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Estimation of foliar nitrogen of rubber trees using hyperspectral reflectance with feature bands

文献类型： 外文期刊

作者： Guo, Peng-Tao 1 ; Li, Mao-Fen 2 ; Luo, Wei 1 ; Cha, Zheng-Zao 1 ;

作者机构： 1.Chinese Acad Trop Agr Sci, Rubber Res Inst, Haikou 571101, Hainan, Peoples R China

2.Chinese Acad Trop Agr Sci, Hainan Prov Key Lab Pract Res Trop Crops Informat, Inst Sci & Tech Informat, Haikou 571101, Hainan, Peoples R China

关键词： Leaf nitrogen concentration; Artificial neural networks; Competitive adaptive reweighted sampling; Successive projections algorithm

期刊名称：INFRARED PHYSICS & TECHNOLOGY （ 影响因子：2.638； 五年影响因子：2.581 ）

ISSN： 1350-4495

年卷期： 2019 年 102 卷

页码：

收录情况： SCI

摘要： Leaf nitrogen is an indispensable parameter for simulating biogeochemical cycling in ecosystems. However, detailed information about leaf nitrogen of rubber trees (Hevea brasiliensis) in the tropical regions is rare. Aims of the current study were to determine the feature bands of foliar nitrogen of rubber trees and to examine the ability of these bands to estimate leaf nitrogen concentration (LNC). In this work, a hybrid variable selection method namely competitive adaptive reweighted sampling (CARS) combined with successive projections algorithm (SPA) (CARS-SPA) was used to extract feature bands for predicting LNC of rubber trees. Two hundred leaf samples were gathered from five fields through April to November (tapping season) in 2014. These samples were divided into calibration dataset (n = 140) and prediction dataset (n = 60) using the Kennard-Stone algorithm. Sixty bands were determined from the first derivative spectrum using the CARS-SPA. Among these bands, not only the commonly used absorption features of chlorophyll, protein, and the red edge position were included, but also those of starch, cellulose, and lignin were selected. All the 60 bands were used as input variables for the partial least squares regression (PLSR) and artificial neural networks (ANN) models to estimate LNC of rubber trees. The determination coefficient of calibration (r(c)(2)) and prediction (r(p)(2)), and root mean square error of calibration (RMSEC) and prediction (RMSEP), as well as normalized RMSEC (nRMSEC) and normalized RMSEP (nRMSEP) were employed to evaluate performances of these models. Results indicated that CARS-SPA-ANN (r(c)(2) = 0.82, RMSEC = 0.24%, nRMSEC = 7.76%; r(p)(2) = 0.78, RMSEP = 0.22%, nRMSEP = 6.73%) outperformed the other selected models except CARS-PLSR (r(c)(2) = 0.91, RMSEC = 0.17%, nRMSEC = 5.46%; r(p)(2) = 0.80, RMSEP = 0.21%, nRMSECP = 6.44%). However, CARS-SPA-ANN contained much less bands and was more stable than CARS-PLSR. In conclusion, hyperspectral feature bands in combination with ANN could accurately and robustly estimate LNC of rubber trees.

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