摘要
以东北地区典型地带的粳稻为例,利用植被指数测量仪Plant Pen,同时测量了粳稻叶片植被指数NDVI和PRI,并根据粳稻生长发育进程分成了与物候一致的4个生育时期。首先利用二元定距变量相关分析的方法对NDVI和PRI进行相关性分析;然后,分别利用线性回归和Cubic曲线回归建立NDVI拟合PRI的回归模型,并对回归模型进行拟合优度检验和精度验证,同时对线性回归模型与Cubic曲线回归模型的拟合效果和检验结果进行对比分析。结果表明,粳稻叶片植被指数NDVI和PRI在各生育时期均有极显著的相关关系,在粳稻生长发育进程中,相关性越来越高;线性回归模型和Cubic曲线回归模型均能使NDVI较好地拟合PRI,在粳稻生长发育进程中,拟合效果也越来越好;Cubic曲线回归模型在粳稻4个生育期平均相应的指标值判定系数(R2)、均方根误差(RMSE)、绝对百分误差(MAPE)分别为0.8055、0.0358、0.534%,而线性回归模型的相应指标为0.7653、0.0488、1.365%。Cubic曲线回归模型的RMSE和MAPE值较小且R2较大。因此其拟合优度和检验精度均优于单纯的线性回归模型,可作为NDVI反演PRI一种参考模型。
Typical japonica rice in the northeast area was taken as an example,the japonica rice leaf vegetation index NDVI and PRI were measured by using vegetation index measuring instrument Plant Pen,and the growth process of rice was divided into four growth periods in accord with the phenological process. Firstly,the correlation analysis of NDVI and PRI was carried out by using the method of dual distance variable correlation analysis; Then,the NDVI fitting regression model of PRI was established by using linear regression and Cubic curve regression,and the goodness of fit and accuracy of regression model were verified; meanwhile,the fitting effect and test results of linear regression model with Cubic curve regression model were analyzed. The results showed that the leaf vegetation index NDVI and PRI in different growth periods of japonica rice showed significant correlation,and the correlation in-creased with the growth process of japonica rice. Both of the linear regression model and Cubic curve regression model could make good fitting PRI,NDVI in japonica rice growth process,and the fitting effect became better and better.The four corresponding indexes determination coefficient（R2）,root mean square error（RMSE）,absolute percentage error（MAPE） of Cubic curve regression model were 0. 805 5,0. 035 8,0. 534%; and those of the linear regression model were 0. 765 3,0. 048 8,1. 365%. It was obvious that the Cubic curve regression model had smaller RMSE and MAPE values and larger R2 value. Thus,its goodness of fit and inspection accuracy were better than the simple linear regression model,which could be used as a reference model for NDVI inversion PRI.
出处
《浙江农业学报》
CSCD
北大核心
2016年第12期1963-1969,共7页
Acta Agriculturae Zhejiangensis
基金
国家重点研发计划重点专项项目(2016YFD0200600)
辽宁省科技特派项目(2014104017)
北京农业质量标准与技术研究中心开放性课题项目(2015)
关键词
粳稻
NDVI
相关关系
回归分析
japonica rice
NDVI
correlation
regression analysis
