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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Jun 2022
Research article |
| 28 Jun 2022
| 28 Jun 2022
Integrating process-related information into an artificial neural network for root-zone soil moisture prediction
Roiya Souissi
CORRESPONDING AUTHOR
CESBIO – Centre d'Etudes Spatiales de la Biosphère, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, Toulouse, France
Mehrez Zribi
CESBIO – Centre d'Etudes Spatiales de la Biosphère, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, Toulouse, France
Chiara Corbari
Department of Civil and Environmental Engineering (DICA), Polytechnic University of Milan, 20133 Milan, Italy
Marco Mancini
Department of Civil and Environmental Engineering (DICA), Polytechnic University of Milan, 20133 Milan, Italy
Sekhar Muddu
Department of Civil Engineering, Indian Institute of Science,
Bangalore 560012, India
Sat Kumar Tomer
Satyukt analytics Pvt Ltd, Sanjay Nagar Main Rd, MET Layout,
Bengaluru, Karnataka 560094, India
Deepti B. Upadhyaya
Department of Civil Engineering, Indian Institute of Science,
Bangalore 560012, India
Satyukt analytics Pvt Ltd, Sanjay Nagar Main Rd, MET Layout,
Bengaluru, Karnataka 560094, India
Ahmad Al Bitar
CESBIO – Centre d'Etudes Spatiales de la Biosphère, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, Toulouse, France
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Cited
15 citations as recorded by crossref.
- Root zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture product N. Ouaadi et al. https://doi.org/10.1016/j.agwat.2025.109507
- Choosing the appropriate methodology to monitor soil organic carbon (SOC) in croplands: aligning methods with evolving monitoring reporting verification (MRV) frameworks A. Ihasusta et al. https://doi.org/10.1080/17583004.2026.2638317
- Land-atmosphere and ocean–atmosphere couplings dominate the dynamics of agricultural drought predictability in the Loess Plateau, China J. Luo et al. https://doi.org/10.1016/j.jhydrol.2024.132225
- Estimating maize root zone soil moisture by assimilating high spatiotemporal resolution optical and radar remote sensing into the WOFOST-HYDRUS coupled model L. Li et al. https://doi.org/10.1016/j.jhydrol.2024.132618
- Comprehensive quality assessment of satellite- and model-based soil moisture products against the COSMOS network in Germany T. Schmidt et al. https://doi.org/10.1016/j.rse.2023.113930
- Novel deep learning algorithm in soil erodibility factor predicting at a continental scale A. Shirzadi et al. https://doi.org/10.1016/j.iswcr.2025.09.008
- Integration of artificial intelligence and remote sensing for crop yield prediction and crop growth parameter estimation in Mediterranean agroecosystems: Methodologies, emerging technologies, research gaps, and future directions W. Demissie et al. https://doi.org/10.1016/j.eja.2025.127894
- Soil moisture estimation at 1-km resolution over croplands and grasslands using sentinel-1/2 and SMOS-IC data: algorithm and validation A. Karami et al. https://doi.org/10.1080/22797254.2026.2622132
- Estimating root zone soil moisture in farmland by integrating multi-source remote sensing data based on the water balance equation X. Bai et al. https://doi.org/10.1016/j.agwat.2025.109544
- A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing M. Li et al. https://doi.org/10.3390/rs15225361
- Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model T. Wu et al. https://doi.org/10.1109/JSTARS.2025.3564182
- Soil Moisture Prediction Using Remote Sensing and Machine Learning Algorithms: A Review on Progress, Challenges, and Opportunities M. Lamichhane et al. https://doi.org/10.3390/rs17142397
- Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning M. Celik et al. https://doi.org/10.3390/rs14215584
- An Overview of Machine-Learning Methods for Soil Moisture Estimation M. Taheri et al. https://doi.org/10.3390/w17111638
- Estimation of multi-layer soil moisture in agricultural irrigation areas based on a feature-level integrated LSTM-XGBoost model D. Wang et al. https://doi.org/10.1016/j.agwat.2026.110379
15 citations as recorded by crossref.
- Root zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture product N. Ouaadi et al. https://doi.org/10.1016/j.agwat.2025.109507
- Choosing the appropriate methodology to monitor soil organic carbon (SOC) in croplands: aligning methods with evolving monitoring reporting verification (MRV) frameworks A. Ihasusta et al. https://doi.org/10.1080/17583004.2026.2638317
- Land-atmosphere and ocean–atmosphere couplings dominate the dynamics of agricultural drought predictability in the Loess Plateau, China J. Luo et al. https://doi.org/10.1016/j.jhydrol.2024.132225
- Estimating maize root zone soil moisture by assimilating high spatiotemporal resolution optical and radar remote sensing into the WOFOST-HYDRUS coupled model L. Li et al. https://doi.org/10.1016/j.jhydrol.2024.132618
- Comprehensive quality assessment of satellite- and model-based soil moisture products against the COSMOS network in Germany T. Schmidt et al. https://doi.org/10.1016/j.rse.2023.113930
- Novel deep learning algorithm in soil erodibility factor predicting at a continental scale A. Shirzadi et al. https://doi.org/10.1016/j.iswcr.2025.09.008
- Integration of artificial intelligence and remote sensing for crop yield prediction and crop growth parameter estimation in Mediterranean agroecosystems: Methodologies, emerging technologies, research gaps, and future directions W. Demissie et al. https://doi.org/10.1016/j.eja.2025.127894
- Soil moisture estimation at 1-km resolution over croplands and grasslands using sentinel-1/2 and SMOS-IC data: algorithm and validation A. Karami et al. https://doi.org/10.1080/22797254.2026.2622132
- Estimating root zone soil moisture in farmland by integrating multi-source remote sensing data based on the water balance equation X. Bai et al. https://doi.org/10.1016/j.agwat.2025.109544
- A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing M. Li et al. https://doi.org/10.3390/rs15225361
- Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model T. Wu et al. https://doi.org/10.1109/JSTARS.2025.3564182
- Soil Moisture Prediction Using Remote Sensing and Machine Learning Algorithms: A Review on Progress, Challenges, and Opportunities M. Lamichhane et al. https://doi.org/10.3390/rs17142397
- Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning M. Celik et al. https://doi.org/10.3390/rs14215584
- An Overview of Machine-Learning Methods for Soil Moisture Estimation M. Taheri et al. https://doi.org/10.3390/w17111638
- Estimation of multi-layer soil moisture in agricultural irrigation areas based on a feature-level integrated LSTM-XGBoost model D. Wang et al. https://doi.org/10.1016/j.agwat.2026.110379
Saved (final revised paper)
Latest update: 04 Sep 2026
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.
In this study, we investigate the combination of surface soil moisture information with...
