研究的目的是利用高光谱遥感尽可能早地识别出健康与遭受条锈病胁迫的小麦。 通过人工田间诱发不同等级条锈病， 在不同生育期测定感染不同严重程度条锈病的冬小麦冠层光谱及病情指数(disease index， DI)。 对测定的光谱进行平滑并计算一阶微分值， 并用两种方法分别提取光谱红边位置(red edge position， REP)与黄边位置(yellow edge position， YEP)： (1)一阶微分最大值法； (2)Cho and Skidmore方法。 研究表明随着病情严重度的增加， REP逐渐向短波方向移动， YEP逐渐向长波方向移动， 而REP-YEP则迅速的减小。 分别对比分析了REP， YEP以及REP-YEP预测DI的能力， 结果表明， 以REP-YEP为变量的模型预测DI的精度最好， 模型估测绝对误差(RMSE)仅为6.22， 相对误差(relative error， RE)为14.3%， 且能够提前12 d识别出健康与病害胁迫的小麦。 该研究不仅可为将来利用高光谱遥感大面积监测小麦病害提供理论与技术支持， 而且对精准农业的实施也具有重要意义与实际应用价值。

The objective of the present paper is to identify healthy wheat and disease wheat by using hyeprspectral remote sensing as soon as possible. The canopy spectral reflectance of winter wheat infected by different severity yellow rust was measured and the disease indices (DI) were investigated in the field respectively. Smoothing the canopy spectra and calculating the first derivative values, the two methods were used to calculate the red edge position (REP) and yellow edge position (YEP) of the first derivative values: (a) maximum of the first derivative value; (b) Cho and Skidmore method. The result showed that REP gradually shifted to short-wave band, and the YEP gradually shifted to long-wave band with disease severity increasing, however, REP-YEP quickly became smaller. Analyzing and comparing the prediction precision of REP, YEP and REP-YEP for DI, the result indicated that the model REP-YEP as variable has the best estimation precision for DI than REP and YEP, the model estimation error is 6.22, and relative error is 14.3%, and it could identify healthy and disease wheat 12 days before the disease symptom apparently appeared. Therefore, this study not only can provide theory and technology for large areas monitoring of wheat disease by using hyperspectral remote sensing in the future, but also has the important meaning and practical application value for implementing precision agriculture.