3 METHOD ICS AND RESULTS OF THE INTERPRETA
TION OF SPECTRAL SIGNATURE DATA
The typical spectral course of the remission
or vegetative objects and of bare soil is
well known. Primarily the spectral signa
ture in the whole spectral band 0.4 -
2.5 /urn is determined by the architecture
of tne stock. The more oven and horizontal
the leaves of the stock are and the higher
i.a. the remission capacity within this
whole spectral range. In the spectral band
0.4 - 0.7 /urn the incident radiation is
absorbed by the pigments in the leaves
(e.g. by the chlorophyll absorption at
0.63 - 0.69 /um) and in the spectral band
1.3 - 2.5 /urn by the water contained in
the leaves. The depth of the absorption
bands first of all depends on the primary
emission with reference to the architecture
os well as the chlorophyll and water content.
The degree of soil coverage of stocks without
complete coverage has a decisive influence on
the spectral signature, which then become a
mixed signature. Especially in the spectral
band C.4 - 0.7 7 um the strength of the
ground cover-influence is determined by the
colour contrast between soil and plant (the
colour contrast is esp. dependent on the
pigmentcontent of the plant as well as on
the humus and water content of the soil),
and in the spectral band 1.3 - 2,5 /um it
is determined by the contrast between the
water content of the plant and that of the
soil. In the thermal infrared band the
spectral signature reflects the temperature
of the stock of plants which results from
the surrounding air temperature, the
architecture of the stock of plants, its
evapotranspiration as well as the wind
conditions. The evapotranspiration itself
depends on the size of the perspiring leaf
area per rn^ soil (LAI), on the species
and site-specific intensity of the vegetable
discharge and the évapotranspiration pro
portion of the soil.
Figure 3 shows four exmples for concrete
signatures measured with the radiometer from
the helicopter. The cases discussed here
ore disticntly marked in the whole spectral
course of the signatures. Taking - as in
the biogeographical surveys - extent and
capacity of the assimilation apparatus as
well as the accumulated assimilation pro
ducts as a measure for the productivity of
stocks it con altogether be derived that a
stock is the more productive, the lower the
indices of the spectral signatures during
the chlorophyll absorption, of the water
absorption bands and in the thermal infrared
are, and the higher the indices the near
infrared remission plateau shows. This
criterion - in the following named producti
vity criteriton - was applied to the spectral
signature data ascertained along the
trajectories to assess the stocks situated
in the measuring alignements according to
their yield formation. The formulated
productivity criterion was concretized as
follows and used for the cleavage of a
quantity of spectral signature indices into
two parts representing a more productive and
a less productive target state: if the spec
tral signature indices of a target in the
channels 3,7 and 8 are lower than the
average signature indices of the target
quantity to be binarized in these channels
and if the spectral signature index in
channel 4 is higher than the average index
in channel 4 it will be grouped into the
Figure 3. Examples for concrete signatures
measured with the radiometer from the
helicopter
more vital part. If this condition is not
fulfilled the target will be grouped into
the less vital port. This criterion is
applied hierarchically to the quantity of
targets as long as there is onl„ one target
in each hierarchy branch left or until the
targets left can no longer be divided. As
a result this procedure leads to an order
of precedence of targets or groups of tar
gets in relation to their degree of pro
ductivity.
In a first duct those spectral signature
indices were taken together as a target
quantity which corresponded to alignement
parts with beet and grain crops. In a second
duct the two alignement parts were divided
into two parts of about equal length.
Independently they were clustered into 10
classes with the cluster algorithm KMEANS.
The cluster averages in all 8 channels as
well as the distribution of the clusters on
the trajectories were thereby stated. Those
clusters corresponding to beet and grain
crops form the target quantity for the
application of tine productivity criterion.
In both cases this leads to an order of
precedence of targets, which begins with
the most porductive target (or target
quantity) on both alignements and ends up
with the least productive target (or target
quantity). To qualify these statements the
average soil measuring indices of test pill
areas were adjoined to this order of
precedence in a way that the soil measuring
indices for minimum and maximum stock quality
of a test pill part corresponds to the lowest
respectively highest rank of this test pill
area. The average was formed when the soil
measuring data of several test pill areas
could be adjoined to a rank.
