295 
August 
Italy, dur- 
1 1 , ¿2 ] such 
is accepting 
Je can final- 
,ng for each 
] and [1 1 , 
re not the 
f the 
:round cover', 
which a 
Figure 2. Effect of intercropping on the Transformed 
Vegetation Index (TVI) as obtained with LANDSAT Multi- 
spectral Scanner (MSS) measurements in band 7 (MSS 7: 
0.8 to 1.1 pm) and band 5 (MSS 5: 0.6 to 0.7 pm) for 
irrigation districts in the region of Mendoza in 
Argentina 
given fraction, 80% say, of the observed area reach 
this stage. 
2.3 Intercropping 
Intercropping is a characteristing feature of small 
holders agricultural land. Sub-pixel combinations of 
different crops will give pixel reflectances which 
cannot be predicted from the reflectances of the sub 
pixel components. One feasible approach is by apply 
ing directly the LANDSAT measurements to establish 
typical temporal profiles for each main intercropping 
pattern. 
Such results are given in Figure 2. It can be seen 
that the TVI-values for the combination 'vineyard 
with olive trees' lie between the TVI-values for 
'vineyard' and 'olive trees'. Our opinion is that the 
practical aspects of agriculture can be efficiently 
translated in terns of LANDSAT measurements by com 
bining directly field inquiries on a number of plots 
with the LANDSAT measurements applying to these plots. 
In the following sections we will illustrate how 
this can be done and how to assess the significance 
of the observed differences between agricultural 
units, as defined in terms of crops and of agricul 
tural practices. 
3 APPROACH 
Differences between crops in growth cycle are better 
defined than differences in spectral reflectances. So 
multitemporal crop discrimination on the basis of 
growth cycle is in principle easier than monotemporal 
crop discrimination on the basis of spectral reflec 
tances. After having established characteristic val 
ues of some vegetation index for each crop and growth 
stage, crop mapping is basically done by applying 
simple and fast density-slicing procedures. 
The required characteristic values can be estab 
lished by combining published measurements of spec 
tral-temporal crop properties with crop phenology in 
a particular area (Azzali 1985a, 1985b, 1986). To es 
tablish identification labels for the crops being 
cultivated in the Po valley different vegetation in 
dices have been combined to give a multi-index multi 
temporal crop identification method. The identifica 
tion scheme is presented in Figure 3. This scheme has 
been established by comparing different selections of 
overpass-dates and of index-values. The availability 
of a large number of options makes crop discrimina 
tion easier. Moreover, as shown in Figure 3, many 
crops can be discriminated in the early growing sea 
son. This is essential if one intends to apply LAND 
SAT TM measurements during the second part of the 
growing season to detect crop stress, e.g. due to 
water shortage. 
The irrigation districts of Mendoza, Argentina are 
characterized by a mediterranean - small holders - 
type of agriculture. Intercropping is a widespread 
occurrence, thus making it impossible to establish 
crop identification labels on the basis of literature, 
the more so since spectral measurements on vineyards 
and olive trees are quite rare. So, the characteristic 
vegetation index values have been established by lo 
cating a number of ground-control plots in three 
LANDSAT-images during the growing season 1984-1985 
and then extracting the required reflectance measure 
ments from each image. The result is presented in Ta 
ble 2, which shows that there is at least one combina 
tion of index values and satellite overpass dates 
Table 2. Characteristic values of the ratio 1^ = 
(MSS 7/MSS 5). 60 and of Io - TVI.100 applying to the 
main crops cultivated in Mendoza, Argentina, as ob 
tained with LANDSAT MSS measurements 
Crop 
Dates 
28-8 
-1984 
27-2' 
-1985 
15-3 
-1985 
T 1 
I 2 
X 1 
T 2 
T 1 
h 
Onion 
69 
83 
89 
87 
82 
86 
Vineyard 
73 
82 
140 
98 
125 
96 
Alfalfa 
121 
96 
143 
98 
158 
100 
Olive trees 
105 
93 
122 
96 
96 
90 
Vineyard with grass 
155 
100 
150 
100 
107 
93 
Olive trees with 
vineyard 
79 
85 
128 
97 
114 
94 
Fruit trees 
67 
80 
113 
88 
113 
88 
Table 3. Cultivated area (1980 
farming year) as 
percentage of the total agricultural 
area for the 5 
main crops cultivated 
in Crande 
Bonifica Ferrarese: 
A, as estimated by means of the 
multi 
-temporal multi- 
index method; B, according 
to ISTAT agricultural 
statistical data 
Crop 
A 
B 
Winter wheat 
20. 
5 
23 
Alfalfa 
21. 
5 
14.5 
Corn 
9 
8 
Sugar beet 
16 
20 
Trees 
16 
12 
Rice 
3 
3