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基于连续小波变换定量反演冬小麦叶片含水量的研究
The Quantitative Retrieval of The Water Content in Winter Wheat Leaves Based on Continuous Wavelet Transform
投稿时间:2019-08-21  修订日期:2019-10-12
DOI:
中文关键词:  叶片含水量  高光谱  连续小波变换  偏最小二乘  
英文关键词:Water Content of Wheat Leaves  Hyperspectral  Continuous Wavelet Transform  Partial Least Squares  
基金项目:国家重点研发计划(2016YFD0300609)、北京市农林科学院科技创新能力建设专项(KJCX20170705)、国家自然科学基金(41401419)、河北省教育厅青年基金(QN2019213)
作者单位E-mail
王延仓 北华航天工业学院计算机与遥感信息技术学院 yancangwang@163.com 
张萧誉   
金永涛   
顾晓鹤   
刘原萍   
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中文摘要:
      水是冬小麦植株开展理化反应的重要物质基础,保持水的供给平衡是冬小麦健康生长的基础保障。针对利用冬小麦冠层高光谱数据精准反演叶片含水量的问题,本文以河北省衡水市安平县为研究区,以野外高光谱数据为数据源,通过提取、筛选其光谱特征敏感波段为切入点,应用光谱指数、连续小波变换进行光谱处理,并采用偏最小二乘法构建冬小麦叶片含水量的定量反演模型,结果表明:在连续小波变换下,基于1尺度构建的冬小麦叶片含水量的反演模型为最优模型,其建模精度的R2=0.756、RMSE=0.994%,验证精度R2=0.766、RMSE=1.713%;连续小波变换可将冠层光谱信息进行二次分配,能有效将有益信息与噪声信息进行分离,提升光谱信息对冬小麦叶片水含量的敏感性,增强冬小麦叶片水含量的预测精度与稳定性。
英文摘要:
      The water is an important material basis for the physical and chemical reactions of winter wheat plants. Maintaining the balance of water supply is the basic guarantee for the healthy growth of winter wheat. In order to accurately retrieve leaf water content with Winter Wheat Canopy Hyperspectral data, this study conducted by using field hyperspectral data which is collected in Anping country of Hebei province. Using spectral index and continuous wavelet transform to transform spectrum from the point of extracting and screening spectral sensitive bands of Winter Wheat Canopy Hyperspectral data. The quantitative inversion model of wheat water content was constructed by partial least squares method. The results show that under wave transform, the diagnostic model of water content in winter wheat based on 1 scale is the optimal model, and its modeling accuracy R2 = 0.756, RMSE = 0.994%, verification accuracy R2 = 0.766, RMSE = 1.713%. The continuous wavelet transform can distribute canopy spectral information again, which can effectively separate useful information from noise information, enhance the sensitivity of spectral information to the content of water of winter wheat leaf, and so the continuous wavelet transform can improve the accuracy and stability of winter wheat leaf water content prediction.
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