Canopy temperature variability as an indicator of crop water stress severity

Canopy temperature variability as an indicator of crop water stress severity 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Irrigation Science Springer Journals

Canopy temperature variability as an indicator of crop water stress severity

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Publisher
Springer-Verlag
Copyright
Copyright © 2005 by Springer-Verlag
Subject
Life Sciences; Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution; Forestry; Soil Science & Conservation ; Plant Sciences ; Agriculture
ISSN
0342-7188
eISSN
1432-1319
D.O.I.
10.1007/s00271-005-0022-8
Publisher site
See Article on Publisher Site

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.

Journal

Irrigation ScienceSpringer Journals

Published: Jan 4, 2006

References

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