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Estimation of leaf chlorophyll content in winter wheat using variable importance for projection (VIP) with hyperspectral data

机译:利用高光谱数据对冬小麦冬小麦叶片叶绿素含量的估算

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Accurate estimation of leaf chlorophyll content (LCC) has great significance in study of the winter wheat, which is important for indicating nutrition status and photosynthetic. Selecting the closed related variable is the key to LCC monitoring. The variable importance for projection (VIP), applied to little samples and strong correlation data, is one of variable selection methods. In this study, VIP was used to select spectral variables, which includes reflectance spectra, first derivative spectra, vegetation indices and absorption or reflectance position features. The grey relational analysis (GRA) was used as a comparison. The results showed that (1) the VIP technology could be used to variable selection and had a strong correlation. (2) Reflectance spectra with the VIP method displayed the best accuracy, with R~2 and RMSE of 0.42 and 0.663mg/g, respectively. (3) Vegetation indices using GRA had higher estimation than VIP method, with R~2 and RMSE of 0.52 and 0.607 mg/g, respectively. (4) The VIP had more superiority and higher accuracy than the GRA in all kinds of hyperspectral features except vegetation indices. Therefore, the VIP technology could be used to the estimation of LCC and had a relatively good accuracy.
机译:精确估计叶片叶绿素含量(LCC)在冬小麦的研究方面具有重要意义,这对于指示营养状况和光合作用是重要的。选择关闭的相关变量是LCC监控的关键。应用于小样本和强相关数据的投影(VIP)的变量重要性是可变选择方法之一。在该研究中,VIP用于选择光谱变量,其包括反射谱,第一导数谱,植被指数和吸收或反射位置特征。灰色关系分析(GRA)用作比较。结果表明,(1)VIP技术可用于可变选择并具有很强的相关性。 (2)具有VIP方法的反射光谱显示出最佳精度,R〜2和RMSE分别为0.42和0.663mg / g。 (3)使用GRA的植被指数比VIP方法具有更高的估计,R〜2和RMSE分别为0.52和0.607 mg / g。 (4)除了植被指数之外,VIP在各种高光谱特征中具有比GRA更多的优势和更高的精度。因此,VIP技术可用于估计LCC并具有相对良好的准确性。

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