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Open AccessArticle

Global Surface Soil Moisture Dynamics in 1979–2016 Observed from ESA CCI SM Dataset

1
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(5), 883; https://doi.org/10.3390/w11050883
Received: 14 March 2019 / Revised: 13 April 2019 / Accepted: 23 April 2019 / Published: 26 April 2019
(This article belongs to the Special Issue Effects of Climate Change on Water Resources)
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Abstract

Soil moisture (SM) is an important variable for the terrestrial surface system, as its changes greatly affect the global water and energy cycle. The description and understanding of spatiotemporal changes in global soil moisture require long time-series observation. Taking advantage of the European Space Agency (ESA) Climate Change Initiative (CCI) combined SM dataset, this study aims at identifying the non-linear trends of global SM dynamics and their variations at multiple time scales. The distribution of global surface SM changes in 1979–2016 was identified by a non-linear methodology based on a stepwise regression at the annual and seasonal scales. On the annual scale, significant changes have taken place in about one third of the lands, in which nonlinear trends account for 48.13%. At the seasonal scale, the phenomenon that “wet season get wetter, and dry season get dryer” is found this study via hemispherical SM trend analysis at seasonal scale. And, the changes in seasonal SM are more pronounced (change rate at seasonal scales is about 5 times higher than that at annual scale) and the areas seeing significant changes cover a larger surface. Seasonal SM fluctuations distributed in southwestern China, central North America and southern Africa, are concealed at the annual scale. Overall, non-linear trend analysis at multiple time scale has revealed more complex dynamics for these long time series of SM. View Full-Text
Keywords: time series; multiple time scale; nonlinear trend; satellite data; seasonal soil moisture time series; multiple time scale; nonlinear trend; satellite data; seasonal soil moisture
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Pan, N.; Wang, S.; Liu, Y.; Zhao, W.; Fu, B. Global Surface Soil Moisture Dynamics in 1979–2016 Observed from ESA CCI SM Dataset. Water 2019, 11, 883.

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