ESTIMATING LEAF NITROGEN CONCENTRATION IN BARLEY BY COUPLING HYPERSPECTRAL MEASUREMENTS WITH OPTIMAL COMBINATION PRINCIPLE

文献类型： 外文期刊

第一作者： Xu, Xingang

作者： Xu, Xingang;Zhao, Chunjiang;Song, Xiaoyu;Yang, Xiaodong;Yang, Guijun

作者机构：

关键词： Leaf nitrogen concentration;Normalized reflectance;Hyperspectral parameters;Optimal combination principle;Linear programming algorithm;Estimating model

期刊名称：INTELLIGENT AUTOMATION AND SOFT COMPUTING （ 影响因子：1.647； 五年影响因子：1.469 ）

ISSN： 1079-8587

年卷期： 2014 年 20 卷 4 期

页码：

收录情况： SCI

摘要： Leaf nitrogen concentration (LNC), as a key indicator of nitrogen (N) status, can be used to evaluate N nutrient levels and improve fertilizer regulation in fields. Due to the non-destructive and quick detection, hyperspectral remote sensing with hundreds of very narrow bands plays an unique role in monitoring LNC in crop, but most of the current methods using hyperspectral techniques are still based on spectral univariate analyses, which often bring about the unstability of the models for LNC estimates. By introducing the optimal combination principle to conduct multivariate analyses and form the combination model, this paper proposes a new method with hyperspectral measurments to estimate LNC in barley. First, this study analyzed the relationships between LNC in barley and three types of spectral parameters including spectral position, area features, vegetation indices, and established the quantitative models of determining LNC with the key spectral variables, then using the optimal combination method with linear programming algorithm conducted multivariate analyses for accuracy improvements by calculating the optimal weights to construct the combination model of evaluating LNC. The results showed that most of the three types of spectral variables had significant correlations with LNC under confidence level of 1%, and the univariate models with the key spectral variables (such as Dr and (lambda r + lambda b)/lambda y)) could well describe the dynamic pattern of LNC changes in barley with the determination coefficients (R 2) of 0.620 and 0.622, and root mean square errors (RMSE) of 0.619 and 0.620, respectively, but by comparison the combination model with Dr and lambda b/lambda y exhibited the better fitting with R-2 of 0.702 and RMSE of 0.574. This analysis indicates that hyperspectral measurements displays good potential to effectively estimate LNC in barley, and the optimal combination (OC) method has the better adaptability and accuracy due to the optimal selection of spectral parameters responding LNC, and can be applied for reliable estimation of LNC. The preliminary results of coupling hyperspectral measurements with optimal combination principle to estimate LNC can also provide new ideas for hyperspectral monitoring of other biochemical constituents.

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