219
! through the
(l-exp(-k.t))
die relation-
¡ctance and
ìpirical
>e used:
(14)
le for the
of extinction
;ers are
Finally the
SOIL COVER (X) SAIL MO p EL SOIL COVER (X) SAIL MODEL
SAIL MODEL
SAIL MODEL
W
(15)
>f the
)EL
isented earlier
i with the
¡4) . This
>n of plant
section 2.2),
¡1 have been
red re fie c-
24.2%) ;
red reflec-
= 12.1%) ;
.ectance =
.e: 45°) .
> be vertically
[le leaf were
= 8%, red
ice = 45%.
Г the follow-
) .5) 5.0 (1.0)
i factors were
>r each of the
i able to
led soil
.1 cover with
>er) .
Figure 3: Soil cover (new definition) as a function of
green and red reflectance, respectively, for a spheric
cal leaf angle distribution,
xx : calculated points SAIL model
— : simplified reflectance model.
asymptotic values for the infrared reflectance,
calculated from the SAIL model. A changing leaf angle
distribution during the growing season of a crop may
disturb the relationship between corrected infrared
reflectance and LAI. However, Clevers also showed
with real field data that leaf angle distribution of
cereals may be considered constant during the
vegetative and generative stage, respectively.
A correction can be made for differences in soil
moisture content by subtracting the contribution of
the soil detectable by the sensor from the measured
infrared reflectance (equation 6). If soil reflectance
is known, equation (6) may be combined with e.g.
equation (3) in carder to ascertain this corrected
infrared reflectance. This method will be called
method 0 (indicating that it cannot be applied
without knowing soil reflectances explicitly). In
practice, however, soil reflectances often are not
known. Then equation (9) can be applied, taking into
account the constant ratios of soil reflectance
between spectral bands. This method will be called
method 1. Results for both methods are given in
figure 5. Both methods gave essentially the same
results, which supports the validity of equation (9)
for correcting the infrared reflectance for soil
background.
Because the only correction made is for soil visible
to the eye and not for the soil underneath vegetation,
some influence of soil background will still remain.
This is illustrated in figure 6. Even with such a
large range in soil reflectances, differences between
curves were not very large. In reality, fluctuations
in soil moisture content underneath vegetation will
be less than those on bare soil.
SAIL MODEL LAI SAIL MODEL
Figure 5: Two methods for correcting for differences
in soil moisture content in estimating LAI. Spherical
leaf angle distribution (for explanation of symbols
see figure 4).
SAIL MODEL
CORR. INFRARED REFL. <X)
Figure 6: Influence of soil background on the
regression of LAI on corrected infrared reflectance.
il clearly
:over, accor-
red
cor a dry soil
1 for a wet
xort the
new definition
>n together
itions (2)
ìal definition
ince is cor-
cly this
nr estimating
3 by using
a black
i does not
juation (15)
jure 4,
: describing
cared
Le distribu-
)) show that
juite distinct
SAIL MODEL
Figure 4: LAI as a function of the infrared reflec
tance for a black soil with a spherical leaf angle
distribution.
xx : calculated points SAIL model
— : simplified reflectance model.
(Rw is used for r . and a is used for a in this
o°, lr
graph; CV = coefficient of variation).
A more extensive verification of the new model by
means of calculations with the SAIL model for several
leaf angle distributions and also for skylight only
are presented by Clevers (1986b).
5 CONCLUSIONS
1. If soil cover is redefined as in chapter 3, then
the reflectance in a spectral band in the visible
region of the electromagnetic spectrum decreases
linearly with increasing soil cover (equation 2 and
3) .
2. It was shown to be possible to get around the
problem of an unknown soil moisture content (and so
an unknown soil reflectance) in estimating LAI. Under
the assumption that there was a constant ratio between
the reflectance factors of bare soil in different
spectral bands, independent of soil moisture content,
a combination of green, red and infrared reflectances
