78 citations as recorded by crossref.

1. Deep learning-based downscaling of SMAP data for surface and root-zone soil moisture mapping S. Rabiei et al. https://doi.org/10.1016/j.jhydrol.2026.135901
2. Physics-informed neural networks enhanced by data augmentation: a novel framework for robust soil moisture estimation using multi-source data fusion J. Hu et al. https://doi.org/10.1016/j.jhydrol.2025.134320
3. Temporal and geographic extrapolation of soil moisture using machine learning algorithms E. Chrysanthopoulos & A. Kallioras https://doi.org/10.1016/j.catena.2025.109156
4. A Snow Depth Retrieval Method Based on Super-Resolution Brightness Temperature Reconstruction and Multimodal Feature Synergy Y. Bai et al. https://doi.org/10.1109/TGRS.2026.3653456
5. From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction J. Liu et al. https://doi.org/10.5194/hess-29-6811-2025
6. The Future of Vineyard Irrigation: AI-Driven Insights from IoT Data S. Stojanova et al. https://doi.org/10.3390/s25123658
7. Exploring soil moisture dynamics and variability across scales and geological settings using gaussian mixture-long short-term memory networks B. Bischof et al. https://doi.org/10.1016/j.jhydrol.2025.134364
8. A novel soil moisture evaluation framework incorporating brightness temperature and a high-resolution 1 km summer brightness temperature dataset Z. Zhu et al. https://doi.org/10.1080/15481603.2025.2491169
9. 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 https://doi.org/10.3390/w17162379
10. Enhancing flood prediction through physics-driven typhoon feature engineering and machine learning Z. Zhang et al. https://doi.org/10.1371/journal.pone.0346237
11. Interpretable soil moisture prediction with a knowledge-guided deep learning approach Y. Wang et al. https://doi.org/10.5194/hess-30-2973-2026
12. Graph-Transformer for Spatiotemporal Soil Moisture Forecasting Using Multimodal Remote Sensing Data M. Saki et al. https://doi.org/10.1109/ACCESS.2026.3669499
13. Spatio-temporal prediction of soil hydro-thermal response in embankment to the varying climatic conditions using a Two-Step LSTM-ML approach N. An et al. https://doi.org/10.1016/j.trgeo.2025.101648
14. Rolling forecast of soil moisture under non-stationary conditions: a robust framework incorporating time-varying dynamics within and between variables C. Yu et al. https://doi.org/10.1016/j.jhydrol.2025.133832
15. SM-YOLO: an improved off-road trafficability detection network in complex field environment for autonomous driving F. Yang et al. https://doi.org/10.1088/2631-8695/ae2784
16. Transferable field-scale daily soil moisture forecasting in cotton with a minimal-input LSTM for irrigation scheduling Q. Su et al. https://doi.org/10.1016/j.atech.2026.102178
17. Earth observation and machine-learning–based mapping of 0–1 m soil moisture at 10-cm intervals in a permafrost-affected basin Y. Xiao et al. https://doi.org/10.1016/j.jag.2026.105147
18. Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review B. Nsoh et al. https://doi.org/10.3390/s24237480
19. Hybrid and Physics-Informed AI Models for Soil Water Dynamics in Sustainable Agriculture—A Review P. Filipowicz & B. Saletnik https://doi.org/10.3390/su18147452
20. Soil Moisture Prediction Using the VIC Model Coupled with LSTMseq2seq X. Zhang et al. https://doi.org/10.3390/rs17142453
21. A shapley additive exPlanations-informed, threshold-based environmental variable optimization strategy for enhancing soil organic carbon content Z. Wu et al. https://doi.org/10.1016/j.compag.2026.111694
22. A hybrid method coupling physical process-driven model with generative deep learning for probabilistic flood forecasting X. Yang et al. https://doi.org/10.1016/j.jhydrol.2026.135319
23. A novel hyper-tuned hybrid deep learning architecture for extended forecasting horizons: Application for soil moisture A. Amamou et al. https://doi.org/10.1016/j.iswa.2026.200666
24. 加强融合：可解释人工智能推动地球系统科学发展 菲. 黄 et al. https://doi.org/10.1360/N072025-0316
25. Urban flood risk analysis using presence-only machine learning approach: an integrated MaxEnt-cloud model framework in Harbin, China J. Hu et al. https://doi.org/10.1007/s11069-025-07452-4
26. Can deep learning outperform mechanistic modeling of peatland water table dynamics? H. Van Nieuwenhove et al. https://doi.org/10.1088/3049-4753/ae7726
