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作物学报 ›› 2007, Vol. 33 ›› Issue (07): 1141-1145.

• 研究论文 • 上一篇    下一篇

基于多视角反射光谱的冬小麦冠层叶片氮素营养监测研究

肖春华1,2;李少昆1,2,*;王克如1,2;卢艳丽1;谢瑞芝1;高世菊1   

  1. 1新疆生产建设兵团绿洲生态农业重点实验室/石河子大学农学院,新疆石河子 832003;2中国农业科学院作物科学研究所/国家农作物基因资源与基因改良重大科学工程,北京 100081
  • 收稿日期:2006-10-17 修回日期:1900-01-01 出版日期:2007-07-12 网络出版日期:2007-07-12
  • 通讯作者: 李少昆

Prediction Canopy Leaf Nitrogen Content of Winter Wheat Based on Reflectance Spectra in Different Directions

XIAO Chun-Hua12,LI Shao-Kun12*,WANG Ke-Ru12,LU Yan-Li1,XIE Rui-Zhi1,GAO Shi-Ju1   

  1. 1 Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crop/ Center of Crop High-Yield Research, Shihezi 832003, Xinjiang; 2 Institute of Crop Sciences, Chinese Academy of Agricultural Sciences /National Key Facility for Crop Gene Resources & Genetic Improvement, Beijing 100081, China
  • Received:2006-10-17 Revised:1900-01-01 Published:2007-07-12 Published online:2007-07-12
  • Contact: LI Shao-Kun

摘要:

通过2个蛋白质含量不同的冬小麦品种和4个氮素水平的试验,研究传感器在垂直冬小麦垄平面上,冠层不同观测角度的反射光谱与叶片氮素营养的关系,改进小麦冠层氮素光谱诊断的理论与方法。结果表明,在本试验选择的7种植被指数光谱特征参量中,2个小麦品种均表现为比值植被指数(RVI[670,890])与冠层叶片氮素含量(CLNC)相关性最高;不同视角的RVI与冠层叶片氮素含量关系中,0°、30°和90°的相关性最高;利用0°、30°和90°的RVI与CLNC进行模型拟合,其模型的决定系数是0°﹥30°﹥90°;在建立的0°模型中,京411小麦模型RMSE为0.2915,预测准确率为90.2%,中优9507模型RMSE为0.3827,预测准确率为87.2%。本研究证明改变反射光谱观测角度,能够提高冠层叶片氮素含量的光谱预测精度,不同小麦品种其光谱特征与冠层叶片氮素含量关系不同,在应用中要根据不同的小麦品种建立相应的模型。

关键词: 冠层叶片氮素含量(CLNC), 观测角度比值植被指数(RVI), 模型, 冬小麦

Abstract:

In order to improve the method of nitrogen spectral diagnosis, we conducted a experiment with two winter wheat (Triticum aestivum L.) cultivars Jing 411(low protein content) and Zhongyou 9507(high protein content) under nitrogen application levels of 0, 100, 200, and 400 kg N ha-1. The wheat canopy spectral reflectance over 350–2 500 nm and canopy leaf nitrogen contents (CLNC) were measured at tillering, jointing, heading, milking, and mature stages. Seven vegetation indices (VIs) with view angles of 0°, 30°, 60°, 90°, 120°, 150°, and 180° to the vertical line to wheat row were compared with corresponding CLNC. The results indicated that CLNC had most significant correlations with RVI[670,890] among seven spectrum vegetation indices, especially in the view angles of 0°, 30°, and 90°, and the predicting models for CLNC were further established on the basis of the correlations. The coefficient of determination (R2) of three models were sequenced as: 0°>30°>90°. Root mean square error (RMSE) and R2 between measured and estimated CLNC were employed to test the model reliability and predicting accuracy. RMSE and R2 were different in different view angles and cultivars, the model based on the view angle of 0°had RSME of 0.2915 and R2 of 0.902 for Jing 411, and RSME of 0.3824 and R2 of 0.872 for Zhongyou 9507. There were lower RMSE and higher R2 in the model established based on the view angle of 0°than in that of other angles. The correlation was different between canopy spectral reflectance and CLNC in different cultivars, indicating that different predicting models should be selected based on different cultivars.

Key words: Canopy leaf nitrogen content(CLNC), Observational angle RVI, Model, Winter wheat (Triticum aestivum L.)

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