摘要
研究利用美国产ASD地物光谱仪,获取新疆北部地区棉花冠层关键生育时期的高光谱数据,采用红边积分面积变量估测棉花冠层叶片的全氮含量,对反射光谱进行一阶微分,应用一阶微分光谱数据,衍生出基于光谱位置变量的分析方法,以红边积分面积(SDr)为自变量,冠层全氮(TN)含量为因变量,做相关分析与处理,构建新陆早6号红边积分面积与冠层叶片TN含量的相关数学模型。研究在不同水处理条件下,对棉花冠层单叶叶绿素含量和单叶全氮含量做相关分析,结果表明:叶绿素含量与TN含量呈显著的正相关(R=0.8723,n=39),叶绿素含量能有效的估计棉花单叶TN含量;红边积分面积变量与冠层TN含量呈显著的相关性,相关系数是0.7394(n=40),利用构建的相关模型可以较为精确地估测棉花两个品种新陆早6号与8号冠层叶片的全氮含量,RMSE分别为0.3859和0.4272。研究认为红边积分面积变量具有预测棉花冠层全氮含量的应用潜力,研究得出利用3边面积变量构造的数学模型对反演作物冠层TN含量有较高应用价值。研究认为,红边位移现象结合红边幅度的变化的研究,用于诊断棉花水分胁迫也是可行的,关键是建立相应合理的诊断指标体系。研究结果证明:1随着棉花的生长发育,叶片的生理生化参数发生变化,冠层的生理生化参数随之发生变化;2.棉花叶片叶绿素含量与叶片的全氮含量相关性显著(R=0.8723,n=38),通过建立数学模型,可以估测叶片中全氮的含量;3由一阶微分光谱衍生出基于光谱"红边"位置变量的分析方法,使我们认识到"红边"的变幅、形状和面积包含了各个波段的信息,这些波段综合产生的变量所构造的模型,为棉花氮素营养参数的估计提供了预测能力;4如果棉花叶绿素含量高,说明水分充足、氮代谢旺盛,植株处于生长旺盛时期,红边向蓝光方向发生了位移。利用红边位移现象结合红边幅度的变化的研究,用于诊断棉花水分胁迫也是可行的,关键是建立相应合理的诊断指标体系。
In this paper, the hyperspectral data of cotton canopies grown in north Xinjiang at the main growing stage under water stress are derived by using an ASD spectrocoparator made in USA, the red-edge integral areas are used to estimate the total N content in leaves of cotton canopies, and the analyzing method based on the spectral position variables is derived from the first differential spectral data. An analysis on the correlation between the red-edge integral areas (used as the independent variables) and the total N contents in leaves of cotton canopies (used as functions) is carried out so as to develop a mathematical model about the correlation between the red-edge integral areas and the total N contents in canopy leaves of cotton variety of Xin Luzao No.6. The correlations between the chlorophyll contents and the total N contents in separate leaves of cotton canopies under the different irrigation water volumes are researched. The results show that there is a significant positive correlation between the chlorophyll content and the total N content in leaves of cotton canopies (R=0.8723, n=39), and the data of chlorophyll contents can be used to effectively estimate the total N content in separate cotton leaves; there is also a significant correlation between the red-edge integral areas and the total N contents in leaves of cotton canopies, and the correlation coefficient is 0.7394 (n=40). The total N contents in canopy leaves of cotton varieties of Xin Luzao No. 6 and No.8 can be accurately estimated by using the developed model, and the values of RMSE are 0.3859 and 0.4272 respectively. It is considered that there is a potentiality to use the variables of red-edge integral areas for predicting the total N contents in leaves of cotton canopies, and it is also feasible that the data of displacement and change of red-edge extent can be used to recognize the moisture stress of cotton plants if a rational recognition system is develop. The conclusions of the study are as follows: (1) The physiological and biochemical properties of both cotton leaves and canopies are changed with cotton growth; (2) There is a significant correlation between the chlorophyll content and total N contents in cotton leaves (R=0.8723, n=38), and the total N contents in cotton leaves can be estimated by a mathematical model; (3) The analyzing method based on the variables of spectral position of 'red edge' of cotton leaves, derived from the first differential spectral data, reveals that the change extent, shape and area of the 'red edge' contain the information of various wavebands, and the capability of predicting N nutrient in cotton leaves and canopies can be provided by using the developed model based on the variables from these wavebands; (4) It is reveals that the moisture supply is sufficient, the N metabolizing in cotton plants is hearty, the cotton plants grow luxuriantly, and the red edge of cotton leaves shifts towards blue light if the chlorophyll content in cotton leaves is high. It is feasible that the data of displacement and change of red-edge extent can be used to recognize the moisture stress of cotton plants if a rational recognition system is develop.
出处
《遥感技术与应用》
CSCD
2005年第3期315-320,共6页
Remote Sensing Technology and Application
基金
国家863计划"数字农业"专项(2003AA209091)
中国科学院知识创新重大项目(K2CX2-404-4)。
