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Quantitative analysis and hyperspectral remote sensing of the nitrogen nutrition index in winter wheat

文献类型： 外文期刊

作者： Liu, Haiying 1 ; Zhu, Hongchun 2 ; Li, Zhenhai 3 ; Yang, Guijun 3 ;

作者机构： 1.Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao, Shandong, Peoples R China

2.Shandong Univ Sci & Technol, Coll Geomat, Qingdao, Shandong, Peoples R China

3.Minist Agr China, Key Lab Quantitat Remote Sensing Agr, Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：3.151； 五年影响因子：3.266 ）

ISSN： 0143-1161

年卷期： 2020 年 41 卷 3 期

页码：

收录情况： SCI

摘要： The nitrogen nutrition index (NNI) is a quantitative and reliable indicator of the nitrogen nutrition distribution or status of crops. The timely and accurate estimation of the NNI is crucial in agriculture management. In this study, the quantitative analysis and hyperspectral remote sensing modelling of the NNI were conducted, in which the hyperspectral remote sensing data and NNI data at different growth stages of winter wheat were measured using ground and unmanned aerial vehicle (UAV) carrying high spectrometer equipment. First, the NNIs of the four growth stages of winter wheat were calculated and statistically analyzed. Then, the hyperspectral characteristics at different growth stages and various NNIs were examined. Second, the representation wavebands of the hyperspectral data, which were sensitive to the NNI of winter wheat, were acquired and evaluated. In addition, hyperspectral models were established and comparatively assessed for the NNI estimation. Finally, the hyperspectral characteristics and the remote sensing estimation of the NNIs were determined on the basis of UAV-based hyperspectral data. The results are as follows. (1) As the NNIs of winter wheat changed, the characteristic of the red shift, the variations in the red edge position, and the near-infrared waveband range of the hyperspectral data became apparent. (2) The green band, red edge, and near-infrared were sensitive to the NNIs of winter wheat, and they could be effectively used for estimating the NNI. Moreover, the multiple statistical regression models, which were based on representative wavebands, performed well in estimating the NNI results for the different growth stages of winter wheat.

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