中国农业科学院农业信息研究所 中国农学会
Agricultural Information Institute of CAAS China Association of Agricultural Science Societies
En
Ch
中国农业科学院农业信息研究所 中国农学会 主办

AI检索

订阅

返回顶部
当前位置: 首页 > 文章 > 基于多植被指数组合的冬小麦地上干生物量高光谱估测 生态学杂志 2018,37 (2) 424-429
Position: Home > Articles > Hyperspectral estimation of aboveground dry biomass of winter wheat based on the combination of vegetation indices. Chinese Journal of Ecology 2018,37 (2) 424-429

基于多植被指数组合的冬小麦地上干生物量高光谱估测

作  者：
贾学勤;冯美臣;杨武德;王超;肖璐洁;孙慧;武改红;张松
关键词：
冬小麦;地上干生物量;植被指数组合;偏最小二乘回归
摘  要：
为了探究多种植被指数组合与偏最小二乘回归(PLSR)结合对于提高冬小麦地上干生物量估测精度的影响,本研究以氮运筹试验为基础,比较分析了18种植被指数与冬小麦地上干生物量的相关性,筛选出相关性较好的植被指数,建立多种植被指数组合的PLSR模型,并对模型进行评价比较.结果表明:除叶绿素归一化植被指数(NPCI)外各植被指数均与冬小麦地上干生物量有良好的相关性,中分辨率陆地叶绿素成像指数(MTCI)、绿色归一化植被指数(GNDVI)、改进红边比值植被指数(MSR705)和特征色素简单比值指数c(PSSRc)4个植被指数相关系数绝对值均达到0.800以上;多植被指数组合构建的PLSR模型中,以PSSRc、MSR705和MTCI 3个植被指数建立的复合式模型建模集(R2=0.719,RMSE=0.316)和验证集(R2=0.696,RMSE=0.346)表现最佳.因此,多种植被指数组合与偏最小二乘回归(PLSR)结合能有效提高冬小麦地上干生物量的估测精度,为更好地实现冬小麦地上干生物量高光谱遥感估测提供有效技术途径.
译  名：
Hyperspectral estimation of aboveground dry biomass of winter wheat based on the combination of vegetation indices.
关键词：
winter wheat%aboveground dry biomass%combination of vegetation index%partial least squares regression.
摘  要：
This study aimed to explore the effects of the combination of various vegetation indices and partial least squares regression (PLSR) on improving the evaluation accuracy of aboveground dry biomass of winter wheat. The experiment was based on nitrogen operation test and was conducted to analyze the correlation between 18 vegetation indices and the aboveground dry biomass of winter wheat. The better vegetation indices were selected to establish the PLSR model as a combined vegetation index, and the model performance was then evaluated. The results showed that, except for the chlorophyll normalized vegetation index (NPCI) , a good correlation was observed between the vegetation indices and aboveground dry biomass of winter wheat. Especially, the correlation coefficients of the four indices, i.e., MERIS terrestrial chlorophyll index (MTCI), green normalized difference vegetation index (GNDVI), modified red edge ratio vegetation index (MSR705), and pigment specific simple ratio carotenoids (PSSRc), were grea- ter than 0.800. Among the PLSR models established with vegetation index combination, the mod- el calibration set ( R2 = 0.719, RMSE = 0. 316 ) and validation set ( R2 = 0.696, RMSE = 0.346) based on the combination of PSSRc, MSR705, and MTCI performed best. Therefore, we concluded that the combination of multiple vegetation indices could improve the estimation accuracy of aboveground dry biomass of winter wheat. This study provides an effective approach for hyperspectral remote sensing estimation of the aboveground biomass of winter wheat.

相似文章

导出引用
计量
文章访问数: 10
HTML全文浏览量: 0
PDF下载量: 0

所属期刊

推荐期刊

友情链接Friendly link
农业信息研究所Agricultural information institute 农业智能知识服务平台Agricultural intelligent knowledge service platform 农业学术服务平台Agricultural Knowledge Service System 中国知网CNKI 维普网VIP 万方WANFANG DATA
联系我们Contact us

电话Tell：010-82109652-808

邮箱E-mail：agrijournal@caas.cn

服务Service
关于我们About us 意见建议Opinions and suggest
版权说明Copyright
单位：中国农业科学院农业信息研究所 中国农学会 © 2017中国农业科学院农业信息研究所 ICP备案号：京ICP备09089781号-46