小波法反演条锈病胁迫下冬小麦冠层叶片全氮含量  

Retrieving canopy leaf total nitrogen content of winter wheat by continuous wavelet transform

作　　者：何汝艳[1] 乔小军[1] 蒋金豹[1] 郭会敏[1] 

机构地区：[1]中国矿业大学(北京)地球科学与测绘工程学院,北京100083

出　　处：《农业工程学报》2015年第2期141-146,共6页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(41101397;41271412)

摘　　要：为监测条锈病胁迫下冬小麦的氮素营养状况,该文通过野外试验测量了感染条锈病的冬小麦冠层光谱数据和相应叶片全氮(leaf total nitrogen,LTN)含量,分析了冬小麦条锈病病情指数(disease index,DI)与LTN之间的关系,对冠层光谱进行了连续小波变换(continuous wavelet transform,CWT)处理得到小波系数,并选择一些高光谱指数,分别利用支持向量机(support vector machine,SVM)回归方法构建了小波系数、高光谱指数与冬小麦LTN含量之间的反演模型。研究表明,随着冬小麦DI增大,LTN含量逐渐减小,相关系数为-0.784;CWT处理得到的小波系数为自变量构建的反演冬小麦LTN含量的模型精度普遍高于高光谱指数为自变量的模型精度,其中以Mexican Hat小波函数处理得到的小波系数423(4)建立的反演模型为最优模型,RMSE为0.315,RE为7.62%。因此,该研究表明可以联合应用CWT与SVM方法对条锈病胁迫下冬小麦LTN含量进行反演,且具有较高的估测精度。该研究成果对小麦作物病害预防、指导作物施肥具有重要现实应用意义。The aim of this paper is to monitor the nitrogen nutrition status of winter wheat under stripe rust stress by hyperspectral remote sensing. The experiment was carried out at Beijing Xiaotangshan Precision Agriculture Experimental Base, China （40＆#176;10.6′N, 116＆#176;16.3′E）. The cultivar of winter wheat was Jingdong 8 which was very susceptible to stripe rust. Canopy spectral reflectance data of winter wheat was collected by an ASD Fieldspec FR spectroradiometer and the disease index （DI） was measured through counting the number of wheat leaf under stripe rust stress artificially in the field. Leaf total nitrogen （LTN） content of winter wheat used to calculate DI was measured in the laboratory. The relationship between DI of stripe rust and LTN content of winter wheat was analyzed. The canopy spectra were processed by the method of continuous wavelet transform （CWT） on 10 scales, therefore, a series of wavelet coefficients were obtained in this way. The correlation coefficients between wavelet coefficients and LTN content were calculated, and then, the wavelet coefficients, which had strong correlation with LTN content, were chosen. Several hyperspectral indices were also selected according to previous research results, namely SR （simple ratio index）, PRI （photochemical reflectance index）, NDVI （normalized difference vegetation index）, OSAVI （optimized soil-adjusted vegetation index）, SIPI （Structure insensitive pigment index）, LIC1 （lichtenthaler index 1）, LIC2 （lichtenthaler index 2）, LIC3 （lichtenthaler index 3）, TVI （triangular vegetation index） and MTVI2 （modified triangular vegetation index 2）, which had high correlations with LTN content. Both wavelet coefficients and hyperspectral indices were used as independent variables of models to retrieve LTN content of winter wheat, and support vector machine （SVM） regression method was used to establish the estimation models. The above estimation models of different types of variables were made a

关 键 词：支持向量机 小波变换 氮 叶片 条锈病 冬小麦 

分 类 号：S127[农业科学—农业基础科学]