|Pinter Jr, Paul
|JONES, D - UNIV OF NEBRASKA
Submitted to: Crop Simulation Workshop Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 4/8/1998
Publication Date: N/A
Technical Abstract: Remote sensing techniques have also been found an appropriate method to assess plant status at a high spatial resolution over an entire farm. The objective of this study was to determine how remotely sensed data can improve a growth model's predictions of actual field scale variability in crop development. The procedures used were evaluated with the crop simulation model CERES-Wheat, and a data set collected during the Free Air Carbon Dioxide Enhancement (FACE) wheat experiments conducted at the Maricopa Agricultural Center in Arizona. It was determined that remotely sensed data can provide at least three key pieces of information on actual crop conditions: 1. Leaf area index (LAI), 2. Growth stage, and 3. Evapotranspiration (ET) rate. LAI was estimated from a vegetation index (VI) using an empirical relationship. New methods were developed to apply rule based systems and fuzzy logic to relate multispectral data to the Zadok's growth stage. Using this information, it was possible to define some of the cultivar coefficients solely from multispectral data. The accuracy of model predictions were also improved by the iterative adjustment model parameters so that early season predictions of LAI and growth stage were accurately predicted. Additionally, remotely sensed estimates of plant surface temperature were used to estimate ET. Methods to interatively adjust the model's soil parameters so that predicted and remotely sensed estimates of ET agree are under development.