冬小麦条锈病单叶光谱特性及严重度反演

Spectral reflectance feature of winter wheat single leaf infected with stripe rust and severity level inversion

对冬小麦条锈病胁迫不同严重度(0、1%、10%、20%、30%、45%、60%和80%)的单叶进行光谱测定,其光谱特征明显,随严重度增加,单叶光谱反射率在可见光550～740nm处增加,差异显著;而近红外平台750～1340nm反射率也呈上升趋势,差异不显著;中红外1350～1600nm反射率上升,差异显著。反射率与严重度相关系数在376～1600nm范围内基本成正相关,达到极显著相关的敏感波段区域为446～725nm与1380～1600nm。入选了493、666nm和1430nm与严重度的相关系数最高的单个波段,利用相关性最好的666nm和相关性最弱的758nm波段组合设计了光谱角度指数SAI(SpectralAngleIndex),与严重度建立的模型相关系数较高。并利用连续统去除法对540～740nm特征反射峰进行归一化定量分析,吸收深度(Depth)与吸收面积(Area)与严重度呈极显著相关,设计的吸收面积指数AAI(AbsorptionAreaIndex)与严重度相关系数最好,可较好地反演单叶严重度。结果表明,条锈病单叶光谱特性明显,利用其光谱反射率可以很好的估算单叶严重度,建立的模型具有很高的反演精度。结果对深入研究冬小麦条锈病害遥感监测机理提供了理论依据。

Leaf spectrum of winter wheat infected with stripe rust was measured using integrating sphere method. Spectral characteristic of single leaf with different severity levels (SL) has a significant difference at yellow light region. Spectral reflectance increases with the increasing of the SL significantly at visible light region and middle-infrared 1350~1600 nm, of which the result is similar to the early research results under canopy condition, but is different at near infrared light region. The mechanism of spectral reflectance changing was demonstrated in this paper. The wavelength bands between 446 nm and 725 nm as well as 1380 nm and 1600 nm were selected out as sensitive bands region to the SL. The individual bands of 493 nm, 666 nm and 1430 nm with the highest correlation coefficient were modeled with SL. The Spectral Angle Index (SAI) was designed to do regression with the single leaf SL using individual band 666 nm with the highest correlation coefficient as well as 758 nm with the lowest correlation coefficient. The feature absorption peak of initial spectral reflectance was normalized quantitatively by the continuum removal method. It indicated that the Depth and the Area of the feature absorption peak were closely related to the single leaf SL. Meanwhile, the Absorption Area Index (AAI) model, which can invert the leaf SL accurately, has been designed in the paper. The results indicate that the reflectance spectral feature is significant and the leaf SL of winter wheat infected with stripe rust can be inversed by the leaf spectral data. The results also provide a good basis for further studying monitoring mechanism of winter wheat stripe rust by remote sensing data.