针对非均质中低分辨率像元的叶面积指数LAI验证中如何布设基本采样单元ESU的问题,提出基于NDVI先验知识的ESU布设方法,并采用不同植被类型、不同均匀程度的地表作为模拟场,分析对比了方法的精度及稳定性.结果显示,本文方法用NDVI先验知识描述植被的生长空间分布信息,能相对准确地划分植被的不同生长水平,有效降低层内方差.在草地和森林地区的试验中,精度与稳定性均优于传统的随机采样、均匀采样和基于分类图的3种采样方法.因此,本文提出的采样方法为大尺度非均质区域LAI地面验证的采样方案提供了新的设计思路.%We propose a new sampling strategy based on Normalized Difference Vegetation Index(NDVI) prior knowledge for Leaf Area Index (LAI) ground measurements of non-homogeneous pixels. The method accuracy and stability have been analyzed in cases of different vegetation types and different pixel heterogeneity. The analysis results show that the proposed method is capable of properly dividing the non-homogeneous area into zones with different vegetation cover levels. It performed more accurate and robust than random sampling, systematic sampling and sampling based on classification in grassland and forest areas. The good performance indicates that this new sampling strategy for the LAI ground measurements may be used to remote sensing product validation for the heterogeneous pixels.
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