42 citations as recorded by crossref.

1. Neural Synthetic Profiles from Remote Sensing and Observations (NeSPReSO) — Reconstructing temperature and salinity fields in the Gulf of Mexico J. Miranda et al. https://doi.org/10.1016/j.ocemod.2025.102550
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3. EOF-UViT Model: A New Deep Learning Model to Reconstruct the Three-Dimensional Salinity Based on Multi-Source Remote Sensing Data X. Han et al. https://doi.org/10.3390/rs18050802
4. Deep learning-driven 3D marine nitrate estimation: uncertainty mitigation through underwater signal exploitation and label augmentation X. Yu et al. https://doi.org/10.3389/fmars.2025.1576558
5. An Updated Estimate of the Indonesian Throughflow Geostrophic Transport: Interannual Variability and Salinity Effect Y. Guo et al. https://doi.org/10.1029/2023GL103748
6. A comparative assessment of different machine learning algorithms for estimating near realistic salinity in the North Indian Ocean A. Paul et al. https://doi.org/10.1016/j.ocemod.2025.102561
7. Reconstructing three-dimensional density from surface data in the North Atlantic Sea through the PIO-Net model Y. Chen et al. https://doi.org/10.1016/j.oceaneng.2025.122086
8. Daily Subsurface Salinity Reconstruction From Multisource Satellite Observations Using Wavelet-Enhanced 3-D Mamba Z. Liang et al. https://doi.org/10.1109/JSTARS.2025.3649568
9. CoastalBench-downscaling: a machine learning benchmark for reconstructing high-resolution three-dimensional coastal fields from surface data B. Yuan et al. https://doi.org/10.1016/j.oceaneng.2026.125851
10. Assessment of wetland ecosystem health in Rarh Region, India through P-S-R (pressure-state-response) model R. Khatun & S. Das https://doi.org/10.1016/j.scitotenv.2024.175700
11. Decreases in South Pacific and South Atlantic sea-air CO2 fluxes caused by extreme precipitation Z. Li et al. https://doi.org/10.1038/s41467-026-69847-6
12. Reconstructing high-resolution subsurface temperature of the global ocean using deep forest with combined remote sensing and in situ observations H. Su et al. https://doi.org/10.1016/j.isprsjprs.2024.09.022
13. Super-resolution reconstruction of subsurface temperature field in South China Sea using satellite observations H. Wu et al. https://doi.org/10.1063/5.0253807
14. Estimation of subsurface salinity and analysis of Changjiang diluted water volume in the East China Sea S. Kim et al. https://doi.org/10.3389/fmars.2023.1247462
15. A deep learning approach to estimate ocean salinity with data sampled with expendable bathythermographs E. Campos et al. https://doi.org/10.1016/j.apor.2024.103997
16. On the global reconstruction of ocean interior variables: a feasibility data-driven study with simulated surface and water column observations A. Garcia-Espriu et al. https://doi.org/10.5194/os-21-2579-2025
17. Enhancing Subsurface Thermal Structures Reconstruction via a Dual-Branch Framework Integrating Surface Remote Sensing and Model-Predicted Upper-Layer Fields C. Zhou et al. https://doi.org/10.1109/JSTARS.2026.3680945
18. Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation A. Giganti et al. https://doi.org/10.3390/rs16213963
19. Reconstruction of the underwater sound speed field of mesoscale eddies in typical sea areas based on the physically-constrained deep learning model Y. Liu et al. https://doi.org/10.1016/j.apor.2026.104988
20. A continual-learning-based multilayer perceptron for improved reconstruction of three-dimensional nitrate concentrations X. Yu et al. https://doi.org/10.5194/essd-17-2735-2025
21. Knowledge-Informed Deep Learning Model for Subsurface Thermohaline Reconstruction From Satellite Observations A. Wang et al. https://doi.org/10.1109/TGRS.2024.3509616
22. OceanVP: A HYCOM based benchmark dataset and a relational spatiotemporal predictive network for oceanic variable prediction Z. Shi et al. https://doi.org/10.1016/j.oceaneng.2024.117748
23. Bias Correction of SMAP L2 Sea Surface Salinity Based on Physics-Informed Neural Network M. Wu et al. https://doi.org/10.3390/rs17183226
24. A Method for Predicting High-Resolution 3D Variations in Temperature and Salinity Fields Using Multi-Source Ocean Data X. Cao et al. https://doi.org/10.3390/jmse12081396
25. A New Region Ocean Sound Velocity Field Model Considering Variation Mechanisms of Temperature and Salt X. Zhao et al. https://doi.org/10.1109/JOE.2025.3582350
26. Application of swarm-based deep neural networks and ensemble models for reconstruction of specific conductance data A. Mahdavi-Meymand et al. https://doi.org/10.1038/s41598-026-38136-z
27. Attention-enhanced deep learning model for reconstruction and downscaling of thermocline depth in the tropical Indian Ocean Z. Feng et al. https://doi.org/10.1016/j.ocemod.2025.102537
28. Strengthening ITF and weakening AMOC: time series evidence of trends and causal pathways to Agulhas variability S. Herho et al. https://doi.org/10.1590/2675-2824074.25092
29. Enhancing Ocean Temperature and Salinity Reconstruction with Deep Learning: The Role of Surface Waves X. Yu et al. https://doi.org/10.3390/jmse13050910
30. Leveraging Land Cover Priors for Isoprene Emission Super-Resolution C. Ummerle et al. https://doi.org/10.3390/rs17101715
31. 3DV-Unet: Eddy-Resolving Reconstruction of Three-Dimensional Upper-Ocean Physical Fields from Satellite Observations Q. Zhu et al. https://doi.org/10.3390/rs17193394
32. Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network L. Howard et al. https://doi.org/10.1017/eds.2026.10033
33. STMSNet: A Novel Spatiotemporal Deep Learning Model for Ocean Subsurface Temperature Reconstruction H. Chen et al. https://doi.org/10.1109/JSTARS.2026.3679664
34. Uncertainty Quantification of Satellite-Based Essential Climate Variables Derived from Deep Learning J. Gou et al. https://doi.org/10.1007/s10712-025-09919-2
35. Reconstruction of Three-Dimensional Temperature and Salinity in the Equatorial Ocean with Deep-Learning X. Yu et al. https://doi.org/10.3390/rs17122005
36. Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach J. Qi et al. https://doi.org/10.1016/j.dynatmoce.2023.101403
37. Forecasting Vertical Profiles of Ocean Currents from Surface Characteristics: A Multivariate Multi-Head Convolutional Neural Network–Long Short-Term Memory Approach S. Kar et al. https://doi.org/10.3390/jmse11101964
38. Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data Z. Gong et al. https://doi.org/10.1080/07038992.2023.2298575
39. Reconstructing 3D ocean subsurface salinity (OSS) from T–S mapping via a data-driven deep learning model J. Zhang et al. https://doi.org/10.1016/j.ocemod.2023.102232
40. Cross-scale 3-D thermohaline modeling via dual-residual swin transformer with multisource ocean observations A. Wang et al. https://doi.org/10.1080/17538947.2025.2607902
41. Machine learning reconstruction of multiyear missing nutrient data along the 137°E section, northwestern Pacific X. Song et al. https://doi.org/10.1016/j.dsr.2025.104638
42. A prior-knowledge-integrated downscaling approach for subsurface thermal structure reconstruction in the tropical Indian Ocean Z. Feng et al. https://doi.org/10.1016/j.dsr2.2025.105589