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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Sep 2026
Research article |
| 17 Sep 2026
| 17 Sep 2026
Evaluation of ASCAT soil moisture retrievals and their potential to detect intraday variability
Estellus, Paris, France
LIRA, Observatoire de Paris, Université PSL, Sorbonne Université, CNRS, Paris, France
Filipe Aires
LIRA, Observatoire de Paris, Université PSL, Sorbonne Université, CNRS, Paris, France
Estellus, Paris, France
Victor Pellet
LMD, École Polytechnique, Palaiseau, France
LIRA, Observatoire de Paris, Université PSL, Sorbonne Université, CNRS, Paris, France
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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.
Soil moisture (SM) plays a key role in weather, agriculture, and water management. While...
