论文标题
非线性复杂PCA用于全球土壤水分时空分析
Nonlinear Complex PCA for spatio-temporal analysis of global soil moisture
论文作者
论文摘要
土壤水分（SM）是水文周期的关键状态变量，以监测气候变化对自然资源的影响。土壤水分在空间和时间上的变化很大，季节性，异常和长期趋势，以及重要的非线性行为。在这里，我们介绍了一种新型的快速和非线性复杂PCA方法，以分析地球表面SM的时空模式。我们使用ESA的SMOS任务在2010 - 2017年期间获得的全球SM估计值。我们的方法揭示了时间和空间模式，趋势和周期性，与标准PCA分解不同。结果表明，其不同组件之间总SM方差的分布，并表明不同区域的表面土壤水分中时间变异的主要模式。还探讨了派生的SM时空模式与El Ni {n} o南部振荡（ENSO）条件的关系。
Soil moisture (SM) is a key state variable of the hydrological cycle, needed to monitor the effects of a changing climate on natural resources. Soil moisture is highly variable in space and time, presenting seasonalities, anomalies and long-term trends, but also, and important nonlinear behaviours. Here, we introduce a novel fast and nonlinear complex PCA method to analyze the spatio-temporal patterns of the Earth's surface SM. We use global SM estimates acquired during the period 2010-2017 by ESA's SMOS mission. Our approach unveils both time and space modes, trends and periodicities unlike standard PCA decompositions. Results show the distribution of the total SM variance among its different components, and indicate the dominant modes of temporal variability in surface soil moisture for different regions. The relationship of the derived SM spatio-temporal patterns with El Ni{ñ}o Southern Oscillation (ENSO) conditions is also explored.
