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

文献类型： 外文期刊

第一作者： He, Peng

作者： He, Peng;Xu, Xingang;Li, Zhenhai;Feng, Haikuan;Yang, Guijun;Zhang, Yongfeng;He, Peng;Xu, Xingang;Li, Zhenhai;Feng, Haikuan;Yang, Guijun;Zhang, Yongfeng;He, Peng;He, Peng;Zhang, Baolei

作者机构：

关键词： spectral indices;variable importance for projection;grey relational analysis;partial least squares regression;leaf chlorophyll content;Winter wheat

期刊名称：REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XVII

ISSN： 0277-786X

年卷期： 2015 年 9637 卷

页码：

收录情况： SCI

摘要： 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.

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