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Journal of Integrative Agriculture  2021, Vol. 20 Issue (9): 2535-2551    DOI: 10.1016/S2095-3119(20)63379-2
Special Issue: 农业生态环境-遥感合辑Agro-ecosystem & Environment—Romote sensing
Agro-ecosystem & Environment Advanced Online Publication | Current Issue | Archive | Adv Search |
An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat
ZHAO Yu1, 2*, WANG Jian-wen1, 3*, CHEN Li-ping1, 2, FU Yuan-yuan1, 2, ZHU Hong-chun3, FENG Hai-kuan1, 2, XU Xin-gang1, 2, LI Zhen-hai1, 2 
1 Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs/Beijing Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China
2 National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China
3 College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, P.R.China
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氮素营养指数(NNI)是作物氮素诊断的可靠指标。然而,目前还没有适用于多生育时期NNI反演的光谱指数。为克服传统NNI直接反演方法(NNIT1)和通过反演生物量(AGB)和植株氮浓度(PNC)进行NNI间接反演方法(NNIT2)在多生育期应用的局限性,本文构建了一个新的NNI遥感指数(NNIRS)。本文基于连续四年(2012–2013(Exp.1),2013–2014(Exp.2),20142015(Exp.3)和20152016(Exp.4))的冬小麦田间试验,采用交叉验证方法利用氮素相关植被指数和生物量相关植被指数构建了遥感关键氮浓度稀释曲线(Nc_RS)和根据NNI构建原理得到的NNIRS进行综合评价。结果表明:(1)由标准叶面积指数决定指数(sLAIDI)和红边叶绿素指数(CIred edge)构建的NNIRS模型表达式为NNIRS=CIred edge/(a×sLAIDIb),在Exp.1/2/4,Exp.1/2/3,Exp.1/3/4和Exp.2/3/4中参数“a”分别等于2.06,2.10,2.08和2.02,参数“b”分别等于0.66,0.73,0.67和0.62;(2)与NNIT1和NNIT2模型相比,NNIRS模型的精度最高(R2的范围为0.50–0.82,RMSE的范围为0.12–0.14);(3)NNIRS在验证数据集中也达到了较好的精度,RMSE分别为0.09,0.18,0.13和0.10。因此,本文认为NNIRS模型在氮素遥感诊断中具有较大的潜力。

The nitrogen nutrition index (NNI) is a reliable indicator for diagnosing crop nitrogen (N) status.  However, there is currently no specific vegetation index for the NNI inversion across multiple growth periods.  To overcome the limitations of the traditional direct NNI inversion method (NNIT1) of the vegetation index and traditional indirect NNI inversion method (NNIT2) by inverting intermediate variables including the aboveground dry biomass (AGB) and plant N concentration (PNC), this study proposed a new NNI remote sensing index (NNIRS).  A remote-sensing-based critical N dilution curve (Nc_RS) was set up directly from two vegetation indices and then used to calculate NNIRS.  Field data including AGB, PNC, and canopy hyperspectral data were collected over four growing seasons (2012–2013 (Exp.1), 2013–2014 (Exp. 2), 2014–2015 (Exp. 3), 2015–2016 (Exp. 4)) in Beijing, China.  All experimental datasets were cross-validated to each of the NNI models (NNIT1, NNIT2 and NNIRS).  The results showed that: (1) the NNIRS models were represented by the standardized leaf area index determining index (sLAIDI) and the red-edge chlorophyll index (CIred edge) in the form of NNIRS=CIred edge/(a×sLAIDIb), where “a” equals 2.06, 2.10, 2.08 and 2.02 and “b” equals 0.66, 0.73, 0.67 and 0.62 when the modeling set data came from Exp.1/2/4, Exp.1/2/3, Exp.1/3/4, and Exp.2/3/4, respectively; (2) the NNIRS models achieved better performance than the other two NNI revised methods, and the ranges of R2 and RMSE were 0.50–0.82 and 0.12–0.14, respectively; (3) when the remaining data were used for verification, the NNIRS models also showed good stability, with RMSE values of 0.09, 0.18, 0.13 and 0.10, respectively.  Therefore, it is concluded that the NNIRS method is promising for the remote assessment of crop N status.
Keywords:  nitrogen nutrition index (NNI)        critical nitrogen dilution curve        standardized leaf area index determining index (sLAIDI)        the red-edge chlorophyll index (CIred edge)  
Received: 08 April 2020   Accepted:
Fund: This research was supported by the earmarked fund for China Agriculture Research System (CARS-03), the National Key Research and Development Program of China (2017YFD0201501 and 2016YFD020060306) and the National Natural Science Foundation of China (41701375 and 61661136003).
Corresponding Authors:  Correspondence LI Zhen-hai, Tel: +86-10-51503215, Fax: +86-10-51503750, E-mail:    
About author:  ZHAO Yu, E-mail:; WANG Jian-wen, E-mail:; * These authors contributed equally to this study.

Cite this article: 

ZHAO Yu, WANG Jian-wen, CHEN Li-ping, FU Yuan-yuan, ZHU Hong-chun, FENG Hai-kuan, XU Xin-gang, LI Zhen-hai. 2021. An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat. Journal of Integrative Agriculture, 20(9): 2535-2551.

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