Nonlinear Complex PCA for Spatio-Temporal Analysis of Global Soil Moisture

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 Niño Southern Oscillation (ENSO) conditions is also explored.

Dominant Features of Global Surface Soil Moisture Variability Observed by the SMOS Satellite

“…Our results showed that the STL procedure, after adequate parameterization, was a solid means to build a soil moisture climatology based solely on the temporal dynamics of the data. Yet, it would be recommendable to assess in future research the additional benefits of using techniques exploiting the temporal and spatial components of the data (e.g., [24,58,59]). The global STL parameterization was thoughtfully chosen after an analysis at 8 selected sites with distinct vegetation seasonality and climatic conditions.…”

“…We show that this observation-driven climatology provides a close representation of the dominant features of temporal variability in the Earth's SM for the period 2010-2016, and allows identifying areas subjected to seasonal, subseasonal and long-term variability. Previous research showed that, despite being a short data set, SMOS provides coherent and reliable SM variability patterns at both seasonal and interannual scales [23,24]. In this work, the Seasonal Trend decomposition using Loess (STL) procedure [25] is implemented and tailored to the first six annual cycles of SMOS data to decompose the temporal variability of the signal.…”