27. Multi-Scale domain adaptation for high-resolution soil moisture retrieval from synthetic aperture radar in data-scarce regions L. Zhu et al. https://doi.org/10.1016/j.jhydrol.2025.133073
28. Integrating Hydrological, Physical, and Chemical Factors for Soil Moisture Prediction Using Advanced Machine Learning Models A. Al-Juaidi https://doi.org/10.1061/JHYEFF.HEENG-6811
29. Predicting Grain Count and Weight of Grape Clusters by Image Processing with Deep Learning E. Kahya https://doi.org/10.1007/s10341-025-01333-7
30. Online MPC irrigation control using ML-based soil moisture prediction A. Benassi et al. https://doi.org/10.1016/j.conengprac.2026.106975
31. Sequence-Aware Deep Learning for Field-Scale Surface Soil Moisture Estimation from Sentinel-1, HLS, and Ancillary Data E. Jahan Nejadi et al. https://doi.org/10.3390/rs18132213
32. High-Resolution Spatiotemporal Mapping of Surface Soil Moisture Using ConvLSTM Model and Sentinel-1 Data A. Hosseinizadeh et al. https://doi.org/10.3390/w17223300
33. 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. https://doi.org/10.1016/j.compag.2024.109031
34. A self-supervised deep learning model for enhanced generalization in soil moisture prediction L. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.133974
35. A lightweight soil moisture prediction model based on irrigation cycle segmentation and Kalman filtering J. Ma et al. https://doi.org/10.3389/fpls.2026.1853119
36. Short-term soil moisture content forecasting with a hybrid informer model L. Wang et al. https://doi.org/10.3389/fsufs.2025.1636499
37. Transformer-based soil moisture simulation for understanding future drying trend globally Y. Liu et al. https://doi.org/10.1016/j.jhydrol.2025.134709
38. Integrating machine learning models with ground sensors to enhance soil moisture prediction in agroecosystems of Texas G. Tefera et al. https://doi.org/10.1016/j.compag.2025.111358
39. A maize-centric framework for explainable artificial intelligence in decoding drought tolerance mechanisms B. Quyoom et al. https://doi.org/10.1007/s44372-026-00485-4
40. Swin-MBUNet for high-precision soil moisture prediction in winter wheat farming D. Zheng et al. https://doi.org/10.1016/j.jafr.2026.103042
41. Development and Comparison of Artificial Neural Networks and Gradient Boosting Regressors for Predicting Topsoil Moisture Using Forecast Data M. Zambudio Martínez et al. https://doi.org/10.3390/ai6020041
42. Explainable transfer learning for subsurface soil moisture prediction S. Ye et al. https://doi.org/10.1016/j.jhydrol.2025.133473
43. Quantifying Field Soil Moisture, Temperature, and Heat Flux Using an Informer–LSTM Deep Learning Model N. Li et al. https://doi.org/10.3390/agronomy15112453
44. High-resolution daily surface soil moisture mapping over the Qinghai–Tibet Plateau via predictors fusion and machine learning X. Gao et al. https://doi.org/10.1016/j.jhydrol.2026.134982
45. Informer–UNet: A Hybrid Deep Learning Framework for Multi-Point Soil Moisture Prediction and Precision Irrigation in Winter Wheat D. Zheng et al. https://doi.org/10.3390/agriculture16060648
46. Enhancing Soil Moisture Forecasting Accuracy with REDF-LSTM: Integrating Residual En-Decoding and Feature Attention Mechanisms X. Li et al. https://doi.org/10.3390/w16101376
47. Applications of Artificial Intelligence in Soil Characterization and Agriculture: A Systematic Review of Techniques, Models, and Applications C. Navarro Rubio et al. https://doi.org/10.3390/agronomy16131241
48. Hybrid LSTM Method for Multistep Soil Moisture Prediction Using Historical Soil Moisture and Weather Data D. Kandamali et al. https://doi.org/10.3390/agriengineering7080260
49. Deep learning approaches for streamflow flash drought prediction across the contiguous United States S. Bakar et al. https://doi.org/10.1016/j.jhydrol.2026.135956
50. Intelligent Modeling of Soil Moisture Variability Using Remote Sensing and Spiking Neural Networks S. El Maachi et al. https://doi.org/10.1016/j.procs.2025.09.258
51. A Robust Sensor-Failure-Tolerant Fuzzy Control Framework with Predictive Data Imputation for Sustainable Precision Irrigation I. Laksmana et al. https://doi.org/10.31436/iiumej.v27i2.3647
