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

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\NALYSIS
md to analyse
rmatted
O I960
X 1981
figure 3. Solar elevation of LANDSAT MSS WRS 182-79
as a function of data.
registered visually to the field boundaries image. An
accuracy of ±1 pixel can be expected by this method.
3.3 Standardization of satellite data
The extraction of meaningful quantitative crop charac
teristics from MSS data collected by LANDSAT sensors
requires the application of correction and standardi
zation procedures. These procedures fall into three
categories, namely
• calibration to standard conditions
• corrections for external effects
• reduction of the data by transformation.
Table 3. Coefficients for satellite-to-satellite
calibration (Rice et al., 1983)
A
B
LANDSAT 2
1.0
0
before
1.0
0
16 July 1975
1.0
0
1.0
0
LANDSAT 2
1.275
-1.445
after
1.141
-2.712
16 July 1975
1.098
-2.950
0.470
0.446
LANDSAT 3
1.1371
0
any
1.1725
0
date
1.2470
0
1.1260
0
3.3.2 Normalization of the data to a fixed sun angle
The LANDSAT sensors have the capability of collecting
imagery over broad areas with short acquisition data
times. Consequently, to a good approximation, the
data for each overpass on flat terrain are obtained
under a constant, normal sensor viewing angle and
constant solar illumination angle. This solar illu
mination angle has a seasonal variation which may be
normalized to a first approximation by considering
the following condition: If the surface behaves
according to Lambert's Laws of Illumination (Colwell,
1984), then the radiance in the simplified special
case as defined above is given by
d
fined
from satellite
ach test site
see Table 2).
is of display-
wth stages for
'9 in 1981 (in
3
329
329-3
39 days
test site at
jred in raster
onitored field
lata sets were
3.3.1 Recalibration of the data to a standard LANDSAT
count
Due to differences in the radiometric processing
carried out on MSS products produced by the EROS Data
Center and SRSC a conversion was required.
In order to calibrate SRSC data to EROS digital
count, the relationship between the digital values of
each pixel and the scene radiance observed by the
satellite was determined for both receiving stations
and compared (see Table 4). The linear transformation
y' = A»y + B
was derived and used to calibrate SRSC data to the
EROS LANDSAT 2 count, where
y is the vector representing the MSS data in
SRSC defined digital count
y' is the vector representing MSS data recali
brated to EROS defined digital count,
and
A and B are given in Table 5.
Furthermore, satellite-to-satellite calibration is
required for the effective utilization of data from
two satellites. It is conventional to use EROS LAND
SAT 2 before 16 July 1975 as the standard reference
and recalibrate other data to this standard. Data
were recalibrated to the standard reference (EROS
LANDSAT 2 before 16 July 1975) by means of a further
linear transformation defined by
y" = C*y' + D
where y' is defined above
y" is the vector representing MSS data recali
brated to LANDSAT 2 MSS data before 16 July
1975 defined in terms of EROS digital count.
The coefficients of the linear transformation C and
D as prepared by Parris and Rice (Rice et al., 1983)
were used (see Table 3).
L(\,0) = Lcos9
for a wavelength X and effective solar incidence
angle 9.
Each band of the MSS may be normalized by
cosa
y II I — ______ y"
COS0
where y"* and y" are the vectors representing the
MSS data before and after normalization
0 is the solar zenith angle at the time of
data acquisition and
a is a constant angle (39 ° being accepted by
US investigators as the standard).
The solar elevation angles were extracted from the
ancillary data on each of the LANDSAT data sets. The
temporal variations of the solar elevation angle for
the WRS 182-79 for the 1980-81 season are displayed
in Fig. 3. The elevation angle varies between 54 °
in midsummer to 24 ° in midwinter.
3.3.3 Reduction of the data by transformation
A preliminary examination of the structure of MSS
data by means of scatter plots discloses that there
is a high degree of correlation between data recorded
in the visible channels, MSS 4 and MSS 5, and between
the data recorded in the two infrared bands MSS 6 and
MSS 7. Figure 4 illustrates these correlations and
the inherent redundancies in the MSS data. It also
suggests that the dimensionality of the MSS data can
be reduced. Principal component analysis (PCA) is
the standard statistical technique used to determine
the dimensionality of the point distribution in a
multi-dimensional space. A PCA on the MSS data for a
typical test site reveals that the first component
(PCI) accounts for the majority of the scene variance
(98.7%), and the second component (PC2) accounts for
1.27% of the total variance. The remaining two com-