State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality
State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality
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DOI：
10.1007/s00477-021-02017-w
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发表时间：
2021-04
影响因子：
4.2
通讯作者：
M. Gong;Claire Miller;M. Scott;Ruth A. O’Donnell;S. Simis;S. Groom;A. Tyler;P. Hunter;E. Spyrakos
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文献类型：
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作者：
M. Gong;Claire Miller;M. Scott;Ruth A. O’Donnell;S. Simis;S. Groom;A. Tyler;P. Hunter;E. Spyrakos
卫星遥感可提供对了解环境至关重要的环境变量的指示性测量。卫星图像的空间和时间覆盖范围使科学家能够以前所未有的规模调查环境变量的变化。然而，从这样的图像中识别时空模式是具有挑战性的，这是由于数据的复杂性，这可能是大的体积，但在个别图像稀疏。本文提出了一种新的方法，状态空间功能主成分分析（SS-FPCA），识别时空模式处理卫星反演，同时降低数据的维数，通过使用功能主成分。此外，我们的方法可以用来产生稀疏区域的插值。提出了一种基于交替期望-条件最大化框架的算法来估计模型。估计参数的不确定性进行了研究，通过参数自助程序。湖泊叶绿素含量是水质状况的关键信息。这种资料通常只能从有限的现场取样地点获得，或者根本无法从偏远的难以进入的湖泊获得。本文利用欧洲空间局MEDIUM分辨率成像光谱仪观测的青藏高原塔若湖叶绿素含量的时空分布特征，采用SS-FPCA方法对塔若湖叶绿素含量的时空分布特征进行了研究。
Satellite remote sensing can provide indicative measures of environmental variables that are crucial to understanding the environment. The spatial and temporal coverage of satellite images allows scientists to investigate the changes in environmental variables in an unprecedented scale. However, identifying spatiotemporal patterns from such images is challenging due to the complexity of the data, which can be large in volume yet sparse within individual images. This paper proposes a new approach, state space functional principal components analysis (SS-FPCA), to identify the spatiotemporal patterns in processed satellite retrievals and simultaneously reduce the dimensionality of the data, through the use of functional principal components. Furthermore our approach can be used to produce interpolations over the sparse areas. An algorithm based on the alternating expectation–conditional maximisation framework is proposed to estimate the model. The uncertainty of the estimated parameters is investigated through a parametric bootstrap procedure. Lake chlorophyll-adata hold key information on water quality status. Such information is usually only available from limited in situ sampling locations or not at all for remote inaccessible lakes. In this paper, the SS-FPCA is used to investigate the spatiotemporal patterns in chlorophyll-adata of Taruo Lake on the Tibetan Plateau, observed by the European Space Agency MEdium Resolution Imaging Spectrometer.