人工田间会诱发不同等级的小麦条锈病，在不同生育期需测定染病冬小麦冠层光谱以及相应小麦的病情指数。把冠层光谱一阶微分数据与相应的小麦病情指数进行相关分析，采用单变量线性和非线性回归技术，选取部分样本建立小麦的病情指数估测模型，并利用其余的样本对模型进行检验。结果表明，病情指数与一阶微分在432～582nm，637～701nm和715～765nm波长区域内具有极显著的相关性。以蓝边内一阶微分总和(SDb)与红边内一阶微分总和(SDr)的归一化值作为变量的模型是估测病情指数的最佳模型，其RMSE为5．73%。研究表明，可用高光谱信息监测作物的病害情况，且精度较高。利用高光谱遥感监测病害程度及其影响具有实际的应用价值。

The canopy reflectance of winter wheat that infected different severity stripe rust is measured through artificial inoculation and the disease index (DI) of the wheat corresponding to the spectra is acquired in the field. The correlation between DI and the first derivative data of the disease wheat is analyzed. Using linear and non-linear regression methods,and choosing a part of samples,the estimation models about DI of disease wheat have been built,through the test of the other part samples.The result shows that there is high correlation between DI and the first derivative data in the regions of 432～582nm,637～701nm and 715～765nm ,the model contained the normalized value of the sum of first derivative within blue edge(SDb) and sum of first derivative within red edge(SDr) is the best one. The model is to estimate the DI of the disease wheat,and the RMSE is 5.73 %. This study shows canopy hyperspectra data can estimate the disease severity of crops and the inversion precision is higher. The conclusion has great practice and application value to monitor the disease severity and its influence of corps by using hyperspectral remote sensing.