标题：New nni model in winter wheat based on hyperspectral index
作者：Jianwen, Wang ;Zhenhai, Li ;Xingang, Xu ;Hongchun, Zhu ;Haikuan, Feng ;Chang, Liu ;Ping, Gan ;Xiaobin, Xu
作者机构：[Jianwen, Wang ;Hongchun, Zhu ;Ping, Gan ;Xiaobin, Xu ] College of Geomatics, Shandong University of Science and Technology, Qingdao; Shandong; 266590 更多
会议名称：11th IFIP WG 5.14 International Conference on Computer and Computing Technologies in Agriculture, CCTA 2017
会议日期：August 12, 2017 - August 15, 2017
来源：IFIP Advances in Information and Communication Technology
摘要：Nitrogen nutrition index (NNI) can monitor winter wheat nitrogen status precisely. Current studies by remote sensing data are to construct the above-ground biomass (AGB) and plant nitrogen concentration (PNC) with spectral indices, respectively, and then substitute them into established NNI equation. This leads to an accumulation of unavoidable error. Therefore, the objective in the study was to construct a direct NNI equation with remote sensing data to reduce this error. Field measurements data including AGB, PNC and canopy hyperspectral at different winter wheat growth stages during 2012/2013, 2013/2014, 2014/2015, 2015/2016 growing seasons in Beijing, China were collected. This study was endeavored to establish a vegetation index critical N dilution curve (Nvic) with two different spectral indices, RTVI (Red edge Triangular Vegetation Index) and NDVI/PPR (the ratio of the normalized difference vegetation index to the plant pigment ratio), which are sensitive to AGB and PNC, respectively. The vegetation index NNI (NNIvi) was calculated from the ratio between the NDVI/PPR and Nvic. Results showed that (1) Nvic can be described by an equation, Nvic = 1106.4(VIRTVI)−1.512, where RTVI ranged from 2.39 to 22.14; the determination coefficient (R2) was 0.57; (2) The NNI based on the above Nvic dilution curve was in good accordance with the classical NNI, with the root mean square error (RMSE), normalized RMSE (nRMSE) and normalized average error (NAE) of 0.194, 22%, and 11%, respectively. The critical nitrogen dilution model constructed in this study was available for winter wheat nitrogen status monitoring. Thus, this study offers a new method which was suitable and convenient for estimating the NNI of the winter wheat and it can reduce quadric error for constructing NNI through indices directly instead of inversing AGB and PNC.
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