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

被引:1
|
作者
Xu, Xingang [1 ]
Zhao, Chunjiang [1 ]
Song, Xiaoyu [1 ]
Yang, Xiaodong [1 ]
Yang, Guijun [1 ]
机构
[1] Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
来源
基金
北京市自然科学基金; 中国国家自然科学基金;
关键词
Leaf nitrogen concentration; Normalized reflectance; Hyperspectral parameters; Optimal combination principle; Linear programming algorithm; Estimating model; REFLECTANCE RED-EDGE; CHLOROPHYLL CONCENTRATION; PREDICTING NITROGEN; GRAIN PROTEIN; CANOPY; VEGETATION; INDEXES; LIMITS; LEVEL; LAI;
D O I
10.1080/10798587.2014.934596
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
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.
引用
收藏
页码:611 / 623
页数:13
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