Abstract

Remotely sensed data has been identified as an important tool or precision crop management (PCM). The data has been used to assist in the identification of management zones, map crop nutrient status, and detect pest infestations. However, in many of the examples cited, the correlation between a multispectral signature and the variation of interest was limited to single factor experiments (i.e., only one factor was primarily responsible for the variability in crop condition). A water by nitrogen experiment was conducted during the 1999 cotton season near Phoenix, Arizona, where one objective was to test the ability of remotely sensed data to distinguish between water and nitrogen stress. Multispectral (visible, near infrared and thermal) data were collected using a prototype sensor mounted on a linear move irrigation system. Neutron probe data were used to quantify crop water status, and petiole samples were used to determine crop N status. Analysis of this data indicated that it is possible to use remotely sensed data to develop maps of water stress, N status and canopy density when variations in all of these factors are simultaneously present. Additional data analysis is needed before we can determine how accurately these factors can be quantified across the growing season.