| 东北粳稻叶片植被指数NDVI与PRI的相关性分析 |
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| 引用本文： | 陈春玲,马航,许童羽,周云成,于丰华,余昌乐. 东北粳稻叶片植被指数NDVI与PRI的相关性分析[J]. 浙江农业学报, 2016, 28(12): 1963. DOI: 10.3969/j.issn.1004-1524.2016.12.01 |
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| 作者姓名： | 陈春玲 马航 许童羽 周云成 于丰华 余昌乐 |
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| 作者单位： | 1.沈阳农业大学 辽宁省农业信息化工程技术中心,辽宁 沈阳 110161; 2.沈阳农业大学 信息与电气工程学院,辽宁 沈阳110161 |
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| 基金项目： | 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) |
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| 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PlantPen,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。
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| 关 键 词： | 粳稻 NDVI 相关关系 回归分析 |
| 收稿时间： | 2016-04-11 |
Correlation analysis of leaf vegetation index NDVI and PRI of Northeast japonica rice |
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| CHEN Chun-ling,MA Hang,XU Tong-yu,ZHOU Yun-cheng,YU Feng-hua,YU Chang-le. Correlation analysis of leaf vegetation index NDVI and PRI of Northeast japonica rice[J]. Acta Agriculturae Zhejiangensis, 2016, 28(12): 1963. DOI: 10.3969/j.issn.1004-1524.2016.12.01 |
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| Authors: | CHEN Chun-ling MA Hang XU Tong-yu ZHOU Yun-cheng YU Feng-hua YU Chang-le |
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| Affiliation: | 1. Agricultural Informatization Engineering Technology Center in Liaoning Province, Shenyang Agricultural University, Shenyang 110161, China;
2.College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110161, China |
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| Abstract: | 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 PlantPen, 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 increased 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. |
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| Keywords: | japonica rice NDVI correlation regression analysis |
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