武倩雯,熊黑钢,靳彦华,王莉锋,王凯龙.基于多个高光谱参数的玉米叶片叶绿素含量估测模型[J].干旱地区农业研究,2016,34(1):201~205
基于多个高光谱参数的玉米叶片叶绿素含量估测模型
Prediction model on chlorophyll content in maize leaf based on several high spectral parameters
  
DOI:10.7606/j.issn.1000-7601.2016.01.31
中文关键词:  玉米叶片  叶绿素含量  估测模型
英文关键词:maize leaf  chlorophyll content  prediction models
基金项目:国家自然科学基金(41171165);北京市属高等学校高层次人才引进与培养计划项目(IDHT20130322);北京联合大学人才强校计划资助项目(BPHR2012E01)
作者单位
武倩雯 新疆大学资源与环境科学学院教育部绿洲生态重点实验室 新疆 乌鲁木齐 830046 
熊黑钢 新疆大学资源与环境科学学院教育部绿洲生态重点实验室 新疆 乌鲁木齐 830046北京联合大学应用文理学院 北京 100083 
靳彦华 新疆大学资源与环境科学学院教育部绿洲生态重点实验室 新疆 乌鲁木齐 830046 
王莉锋 新疆大学资源与环境科学学院教育部绿洲生态重点实验室 新疆 乌鲁木齐 830046 
王凯龙 新疆大学资源与环境科学学院教育部绿洲生态重点实验室 新疆 乌鲁木齐 830046 
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中文摘要:
      采用FieldSpecPro3光谱仪和SPAD-502叶绿素仪分别测定玉米叶片的光谱与其相对应的叶绿素含量,通过分析红边位置、蓝边位置以及绿峰位置等高光谱参数与叶绿素含量的关系,建立叶绿素含量的单、双和多变量光谱预测模型。结果表明: 在可见光区域,玉米叶绿素含量高,光谱反射率低,而进入近红外区则刚好相反,叶绿素含量高,光谱反射率高; 红边位置、绿峰位置及蓝边位置各高光谱参数与叶绿素含量的相关性均达极显著。其中红边位置与叶绿素含量的相关性最高,相关系数达0.84; 利用所选的3个高光谱参数分别建立的单、双以及三变量模型,虽然大多数模型的精度R2大于0.71,但分析对比得出利用红边、蓝边及绿峰位置3个变量建立的模型具有最大模型精度R2、最小标准误差(S)和均方根误差(RMSE),因此其模型预测能力较优。
英文摘要:
      In this research, FieldSpecPro3 spectroscopy and SPAD-502 chlorophyll meter were used to measure the spectrum of corn leaf chlorophyll content and its amount respectively. Through the analysis on the relationships between chlorophyll content and parameters of red, blue, and green edge positions and spectral peak positions, single, double and multivariate spectral prediction models about the chlorophyll content were established. The results showed that in the visible spectrum, the higher corn chlorophyll content, the lower reflectance spectrum. However, exact opposite was observed in the infrared spectrum. Spectral parameters of red edge, green peak and blue edge positions were in significant correlations with the chlorophyll content, reaching up to 0.84 between red edge position and the chlorophyll content. As a result, single, double and three-variable models using three high spectral parameters were established. Although the accuracy of R2 were mostly greater than 0.71, it was further suggested that three-variable model had the best accuracy in R2, the minimal standard deviation (S) and root mean square error (RMSE), which might provide better prediction results than the other two models.
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