Articles | Volume 13, issue 4
Earth Syst. Sci. Data, 13, 1711–1735, 2021
https://doi.org/10.5194/essd-13-1711-2021
Earth Syst. Sci. Data, 13, 1711–1735, 2021
https://doi.org/10.5194/essd-13-1711-2021

Data description paper 27 Apr 2021

Data description paper | 27 Apr 2021

Gap-free global annual soil moisture: 15 km grids for 1991–2018

Mario Guevara et al.

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AR by Rodrigo Vargas on behalf of the Authors (19 Jan 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (26 Jan 2021) by David Carlson
ED: Publish as is (05 Mar 2021) by David Carlson
AR by Rodrigo Vargas on behalf of the Authors (12 Mar 2021)  Author's response    Manuscript
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Short summary
Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a machine learning approach to increase the spatial resolution of satellite-derived soil moisture information. The outcome is a dataset of gap-free global mean annual soil moisture predictions and associated uncertainty for 28 years (1991–2018) across 15 km grids. This dataset has higher agreement with in situ soil moisture and precipitation measurements. Results show a decline of global annual soil moisture.