Remote Sensing of Leaf and Canopy Nitrogen Status in Winter Wheat (Triticum aestivum L.) Based on N-PROSAIL Model
Remote Sensing of Leaf and Canopy Nitrogen Status in Winter Wheat (Triticum aestivum L.) Based on N-PROSAIL Model
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基于N-PROSAIL模型的冬小麦叶片和冠层氮素状况遥感
DOI：
10.3390/rs10091463
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发表时间：
2018-09
期刊：
影响因子：
5
通讯作者：
Chunjiang Zhao
中科院分区：
文献类型：
--
作者：
Zhenhai Li;Xiuliang Jin;Guijun Yang;Jane Drummond;Hao Yang;Beth Clark;Zhenhong Li;Chunjiang Zhao
植物氮素（N）信息已被广泛地通过经验技术使用高光谱数据估计。然而，N谱响应的物理模型反演方法很少发展，仍然是一个挑战。在本研究中，N-PROSAIL模型的基础上，基于N基前景模型和SAIL模型冠层模型的构建，并用于反演作物氮素状况的叶片和冠层尺度。结果表明，第三参数（3rd-par）检索策略（叶面积指数（LAI）和叶氮密度（LND）优化，其中N-PROSAIL模型中的其他参数在每个生长阶段设置为不同值）对LAI和LND的估计表现出最高的准确性，其R2和RMSE值分别为0.80和0.69，0.46和21.18 µg·cm−2，分别叶片氮浓度（LNC）的R2和RMSE分别为0.75和0.38%;冠层氮密度（CND）的R2和RMSE分别为0.82和0.95 g·m−2。N-PROSAIL模型的反演效果优于植被指数回归模型（LNC：RMSE = 0.48 − 0.64%; CND：RMSE = 1.26 − 1.78 g·m−2）。本研究表明，利用N-PROSAIL模型在小麦叶片和冠层尺度上进行作物氮素诊断的潜力。
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 R2 and RMSE values of 0.80 and 0.69, and 0.46 and 21.18 µg·cm−2, respectively. It also showed good results with R2 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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影响因子：
6.2
作者：
Xingang Xu;C. Zhao;Ji‐Hua Wang;Jingcheng Zhang;Xiao-yu Song
通讯作者：
Xingang Xu;C. Zhao;Ji‐Hua Wang;Jingcheng Zhang;Xiao-yu Song
影响因子：
13.5
作者：
Combal, B;Baret, F;Wang, L
通讯作者：
Wang, L
影响因子：
3.4
作者：
Xiaohua Zhu;Yingshi Zhao;Xiaoming Feng
通讯作者：
Xiaohua Zhu;Yingshi Zhao;Xiaoming Feng
DOI：
10.1111/j.0033-0124.1965.00020.x
发表时间：
--
期刊：
--
影响因子：
--
作者：
Daniele Marinelli;M. Dalponte;L. Frizzera;Erik Næsset;D. Gianelle;Marie Weiss
通讯作者：
Daniele Marinelli;M. Dalponte;L. Frizzera;Erik Næsset;D. Gianelle;Marie Weiss
影响因子：
5.8
作者：
Ecarnot, Martin;Compan, Frederic;Roumet, Pierre
通讯作者：
Roumet, Pierre