植被叶片生化组分光谱响应分析与估测

Analysis and estimation of spectral response of biochemical components in vegetation leaves

【目的】传统植被生化组分化学分析法具有破坏性且耗费较多人力物力等缺点,高光谱技术可以为快速无损地估测植被生化组分含量提供有效手段。【方法】利用LOPEX’93数据集,对原始光谱进行了4种光谱变换(一阶微分DR、取倒数1/R、取对数log R、倒数的对数log(1/R)),提取了“三边”参数、光谱指数、全波段光谱指数,并分析它们与叶绿素含量(C_ab)、干物质含量(LMA)、等效水厚度(EWT)的相关性,将提取的相关性高的光谱特征作为输入变量,分别采用偏最小二乘回归(PLSR)、支持向量机(SVM)、随机森林(RF)建立模型并进行预测,以模型的决定系数R2和均方根误差RMSE作为判定模型优劣的指标。【结果】4种光谱变换中最有效的方式是一阶微分,其中,一阶微分波段D742、D535对C_ab有较强的光谱响应;D1700、D1229、D2192对LMA具有较高的响应;D1145、D1302、D955对EWT具有较高的响应。“三边”参数虽然在一定程度上提高了与叶绿素的相关性,但是相关性不如一阶微分光谱与光谱指数。在光谱指数的分析方法中,对C_ab响应较高的光谱指数有DD、NDVI842,对LMA和EWT具有较高响应的光谱指数有NDLMA、NDMI和Ratio975、Ratio1200。构建的4种全波段光谱指数中,RI、DI、NDVI、TVI与C_ab相关性分别是0.64、0.66、0.64、0.67,与传统光谱指数相比相关性得到明显的增强。将以上关于C_ab、LMA、EWT的光谱特征分别参与建模并估测,最佳估测模型分别为SVM、PLSR、RF,R2分别为0.73、0.75、0.93,RMSE分别为9.62、0.001 274、0.000 971。【结论】一阶微分变换及全波段光谱指数可以提高光谱对生化组分的响应能力,构建的SVM、PLSR、RF模型总体上可以实现对植被叶片C_ab、LMA、EWT的估测,为高光谱数据精细化反演植被生化组分提供了参考。

【Objective】Traditional chemical analysis of vegetation biochemical components is destructive and labor-intensive,and hyperspectral techniques can provide an effective means for rapid and non-destructive estimation of vegetation biochemical components.【Method】Based on the LOPEX’93 data set,four transformed spectra(first-order differential DR,reciprocal 1/R,logarithmic logR,reciprocal logarithmic log(1/R))were performed on the original spectra,and the trilateral parameters,spectral indices,and full-band spectral indices were extracted and the correlations with chlorophyll content(C_ab),dry matter content(LMA),and equivalent water thickness(EWT)were analyzed,and the extracted spectral features with high correlations were used as input variables,and the models were built and predicted by partial least squares regression(PLSR),support vector machine(SVM),and random forest(RF),respectively.2 The decision coefficient R and root mean square error(RMSE)were used as indicators to determine the quality of the models.【Result】The most effective method among the four spectral transformations was the first-order differentiation,in which the first-order differential bands D742 and D535 had strong spectral responses to C_ab;D1700,D1229,and D2192 had high responses to LMA;D1145,D1302,and D955 had high responses to EWT.Although the trilateral parameters improved the correlation with chlorophyll to some extent,the correlation was not as good as the first-order differential spectra and spectral indices.Among the four full-band spectral indices constructed,the correlations of RI,DI,NDVI,and TVI with C_ab were 0.64,0.66,0.64,and 0.67,and the correlations were significantly enhanced compared with the conventional spectral indices.The above 8,5,5 spectral features about C_ab,LMA,and EWT were 2 involved in modeling and estimation,and the best estimation models were SVM,PLSR,and RF with R of 0.73,0.75,and 0.93,and RMSE of 9.62,0.001274,and 0.000971,respectively.【Conclusion】The first-order differential transform and full-band spectral index can improve the spectral response to biochemical components,and the constructed SVM,PLSR,and RF models can generally achieve the estimation of vegetation leaf C_ab,LMA and EWT,which provide a reference for refining the inversion of vegetation biochemical components from hyperspectral data.