Simple size for ground and remotely sensed data
Simple size for ground and remotely sensed data
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DOI：
10.1016/0034-4257(86)90012-x
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发表时间：
1986-08
影响因子：
13.5
通讯作者：
P. Curran;H. D. Williamson
中科院分区：
文献类型：
--
作者：
P. Curran;H. D. Williamson
采样数据用于校准和确定遥感数据和遥感数据产品的精度。本文讨论了在这些样本的大小和误差之间实现平衡的问题，不是在场景的水平上，而是在场景内的站点的水平上。以石灰岩草原区域为例，确定了表征大范围草地所需的样本量。在95%的置信水平下，最大误差为5%，注意到每个领域的最小样样量在地面辐射测量和机载多光谱扫描仪测量的1-58和绿叶面积指数测量的142-293之间变化。收集如此大的样本在遥感中是不寻常的。这篇综述的结论是，有必要提高对抽样误差大小的认识，并尝试使用已知的数据空间自相关来减少给定样本量的误差。
Sampled data are used to calibrate and determine the accuracy of both remotely sensed data and the products of remotely sensed data. This paper discusses the problems of achieving a balance between the size and the error of these samples, not at the level of the scene but at the level of the sites within that scene. Using an area of limestone grassland as an illustrative example, the sample size required to characterize a wide range of grassland fields was determined. With a maximum of 5% error at the 95% confidence level the minimum sample size per field was noted to vary between 1–58 for ground radiometric and airborne multispectral scanner measurements and 142–293 for green leaf area index measurements. The collection of such large sample sizes is unusual in remote sensing. This review concludes that there is a need for an increased awareness of the magnitude of the sampling error and an attempt should be made to use the known spatial autocorrelation in the data to reduce error for a given sample size.