Articles | Volume 16, issue 5
https://doi.org/10.5194/gmd-16-1553-2023
https://doi.org/10.5194/gmd-16-1553-2023
Model description paper
 | 
17 Mar 2023
Model description paper |  | 17 Mar 2023

Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats

Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen

Viewed

Total article views: 3,513 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,866 573 74 3,513 175 64 60
  • HTML: 2,866
  • PDF: 573
  • XML: 74
  • Total: 3,513
  • Supplement: 175
  • BibTeX: 64
  • EndNote: 60
Views and downloads (calculated since 30 Sep 2022)
Cumulative views and downloads (calculated since 30 Sep 2022)

Viewed (geographical distribution)

Total article views: 3,513 (including HTML, PDF, and XML) Thereof 3,458 with geography defined and 55 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.