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.

Viewed

Total article views: 1,777 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,265 490 22 1,777 25 38
  • HTML: 1,265
  • PDF: 490
  • XML: 22
  • Total: 1,777
  • BibTeX: 25
  • EndNote: 38
Views and downloads (calculated since 22 Sep 2020)
Cumulative views and downloads (calculated since 22 Sep 2020)

Viewed (geographical distribution)

Total article views: 1,526 (including HTML, PDF, and XML) Thereof 1,520 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Jun 2022
Download
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.