52. 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. https://doi.org/10.3390/agronomy15030696
53. A Soil-Moisture-Constrained ML Framework for High-Resolution Rainfall Estimation from Satellite Data S. Talha et al. https://doi.org/10.1007/s41748-026-01138-y
54. 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. https://doi.org/10.3390/rs17050753
55. Machine learning approaches for enhanced estimation of reference evapotranspiration (ETo): a comparative evaluation A. Farag https://doi.org/10.1038/s41598-025-23166-w
56. A hybrid ConvLSTM-Nudging model for predicting surface soil moisture in the Qilian Mountains, China M. Fan et al. https://doi.org/10.1007/s40333-025-0112-9
57. Multi-Depth Soil Moisture Prediction Using Machine Learning Across Türkiye's Diverse Environments M. Demir https://doi.org/10.15832/ankutbd.1809955
58. Bridging the gap: Explainable AI for advancing Earth System Science F. Huang et al. https://doi.org/10.1007/s11430-025-1898-3
59. ASCAT soil moisture retrieval using deep learning: a focus on localization strategy L. Dinh https://doi.org/10.3389/frsen.2025.1718353
60. Integrating multiple physical processes with deep learning for the prediction of coupled water-vapor-heat water flux in unsaturated zone Y. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.133654
61. Enhancing soil moisture prediction via a physics-informed xLSTM with multi-variate temporal dynamics L. He et al. https://doi.org/10.1080/19942060.2026.2678125
62. Design and Experiment of an Internet of Things-Based Wireless System for Farmland Soil Information Monitoring G. Ou et al. https://doi.org/10.3390/agriculture15050467
63. Multi-layer grid-scale soil moisture estimation using spatiotemporal deep learning methods with physical constraints T. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.133086
64. Integrating MFDFA with hybrid deep learning for enhanced daily precipitation-runoff prediction in the Minjiang River Basin H. Zeng et al. https://doi.org/10.2166/hydro.2025.043
65. Artificial Intelligence for Remote Sensing: Progress, Challenges, and Perspectives Y. Shang et al. https://doi.org/10.1109/JSTARS.2026.3656185
66. Research on LSTM Soil Temperature and Humidity Prediction Model Based on Optimized Particle Swarm Optimization Attention Mechanism 锦. 付 https://doi.org/10.12677/csa.2026.165171
67. Climate-aware hybrid 1D-CNN-LSTM model for multi-layer soil moisture prediction in tropical Cocoa plantations S. Shawon et al. https://doi.org/10.1016/j.csag.2025.100096
68. Optimizing irrigation decisions with Seq2Seq modeling and deep reinforcement learning A. Mukti et al. https://doi.org/10.1016/j.compag.2026.111448
69. Explainable Quantile CNN-LSTM model for uncertainty-aware multi-layer soil moisture prediction in tropical cocoa plantations S. Shawon et al. https://doi.org/10.1038/s41598-026-48517-z
70. Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model T. Wu et al. https://doi.org/10.1109/JSTARS.2025.3564182
71. Prediction of moisture content in gray bricks of historical buildings based on infrared images and deep learning J. Yue et al. https://doi.org/10.1016/j.jobe.2026.115199
72. Distributed Deep Learning and Intelligent Soil–Water Analytics in Precision Agriculture: A Comprehensive Review P. Lemenkova https://doi.org/10.3390/land15071125
73. Improving global soil moisture prediction based on Meta-Learning model leveraging Köppen-Geiger climate classification Q. Li et al. https://doi.org/10.1016/j.catena.2025.108743
74. Enhancing dissolved oxygen prediction through a hybrid attention-based LSTM model with wavelet transform and auto-encoder N. Shiri et al. https://doi.org/10.1016/j.jwpe.2026.109664
75. A Soil Moisture Prediction Model Based on GCN-LSTM Network Incorporating Channel and Temporal Attention J. Wang et al. https://doi.org/10.3390/w18111308
76. 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. https://doi.org/10.1007/s12665-025-12514-8
77. Soil Moisture Prediction in Pavement Layers Using LSTM Neural Networks A. Tophel et al. https://doi.org/10.1007/s10706-025-03293-x
78. A comparative assessment of a hybrid approach against conventional and machine-learning daily streamflow prediction in ungauged basins S. Lee & D. Kim https://doi.org/10.1016/j.ejrh.2025.102854