Nonlinear Complex PCA for spatio-temporal analysis of global soil moisture

TL;DR

This paper introduces a novel nonlinear complex PCA method to analyze global soil moisture data, revealing detailed spatio-temporal patterns, trends, and their relation to climate phenomena like ENSO.

Contribution

The paper presents a fast, nonlinear complex PCA approach that uncovers detailed spatio-temporal modes and trends in global soil moisture data, surpassing standard PCA capabilities.

Findings

Identifies dominant temporal variability modes in soil moisture.

Unveils spatial distribution of soil moisture variance.

Explores relationship between soil moisture patterns and ENSO.

Abstract

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{\~n}o Southern Oscillation (ENSO) conditions is also explored.