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

第一作者： Li, Zhenhai

作者： Li, Zhenhai;Yang, Guijun;Yang, Hao;Zhao, Chunjiang;Li, Zhenhai;Yang, Guijun;Yang, Hao;Zhao, Chunjiang;Li, Zhenhai;Li, Zhenhong;Jin, Xiuliang;Drummond, Jane;Clark, Beth

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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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