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Remote Sens. 2019, 11(1), 95; https://doi.org/10.3390/rs11010095

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

1
Image Processing Laboratory, Universitat de València, C/Catedrático José Beltrán 2, 46980 València, Spain
2
Institut de Ciències del Mar, CSIC, Pg. Maritim de la Barceloneta 37-49, 08003 Barcelona, Spain
3
European Centre for Medium-Range Weather Forecasts (ECMWF), Copernicus department, Shinfield Park, Reading RG2 9AX, UK
*
Author to whom correspondence should be addressed.
Received: 23 November 2018 / Revised: 23 December 2018 / Accepted: 29 December 2018 / Published: 8 January 2019
(This article belongs to the Special Issue Ten Years of Remote Sensing at Barcelona Expert Center)
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Abstract

Soil moisture observations are expected to play an important role in monitoring global climate trends. However, measuring soil moisture is challenging because of its high spatial and temporal variability. Point-scale in-situ measurements are scarce and, excluding model-based estimates, remote sensing remains the only practical way to observe soil moisture at a global scale. The ESA-led Soil Moisture and Ocean Salinity (SMOS) mission, launched in 2009, measures the Earth’s surface natural emissivity at L-band and provides highly accurate soil moisture information with a 3-day revisiting time. Using the first six full annual cycles of SMOS measurements (June 2010–June 2016), this study investigates the temporal variability of global surface soil moisture. The soil moisture time series are decomposed into a linear trend, interannual, seasonal, and high-frequency residual (i.e., subseasonal) components. The relative distribution of soil moisture variance among its temporal components is first illustrated at selected target sites representative of terrestrial biomes with distinct vegetation type and seasonality. A comparison with GLDAS-Noah and ERA5 modeled soil moisture at these sites shows general agreement in terms of temporal phase except in areas with limited temporal coverage in winter season due to snow. A comparison with ground-based estimates at one of the sites shows good agreement of both temporal phase and absolute magnitude. A global assessment of the dominant features and spatial distribution of soil moisture variability is then provided. Results show that, despite still being a relatively short data set, SMOS data provides coherent and reliable variability patterns at both seasonal and interannual scales. Subseasonal components are characterized as white noise. The observed linear trends, based upon one strong El Niño event in 2016, are consistent with the known El Niño Southern Oscillation (ENSO) teleconnections. This work provides new insight into recent changes in surface soil moisture and can help further our understanding of the terrestrial branch of the water cycle and of global patterns of climate anomalies. Also, it is an important support to multi-decadal soil moisture observational data records, hydrological studies and land data assimilation projects using remotely sensed observations. View Full-Text
Keywords: SMOS; soil moisture; climatology; trends; signal decomposition SMOS; soil moisture; climatology; trends; signal decomposition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Piles, M.; Ballabrera-Poy, J.; Muñoz-Sabater, J. Dominant Features of Global Surface Soil Moisture Variability Observed by the SMOS Satellite. Remote Sens. 2019, 11, 95.

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