何汝艳, 乔小军, 蒋金豹, 郭会敏. 小波法反演条锈病胁迫下冬小麦冠层叶片全氮含量[J]. 农业工程学报, 2015, 31(2): 141-146. DOI: 10.3969/j.issn.1002-6819.2015.02.020
    引用本文: 何汝艳, 乔小军, 蒋金豹, 郭会敏. 小波法反演条锈病胁迫下冬小麦冠层叶片全氮含量[J]. 农业工程学报, 2015, 31(2): 141-146. DOI: 10.3969/j.issn.1002-6819.2015.02.020
    He Ruyan, Qiao Xiaojun, Jiang Jinbao, Guo Huimin. Retrieving canopy leaf total nitrogen content of winter wheat by continuous wavelet transform[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(2): 141-146. DOI: 10.3969/j.issn.1002-6819.2015.02.020
    Citation: He Ruyan, Qiao Xiaojun, Jiang Jinbao, Guo Huimin. Retrieving canopy leaf total nitrogen content of winter wheat by continuous wavelet transform[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(2): 141-146. DOI: 10.3969/j.issn.1002-6819.2015.02.020

    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

    • 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。

       

      Abstract: Abstract: 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°10.6′N, 116°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 comparison. Cross-validation method was used to examine estimation model accuracies of winter wheat canopy LTN content. The test results showed that LTN content of winter wheat decreased gradually when DI of stripe rust increased and the correlation coefficient between LTN content and DI was -0.784 (n=33). The research results indicated that the model prediction accuracies of SR, PRI, LIC2, LIC3, TVI and MTVI2 were very low, the values of R2 were less than 0.700, and root mean square error (RMSE) and relative error (RE) were both high. The estimated models of NDVI, OSAVI, SIPI and LIC1 had higher accuracies than aforementioned hyperspectral indices. However, the accuracies of LTN content estimation models which were built by wavelet coefficients obtained by CWT were generally higher than those of hyperspectral indices. The optimal model was established by wavelet coefficient 423(4) which was acquired by Mexican hat wavelet function, and RMSE and RE were 0.315 and 7.62%, respectively. The wavelet coefficient 423(4) was located in 423 nm where was near to 430 nm which is the nitrogen absorption waveband. The estimation model of wavelet coefficient 663(5) was secondary, which had the prediction RMSE of 0.345 and RE of 8.28%. The variable of 663(5) obtained by Daubechises (Db5) wavelet function was also close to the nitrogen absorption waveband which lies in 660 nm. Therefore, the method of jointing CWT and SVM regression is feasible to retrieve LTN content of winter wheat under stripe rust stress and the estimation models have high accuracies. There is an important practical meaning for preventing diseases of wheat and instructing crop fertilization.

       

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