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

Guevara, Mario; Taufer, Michela; Vargas, Rodrigo

doi:https://doi.org/10.5194/essd-13-1711-2021

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.

https://doi.org/10.5194/essd-13-1711-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 4

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, Michela Taufer, and Rodrigo Vargas

Data sets

Gap-Free Global Annual Soil Moisture: 15km Grids for 1991-2018 M. Guevara, M. Taufer, and R. Vargas https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e

Soil moisture training dataset ESA-CC https://www.esa-soilmoisture-cci.org/

Soil moisture validation dataset ISMN https://ismn.geo.tuwien.ac.at/en/

A Global Database of Soil Respiration Data, Version 4.0 B. P. Bond-Lamberty and A. M. Thomson https://doi.org/10.3334/ORNLDAAC/1578

LBA-ECO CD-32 Flux Tower Network Data Compilation, Brazilian Amazon: 1999-2006 S. R. Saleska, H. R. da Rocha, A. R. Huete, A. D. Nobre, P. Artaxo, and Y. E. Shimabukuro https://doi.org/10.3334/ORNLDAAC/1174

Model code and software

Supporting R code R. Vargas https://github.com/vargaslab/Global_Soil_Moisture

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.