Use of remotely-sensed information in agricultural crop growth models

Abstract Four techniques are described for incorporating remotely-sensed information into agricultural crop growth models. The techniques are demonstrated using a simple model of the above-ground growth of a uniform maize monoculture. Use of remotely-sensed data as model input is relatively simple but requires frequent observations. Infrequent observations may be used to update the model simulation, but final results are sensitive only to the latest observation. Re-initialization and re-parameterization may be used to adjust model initial conditions and parameter values to fit simulated growth to remotely-sensed observations.

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