近年来水资源短缺问题日益严重， 部分地区由于农业灌溉用水不足导致庄稼减产农民利益受损。 大豆是一种需水量较大的农作物， 一旦水分亏缺将直接影响大豆植株的形态和生长发育， 从而造成大豆品质降低和产量减少。 大豆叶片的水分状况可真实地反映植株水分受土壤水分亏缺的影响程度， 因此， 大豆冠层叶片水分含量的快速获取成为一种需要。 太赫兹辐射在水中的强烈衰减使其成为一种非常灵敏的非接触式探针， 可以快速、 无损地检测叶片含水量。 因此基于太赫兹光谱这一新技术进行大豆冠层叶片含水量的检测研究， 用于实时监测田间大豆的健康状况。 实验选用中黄13号大豆进行栽培， 为尽可能模拟田间不同程度的干旱胁迫状况， 将开花期大豆进行5个不同梯度： 正常供水、 轻度干旱胁迫、 中度干旱胁迫、 重度干旱胁迫、 严重干旱胁迫（分别占田间最大持水量的80%， 65%， 50%， 35%， 20%）的水分灌溉， 每个梯度设置3个重复。 利用人工称重法与便携式土壤水分速测仪结合将土壤含水量调控到各水分梯度要求。 然后， 将实验大豆植株运回实验室并利用透射式太赫兹时域光谱仪进行样本扫描， 每个梯度采集18片冠层叶片， 共90个样本， 以2∶1的比例分为校正集和预测集。 在获取各样本时域光谱数据后， 根据Dorney和Duvillaret提出的模型进行了光学参数的提取， 得到各样本的吸收系数谱以及折射率谱。 定性分析了太赫兹时域光谱、 吸收系数、 折射率随水分胁迫程度不同的变化情况。 实验发现： 随着水分胁迫程度的降低， 时域光谱的峰值呈不断衰减趋势， 且均低于空白参考峰值， 同时有明显的时间延迟。 吸收系数值随干旱胁迫程度的加剧逐渐降低； 折射率值同样随干旱胁迫程度的加剧逐渐降低。 并利用偏最小二乘（PLS）和多元线性回归(MLR)方法定量研究了时域光谱、 吸收系数、 折射率光谱数据与叶片含水率的相关关系。 结果表明， 太赫兹波对大豆叶片水分差异十分敏感， 基于时域光谱最大值和最小值的MLR预测精度最高， 预测集相关性（rp）达-0.939 3， 均方根误差（RMSEP）为0.049 5。 研究表明太赫兹光谱技术应用于大豆冠层叶片含水量观测具有良好的可行性， 为开展大豆冠层含水量信息快速获取， 实现科学节水管理与灌溉决策提供了新的检测手段和实验依据。

With the increasingly serious situation of water resources shortage, the shortage of agricultural irrigation water in some areas has resulted in reduction of crop and damages the farmers’ interests. Soybean is kind of crop with high water requirement. Once the water deficiency will directly affect the morphology and growth, the quality and the yield will be reduced. Because water status of soybean leaves can truly reflect the degree of soil water deficit, a tool for water content measurements is in great need. The strong attenuation of terahertz radiation in water makes it a contactless probe, which can be used to detect the water status of leaves quickly. As a result, terahertz spectroscopy technology was studied to rapidly and conveniently estimate water content in soybean canopy leaf, so as to monitor the health status in real time. Zhong-huang 13 soybean cultivars were cultivated in our experiment. In order to simulate the drought stress of different degrees in the field, 5 different gradients of flowering soybean were carried out: normal watering, mild drought stress and moderate drought, severe drought, more severe drought stress (accounted for 80%, 65%, 50%, 35%, 20% of the maximum water holding capacity in the field, respectively) and 3 repetitions were set per gradient. The artificial weighing method combined with the portable soil moisture measuring instrument was used to regulate soil moisture content to meet the requirements of the various water gradients . Then, the experimental soybean were transported to the laboratory, and the samples were scanned by terahertz time domain spectrometer. 18 canopy leaves for each gradient, a total of 90 samples were collected. It was divided into calibration set and prediction set at 2∶1 ratio. After obtaining the time domain spectral data of each sample, the absorption coefficient spectrum and the refractive index spectrum of each sample were calculated by the data processing method of Dorney and Duvillaret. The changes of time domain spectroscopy, absorption coefficient and refractive index with water drought stress were qualitatively analyzed. It was found that the peak value of time domain spectrum was decreasing with the degree of water stress decreasing, which was lower than the reference value. At the same time, there was a significant time delay. The number of absorption coefficient gradually decreased with the aggravation of drought stress, and the refractive index value the same decreased. Moreover, partial least squares (PLS) and multiple linear regression (MLR) were used to quantitatively study the correlation between time domain spectrum, absorption coefficient, refractive index spectrum data and leaf water content, respectively. The results showed that, terahertz was sensitive to differences of leaf water content. And the MLR model based on maximum and minimum values in time domain spectral performed the best, in which correlation coefficient (rP) and root mean square error of prediction set (RMSEP) were -0.939 3 and 0.049 5, respectively. This study showed that the application of terahertz technology in leaf water content estimation has good feasibility. It will provide a new detection tool and experimental basis for rapid monitoring of water content in soybean canopy and scientific water-saving irrigation management.