【摘要】 综合利用反射率光谱在作物生化参数探测的优势和叶绿素荧光在光合生理诊断的优势,开展了日光诱导叶绿素荧光（SIF）和反射率光谱指数协同的小麦条锈病光谱探测研究,以期提高小麦条锈病病情严重度的预测精度。利用O₂-A波段（760 nm）的SIF信号和对小麦条锈病病情严重度敏感的7种反射率光谱指数,基于支持向量机（SVM）、逐步回归（SR）以及神经网络（BP）算法,定量分析了反射率光谱指数和反射率光谱指数与SIF协同的小麦条锈病病情严重度（DI）光谱探测模型的预测精度。结果表明:①SIF与小麦条锈病病情严重度之间存在极显著的负相关关系,SIF与DI间的响应能有效地应用于小麦条锈病的遥感探测;②SIF结合反射率光谱指数的小麦条锈病病情严重度光谱模型探测精度均高于反射率光谱指数模型,SIF能够显著提高小麦条锈病病情严重度的光谱探测精度;③无论是利用反射率光谱指数还是SIF结合反射率光谱指数作为小麦条锈病病情严重度预测模型的输入参数,训练模型以BP模型的预测精度最高,但验证结果表明SVM与SR方法构建的病情严重度预测模型效果较优。更多还原

【Abstract】 Detection of wheat stripe rust is important for agriculture management and decision,this paper aims to improve detection accuracy of the disease severity of wheat stripe rust by integrating the advantages of reflectance spectroscopy in the detection of crop biochemical parameters and the advantages of chlorophyll fluorescence in photosynthetic physiology diagnosis.Firstly,the solar-induced chlorophyll fluorescence（SIF） at O₂-A band（760 nm） was calculated using the 3FLD algorithm,and seven spectral indices sensitive to wheat stripe rust were investigated for estimating the disease severity.Then,three classic statistical modelling methods,including Support Vector Machine（SVM）,Stepwise Regression（SR） and BP neural network（BP）,were used to quantitatively investigated the performance of the spectral indices and SIF for detection of winter wheat stripe rust severity.The results show that:（1） there is a significantly negative correlation between SIF and the severity of wheat stripe rust.The relationship between SIF and DI can be effectively applied to detect wheat stripe rust.（2） the spectral models based on SIF combined with spectral indices are more accurate than those based on spectral indices.SIF can significantly improve the detection accuracy of the disease severity of winter wheat stripe rust.（3） compared to the SVM and SR methods,the training model constructed by the BP neural network has the highest prediction accuracy whether using the spectral indices or SIF combined spectral indices.However,the verification results show that the disease severity prediction model constructed by SVM and SR method have a better prediction.更多还原