Spatial deconvolution of yield meter data.
Data for yield maps can be obtained from modern combine harvesters
equipped with a differential Global Positioning System and an Yield
Monitoring System. Due to a delay/smoothing effect in the combine
harvester the recorded yield for a location actually represents a
shifted weighted average of harvested yield over a finite region
along the swath. This is modelled as a spatial convolution of the
true unobserved yield and an impulse response function (IRF). The
assumption of Gaussian data together with a simultaneously
parametric modelling of both the impulse response function and the
spatial covariance function (SCF) of the unobserved yield, enables us
to compute maximum likelihood parameter estimates of model
parameters. The suggested procedure of simultaneously estimating
the parameters of the IRF and SCF gives more precise results
compared to methods of estimating the impulse response function
where no spatial information is used. The inferred statistical
model for the yield meter data may be used to predict the ``true''
yield on a dense grid of locations given the observed smoothed yield
data resulting in more accurate yield maps than maps based on raw
data.
Last modified: Thu Nov 2 11:47:52 MET 2000