Precision irrigation management using modeling and remote sensing approaches.

A synergy between remote sensing and crop simulation models is proposed as a new method for managing irrigations in precision agriculture. The remote sensing component provides the ability to assess plant water status at high spatial resolution and the crop model provides data at high temporal frequency. The objective of this study was to integrate the crop water stress index (CWSI) and the simulation model CERES-Wheat to provide data on within-field variability in plant water requirements and yield response. The accuracy of the procedure was evaluated using a data set collected during the Free Air Carbon Dioxide Enhancement (FACE) wheat experiments conducted at the Maricopa Agricultural Center in Arizona. The method was very sensitive to overestimation of the CWSI under dry conditions with a potential for inaccurately predicted soil water contents. However, the combined approach allowed the model to provide reasonable yield prediction of water stressed plots using only CWSI measurements during the season to indicate inadequate plant available water. These initial results are encouraging; however, additional analysis of the data on a plot-by-plot basis is necessary before specific conclusions can be made about the suitability of this method for precision farming applications.