Articles | Volume 28, issue 4
https://doi.org/10.5194/hess-28-917-2024
© Author(s) 2024. 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-28-917-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Feb 2024
Research article |
| 27 Feb 2024
| 27 Feb 2024
A comprehensive study of deep learning for soil moisture prediction
Yanling Wang
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Liangsheng Shi
CORRESPONDING AUTHOR
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Yaan Hu
State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, China
Xiaolong Hu
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Wenxiang Song
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Lijun Wang
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
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Cited
17 citations as recorded by crossref.
- Temporal and geographic extrapolation of soil moisture using machine learning algorithms E. Chrysanthopoulos & A. Kallioras 10.1016/j.catena.2025.109156
- Design and Experiment of an Internet of Things-Based Wireless System for Farmland Soil Information Monitoring G. Ou et al. 10.3390/agriculture15050467
- Multi-Scale domain adaptation for high-resolution soil moisture retrieval from synthetic aperture radar in data-scarce regions L. Zhu et al. 10.1016/j.jhydrol.2025.133073
- Multi-layer grid-scale soil moisture estimation using spatiotemporal deep learning methods with physical constraints T. Zhang et al. 10.1016/j.jhydrol.2025.133086
- Enhancing Soil Moisture Forecasting Accuracy with REDF-LSTM: Integrating Residual En-Decoding and Feature Attention Mechanisms X. Li et al. 10.3390/w16101376
- A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset Z. Zhu et al. 10.1080/15481603.2025.2491169
- Predicting Grain Count and Weight of Grape Clusters by Image Processing with Deep Learning E. Kahya 10.1007/s10341-025-01333-7
- Sensor records can be used to forecast complex soil moisture dynamics with symbiosis of empirical nonlinear dynamics and echo state neural network AI R. Huffaker et al. 10.1016/j.compag.2024.109031
- Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model T. Wu et al. 10.1109/JSTARS.2025.3564182
- Improving global soil moisture prediction based on Meta-Learning model leveraging Köppen-Geiger climate classification Q. Li et al. 10.1016/j.catena.2025.108743
- Development of a Drought Monitoring System for Winter Wheat in the Huang-Huai-Hai Region, China, Utilizing a Machine Learning–Physical Process Hybrid Model Q. Mi et al. 10.3390/agronomy15030696
- Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review B. Nsoh et al. 10.3390/s24237480
- Evaluating the Performance of Satellite-Derived Soil Moisture Products Across South America Using Minimal Ground-Truth Assumptions in Spatiotemporal Statistical Analysis B. Mousa et al. 10.3390/rs17050753
- Development and Comparison of Artificial Neural Networks and Gradient Boosting Regressors for Predicting Topsoil Moisture Using Forecast Data M. Zambudio Martínez et al. 10.3390/ai6020041
- Explainable transfer learning for subsurface soil moisture prediction S. Ye et al. 10.1016/j.jhydrol.2025.133473
- Spatial heterogeneity response of soil salinization inversion cotton field expansion based on deep learning J. Zhang et al. 10.3389/fpls.2024.1437390
- Predicting Grain Count and Weight of Grape Clusters by Image Processing with Deep Learning E. Kahya 10.1007/s10341-025-01333-7
15 citations as recorded by crossref.
- Temporal and geographic extrapolation of soil moisture using machine learning algorithms E. Chrysanthopoulos & A. Kallioras 10.1016/j.catena.2025.109156
- Design and Experiment of an Internet of Things-Based Wireless System for Farmland Soil Information Monitoring G. Ou et al. 10.3390/agriculture15050467
- Multi-Scale domain adaptation for high-resolution soil moisture retrieval from synthetic aperture radar in data-scarce regions L. Zhu et al. 10.1016/j.jhydrol.2025.133073
- Multi-layer grid-scale soil moisture estimation using spatiotemporal deep learning methods with physical constraints T. Zhang et al. 10.1016/j.jhydrol.2025.133086
- Enhancing Soil Moisture Forecasting Accuracy with REDF-LSTM: Integrating Residual En-Decoding and Feature Attention Mechanisms X. Li et al. 10.3390/w16101376
- A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset Z. Zhu et al. 10.1080/15481603.2025.2491169
- Predicting Grain Count and Weight of Grape Clusters by Image Processing with Deep Learning E. Kahya 10.1007/s10341-025-01333-7
- Sensor records can be used to forecast complex soil moisture dynamics with symbiosis of empirical nonlinear dynamics and echo state neural network AI R. Huffaker et al. 10.1016/j.compag.2024.109031
- Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model T. Wu et al. 10.1109/JSTARS.2025.3564182
- Improving global soil moisture prediction based on Meta-Learning model leveraging Köppen-Geiger climate classification Q. Li et al. 10.1016/j.catena.2025.108743
- Development of a Drought Monitoring System for Winter Wheat in the Huang-Huai-Hai Region, China, Utilizing a Machine Learning–Physical Process Hybrid Model Q. Mi et al. 10.3390/agronomy15030696
- Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review B. Nsoh et al. 10.3390/s24237480
- Evaluating the Performance of Satellite-Derived Soil Moisture Products Across South America Using Minimal Ground-Truth Assumptions in Spatiotemporal Statistical Analysis B. Mousa et al. 10.3390/rs17050753
- Development and Comparison of Artificial Neural Networks and Gradient Boosting Regressors for Predicting Topsoil Moisture Using Forecast Data M. Zambudio Martínez et al. 10.3390/ai6020041
- Explainable transfer learning for subsurface soil moisture prediction S. Ye et al. 10.1016/j.jhydrol.2025.133473
2 citations as recorded by crossref.
Latest update: 02 Jun 2025
Short summary
LSTM temporal modeling suits soil moisture prediction; attention mechanisms enhance feature learning efficiently, as their feature selection capabilities are proven through Transformer and attention–LSTM hybrids. Adversarial training strategies help extract additional information from time series’ data. SHAP analysis and t-SNE visualization reveal differences in encoded features across models. This work serves as a reference for time series’ data processing in hydrology problems.
LSTM temporal modeling suits soil moisture prediction; attention mechanisms enhance feature...
