Articles | Volume 22, issue 10
https://doi.org/10.5194/hess-22-5341-2018
https://doi.org/10.5194/hess-22-5341-2018
Research article
 | 
17 Oct 2018
Research article |  | 17 Oct 2018

Global downscaling of remotely sensed soil moisture using neural networks

Seyed Hamed Alemohammad, Jana Kolassa, Catherine Prigent, Filipe Aires, and Pierre Gentine

Viewed

Total article views: 4,560 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,786 1,704 70 4,560 448 60 70
  • HTML: 2,786
  • PDF: 1,704
  • XML: 70
  • Total: 4,560
  • Supplement: 448
  • BibTeX: 60
  • EndNote: 70
Views and downloads (calculated since 08 Feb 2018)
Cumulative views and downloads (calculated since 08 Feb 2018)

Viewed (geographical distribution)

Total article views: 4,560 (including HTML, PDF, and XML) Thereof 4,222 with geography defined and 338 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (preprint)

Latest update: 27 Feb 2024
Download
Short summary
A new machine learning algorithm is developed to downscale satellite-based soil moisture estimates from their native spatial scale of 9 km to 2.25 km.