Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 12
Articles | Volume 26, issue 12
https://doi.org/10.5194/hess-26-3263-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-3263-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 12
Research article
 | 
28 Jun 2022
Research article |  | 28 Jun 2022

Integrating process-related information into an artificial neural network for root-zone soil moisture prediction

Roiya Souissi, Mehrez Zribi, Chiara Corbari, Marco Mancini, Sekhar Muddu, Sat Kumar Tomer, Deepti B. Upadhyaya, and Ahmad Al Bitar

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Latest update: 04 Nov 2024
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Short summary
In this study, we investigate the combination of surface soil moisture information with process-related features, namely, evaporation efficiency, soil water index and normalized difference vegetation index, using artificial neural networks to predict root-zone soil moisture. The joint use of process-related features yielded more accurate predictions in the case of arid and semiarid conditions. However, they have no to little added value in temperate to tropical conditions.
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