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Multi-LUTs method for canopy nitrogen density estimation in winter wheat by field and UAV hyperspectral

文献类型： 外文期刊

作者： Li, Zhenhai 1 ; Li, Zhenhong 2 ; Fairbairn, David 2 ; Li, Na 3 ; Xu, Bo 1 ; Feng, Haikuan 1 ; Yang, Guijun 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Newcastle Univ, Sch Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England

3.Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100191, Peoples R China

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

关键词： Canopy nitrogen density; Look up table; Unmanned aerial vehicle platforms; Hyperspectral image; Winter wheat

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2019 年 162 卷

页码：

收录情况： SCI

摘要： Unmanned aerial vehicle (UAV) based hyperspectral images linked to a radiative transfer model can provide a promising approach for high throughput monitoring of plant nitrogen (N) status. In this study, multiple lookup tables (Multi-LUTs), each LUT corresponding to one growth stage, were constructed based on the N-PROSAIL model, a radiative transfer model, and LUT size was optimized for improving computing efficiency. The objective is to use the constructed Multi-LUTs for estimating canopy N density (CND) in winter wheat. Results suggest that Multi-LUTs of leaf area index, leaf N density and two spectral indices (MSR and MCARI/MTVI2) in winter wheat demonstrate good performance of CND estimation; and LUTs with the optimal size of 6000 rows can yield good accuracy. The R-2 and nRMSE values of the regression relationship between estimated and measured CND were 0.83 and 0.23 from field hyperspectral data, and 0.69 and 0.27 from UAV based hyper-spectral imagery during the 2014-2015 growing season. CND by Multi-LUTs method was also accurately estimated from field hyperspectral data during the 2013-2014 growing season, with R-2 and nRMSE values of 0.74 and 0.26. The estimation accuracy of CND based UAV data was a slightly lower than based field data. The resultant thematic CND map accurately exhibits CND variability at varying spatial and temporal scales. Results from this study confirmed the potential of combining UAV based hyperspectral imagery and physical optics approach for estimating CND in winter wheat.

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