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张娟娟,熊淑萍,翟清云,张钰洋,马新明.不同土壤质地小麦叶片叶绿素的高光谱响应及估测模型[J].麦类作物学报,2014,34(5):642
不同土壤质地小麦叶片叶绿素的高光谱响应及估测模型
Hyper-spectral Remote Sensing Response and Estimation Model of Leaf Chlorophyll Content of Wheat under Different Soil Textures
  
DOI:10.7606/j.issn.1009-1041.2014.05.11
中文关键词:  小麦  土壤质地  施氮量  叶绿素含量  高光谱遥感  估测模型
英文关键词:Wheat  Soil texture  Nitrogen application amount  Chlorophyll content  Hyper-spectral remote sensing  Estimation model
基金项目:河南省科技攻关项目(112102110030);河南省教育厅项目(14A210002);国家公益性行业(农业)科研专项(201303109-6);河南省小麦产业技术体系岗位专家专项(S2010-01-G04)
作者单位
张娟娟,熊淑萍,翟清云,张钰洋,马新明 (1.河南农业大学信息与管理科学学院河南郑州 450002 2.河南农业大学农学院/河南省粮食作物生理生态与遗传改良重点实验室河南郑州 450002) 
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中文摘要:
      为给小麦生长过程中叶绿素的实时监测和氮肥调控提供参考,设置3种不同土壤质地(沙土、壤土和粘土)、5种不同施氮水平(0、120、225、330和435 kg·hm-2)和3个河南省主栽小麦品种(矮抗58、周麦22和郑麦366),同步测定小麦主要生育时期冠层光谱反射率和叶绿素(Chla+b)含量,系统分析了3种土壤质地条件下小麦Chla+b含量与350~1 050 nm波段范围内冠层光谱参数的相关关系。结果表明,3种土壤质地下小麦叶绿素的冠层光谱响应趋势基本一致。光谱指数REPIG 和mND705对叶片Chla+b含量的监测效果较好,建模决定系数分别为0.76和0.75。利用独立样本数据对用于建模的此二光谱参数进行检验,其预测效果表现较为稳定,预测决定系数分别为0.87和0.85,均方根偏差分别为0.46和0.48。说明利用光谱指数REPIG 和mND705为自变量建立的估测模型可以较好地预测当地生产条件下小麦叶片叶绿素,同时为氮肥施用及调控提供技术依据。
英文摘要:
      Using hyper-spectral remote sensing to draw the leaf chlorophyll information of wheat, thereby establishing the spectrum monitoring model of chlorophyll, is important for real-time monitoring of chlorophyll and nitrogen fertilizer in wheat growth. Field experiments were conducted with three different soil textures (sand, loam and clay) and five nitrogen levels (0, 120, 225, 330 and 435 kg·hm-2) in three wheat cultivars of Henan (Aikang 58, Zhoumai 22 and Zhengmai 366). The hyper-spectral reflectance and Chla+b content of canopy were taken by synchronous measurement during main growth stages of wheat. The quantitative relationship systematically between wavelength range of 350~1 050 nm and wheat chlorophyll under three soil textures was analyzed. The results showed that the trend was almost consistent in the hyper-spectral reflectance of leaf chlorophyll under different soil textures. Spectral index REPIG and mND705 performed well for monitoring of the leaf Chla+b content, the determination coefficient(r) were 0.76 and 0.75, respectively. Above hyper-spectral parameters of monitoring model were tested using independent sample. The results confirmed that the most stable performance from the model based on REPIG and mND705, with the predictive determine coefficient (r) of 0.87 and 0.85, respectively, and the root mean square error (RMSE) of 0.46 and 0.48, respectively. In conclusion, the monitoring model which used the hyper-spectral parameter of REPIG and mND705 as independent variables, could better estimate the leaf chlorophyll content of wheat in different soil textures. Furthermore, it provided the technical basis for the application and regulation of nitrogen fertilizer.
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