Hello,
Thank you all for your comments which were very helpful and
informative.  The reason I asked the question is because I am attempting to
incorporate infrequent (1-3 scenes per growing season) remotely sensed data
(Landsat 7 ETM+) into CERES-Wheat to see if it improves estimates of soil
water content and grain yield.  There are two methods described in the
literature that I am considering to accomplish this objective.  Both
methods essentially fine the 'best' fit of the simulated growth curve to
the remotely sensed data.  One method, however, will only change the
initial value of LAI, and is only applicable in a growth model which is
sensitive to initial conditions of LAI, the literature terms this the
re-initiation technique.  The other method results in new parameter values
for all parameters used in the equation to estimate LAI, the equation to
estimate LAI is
         LAI = (PLA-SENLA)*Plants/10000
So the equation to determine PLA and SENLA and there parameters will also
be changed.  I have not figured out all the parameters involved yet but I
believe there will be many.  This technique, termed the re-parameterization
technique, is consequently much more difficult to implement
programmatically and will result in greater computation time than the
re-initiation technique which is relatively simple.  It seems to me that
the re-parameterization technique will account for more of the variability
in inputs than the re-initialization technique.  However, based on some of
your comments changing the initial value of LAI based on remotely sensed
estimates of LAI (assumming these are correct) does have some merit and
might help to improve CERES-Wheat estimates.  Maybe it is best to try the
simplest solution first.  Does anyone have any comments on this.  Thank you
for your help.

Derek McNamara

Derek McNamara
Graduate Research Assistant
Mountain Research Center, Montana State University
P.O. Box 173490, 106 AJM Johnson Hall
Bozeman, MT 59717-3490, USA
406-994-5073
[log in to unmask]