the reason of the better

performance of these last VIs.

Table 1

Vegetation indices analyzed in this work. The soil line is NIR soil = A + BR

Vegetation index Reference

RVI ¼ NIR R

Pearson & Miller,

1972

NDVI ¼ NIR R NIR þ R

Rouse, Haas, Schell,Deering, & Harlan,

1974

PVI ¼ NIR BR A ffiffiffiffiffiffiffiffiffiffiffiffiffiffi1 þ B2

p Richardson &Wiegand, 1977

SAVI ¼ NIR R NIR þ R þ L ð1 þ LÞ

Huete, 1988

TSAVI ¼ Bð NIR BR AÞ R þ Bð NIR AÞ þ X ð1 þ B2Þ

Baret & Guyot, 1991

MSAVI

¼2NIR þ 1

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffið2NIR þ 1Þ2 8ð NIR RÞ

q 2

Qi, Chehbouni,

Huete, Kerr, &

Sorooshian, 1994

OSAVI ¼ NIR R NIR þ R þ Y

Rondeaux, Steven,

& Baret, 1996

Fig. 1. NIR reflectance versus red reflectance for the Quercus canopy, as

described in Section 3 (Experiment I). Different symbols are used for

different LAI values, which are also indicated in the graph. The straight line

corresponding to LAI = 0 (bare soils) is the soil line for this particular data

set. Similar isolines were found in the other experiments.

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2.2. Designing the GESAVI

A vegetation canopy scatters and transmits a significant

amount of NIR radiation towards the soil surface, irradiating

the soil underneath as well as in between individual plants.

A fraction of the NIR radiation incident on the soil, fraction

that is determined by the optical properties of the soilsurface, is reflected back towards the sensor and scattered

and transmitted by the canopy. By contrast, red light is

strongly absorbed by the uppermost leaf layers of the

canopy, and irradiance at the soil surface is limited to that

received direct ly from the sun and sky through canopy gaps

(Huete, 1988).

Since the variance of reflectance appears to be mainly

associated with the fractions of illuminated and viewed soil

and plant components, it is reasonable to assume that the

reflectances R and NIR of a vegetation canopy vary propor-

tionally to a soil parameter s and a vegetation parameter v .

Reflectance in the red region increases with s and decreases

with v , while the near-infrared reflectance increases propor-

tionally to both s and v . The reflectance NIR incorporates

also a nonlinear (interaction) term related to multiple scat-

tering. Thus, it can be written that

R ¼ a þ bs cv ð1aÞ

NIR ¼ d þ es þ fv þ gsv ð1bÞ

where a, b, c, d , e, f , and g are unknown constants. To reduce

the number of constants, the soil line NIR soil = A + BR can be

used as a reference. Since v = 0 for this line, elimination of

parameter s between Eqs. (1a) and (1b) yields

A ¼ d eab

ð2aÞ

B ¼ eb

ð2bÞ

The vegetation isolines (i.e., lines with constant v )

predicted by this model are given by

NIR ¼ A Vþ B V R; v constant ð3Þ

with

A V ¼ ðd þ fv Þ ðe þ gv Þða cv Þb

ð4aÞ

B V ¼ e þ gv b

ð4bÞ

These vegetation isolines are linear but not parallel to the

soil line (except for the trivial case g = 0) since B V p B.

They intercept the soil line at a cross point with a red

reflectance given by

Rcross ¼ A V A B V

B ¼ bf þ ec ag þ cgv

g


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interpretation: GESAVI is proportional to tan(h), which

adopts a similar value for all the points situated over the

same vegetation isoline (for example, 1 and 1 V), independ-

ently of soil background optical properties.

The isolines predicted by this index are neither parallel

(as the ones derived from PVI) nor convergent at the origin

(like NDVI or RVI). As shown in Eq. (5), the vegetationisolines intercept the soil line at different cross points

depending on the vegetation amount. This can also be

observed in Fig. 2. In general, Z decreases at increasing

the vegetation cover. However, in this paper variations in

Rcross are considered negligible for simplicity. This fact

allows us to consider that all the isolines are convergent

in a point, usually situated in the third quadrant, but not

necessarily on the bisectrix of the third quadrant, unlike the

SAVI. This hypothesis may be more limited for dense

canopies. Future refinements are being investigated to

parameterize Z as a function of the canopy condition.

