299 
TVI-values; 
ation of plot 
= signifi- 
sampies 
amples in 
amples in- 
IA3 
IA4 
hs 
hs 
hs 
hs 
hs 
hs 
s 
hs 
” 
ns 
hs 
hs 
hs 
hs 
hs 
hs 
ns 
ns 
- 
ns 
hs 
hs 
hs 
hs 
hs 
hs 
hs 
hs 
“ 
ns 
From our point 
ay of comparing 
which one 
ences in TVI 
B we see that 
lot, the MSS 
e difference 
MSS measure- 
It is only 
given field 
better sig- 
3 is 'highly 
ifferent points 
at the per- 
int of view, 
however, true 
y, one can 
e 23,305 m^ of 
nd TM 7 
p-specific 
shed. 
IGNIFICANT 
;nificant dif- 
ed the means 
of a large 
'ith the MSS 
.%, significant 
:se percentages 
irge relative 
ir these differ- 
mt or signifi 
cant after the proper significance test. 
Let us now take this 4% as an approximate indica 
tion of the relative change in TVI which will be 
assessed as not significant. We have then to estab 
lish whether we can measure TVI with this accuracy. 
The nominal radiometric accuracy of TM measurements 
is 0.5% on reflectance. So we can calculate the 
nominal accuracy on TVI by means of error analysis 
of the formula defining this vegetation index. This 
results in a 0.2% nominal accuracy. 
We can, therefore, conclude that we can in prin 
ciple measure significant differences in TVI-values 
with satisfactory accuracy. 
Actual radiometric accuracy of TM is, however, 
quite different from nominal, If, for example, we 
take the results of Slater (1986), accuracy on re 
flectances is 0.5 in TM 3 and 1.7 in TM 4 at visibil 
ity greater than 10 km. Accuracy on TVI will there 
fore be 0.2% which confirms the nominal value. 
It must finally be stressed that the multitemporal 
analysis of satellite images requires that the basic 
digital count values be transformed into reflectance 
values (Menenti 1984, Kuipers and Menenti 1986). 
Furthermore measurements on different overpass dates 
have to be corrected for atmospheric effects or at 
least normalized with each other by taking one of 
the images as a reference. 
metrization of land-surface characteristics, use of 
satellite data in climate studies and first results 
of ISLSCP. ESA SP-248. 
Menenti, M. 1984. Physical aspects and determination 
of evaporation in deserts applying remote sensing 
techniques. Report ns 10 (special issue). Institute 
for Land and Water Management Research (ICW), Wa- 
geningen. 202 pp. 
Miller, G.P., M. Fuchs, M.J. Hall, G. Asrar, E.T. 
Kanemasu & D.E. Johnson 1984. Analysis of seasonal 
multispectral reflectances of small grains. Remote 
sensing of environment 14:153-167. 
Slater, P.N. 1986. Variations in in-flight absolute 
radiometric calibration. Proc. ISLSCP-conference on 
parametrization of land-surface characteristics, 
use of satellite data in climate studies and first 
results of ISLSCP. ESA SP-248. 
Steel, R.G. & J.H. Torrie 1960. Principles and 
procedures of statistics. New York, Me Graw-Hill. 
481 pp. 
6 CONCLUSIONS 
We conclude that differences in temporal and spectral 
patterns relating to crop phenology and intercropping 
can be obtained from LANDSAT measurements. Plots of 
0.5 ha and 2 ha must be considered when applying TM 
and MSS measurements respectively. It is demonstrated 
that a multi-index multi-temporal crop discrimination 
scheme can be established for three rather different 
agricultural areas. Operational crop monitoring by 
satellites in a given region, however, is only 
feasible after such pre-operational investigations, 
as presented in this paper. 
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