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An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat

文献类型： 外文期刊

作者： ZHAO Yu 1 ; WANG Jian-wen 1 ; CHEN Li-ping 1 ; FU Yuan-yuan 1 ; ZHU Hong-chun 3 ; FENG Hai-kuan 1 ; XU Xin-gang 1 ; LI, Z 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Minist Agr & Rural Affairs, Key Lab Quantitat Remote Sensing, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China

关键词： nitrogen nutrition index (NNI); critical nitrogen dilution curve; standardized leaf area index determining index (sLAIDI); the red-edge chlorophyll index (CIred edge)

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：2.848； 五年影响因子：2.979 ）

ISSN： 2095-3119

年卷期： 2021 年 20 卷 9 期

页码：

收录情况： SCI

摘要： 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 & times;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. Superscript/Subscript Available

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