3. Materials and methods

To analyze the sensitivity of GESAVI to soil brightness

and soil color, two high-resolution reflectance data sets (I

and II) from two laboratory experiments (Garcı́a-Haro,

Gilabert, & Meliá, 1996; González-Piqueras, 1998) and a

third data set (III), obtained by applying a radiosity model to

simulate heterogeneous vegetation canopy scenes (Garcı́a-

Haro, Gilabert, & Meliá, 1999), were used. VIs (including

GESAVI, RVI, NDVI, PVI, and other SAVI-family VIs)

were computed and their performance was investigated

based on their correlation with LAI in the presence of diverse soil backgrounds.

In Experiment I, a set of 21 plots was designed, consist-

ing of seven varying amounts of vegetation (Quercus ilex

rotundifolia) over three different soil backgrounds. Cano-

pies with different vegetation amounts (LAI compressed

between 0 and 2.4) were obtained by uniformly inserting

Quercus plants of about 25-cm height in finely spaced holes

distributed over boxes of dimensions 30 50 cm. Theoriginal soil background was mainly composed of red clay

conglomerate. Changing soil brightness effect was obtained

by covering the soil with two varying levels of coal (16 and

40 g m 2, respectively). Coal was proved to have a low

reflectance in all wavelengths and therefore it reduces the

soil brightness. Similarly, in Experiment II, different vege-

tation canopies of Pinus pinea with LAI between 0 and 2.14

were obtained over three soil backgrounds (phyllite, red

clay, and marl) with a clearly different spectral response. In

this case, a previous series of reflectance measurements was

carried out to better estimate the soil line. It consisted of

changing arbitrarily the moisture content of the three soils

considered in the Experiment II.

As mentioned before, in these two laboratory experi-

ments, the biophysical parameter selected to characterize the

vegetation canopies was the LAI. LAI measurements were

t aken by means of a LICOR-2000 LAI canopy analyzer

(Welles & Norman, 1991), which provides an indirect

procedure to estimate LAI based on the attenuation of the

diffuse hemispherical sky radiation in the ultraviolet region

through the canopy. The standard deviation of the data was

below 0.1 in all the cases.

Concerning the reflectance data, the mean and standarddeviation reflectance spectrum (from 400 to 2500 nm)

were obtained for each plot using a GER SIRIS spectror-

adiometer. Standard deviations were found to be less than

1% for wavelengths from 400 to 900 nm. Beyond 900 nm,

the standard deviations rose continuously due to random

instrumental noise. The reflectances for the red and near-

infrared bands for Landsat Thematic Mapper (TM3 and

TM4, respectively) were derived by convolving the high-

spectral-resolution data with the relative response cur ves

(Markham & Barker, 1985) for these two TM bands. Fig.

1 shows the data set corresponding to Experiment I.

Different symbols have been used to represent plots with

different LAI values, which has been indicated in the

graph.

In Experiment III, simulation modelling of canopy BRF

was applied to obtain different scenes with varying soil

background and vegetation architectural characteristics by

means of a radiosity model (Garcı́a-Haro et al., 1999)

developed for heterogeneous canopies. These canopies are

approximated by an arbitrary configuration of plants or

clumps of vegetation, placed on the ground surface in a

prescribed manner. Plants are treated as porous cylinders

formed by aggregations of layers of leaves. This model

explicitly computes the radiation leaving each individual

surface, taking into account multiple scattering processes between leaves and soil, and occlusion of neighbouring

plants. In the actual case, R and NIR reflectance scenes

corresponding to 2-m shrub canopies with varying LAI

values (from 0 to 2.35) over three different soil backgrounds

(phyllite, red clay, and marl) were generated.

In Experiments I and II, the maximum LAI value

corresponds to dense canopies with a 100% fractional cover,

whereas in Experiment III the maximum LAI value corre-

sponds to a canopy with a fractional cover about 50%. Thus,

the reflectance distribution for this last experiment does not

reach the upper side of the reflectance triangle, indicating

that at maximum LAI levels the canopy reflectance is still

seriously affected by soil optical properties (González-

Piqueras, 1998).

4. Results

In all cases, the soil line equation has been obtained (see

Table 2) in addition to its cross point with each vegetation

isoline. An average value of Z u Rcross was found for theExperiment III data set, which resulted to be very close to

0.35. This value translated into low GESAVI errors for a

range of canopy densities, primarily sparse and intermediate



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(González-Piqueras, 1998). The choice of parameter Z

appears to be critical in minimizing the soil background

effect. However, we considered an identical Z value for data

sets I and II, in order to test the reliability of GESAVI as

computed without empirical adjustment of Z to the specific

conditions of each particular experiment, thus enabling its

direct comparison with other general indices.

