Abstract
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 \( {\left( {\sigma _{{T_{{\text{c}}} }} } \right)} \) to the more complex and data intensive crop water stress index (CWSI). For low water stress, field \( \sigma _{{T_{{\text{c}}} }} \) was used to quantify water deficit with some confidence. For moderately stressed crops, the \( \sigma _{{T_{{\text{c}}} }} \) 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 \( \sigma _{{T_{{\text{c}}} }} \) is not recommended. For all applications of \( \sigma _{{T_{{\text{c}}} }} , \) one must account for variations in irrigation uniformity, field root zone water holding capacity, meteorological conditions and spatial resolution of T c data. These sensitivities limit the operational application of \( \sigma _{{T_{{\text{c}}} }} \) for irrigation scheduling. On the other hand, \( \sigma _{{T_{{\text{c}}} }} \) was most sensitive to water stress in the range in which most irrigation decisions are made, thus, with some consideration of daily meteorological conditions, \( \sigma _{{T_{{\text{c}}} }} \) 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.
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Notes
At this point in the discussion, it is important to define three measures of surface temperature: T c, T o and T s. T c is the canopy temperature, defined by Norman et al. (1995) as the temperature at which the “vegetation dominates the (measurement) field of view minimizing the effect of soil”. T o is the temperature of the soil surface. T s is the surface composite temperature, defined by Norman et al. (1995) as the “aggregate temperature of all objects comprising the surface”, which was shown by Kustas et al. (1990) to be a linear function of T c and T o. When the surface is completely covered by vegetation, T s=T c and when the surface is bare soil, T s=T o.
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Acknowledgements
This research was made possible with funding and support provided by NASA Stennis Space Center. Additional support was provided through the Cooperative Research Program of the Organisation for Economic Cooperation and Development (OECD). The authors would like to thank the staff at the University of Arizona Maricopa Agricultural Center and pilot Larry Hinton for their research support and collaboration.
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González-Dugo, M.P., Moran, M.S., Mateos, L. et al. Canopy temperature variability as an indicator of crop water stress severity. Irrig Sci 24, 233–240 (2006). https://doi.org/10.1007/s00271-005-0022-8
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DOI: https://doi.org/10.1007/s00271-005-0022-8