Use of Remotely-Sensed Data in Cotton Growth Model

Remote sensing data can be integrated intocrop models, making simulation improved. A crop modelthat uses remote sensing data was evaluated for itscapability, which was performed through comparing threedifferent methods of canopymeasurement for cotton(Gossypium hirsutumL.). The measurement methods usedwere leaf area index (LAI), hand-held remotely sensedperpendicular vegetation index (PVI), and satellite remotelysensed PVI. Simulated values of cotton growth and lintyield showed reasonable agreement with the correspondinghand-held remotely sensed PVI were used for modelcalibration. Meanwhile, simulated lint yields involving thesatellite remotely sensed PVI were in rough agreement withthe measured lint yields. We believe this matter could beimproved by using remote sensing data obtained from finerresolution sensors. The model not only has imple inputr rements but also is easy to use. It promises to expand its applicability to other egions for crop production, and to be applicable to regional crop growth monitoring andyield mapping projects.