With R and NIR reflectance and the soil-adjustment

coefficient values ( L = 0.5, X = 0.08, Y = 0.16, Z = 0.35) all

the vegetation indices shown in Table 1 and the GESAVI

were calculated and next they were represented versus LAI.

As an example, Fig. 3 shows NDVI, SAVI, and GESAVI as

a function of the LAI, for the reflectance data obtained in thethree experiments. Each symbol corresponds to a different

soil background. The quantitative comparison of GESAVI

with other common indices (e.g., improved versions of

SAVI) is presented later.

It can be seen that, in the LAI interval considered, the

three vegetation indices increase with LAI, although the first

one shows a more pronounced exponential behaviour pre-

senting a plateau after a threshold value depending on the

reflectance data set. This saturating effect of NDVI has been

widely reported in the literature. The SAVI and the GESAVI

show a more linear variation with LAI. As shown, plots with

identical canopy cover present different NDVI values for the

three soil backgrounds. There seems to be a systematic

tendency to produce larger VI values for darker soils than

for lighter ones as demonstrated by Bausch (1993) anddiscussed by Garcı́a-Haro et al. (1996). On the contrary,

SAVI and GESAVI values seem to be less affected by soil-

brightness variations and, thus, VI values obtained for a

given canopy cover are rather the same, independently of

soil background. As expected (Huete, 1988), soil influences

are prevalent in partially vegetated canopies (in the actual

case, they are more significant in the range from LAI = 0.5

to LAI = 1.5). From an operational point of view, this

reveals a more efficiency of SAVI and GESAVI to normal-

Table 2

Soil line equation for each experiment

Experiment I NIR = 0.009+ 1.34 R (r 2 = 0.99)Experiment II NIR =0.010 +1.17 R (r 2 = 0.98)

Experiment III NIR = 0.011 + 1.16 R (r 2 = 0.99)

Fig. 3. NDVI, SAVI, and GESAVI values versus LAI obtained in the three experiments for the three soil backgrounds. Experiment I: Symbols (.), ( ), and (o)refer to plots with 0, 16, and 40 g m 2 coal cover, respectively. Experiments II and III: Symbols (.), ( ), and (o) refer to plots with phyllite, red clay, andmarl background, respectively.

Table 3

Value of the signal-to-noise ratio for all the vegetation indices considered

and for the three data sets (Experiments I, II, and III)

Vegetation index Experiment I Experiment II Experiment III

RVI 0.012 0.040 0.025

NDVI 0.013 0.040 0.029

PVI 0.016 0.100 0.040

SAVI 0.037 0.083 0.077TSAVI 0.025 0.111 0.048

MSAVI 0.023 0.067 0.083

OSAVI 0.027 0.125 0.050

GESAVI 0.045 0.200 0.071

Outstanding values appear in bold face.

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Fig. 4. Efficiency of the different VIs as measured by means of T (%) (see text) as a function of LAI for the three data sets.



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ize the soil effect and consequently, a major applicability in

areas presenting sparse vegetation and high lithological

variability. Although not shown for brevity improved ver-

sion of SAVI, such like OSAVI and MSAVI, showed a

similarly good behavior.

To establish a quantitative comparison among the effi-

ciency to normalize soil spectral contribution of all the VIsconsidered in the study, two parameters that take into

account the VI dispersion for each LAI level as due to soil

background and their global ranges of variation were

considered. They will both result to be independent of the

range of variation of each VI.

The first one was defined by Leprieur et al. (1994) based

on the concept of signal-to-noise ratio (S/N). They propose

that the ‘‘signal’’ of interest is given by the difference

between the average index value for the maximum LAI

canopy and the average index value for the minimum LAI

canopy. The measurement of ‘‘noise’’ is taken by the area

between the maximum and minimum curves (i.e., the

product of the range of variation of the index due to changes

in soil spectral properties by the interval of LAI for which

this range is valid). As we are interested in selecting an

index as sensitive as possible to vegetation amount but also

as insensitive as possible to the soil optical properties, the

signal-to-noise ratio should increase in proportion to VI

efficiency. The first criterion to evaluate the efficiency of a

VI is then based on

S ðVIÞ N ðVIÞ ¼

VIðLAImaxÞ VIðLAIminÞZ LAImaxLAImin ½

maxVI

ðLAI

Þ minVI

ðLAI

Þd

ðLAI

Þ ð8Þ

Results obtained for the three data sets considered in the

present study are shown in Table 3. The three best VIs for

each case appear in bold face.

