中文摘要：

通过人工田间诱发不同等级小麦条锈病，在不同生育期测定染病冬小麦冠层光谱及其病情指数（disease index，DI）。利用主成分分析法提取冠层光谱350-1350nm范围内的前5个主成分（principal components，PCs），以及一阶微分光谱在蓝边（490-530nm），黄边（550-582nm）和红边（630-673nm）内的前3个PCs，并利用逐步回归法建立反演模型，其结果分别与植被指数经验模型进行对比，结果表明：以一阶微分PCs为变量的模型精度优于其他模型，其RMSE为7．65，相对误差为15．59％。通过对预测值与实测值对比发现，以微分指数SDr’／SDg’为变量的模型适合监测冬小麦早期病情，而以一阶微分PCs为变量的模型特别适合监测冬小麦条锈病病情较严重期。研究结果对利用高光谱遥感监测与评估小麦病害程度具有实际应用价值。

英文摘要：

The canopy reflectance of winter wheat infected by yellow rust with different severity was measured through artificial inoculation, and the disease index （DI） of the wheat corresponding to the spectra acquired in the field was obtained. Principal component analysis（PCA）was used to compute the first 5 principal components （PCs） of canopy spectra in the 350-1 350 nm range and the first 3 PCs of first-order derivative in blue edge （490-530 nm）, yellow edge （550-582 nm） and red edge （630-673 nm）, respectively. Step-wise regression was used to build models, the results of those models are compared with that of VI-empirical models, and the result shows that the model based on PCs of first-order derivative is particularly accurate compared to others, with the RMSE of 7. 65 and relative error of 15.59%. Comparison was made between the estimated DI and the measured DI, indicating that the model based on SDr＇/SDg＇ is suitable to monitoring early disease and the model based on PCs of first-order derivative is suitable to monitoring the more severe disease of yellow rust of winter wheat. The conclusion has great practical and application value to acquiring and evaluating wheat disease severity using hyperspectral remote sensing, and has an important meaning for increasing yields of crops and ensuring security of food supplies.