Hi, we did this using the sugar beet model SUCROS. Research in N France found
that the initial leaf area for beets can vary, depending on the seeding to
emergence duration, seeding depth, crust, etc. Also, the relative growth rate
for the exponential curve was negatively affected by poor emergence conditions.
(we concentrated on these since emergence conditions were often sub-optimal
there). We adjusted these within the range found by the field experiemnts by
assimilating the reflectance data in and minimising the differences between
observed and simulated. I agree with Tony that certain parameters should not be
touched. You can initialise the model by changing the seeding or emergence
dates if these are not known. It is advisable to evaluate certain parameter
values to see which ones have natural variability. Density would be most
variable i would think (initialising).

I am now doing similar work with dssat. i have not crossed that bridge yet.

regards, chris duke

Derek McNamara wrote:

> 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]

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