Articles | Volume 13, issue 4
https://doi.org/10.5194/essd-13-1711-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-1711-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
27 Apr 2021
Data description paper |
| 27 Apr 2021
| 27 Apr 2021
Gap-free global annual soil moisture: 15 km grids for 1991–2018
Mario Guevara
Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA
present address: University of California Riverside, Environmental Sciences|USDA-ARS, U.S. Salinity Laboratory, Riverside, CA, USA
Michela Taufer
Department of Electrical Engineering and Computer Science, The
University of Tennessee, Knoxville, TN, USA
Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA
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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.
Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a machine...
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