Measuring leaf nitrogen concentration in-winter wheat using double-peak spectral reflection remote sensing data
Measuring leaf nitrogen concentration in-winter wheat using double-peak spectral reflection remote sensing data
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DOI：
10.1016/j.fcr.2014.01.010
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发表时间：
2014-03-15
影响因子：
5.8
通讯作者：
Guo, Tian-Cai
中科院分区：
文献类型：
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作者：
Feng, Wei;Guo, Bin-Bin;Guo, Tian-Cai
遥感技术是一种快速、无损的作物氮素营养状况评估方法。以冬小麦（Triticum aestivum L.）考察在河南省郑州、焦作、开封3个不同地点进行了3年冬小麦田间试验。为提高LNC的预测精度和稳定性，提出了一种新的双峰面积指数。常用的最佳红边光谱指数被用来监测LNC模型。现有的蔬菜指数和LNC之间的关系的分析表明，红边光谱参数是最敏感的，在这种情况下。对LNC与mND 705和REPle进行积分线性回归，以描述LNC模式的动态性质，得到的系数（R-2）分别为0.83和0.82，标准误差（SE）分别为0.414和0.424。这些新的双峰面积参数是基于红边特征的分析和基于REPig划分的双峰面积的最佳归一化差（NDDAig）构造的，其形式为计算（R-755 + R-680 - 2 x R-REPig）/（R-755 - R-680），发现与LNC高度相关（最高R-2=0.85;最低SE=0.385）。当独立数据拟合到推导的方程中时，使用mND 705、REPle和NDDAig测量和估计的LNC之间的平均相对误差（RE）值分别为14.1%、13.7%和11.5%，表明NDDAig的上级拟合和更好的性能。这些结果表明，模型可以准确地估计LNC在小麦，和新的双峰面积指数是更有效的LNC建模比以前报道的红边指数。(C)2014爱思唯尔有限公司版权所有。
Remote sensing is proving to be a rapid non-destructive method for crop nitrogen (N) status assessment. In this study, quantitative relationships between leaf N concentration (LNC) and ground-based canopy hyperspectral reflectance in winter wheat (Triticum aestivum L.) were investigated. Winter wheat field experiments were conducted over three years at different sites (Zhengzhou, Jiaozuo and Kaifeng) in Henan, China. Different N rates and wheat cultivars were tested, and a novel double-peak area index was developed to improve the prediction accuracy and stability of LNC measurement. The common optimal red-edge spectral indices were used to monitor the LNC models. Analysis of the relationship between existing vegetable indices and LNC indicated that red-edge spectral parameters were the most sensitive in this case. Integrated linear regression of LNC with mND705 and REPle was performed to describe the dynamic nature of the LNC patterns, giving coefficients (R-2) of 0.83 and 0.82, and the standard errors (SE) of 0.414 and 0.424, respectively. These novel double-peak area parameters were constructed based on analysis of the red-edge characteristics, and the optimal normalized difference of the double-peak areas based on REPig division (NDDAig), in the form of (R-755 + R-680 - 2 x R-REPig)/(R-755 - R-680), were calculated and found to be highly correlated with LNC (highest R-2=0.85; lowest SE=0.385). When independent data were fit into the derived equations, the average relative error (RE) values were 14.1%, 13.7% and 11.5% between measured and estimated LNC using mND705, REPle and NDDAig, respectively, indicating a superior fit and better performance for NDDAig. These results suggest that the models can accurately estimate LNC in wheat, and the novel double-peak area index is more effective for modeling LNC than previously reported red-edge indices. (C) 2014 Elsevier B.V. All rights reserved.