主成分分析法与植被指数经验方法估测冬小麦条锈病严重度的对比研究

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350～1 350 nm范围内的前5个主成分(principal components，PCs)，以及一阶微分光谱在蓝边(490～530 nm)，黄边(550～582 nm)和红边(630～673 nm)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。

Comparison of Principal Component Analysis with VI-Empirical Approach for Estimating Severity of Yellow Rust of Winter Wheat

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. Ste...