Study on the Response Model of Spectral Signatures of Vegetation Leaves on the Stress Level for Sooty Mould
YANG Xing-chuan1, 2, 3, LUO Hong-xia3, ZHAO Wen-ji1, 2*, CHENG Yu-si3, WANG Hao-fei1, 2
1. Key Laboratory of 3D Information Acquisition and Application of Ministry of Education, Capital Normal University, Beijing 100048, China
2. Beijing Key Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing 100048, China
3. School of Geographical Sciences, Southwest University, Chongqing 400715, China
Abstract：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.
Key words：Sooty mould; Cinnamomum Septentrionale; Disease monitoring; Hyperspectral; Response model
杨兴川，罗红霞，赵文吉，程玉丝，王皓飞. 植被叶片光谱特征对烟煤病胁迫程度的响应模型研究[J]. 光谱学与光谱分析, 2017, 37(09): 2873-2878.
YANG Xing-chuan, LUO Hong-xia, ZHAO Wen-ji, CHENG Yu-si, WANG Hao-fei. Study on the Response Model of Spectral Signatures of Vegetation Leaves on the Stress Level for Sooty Mould. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(09): 2873-2878.
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