烟煤病是我国南方热带、 亚热带地区一种非常普遍的植物病害, 对我国农业生产造成巨大危害, 对其监测预报是实施有效治理措施的重要基础和依据。 为建立以高光谱数据为基础的烟煤病严重程度反演模型, 在重庆北碚城区采集50个银木叶片样本, 利用ASD FieldSpec HandHeld光谱仪获取高光谱数据, 通过数码相机和ENVI软件获取叶面积数据, 将银木叶片烟煤病面积与整个叶片面积的比例作为烟煤病的严重程度, 建立相关性最大波段的烟煤病反演模型, 探究烟煤病严重程度与光谱曲线之间可能存在的关系。 结果表明: 单叶尺度下, 健康叶片在560 nm波段附近有明显的反射峰, 随着烟煤病严重程度增大, 反射峰逐渐消失, 在可见光与近红外波段, 总体上光谱反射率与烟煤病病情严重程度呈负相关性。 500～650和720～850 nm为烟煤病的光谱敏感波段, 其中相关度最大值点为550 nm波段, 相关系数达到-0.72。 在烟煤病严重程度与叶片波段原始光谱信息及多波段组合关系研究中, 单叶尺度下785 nm波段高光谱参数与烟煤病严重程度建立的回归模型的决定系数（R2）最大, 为0.875。 通过模型的显著性检验和预测精度检验, 785nm 处的光谱反射率建立的二次曲线模型为最优。 证明在单叶尺度下, 基于785 nm波段的二次曲线模型反演烟煤病的效果较为理想。

Sooty mould is one of very common plant diseases in the tropical and subtropical regions of southern China, which does great harm to China’s agricultural production. Monitoring and forecasting this disease provide a significant foundation and basis for the implementation of effective governance measures. To establish hyperspectral based data for Sooty mould severity level inversion model, this study collected 50 Cinnamomum Septentrionale samples’ hyperspectral data by ASD FieldSpec HandHeld type spectrometer in Beibei, Chongqing. Leaf area data was obtained through the digital camera and ENVI software, and the sooty mould severity was calculated by the ratio of sooty mould area and the whole leaf area. Then, a regression model was established by the maximum correlation between the sooty disease severity and the spectral reflectance data. This study shows that health leaves at around 560 nm band have obvious reflection peak, and when the severity of Sooty mould increases, the reflection peak gradually disappears. The spectral reflectance is negatively correlated with Sooty mould disease level in visible light and near infrared band, and the 500～650 and 720～850 nm are the sensitive spectral bands of Sooty mould. The correlation maximum point is in the 550 nm band, and the correlation coefficient reaches -0.72. By the analysis of Sooty mould severity with multi band spectral reflectance data, this study concludes that the 785 nm band has the largest correlation with disease severity and its regression model Sooty mould. The coefficient of determination (R2) reaches 0.875. Through the significance test and the prediction accuracy of the model, the conic model established at 785 nm is the best, which proved that the conic model based on the 785 nm band is effective in inversion of Sooty mould severity in single leaf scale.