小麦条锈病是影响我国小麦产量的主要病害之一, 在小麦受到条锈病菌侵染初期探测到病害信息, 对小麦条锈病的防控以及产量和品质的提高具有更为重要的意义。 反射率光谱主要反映植被生化组分的浓度信息, 而日光诱导叶绿素荧光则对植物光合生理变化响应灵敏。 为了更好地实现小麦条锈病病情严重度的遥感探测, 尤其是条锈病的早期探测, 对日光诱导叶绿素荧光和反射率光谱数据监测小麦条锈病病情严重度的敏感性进行了对比分析。 首先利用地物光谱仪测定了不同病情严重度的小麦冠层光谱数据, 基于夫琅和费暗线原理利用3FLD(three-band Fraunhofer Line Discrimination)方法提取了小麦条锈病不同病情严重度下的日光诱导叶绿素荧光数据, 然后分别利用反射率光谱数据和日光诱导叶绿素荧光数据构建小麦条锈病不同发病状态下的遥感探测模型, 并通过保留样本交叉检验方式对预测模型精度进行了评价。 结果表明: （1）当小麦条锈病病情指数低于20%时, 日光诱导叶绿素荧光对小麦条锈病病害信息的响应比反射率光谱数据更为敏感, 以日光诱导叶绿素荧光为自变量构建的小麦条锈病病情严重度预测模型达到了极显著性水平, 能够在植被叶绿素含量或叶面积指数发生变化之前探测到植物的胁迫状态, 实现作物病害的早期诊断, 而反射率光谱数据则难以探测到条锈病病害信息; （2）在小麦条锈病病情严重度处于中度发病(20%＜DI≤45%)状态时, 虽然日光诱导叶绿素荧光和反射率光谱数据均能实现小麦条锈病病情严重度的遥感探测, 但利用日光诱导叶绿素荧光数据构建的预测模型优于反射率光谱数据; （3）当小麦条锈病病情严重度达到重度水平(DI＞45%)时, 利用反射率光谱数据和日光诱导叶绿素荧光数据构建的小麦条锈病病情严重度预测模型均达到了极显著性水平, 两种数据均能够较好地实现小麦条锈病病情严重度的遥感探测。 该研究结果对提高小麦条锈病的遥感探测精度具有重要的意义, 为利用TanSat等卫星的荧光数据进行小麦条锈病的早期探测提供了参考依据。

Stripe rust of wheat is one of the hazardous diseases which affects the wheat yield in China. It is more significant to early detect wheat stripe rust infection information for the prevention of wheat stripe rust and the improvement of yield and quality. Considering that reflectance spectra are sensitive to variations in the concentration of plant biochemical components, and the sun-induced chlorophyll fluorescence is sensitive to variations in plant photosynthetic physiology. In order to preferably detect the severity of wheat stripe rust disease by remote sensing, especially the earlier detection of wheat stripe rust disease, this study made a comparative analysis of the sensitivity of sun-induced chlorophyll fluorescence and reflectance spectrum data to monitor the severity of wheat stripe rust disease. First used the ASD Field Spec Pro NIR spectrometer to determine the wheat canopy spectral data of different illness severity, on the basis of the principle of fraunhofer line to extracted sun-induced chlorophyll fluorescence data by the method of 3FLD under different illness severity, then respectively induced by reflectance spectra data and sun-induced chlorophyll fluorescence data to construct at different conditions of wheat stripe rust of remote sensing detection model, and through the retained sample cross terms of inspection on the forecast model accuracy is evaluated. The result shows that: (1) when the severity of wheat stripe rust disease was less than 20%, the sun-induced chlorophyll fluorescence response of wheat stripe rust disease information was more sensitive than reflectance spectra data, and the sun-induced chlorophyll fluorescence as the independent variable to build the forecasting model of wheat stripe rust disease severity reached the extremely significant level. It can earlier diagnose the crop diseases by detecting the stress state of plants before the change of chlorophyll content or leaf area index, while it is hard to use the reflectivity spectrum data to detect wheat stripe rust damage information. (2) when the severity of wheat stripe rust disease is in the state of moderate incidence (20%45%), the prediction model of severity of wheat stripe rust disease constructed by using reflectance spectral data and sun-induced chlorophyll fluorescence data has reached the extremely significant level, both of which can preferably detect the severity of wheat stripe rust by remote sensing. The results of this study have great significance for improving the remote sensing detection accuracy of wheat stripe rust, and it provides reference basis for the earlier detection of stripe rust in wheat by using TanSat or other satellite fluorescence data.