摘要
类胡萝卜素(Car)是植物进行光合作用的主要色素之一,在吸收传递光能、保护叶绿素,以及延缓叶片衰老等方面有重要作用。以LOPEX’93数据库为基础,系统分析400~2 500 nm高光谱波段范围内任意两波段组合而成的归一化差值植被指数(NDVI)、比值植被指数(RVI)和差值植被指数(DVI)与双子叶植物叶片Car含量间的定量关系。结果表明,在756 nm处红光波段与809 nm处近红外波段的NDVI_((809,756))、RVI_((809,756)),以及750 nm处红光波段与809 nm处近红外波段的DVI_((809,750))都可以较好地实现Car含量反演,建立的回归预测模型的判定系数(R^2)均大于0.74。对由各植被指数构建的反演模型进行精度验证发现,NDVI_((809,756))和RVI_((809,756))的估算效果相当,且都好于DVI_((809,750)),模型预测精度分别为0.735和0.738,均方根误差分别为1.426 1和1.420 5,平均相对误差分别为13.66%和13.60%。表明基于高光谱数据对双子叶植物叶片Car含量进行估算是可行的。
Carotenoid(Car)is one of the main pigment of photosynthesis for plants.It plays an important role in light absorption and transmission,chlorophyll protection,leaf senescence delaying and so on.Based on LOPEX’93 database,the present study systematically analyzed quantitative relationship within Car content in dicotyledonous plants leaves and normalized difference vegetation index(NDVI),ratio of vegetation index(RVI),and difference vegetation index(DVI).It was shown that NDVI(809,756)and RVI(809,756)combining of infrared band at 756 nm and the near infrared band at 809 nm,and DVI(809,750)combining of infrared band at 750 nm and 809 nm all could achieve better inversion of Car content.Determination coefficient(R 2)of the established regression prediction modes were higher than 0.74.By verifying the accuracy of the inversion model estimated based on vegetation index,it was found that the effects of NDVI(809,756)and RVI(809,756)were comparable and better than that of the DVI(809,750),of which the prediction accuracies were 0.735 and 0.738,respectively,the root mean square errors were 1.426 1 and 1.420 5,respectively,and the average relative errors were 13.66%and 13.60%,respectively.Thus,estimation of Car content in dicotyledonous plants leaves based on the hyperspectral data was feasible.
作者
余昌乐
许童羽
王洋
于丰华
YU Changle;XU Tongyu;WANG Yang;YU Fenghua(College of Information and Electrical Engineering,Shenyang Agricultural University,Shenyang 110161,China;Agricultural Information Engineering Technology Center in Liaoning Province,Shenyang Agricultural University,Shenyang 110161,China)
出处
《浙江农业学报》
CSCD
北大核心
2018年第3期393-398,共6页
Acta Agriculturae Zhejiangensis
基金
国家重点研发计划课题(2016YFD0200708)
