31 citations as recorded by crossref.

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3. Enhancing Soil Moisture Forecasting Accuracy with REDF-LSTM: Integrating Residual En-Decoding and Feature Attention Mechanisms X. Li et al. 10.3390/w16101376
4. The Future of Vineyard Irrigation: AI-Driven Insights from IoT Data S. Stojanova et al. 10.3390/s25123658
5. Hybrid LSTM Method for Multistep Soil Moisture Prediction Using Historical Soil Moisture and Weather Data D. Kandamali et al. 10.3390/agriengineering7080260
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7. A Novel Deep Learning-Based Soil Moisture Prediction Model Using Adaptive Group Radial Lasso Regularized Basis Function Networks (AGRL-RBFN) Optimized by Hierarchical Correlated Spider Wasp Optimizer (HCSWO) and Incremental Learning (IL) C. Cherubini & M. Bala Anand 10.3390/w17162379
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9. Rolling forecast of soil moisture under non-stationary conditions: a robust framework incorporating time-varying dynamics within and between variables C. Yu et al. 10.1016/j.jhydrol.2025.133832
10. 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
11. 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
12. 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
13. Soil Moisture Prediction Using the VIC Model Coupled with LSTMseq2seq X. Zhang et al. 10.3390/rs17142453
14. Integrating multiple physical processes with deep learning for the prediction of coupled water-vapor-heat water flux in unsaturated zone Y. Wang et al. 10.1016/j.jhydrol.2025.133654
15. Design and Experiment of an Internet of Things-Based Wireless System for Farmland Soil Information Monitoring G. Ou et al. 10.3390/agriculture15050467
16. Urban flood risk analysis using presence-only machine learning approach: an integrated MaxEnt-cloud model framework in Harbin, China J. Hu et al. 10.1007/s11069-025-07452-4
17. 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
18. 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
19. Predicting Grain Count and Weight of Grape Clusters by Image Processing with Deep Learning E. Kahya 10.1007/s10341-025-01333-7
20. Integrating MFDFA with hybrid deep learning for enhanced daily precipitation-runoff prediction in the Minjiang River Basin H. Zeng et al. 10.2166/hydro.2025.043
21. 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
22. Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model T. Wu et al. 10.1109/JSTARS.2025.3564182
23. A self-supervised deep learning model for enhanced generalization in soil moisture prediction L. Wang et al. 10.1016/j.jhydrol.2025.133974
24. 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
25. Short-term soil moisture content forecasting with a hybrid informer model L. Wang et al. 10.3389/fsufs.2025.1636499
26. Multi-source remote sensing-based soil moisture retrieval at different depths in the Naqu Region, Tibetan Plateau: a comparative analysis of machine learning models with modified water cloud model preprocessing Z. Tong et al. 10.1007/s12665-025-12514-8
27. Soil Moisture Prediction in Pavement Layers Using LSTM Neural Networks A. Tophel et al. 10.1007/s10706-025-03293-x
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29. Explainable transfer learning for subsurface soil moisture prediction S. Ye et al. 10.1016/j.jhydrol.2025.133473
30. Spatial heterogeneity response of soil salinization inversion cotton field expansion based on deep learning J. Zhang et al. 10.3389/fpls.2024.1437390
31. Predicting Grain Count and Weight of Grape Clusters by Image Processing with Deep Learning E. Kahya 10.1007/s10341-025-01333-7