Canopy temperature variability as an indicator of crop water stress severity
Canopy temperature variability as an indicator of crop water stress severity
复制标题
DOI：
10.1007/s00271-005-0022-8
复制
发表时间：
2006-05-01
影响因子：
3
通讯作者：
Bryant, R
中科院分区：
文献类型：
--
作者：
González-Dugo, MP;Moran, MS;Bryant, R
灌溉调度需要一个可操作的手段来量化植物水分胁迫。遥感可以提供快速测量，并具有当前地面采样技术无法实现的区域覆盖范围。本研究探讨了美国亚利桑那州中部棉田冠层温度高分辨率测量值的变异性与作物水分胁迫之间的关系。利用实测数据和模拟模型，比较了美国亚利桑那州中部棉田冠层温度和作物水分胁迫的标准差。通过使用测量和模拟模型，该分析将冠层温度的标准差（σ（Tc））与更复杂和数据密集的作物水分胁迫指数（CWSI）进行了比较。对于低水分胁迫，场西格玛（Tc）被用来量化水分亏缺与一定的信心。对于中度胁迫的作物，西格玛（Tc）是非常敏感的植物水分胁迫的变化，并与田间尺度CWSI的线性关系。对于高度胁迫的作物，不推荐用σ（Tc）来估计水分胁迫。对于所有应用的西格玛（Tc），必须考虑到灌溉均匀性的变化，田间根区持水量，气象条件和空间分辨率的T-C数据。这些敏感性限制了sigma（Tc）在灌溉调度中的实际应用。另一方面，西格玛（Tc）是最敏感的水分胁迫的范围内，大多数灌溉决策，因此，考虑到一些日常的气象条件下，西格玛（Tc）可以提供一个相对的措施，根区水分供应的时间变化。对于大型灌溉区，这可能是一个经济的选择，以尽量减少用水和最大限度地提高作物产量。
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 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 (sigma(Tc)) to the more complex and data intensive crop water stress index (CWSI). For low water stress, field sigma(Tc) was used to quantify water deficit with some confidence. For moderately stressed crops, the sigma(Tc) 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(Tc) is not recommended. For all applications of sigma(Tc); 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(Tc) for irrigation scheduling. On the other hand, sigma(Tc) 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(Tc) 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.