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Title
Remote sensing for resources development and environmental management
Author
Damen, M. C. J.

221
sled cap - a
poral develop-
andsat.
ata, Purdue
Lve transfer
L. Opt. 21:
A factor
Df spectral
In remote
sources. Rem.
zur Optik der
-601.
stinguishing
tion.
2.
D.W. Deering,
the Great
ellite-1
aington D.C.,
J.A. Schell
nal advance-
ect) of
Final Report,
1 &
ectance
Univ.,
5 pp.
directional
. Sens. Envir.
af layers
modelling:
25-141.
nee of crop
etermined by
roc. Int.
Objects,
udies.
Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986
The application of a vegetation index in correcting
the infrared reflectance for soil background
J.G.P.W.Clevers
Dept, of Landsurveying and Remote Sensing, Wageningen Agricultural University, Netherlands
ABSTRACT: The simplified reflectance model described earlier (Clevers, 1986a) for estimating leaf area index
(LAI) is further simplified for one specific situation. In this model the infrared reflectance is corrected
for soil background and subsequently used for estimating LAI by applying the inverse of a special case of the
Mitscherlich function. In the specific situation that the reflectances of bare soil in the green, red and near-
infrared are equal, the corrected infrared reflectance is ascertained as the difference of total measured in
frared and red reflectances. This approach was confirmed by simulations with the SAIL model.
The above concept was tested at the experimental farm of the Wageningen Agricultural University, by using
reflectance factors ascertained in field trials with multispectral aerial photography. The soil type at the
experimental farm yielded nearly equal reflectances in the green, red and infrared at some moisture content.
The difference between measured infrared and red reflectances provided a satisfactory approximation of the
corrected infrared reflectance. The estimation of LAI by this corrected infrared reflectance for real data
yielded good results in this study, resulting in the ascertainment of treatment effects with larger precision
than by means of the LAI measured in the field by conventional field sampling methods.
1 INTRODUCTION
In the visible region of the electromagnetic spec
trum, vegetation absorbs much radiation and shows
a relatively low reflectance (e.g. Bunnik, 1978;
Clevers, 1986c). This is especially true in the red
region, because of the large absorption of this ra
diation by the chlorophyll in the leaves. In the
visible and near-infrared region the reflectance
and transmittance of a green leaf are approximately
equal (e.g. Goudriaan, 1977; Youkhana, 1983). For a
crop canopy this implies that in the visible region
only the reflectance of the upper layer of leaves
determines the measured reflectance of that canopy.
In the near-infrared region the spectral reflectance
of leaves is high and there is hardly any infrared
absorptance by a green leaf. In this situation
leaves or canopy layers underneath the upper layer
contribute significantly to the total measured re
flectance. This multiple reflectance indicates that
the infrared reflectance may be a suitable estimator
of LAI.
Soil reflectance has an important influence on the
relationship between infrared reflectance and LAI.
At low soil cover, soil reflectance contributes
strongly to the measured reflectance in the differ
ent spectral bands. Soil moisture content is not
constant during the growing season and differences
in soil moisture content greatly influence soil re
flectance. If a multitemporal analysis of remote
sensing data is required, a correction has to be
made for soil background when ascertaining the re
lationship between infrared reflectance and LAI.
Clevers (1986a) has described a simplified, semi-
empirical, reflectance model for estimating LAI.
First of all, soil cover was redefined as: the ver
tical projection of green vegetation, the relative
area of the shadows included, seen by a sensor poin
ting vertically downwards, relative to the total
soil area (in this definition soil cover depends on
the position of the sun). Next, the simplified re
flectance model derived by Clevers was based on the
expression of the measured reflectance as a compo
site reflectance of plants and soil: the measured
reflectance in the green, red and near-infrared
spectral bands is a linear combination of the appar
ent soil cover (new definition) and its complement,
with the reflectances of the plants and of the soil
as coefficients, respectively. For estimating LAI a
corrected infrared reflectance was calculated by
subtracting the contribution of the soil from the
measured reflectance. Combining the reflectance
measurements obtained in the green, red and infra
red spectral bands, enables one to calculate the
corrected infrared reflectance, without knowing
soil reflectances. The main assumption was that
there is a constant ratio between the reflectances
of bare soil in different bands, independent of soil
moisture content: this assumption is valid for many
soil types. Subsequently this corrected infrared re
flectance was used for estimating LAI according to
the inverse of a special case of the Mitscherlich
function. This function contains two parameters that
have to be estimated empirically. Simulations with
the SAIL model (introduced by Verhoef, 1984) con
firmed the potential of this simplified (semi-empir-
ical) reflectance model for estimating LAI.
The starting point of the study of this paper was
the simplified reflectance model for the estimation
of LAI, introduced by Clevers (1986a), using cali
brated reflectance factors. For a specific soil type
a simple vegetation index was derived for correct
ing the infrared reflectance of green vegetation for
soil background. Then the mathematical relationship
between this index and LAI, derived by Clevers, was
applied for estimating LAI. This approach was veri
fied by means of calculations with the SAIL model
and with real field data.
2 DERIVATION OF A VEGETATION INDEX
2.1 Summary of the simplified reflectance model
The simplified reflectance model derived by Clevers
(1986a) was based on the equation:
r = r v . B + r s . (1-B) (1)
r = total measured reflectance
r = reflectance of the vegetation
r V = reflectance of the soil
B S = soil cover.