Agriculture Reference
In-Depth Information
Another prerequisite for every method of crop canopy property recording via
reflectance is that the amount of soil in the viewing area of the sensor is kept low
enough. Because the reflectance from soil is completely different (Fig.
6.2
). So
closed canopies are needed. With closely spaced crops -
e.g.
small grains, colza,
grass, clover and alfalfa - it is much easier to meet this premise than with widely
spaced plants such as maize, beets and sunflower.
However, since the coefficients of determination (r
2
) for sensing the gross pri-
mary productivity (GPP) are very high (Figs.
6.10
and
6.11
), a small percentage of
soil reflectance within the signals can be tolerated. Gitelson et al. (
2008
) even state
that the effects of soil on the accuracy of GPP retrieval of maize are minimized once
the canopy cover exceeds 60 %.
With proximal sensing and early growth stages of crops,
view directing
can help
to avoid sensing errors that are caused by soil. This method aims at restricting the
sensing view to small strips just above the plant rows and thus leaving out bare inter-
row strips or at using an oblique view on canopies in order to avoid reflectance from
soil. Yet with remote sensing, the use of such methods hardly seems possible.
However an
elimination of soil errors
with row crops via special post-processing
of signals might be feasible (Homayouni et al.
2008
; Liu et al.
2008
; Pacheco et al.
2008
). Up to now, such post-processing to eliminate soil errors is not state of the art.
It would lend itself for proximal as well as for remote sensing.
A point to consider is the frequency with which the gross primary productivity
(GPP) should be monitored. High yielding crops often need several treatments with
farm chemicals during the growing season. Accordingly, also several dates for
recording the GPP might be reasonable. Whenever immediate processing of the
signals and simultaneous use for the control of farming operations is feasible, proximal
sensing during these operations would be desirable.
However, this probably would exclude manual inspection and correction of the
results by the farmer prior to the control operation. These human interactions - that
might take care of
e.g.
respective soil and water situations - could easily be imple-
mented by recording and mapping the canopy productivity results in a separate first
step. Processing and combining the results could then take place in a second and
stationary step, thus preparing a final control map for the third step, the respective
site-specific operation. This
multi-step procedure
would lend itself for
remote
productivity sensing
. A definite point for remote sensing is that is can be repeated
rather easily any time, provided no clouds obscure the view. With ground based
proximal sensing, this is not feasible because of the labor that is involved.
How can a farmer use the signals from productivity sensing for defining the yield
expectations? The information about the gross primary production (GPP) indicates
the respective situation at intermediate stages within the growing season. This can
at best help to get an estimation. If GPP maps were obtained at two different growing
stages early in the season, the question is, how the site-specific signals should be
combined into a single map.
A logical reasoning for this would be to expect the final yield to be proportional
to averages or sums of the GPP from the sensing dates. However, the time span and
the temperature between two sensing dates should be considered. The longer the
