论文标题
非线性PCA用于地球观察数据的时空分析
Nonlinear PCA for Spatio-Temporal Analysis of Earth Observation Data
论文作者
论文摘要
遥感观察，产品和模拟是监测我们星球及其气候变异性的基本信息来源。揭示地球数据中空间和时间变异的主要模式对于分析和理解驱动地球系统的基本物理动态和过程至关重要。降低降低方法可以与时空数据集一起使用，并有效地分解信息。传统上，主要成分分析（PCA），也称为地球物理学中的经验正交函数（EOF），传统上已用于分析气候数据。但是，当存在非线性特征关系时，PCA/EOF失败。在这项工作中，我们提出了一种非线性PCA方法来处理时空的地球系统数据。所提出的方法称为旋转的复杂核PCA（简称Rock-PCA），用于再现内核Hilbert空间以考虑非线性过程，在复杂的内核域中运行，以考虑空间和时间的特征，并增加了额外的旋转，以提高灵活性。结果是地球数据立方体明确分辨出时空分解。该方法是无监督的，并且在计算上非常有效。我们说明了其使用合成实验和实际数据发现时空模式的能力。显示了三个基本气候变量分解的结果：基于卫星的全球总生产率（GPP）和土壤水分（SM）和重新分析海面温度（SST）数据。 Rock-PCA方法允许识别其年度和季节性振荡，以及它们的非季节趋势和空间可变性模式。
Remote sensing observations, products and simulations are fundamental sources of information to monitor our planet and its climate variability. Uncovering the main modes of spatial and temporal variability in Earth data is essential to analyze and understand the underlying physical dynamics and processes driving the Earth System. Dimensionality reduction methods can work with spatio-temporal datasets and decompose the information efficiently. Principal Component Analysis (PCA), also known as Empirical Orthogonal Functions (EOF) in geophysics, has been traditionally used to analyze climatic data. However, when nonlinear feature relations are present, PCA/EOF fails. In this work, we propose a nonlinear PCA method to deal with spatio-temporal Earth System data. The proposed method, called Rotated Complex Kernel PCA (ROCK-PCA for short), works in reproducing kernel Hilbert spaces to account for nonlinear processes, operates in the complex kernel domain to account for both space and time features, and adds an extra rotation for improved flexibility. The result is an explicitly resolved spatio-temporal decomposition of the Earth data cube. The method is unsupervised and computationally very efficient.We illustrate its ability to uncover spatio-temporal patterns using synthetic experiments and real data. Results of the decomposition of three essential climate variables are shown: satellite-based global Gross Primary Productivity (GPP) and Soil Moisture (SM), and reanalysis Sea Surface Temperature (SST) data. The ROCK-PCA method allows identifying their annual and seasonal oscillations, as well as their non-seasonal trends and spatial variability patterns.
