  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.
