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Title: COINCIDENT DETECTION OF CROP WATER STRESS, NITROGEN STATUS AND CANOPY DENSITY USING GROUND-BASED MULTISPECTRAL DATA

Author
item Barnes, Edward
item Clarke, Thomas
item RICHARDS, S - US WATER CONS LAB
item COLAIZZI, P - UNIV OF ARIZONA
item HABERLAND, J - UNIV OF ARIZONA
item KOSTRZEWSKI, M - UNIV OF ARIZONA
item WALLER, P - UNIV OF ARIZONA
item CHOI, C - UNIV OF ARIZONA
item RILEY, E - UNIV OF ARIZONA
item THOMPSON, T - UNIV OF ARIZONA

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 6/1/2001
Publication Date: N/A
Citation: N/A

Interpretive Summary: Many agricultural producers are adopting a new management practice called precision farming whereby crop needs are sometimes determined as finely as every meter. Remotely sensed data have been identified as an important information source for precision farming; however, changes in crop size can make plant stress difficult to interpret from remotely sensed data. In this study, methods to minimize the effect of changes in crop size on remotely sensed data were applied that allowed use of data to detect differences in the water and nitrogen status of cotton. The results of this study could ultimately provide agricultural producers and crop consultants a rapid means, using remotely sensed data, to map the amount of water and nitrogen needed by a crop across an entire farm.

Technical Abstract: Remotely sensed data have been identified as an important tool for precision crop management (PCM). The data have been used to assist in the identification of management zones and map crop nutrient status, and to 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 these 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.