基于红边参数的植被叶绿素含量高光谱估算模型

    Hyperspectral models for estimating vegetation chlorophyll content based on red edge parameter

    • 摘要: 利用ASD便携式野外光谱仪和SPAD-502叶绿素计实测了落叶阔叶树法国梧桐、毛白杨叶片的高光谱反射率与叶片绿度,并对原始光谱反射率及一阶导数光谱与叶片绿度进行了相关分析,建立了基于红边位置、峰度系数、偏度系数的叶片叶绿素含量的高光谱估算模型,最后采用红边位置、峰度、偏度作为BP人工神经网络的输入变量进行了叶绿素含量的估算。结果表明:基于红边位置的法国梧桐、毛白杨叶绿素估算模型的决定系数达到0.7366、0.7289;基于峰度、偏度建立的估算模型可以有效提高估算精度,模型的决定系数均达0.8341以上;法国梧桐和毛白杨人工神经网络模型的确定系数决定系数分别达到0.9574和0.9523。与单变量模型相比人工神经网络模型反演精度明显提高,是一种良好的植被叶绿素含量高光谱反演模式。

       

      Abstract: Hyperspectral reflectance and green degree of Platanus orientalis L. and Populus tomentosa Carr. leaves were measured by the ASD portable spectrometer and the portable chlorophyll meter SPAD-502, respectively. The correlations between spectral reflectance, first derivative spectral reflectance and leaf green degree were analyzed. The hyperspectral models for estimating vegetation chlorophyll content based on red edge position, kurtosis and skewness were established, and the red edge position, kurtosis and skewness were used as the input variables of ANN-BP to estimate the content of chlorophyll. The results showed that red edge position of Platanus orientalis L. and Populus tomentosa Carr. had close relations with chlorophyll content, the regression determination coefficients were 0.7366 and 0.7289, respectively. The regression models established with kurtosis and skewness were obtained which could improve the estimating precision effectively, and the least determination coefficients were above 0.8341. The determination coefficients of ANN-BP models of Platanus orientalis L. and Populus tomentosa Carr. were 0.9574 and 0.9523, respectively. Compared with the models of single variable, ANN-BP model was a good hyperspectrum inversion model for estimating vegetation chlorophyll content, which could greatly improve estimation accuracy of vegetation chlorophyll content.

       

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