Substantial progress has been made in developing sensor‐based proximal phenotyping systems for cotton (Gossypium hirsutum L.), but research is needed to improve in‐season prediction of lint yield and to improve accuracy in monitoring crop water stress using such a system. Here, we report on results of a 2‐yr field study in which a proximal remote sensing system (measuring canopy height, spectral indices [normalized difference vegetation index, NDVI], and canopy temperature) was deployed every 2 wk over plots of eight cotton varieties at three rates of ET replacement (0, 45, and 90%). As expected, NDVI was an excellent predictor of canopy and biomass traits, including canopy height and leaf area index (LAI). The strength of correlations between in‐season sensor measurements (NDVI and the canopy‐to‐air temperature difference [T_c – T_a]) and lint yield ranged from poor to fair when analyzed by irrigation rate and excellent when analyzed across all rates. Correlations were weaker in the drier of the two years tested and NDVI was a better and more consistent predictor of yield than T_c − T_a, though multiple linear regression integrating both variables improved results by up to 9%. Combining the T_c − T_a data with onsite atmospheric weather station data allowed calculation of empirical crop water stress index (CWSI) values. Using the CWSI metric, relative differences in crop water stress were clear among ET replacement levels once the canopy width reached the threshold for focusing the infrared temperature sensor on the canopy. This indicated that cotton water stress can be successfully monitored using a proximal phenotyping system, which can be quickly and easily deployed across many plots in research or production settings.

中文翻译：

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棉花表型和生理监测的近端遥感系统

实质性的进展已经在制定棉花基于传感器的近端分型系统（已取得陆地棉L.），但是需要进行研究以改善棉绒产量的季节预测，并提高使用这种系统监测作物水分胁迫的准确性。在此，我们报告了一项为期2年的田间研究的结果，其中每2周在8个棉田上每2周部署一个近端遥感系统（测量冠层高度，光谱指数[归一化植被指数，NDVI]和冠层温度）三种ET替换率（0％，45％和90％）。不出所料，NDVI是冠层和生物量性状（包括冠层高度和叶面积指数（LAI））的出色预测指标。季节内传感器测量值（NDVI和冠层与空气之间的温差[ T _c – T _a]）和皮棉产量，从灌溉率分析到差到中等，而在所有速率下分析都很好。在测试的两年中，干燥度之间的相关性较弱，并且NDVI是比T _c - T _a更好，更一致的产量预测指标，尽管将两个变量综合在一起的多元线性回归最多可将结果提高9％。结合T _c - T _a具有现场大气气象站数据的数据可以计算出经验作物水分胁迫指数（CWSI）值。使用CWSI指标，一旦树冠宽度达到将红外温度传感器聚焦在树冠上的阈值，在ET替代水平之间作物水分胁迫的相对差异就很明显了。这表明可以使用近端表型系统成功监测棉花水分胁迫，该系统可以在研究或生产环境中的许多样地中快速，轻松地部署。