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Monitoring of nitrogen accumulation in wheat plants based on hyperspectral data

文献类型： 外文期刊

作者： Song, Xiao 1 ; Xu, Duanyang 2 ; Huang, Chenchen 3 ; Zhang, Keke 1 ; Huang, Shaomin 1 ; Guo, Doudou 1 ; Zhang, Shuiqing 1 ; Yue, Ke 1 ; Guo, Tengfei 1 ; Wang, Shasha 1 ; Zang, Hecang 1 ;

作者机构： 1.Henan Acad Agr Sci, Inst Plant Nutrient & Environm Resources, Zhengzhou 450002, Henan, Peoples R China

2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China

3.Zhengzhou Univ, Acad Life Sci, Zhengzhou 450002, Henan, Peoples R China

关键词： Nitrogen accumulation; Wheat; Hyperspectral; Remote sensing

期刊名称：REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT （ 影响因子：4.7； ）

ISSN： 2352-9385

年卷期： 2021 年 23 卷

页码：

收录情况： SCI

摘要： Crop nitrogen nutrition is an important indicator for evaluating crop growth. Rapid and non-destructive estimation of nitrogen accumulation in wheat leaves is of great significance for crop nitrogen fertilizer management. Based on field test data from multiple wheat varieties for different locations, years, nitrogen levels, and growth periods, the relationship between 11 canopy hyperspectral parameters and nitrogen accumulation in wheat plants was studied. According to the results of correlation and regression analysis, the flowering period of wheat was selected as the most suitable growth period for crop growth evaluation (the average R-2 was 0.732, and the root mean square error (RMSE) was 0.354). A new vegetation index, NDchI*DDN (referred to as the nitrogen accumulation vegetation index, abbreviated as NAVI), was constructed based on the pairwise combination of traditional vegetation index products. This parameter had a high correlation with plant nitrogen accumulation (R-2 = 0.856), and the root mean square error (RMSE) was 0.296. Tested by independent experimental data, the fitting degree of the plant nitrogen accumulation inversion model established with NAVI as the variable was R-2 = 0.861, the relative error RE = 9.3%, RMSE = 0.398, and the prediction accuracy was significantly higher than other models. Therefore, construction of a NAVI-based plant nitrogen accumulation monitoring model gave ideal test results, which could reduce the limitations of experimental conditions and is expected to provide new important technical support for precise fertilization.

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