It can be seen that, in all the cases, the new index

(GESAVI) presents one of the highest signal-to-noise ratios.

Some other indices such as SAVI and OSAVI also present

high values and, thus, can be considered as belonging to the

most efficient VI group. Traditional indices (RVI and

NDVI) present low values for the signal-to-noise ratio,

indicating their worse performance to retrieve biophysical

characteristics from the vegetation canopies independently

of the soil background.

Results confirmed the lower sensitivity of GESAVI to

soil background in most of the cases, thus becoming the

most efficient index. This good index performance results

from the fact that the isolines in the NIR- R plane are neither

parallel to the soil line (as required by the PVI) nor

convergent at the origin (as required by the NDVI) but they

converge somewhere between the origin and infinity where

the NIR and R negative values are located. This conver-

gence point is not necessarily situated on the bisectrix, as

required by the SAVI family indices.

The second criterion to evaluate the VIs is based on an

LAI dependent parameter (Gilabert et al., 1998), which is

defined as

T VIðLAIÞ ¼ rLAIr̄ 100 ð9Þ

where rLAI refers to the standard deviation for the VI valuescorresponding to given value of LAI and r

¯refers to the

standard deviation of the VI values taking into account the

whole range of variation of LAI. Conversely, this parameter

diminishes as the VI efficiency increases. By using this

procedure, we have obtained the T (%) values as a function

of the LAI for the three data sets of the work. Fig. 4 shows

the values obtained for the three experiments.

It is observed again that RVI and NDVI hardly normalize

soil effects, presenting the maximum T values for inter-

mediate canopies, where the contribution of soil background

is prevalent and multiple scattering effects are more pro-

nounced. For dense canopies (LAI maximum) all the

vegetation indices present the lowest T value since soil

contribution is then less important. As previously shown,

the GESAVI is the index that better normalizes the soil-

induced effects and presents, for the overall range of LAI,

the lowest values for T .

In general, taking into account the data from the three

experiments, it can be observed that the new generation of

indices (SAVI family indices) present a better performance

than the traditional ones. Although the OSAVI shows in

most of the cases a good performance, the new index

defined in this work (GESAVI) presents even a better

efficiency to normalize soil background effects.

5. Discussion

The vegetation index introduced in this work needs a

prior knowledge of the soil line parameters and the soil-

adjustment factor to be suitable for operational monitoring

of vegetation from remotely sensed data. Several studies

(Baret et al., 1993) have shown that a universal soil line

cannot be defined and, therefore, this straight line has to be

determined for each case. A remaining question to be solved

in further studies concerns the sensitivity of GESAVI to the

soil line determination. In fact, a poor knowledge of the soil

line would affect the detection of low amounts of vegeta-

tion.

On the other hand, regarding the soil-adjustment term, a

single value ( Z = 0.35) appears to be optimal to normalize

soil effects. The value of this coefficient was selected to

minimize the variation of canopy reflectance with soil

background for intermediate LAI levels. Nevertheless, its

determination is a crucial point in the performance of

GESAVI, and some more reflectance data sets would be

required to establish a general optimal value of Z . In this

context, different analysis conducted very recently on real

and simulated satellite data have indicated that Z = 0.35 is

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well suited for a range of canopy densities, even though this

value could be less appropriate for very dense canopies. The

variation of Z with LAI has not been still quantitatively

established to recommend the best correction term for

different LAI ranges of the vegetation canopies. However,

the more common situation in a remote sensing context

involves no prior knowledge of LAI. The limitations of GESAVI thus need to be analyzed over several vegetation

canopies. A further study concerning different vegetation

canopies and a wider range of densities would be needed to

ensure GESAVI applicability on a global basis. In that

context, some research is now being carried out to param-

eterize the soil adjustment coefficient Z in terms of infor-

mation contained in the image, to operationally calculate the

GESAVI from satellite.

Acknowledgements

This work was partially supported by the MEDALUS III

Project funded by the European Communities (ENV4-

CT95-0019) and by the CICYT (AMB97-1000-C02-02 and

REN2000-1507-C03-02 GLO).

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a generalized soil-adjusted vegetation index

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