Earth Observation
Earth Observation
EO
Articles | Volume 1, issue 1
Articles | Volume 1, issue 1
https://doi.org/10.5194/eo-1-105-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/eo-1-105-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 1, issue 1
Research article
 | 
17 Sep 2026
Research article |  | 17 Sep 2026

Evaluation of ASCAT soil moisture retrievals and their potential to detect intraday variability

Lan Anh Dinh, Filipe Aires, and Victor Pellet

Viewed

Total article views: 339 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
207 100 32 339 29 35
  • HTML: 207
  • PDF: 100
  • XML: 32
  • Total: 339
  • BibTeX: 29
  • EndNote: 35
Views and downloads (calculated since 28 Apr 2026)
Cumulative views and downloads (calculated since 28 Apr 2026)

Viewed (geographical distribution)

Total article views: 339 (including HTML, PDF, and XML) Thereof 321 with geography defined and 18 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 17 Sep 2026
Download
Short summary
Soil moisture (SM) plays a key role in weather, agriculture, and water management. While satellites can measure SM from space, obtaining accurate, frequent measurements throughout the day remains challenging. Here, we explore how deep learning models can improve sub-daily SM estimates. Our approach focuses on capturing spatial patterns and adapting to local conditions. Using data from the Advanced SCATterometer (ASCAT) satellite instrument, we show that this model can produce reliable SM estimates multiple times a day. 
Read more
Share