Irrigation scheduling requires an operational means to quantify plant water stress. Remote sensing may offer quick measurements with regional coverage that cannot be achieved by current ground-based sampling techniques. This study explored the relation between variability in fine-resolution measurements of canopy temperature and crop water stress in cotton fields in Central Arizona, USA. By using both measurements and simulation models, this analysis compared the standard deviation of the canopy temperature
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$$ {\left( {\sigma _{{T_{{\text{c}}} }} } \right)} $$\end{document} to the more complex and data intensive crop water stress index (CWSI). For low water stress, field
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$$ \sigma _{{T_{{\text{c}}} }} $$\end{document} was used to quantify water deficit with some confidence. For moderately stressed crops, the
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$$ \sigma _{{T_{{\text{c}}} }} $$\end{document} was very sensitive to variations in plant water stress and had a linear relation with field-scale CWSI. For highly stressed crops, the estimation of water stress from
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$$ \sigma _{{T_{{\text{c}}} }} $$\end{document} is not recommended. For all applications of
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$$ \sigma _{{T_{{\text{c}}} }} , $$\end{document} one must account for variations in irrigation uniformity, field root zone water holding capacity, meteorological conditions and spatial resolution of Tc data. These sensitivities limit the operational application of
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$$ \sigma _{{T_{{\text{c}}} }} $$\end{document} for irrigation scheduling. On the other hand,
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$$ \sigma _{{T_{{\text{c}}} }} $$\end{document} was most sensitive to water stress in the range in which most irrigation decisions are made, thus, with some consideration of daily meteorological conditions,
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$$ \sigma _{{T_{{\text{c}}} }} $$\end{document} could provide a relative measure of temporal variations in root zone water availability. For large irrigation districts, this may be an economical option for minimizing water use and maximizing crop yield.
