Platforms and instrumentation for Field High-Throughput Plant Phenotyping (FHTPP) are well developed to measure important traits for crop breeding and agronomic studies. However, the research has focused on morphological and spectral traits; and approaches to estimate major physiological processes such as evapotranspiration (ET) for small experimental plots are lacking. In this study, we put forward a new analytical framework to estimate plot-scale ET by integrating frequent phenotyping data (multispectral and thermal infrared images, canopy reflectance, and LiDAR point clouds) from a FHTPP system (known as NU-Spidercam), the weather data, a simplified two-source energy balance model, and reference ET and crop coefficient calculation. The new plot-scale ET method was tested on five field experiments involving maize and soybean crops over two growing seasons, with the different treatment levels of irrigation water. Estimated plot-scale ET was accumulated across the growing reason for each plot, and its association with grain yield was investigated with regression analysis. The result showed that plot-scale accumulated ET captured the seasonal trend of plot water use and clearly differentiated the irrigation treatments. Strong linear correlations were observed between plot-scale ET and grain yield, with R values ranging from 0.35 to 0.93 (average R = 0.71). Plot-scale ET appeared to be a more steady and stronger predictor of grain yield across the seasons than several other morphological and spectral traits including crop height, green pixel fraction, canopy temperature depression, and red-edge normalized difference vegetation index. High spatial and temporal resolution of the field phenotyping data, along with the new analytical framework reported, successfully estimated ET at small plot scale, which is difficult to achieve with other systems or methods. Our work of estimating ET at the plot-scale can be adopt to other ground-based platforms and drones, thus empowers physiologists, breeders, and agronomists for high-throughput phenotyping of water-use related traits and drought response evaluation.

中文翻译：

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地块尺度作物蒸散量的高通量生理表型分析


用于田间高通量植物表型分析 (FHTPP) 的平台和仪器已得到很好的开发，可以测量作物育种和农艺研究的重要性状。然而，该研究主要集中在形态和光谱特征上。缺乏估计小型实验地的蒸散量（ET）等主要生理过程的方法。在这项研究中，我们提出了一个新的分析框架，通过集成来自 FHTPP 系统（称为 NU-Spidercam）的频繁表型数据（多光谱和热红外图像、冠层反射率和 LiDAR 点云）来估计地块尺度的 ET，天气数据、简化的双源能量平衡模型以及参考蒸散和作物系数计算。新的小区规模 ET 方法在五个田间试验中进行了测试，涉及玉米和大豆作物的两个生长季节，灌溉水的处理水平不同。估计的小区规模蒸散量是根据每个小区的生长原因进行累积的，并通过回归分析研究了其与谷物产量的关联。结果表明，小区尺度的累积蒸散捕获了小区用水的季节趋势，并清楚地区分了灌溉处理。小区尺度 ET 和谷物产量之间存在很强的线性相关性，R 值范围为 0.35 至 0.93（平均 R = 0.71）。与其他几种形态和光谱特征（包括作物高度、绿色像素分数、冠层温度降低和红边归一化差异植被指数）相比，地块尺度的蒸散似乎是整个季节粮食产量更稳定、更强有力的预测因子。 现场表型数据的高空间和时间分辨率以及报告的新分析框架成功地估计了小地块尺度的蒸散量，这是其他系统或方法难以实现的。我们在地块尺度上估算蒸散量的工作可以应用于其他地面平台和无人机，从而使生理学家、育种家和农学家能够对用水相关性状进行高通量表型分析和干旱响应评估。