Remote Sensing of Leaf and Canopy Nitrogen Status in Winter Wheat (Triticum aestivum L.) Based on N-PROSAIL Model
文献类型: 外文期刊
作者: Li, Zhenhai 1 ; Jin, Xiuliang 4 ; Yang, Guijun 1 ; Drummond, Jane 5 ; Yang, Hao 1 ; Clark, Beth 6 ; Li, Zhenhong 3 ; Zhao 1 ;
作者机构: 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
3.Newcastle Univ, Sch Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
4.INRA, NAPV, UMR EMMAH, F-84914 Avignon, France
5.Univ Glasgow, Sch Geog & Earth Sci, Glasgow G12 8QQ, Lanark, Scotland
6.Newcastle Univ, Sch Nat & Environm Sci, New
关键词: leaf nitrogen concentration; canopy nitrogen density; radiative transfer model; hyperspectral; winter wheat
期刊名称:REMOTE SENSING ( 影响因子:4.848; 五年影响因子:5.353 )
ISSN: 2072-4292
年卷期: 2018 年 10 卷 9 期
页码:
收录情况: SCI
摘要: Plant nitrogen (N) information has widely been estimated through empirical techniques using hyperspectral data. However, the physical model inversion approach on N spectral response has seldom developed and remains a challenge. In this study, an N-PROSAIL model based on the N-based PROSPECT model and the SAIL model canopy model was constructed and used for retrieving crop N status both at leaf and canopy scales. The results show that the third parameter (3rd-par) retrieving strategy (leaf area index (LAI) and leaf N density (LND) optimized where other parameters in the N-PROSAIL model are set at different values at each growth stage) exhibited the highest accuracy for LAI and LND estimation, which resulted in R-2 and RMSE values of 0.80 and 0.69, and 0.46 and 21.18 mu g.cm(-)(2), respectively. It also showed good results with R-2 and RMSE values of 0.75 and 0.38% for leaf N concentration (LNC) and 0.82 and 0.95 g.m(-2) for canopy N density (CND), respectively. The N-PROSAIL model retrieving method performed better than the vegetation index regression model (LNC: RMSE = 0.48 - 0.64%; CND: RMSE = 1.26 - 1.78 g.m(-2)). This study indicates the potential of using the N-PROSAIL model for crop N diagnosis on leaf and canopy scales in wheat.
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