Functional PCA for Remotely Sensed Lake Surface Water Temperature Data
Functional PCA for Remotely Sensed Lake Surface Water Temperature Data
复制标题
用于遥感湖面水温数据的函数主成分分析
DOI：
10.1016/j.proenv.2015.05.015
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Gong M
中科院分区：
文献类型：
--
作者：
Gong M
应用函数主成分分析方法对ARC-Lake项目中产生的高维维多利亚湖表层水温数据集进行了分析。在分析中采用了两种不同的观点：建模温度曲线（单变量函数）和温度表面（双变量函数）。后者被证明是一个更好的方法，在降维和模式检测的意义。计算的细节和应用程序的维多利亚湖数据的一些结果。
Functional principal component analysis is used to investigate a high-dimensional surface water temperature data set of Lake Victoria, which has been produced in the ARC-Lake project. Two different perspectives are adopted in the analysis: modelling temperature curves (univariate functions) and temperature surfaces (bivariate functions). The latter proves to be a better approach in the sense of both dimension reduction and pattern detection. Computational details and some results from an application to Lake Victoria data are presented.