Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 5
Articles | Volume 16, issue 5
https://doi.org/10.5194/gmd-16-1553-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1553-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 5
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

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Latest update: 17 Jul 2024
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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.
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