CO2胁迫下大豆叶片红边位置最优算法的研究

大气与环境光学学报 ›› 2016, Vol. 11 ›› Issue (1): 51-60.

CO2胁迫下大豆叶片红边位置最优算法的研究

郝瑞娟 1,2, 王周锋1,2, 王文科1,2＊, 赵亚乾1,3

(1长安大学环境科学与工程学院, 陕西西安710054;
2 旱区地下水文与生态效应教育部重点实验室, 陕西西安 710054;
3爱尔兰都柏林大学土木结构和环境工程系,爱尔兰,英国)

收稿日期:2015-06-19 修回日期:2015-07-20 出版日期:2016-01-28 发布日期:2016-01-28
通讯作者: 王文科（1962-），男，汉，陕西岐山，教授，博士生导师，主要从事旱区河流与地下水转化关系，地下水资源评价、管理、数值模拟以及相关的生态环境保护等方面的教学和科研工作. E-mail:764933538@qq.com
作者简介:郝瑞娟（1979-），女，汉，陕西子长，讲师，博士研究生，主要从事环境地质方面的研究工作。
基金资助:
国家自然科学基金项目(41202164,41230314)、长安大学中央高校基本科研业务费专项资金（2013G1291069）、陕西省地下水与生态环境工程研究中心开放基金（2013G1502041）资助

Optimal Algorithm of Red Edge Position for Soybean Leaf Under CO2 Stress

HAO Ruijuan1,2, WANG Zhoufeng1,2, WANG Wenke1,2, ZHAO Yaqian1,2

(1 School of Environmental Science and Engineering, Chang’an University, Xi’an 710054, China;
2 Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education, Xi’an 710054, China;
3 School of Civil, Structural & Environmental Engineering New Stead Building, University College Dublin, Ireland)

Received:2015-06-19 Revised:2015-07-20 Published:2016-01-28 Online:2016-01-28

摘要/Abstract

摘要：

利用红边参数反演植被特征是定量遥感研究的热点问题之一，红边参数中的红边位置与植被参数显著相关，是监测植被胁迫的一个非常敏感的指标。采用最大一阶导数法、拉格朗日内插法、线性外推法、四点内插法、倒高斯模型法、多项式拟合法六种方法分别计算出不同浓度CO2泄漏胁迫下大豆叶片的红边位置，分析并比较不同算法提取的红边位置变化特征，以确定监测CO2泄漏胁迫下大豆叶片的最佳红边位置的算法。结果表明：提取的红边位置均与大豆叶片叶绿素含量呈极显著的线性相关。其中，最大一阶导数法和拉格朗日内插法相关程度最高，且最大一阶导数法计算简单、稳定性较好，因此可以利用最大一阶导数法提取的红边位置反映大豆叶片叶绿素含量变化，进而监测CO2地质储存泄漏问题。

关键词: 不同算法, 红边位置, CO2监测, 大豆光谱特征

Abstract:

Red edge parameters are widely used to invert vegetation parameters in quantitative remote sense. The red edge position, as a very sensitive indicator for monitoring vegetation stress, is strongly correlated with vegetation biochemical components. In order to obtain the best red edge position algorithm, six red edge position extraction methods, which are red edge position maximum first derivative method, Lagrange method, line extrapolate method, four-point interpolation method, Gaussian method and polynomial fitting method, were compared for soybean leaf under higher CO2 stress. The results show that the different algorithms of red edge position are significantly linear correlation with chlorophyll content of soybean leaf. However, largest first derivative method and Lagrange method are the optimal extract methods to calculate red edge position for soybean leaf under CO2 stress. Moreover, the maximum first derivative method is more simple and stable. The results imply that red edge position changes can reflect plant chlorophyll content and can be used to monitor CO2 leakage during CCS project using aboveground plant remote sensing data.

Key words: different algorithm, red edge position, CO2 monitoring, spectral characteristics of soybean

中图分类号:

S126

郝瑞娟, 王周锋, 王文科, 赵亚乾. CO2胁迫下大豆叶片红边位置最优算法的研究[J]. 大气与环境光学学报, 2016, 11(1): 51-60.

HAO Ruijuan, WANG Zhoufeng, WANG Wenke, ZHAO Yaqian. Optimal Algorithm of Red Edge Position for Soybean Leaf Under CO2 Stress[J]. Journal of Atmospheric and Environmental Optics, 2016, 11(1): 51-60.

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