摘要
无论是叶面积指数(Leaf Area Index,LAI)反演模型的精度验证,还是产品的真实性检验,都离不开地面实测数据的大力支持,而对于地面观测的最大问题便是地面采样点的选择.本文主要对黑河流域生态-水文过程综合遥感观测联合试验(Heihe Watershed Allied Telemetry Experimental Research,Hi WATER)中有关LAI地面测量的采样方案进行评价与优化,随机采样方法的精度和稳定度较低,可以通过增加样点数来优化采样设计.并提出了基于空间非均值地表均值估计(Mean of surfaces with Non-homogeneity,MSN)的LAI地面采样设计方案,通过与随机采样法、系统采样法的比较,突出了MSN采样方法的优势,是三种LAI空间采样设计方案中的最优方法,为黑河流域中游地区植被生化参数的地面采样设计提供了参考.
The ground observation points data is required,no matter how to validate the accuracy of the leaf area index（ LAI） inversion model and the authenticity of products. But the biggest problem for ground observation is the ground sampling points＇ selection. This paper focuses on evaluation and optimization of the LAI ground sampling method in the Heihe Watershed Allied Telemetry Experimental Research（ Hi WATER）. The precision and stability of random sampling method is relatively low,by increasing the number of sampling points to optimize sampling method. A new method that is based on Mean of surfaces with Non-homogeneity（ MSN） LAI ground sampling design was proposed. This paper finds the MSN sampling method is the best method of three kinds of LAI spatial sampling method through comparison with random sampling method and system sampling method. This will provide a reference for the ground sampling design of vegetation biochemical parameters in the middle reaches of Heihe River basin.
作者
家淑珍
JIA Shu-zhen(College of Geographical Sciences,Shanxi Normal University,Linfen 041000,Shanxi,China)
出处
《山西师范大学学报（自然科学版）》
2018年第3期56-64,共9页
Journal of Shanxi Normal University(Natural Science Edition)
关键词
叶面积指数
采样方法
MSN
采样优化
leaf area index
sampling method
MSN
sampling optimization
