104 citations as recorded by crossref.

1. Application of Synthetic DINCAE–BME Spatiotemporal Interpolation Framework to Reconstruct Chlorophyll–a from Satellite Observations in the Arabian Sea X. Yan et al. https://doi.org/10.3390/jmse11040743
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9. Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review S. Cheng et al. https://doi.org/10.1109/JAS.2023.123537
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12. A General Convolutional Neural Network to Reconstruct Remotely Sensed Chlorophyll-a Concentration X. Zhang & M. Zhou https://doi.org/10.3390/jmse11040810
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18. Reconstruction of Daily MODIS/Aqua Chlorophyll-a Concentration in Turbid Estuarine Waters Based on Attention U-NET H. Ye et al. https://doi.org/10.3390/rs15030546
19. Ensemble reconstruction of missing satellite data using a denoising diffusion model: application to chlorophyll a concentration in the Black Sea A. Barth et al. https://doi.org/10.5194/os-20-1567-2024
20. Validation and Calibration of Significant Wave Height and Wind Speed Retrievals from HY2B Altimeter Based on Deep Learning J. Wang et al. https://doi.org/10.3390/rs12172858
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24. Comparison of Cloud-Filling Algorithms for Marine Satellite Data A. Stock et al. https://doi.org/10.3390/rs12203313
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26. Ocean Currents Reconstruction from a Combination of Altimeter and Ocean Colour Data: A Feasibility Study D. Ciani et al. https://doi.org/10.3390/rs13122389
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28. Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method C. Gu et al. https://doi.org/10.3390/s22155600
29. Filling gaps in MODIS NDVI data using hybrid multiple imputation–Machine learning and DINCAE techniques: Case study of the State of Hawaii T. Tran et al. https://doi.org/10.1016/j.advengsoft.2024.103856
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33. Deep‐learning‐based harmonization and super‐resolution of near‐surface air temperature from CMIP6 models (1850–2100) X. Wei et al. https://doi.org/10.1002/joc.7926
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35. Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies R. Fablet et al. https://doi.org/10.1109/TGRS.2023.3268006
36. STA-GAN: A Spatio-Temporal Attention Generative Adversarial Network for Missing Value Imputation in Satellite Data S. Wang et al. https://doi.org/10.3390/rs15010088
37. A deep learning approach for coastal downscaling: The northern Adriatic Sea case-study F. Adobbati et al. https://doi.org/10.1016/j.ocemod.2025.102581
38. Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms B. Buongiorno Nardelli et al. https://doi.org/10.3390/rs14051159
39. Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan D. Putra & P. Hsu https://doi.org/10.3390/ijgi13050162
40. PARAN: A novel physics-assisted reconstruction adversarial network using geostationary satellite data to reconstruct hourly sea surface temperatures S. Jung et al. https://doi.org/10.1016/j.rse.2025.114749
41. Revisiting the Intraseasonal Variability of Chlorophyll-a in the Adjacent Luzon Strait With a New Gap-Filled Remote Sensing Data Set T. Wang et al. https://doi.org/10.1109/TGRS.2021.3067646
42. Potential error underestimation of cross-validation in missing value reconstruction in ocean satellite data M. Yu et al. https://doi.org/10.1007/s13131-025-2536-7
43. Multimodal learning–based reconstruction of high-resolution spatial wind speed fields M. Zambra et al. https://doi.org/10.1017/eds.2024.34
44. Mitigating Masked Pixels in a Climate-Critical Ocean Dataset A. Agabin et al. https://doi.org/10.3390/rs16132439
45. Prediction of Dominant Ocean Parameters for Sustainable Marine Environment D. Menaka & S. Gauni https://doi.org/10.1109/ACCESS.2021.3122237
46. A machine learning approach for spatiotemporal imputation of MODIS chlorophyll-a H. Mohebzadeh et al. https://doi.org/10.1080/01431161.2021.1957513
47. Impact of Parameterized Isopycnal Diffusivity on Shelf‐Ocean Exchanges Under Upwelling‐Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network C. Xie et al. https://doi.org/10.1029/2022MS003424
48. Physics-informed neural data assimilation for high-resolution coastal SPM reconstruction from model and satellite data W. Chen et al. https://doi.org/10.1016/j.apor.2025.104871
49. A Global Ocean Oxygen Database and Atlas for Assessing and Predicting Deoxygenation and Ocean Health in the Open and Coastal Ocean M. Grégoire et al. https://doi.org/10.3389/fmars.2021.724913
50. CARE-SST: context-aware reconstruction diffusion model for sea surface temperature M. Choo et al. https://doi.org/10.1016/j.isprsjprs.2025.01.001
51. Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring S. Mukonza & J. Chiang https://doi.org/10.3390/environments10100170
52. AsyDM-SST: Asynchronous Diffusion Model based on Schrödinger Bridge for Sea Surface Temperature reconstruction Y. Wang et al. https://doi.org/10.1016/j.apor.2026.105117
53. Enhanced Reconstruction of Satellite-Derived Monthly Chlorophyll a Concentration With Fourier Transform Convolutional-LSTM S. Chen et al. https://doi.org/10.1109/TGRS.2024.3394399
54. Evolving a Bayesian network model with information flow for time series interpolation of multiple ocean variables M. Li et al. https://doi.org/10.1007/s13131-021-1734-1
55. Spurious seasonality in satellite turbidity monitoring driven by K1 tidal aliasing S. Cao et al. https://doi.org/10.1016/j.rsase.2026.102088
56. Estimation of daytime all-sky sea surface temperature from Himawari-8 based on multilayer stacking machine learning H. He et al. https://doi.org/10.1016/j.jag.2024.104055
57. Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies and Opportunities H. Yang et al. https://doi.org/10.1145/3748259
58. SVIFNN: Robust Inpainting Fourier Neural Network for SST Scientific Visualization Image Leveraging Significant Stability and Nonsignificant Anomalies Z. Zuo et al. https://doi.org/10.1109/TGRS.2025.3567422
59. A daily reconstructed chlorophyll-a dataset in the South China Sea from MODIS using OI-SwinUnet H. Ye et al. https://doi.org/10.5194/essd-16-3125-2024
60. Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al. https://doi.org/10.3390/jmse11020340
61. Super-resolution of subsurface temperature field from remote sensing observations based on machine learning H. Su et al. https://doi.org/10.1016/j.jag.2021.102440
62. Machine learning for the physics of climate A. Bracco et al. https://doi.org/10.1038/s42254-024-00776-3
63. Global Daily Column Average CO2 at 0.1° × 0.1° Spatial Resolution Integrating OCO-3, GOSAT, CAMS with EOF and Deep Learning F. Antezana Lopez et al. https://doi.org/10.1038/s41597-024-04135-w
64. Seamless finer-resolution soil moisture from the synergistic merging of the FengYun-3 satellite series D. Hagan et al. https://doi.org/10.1038/s41597-025-05263-7
65. Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives M. Buzzicotti https://doi.org/10.1209/0295-5075/acc88c
66. Hybrid machine learning data assimilation for marine biogeochemistry I. Higgs et al. https://doi.org/10.5194/bg-23-315-2026
67. CLOINet: ocean state reconstructions through remote-sensing, in-situ sparse observations and deep learning E. Cutolo et al. https://doi.org/10.3389/fmars.2024.1151868
68. Deep learning for sea surface temperature reconstruction under cloud occlusion A. Asperti et al. https://doi.org/10.1016/j.apor.2026.105038
69. 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
70. Investigating ocean surface responses to typhoons using reconstructed satellite data C. Ji et al. https://doi.org/10.1016/j.jag.2021.102474
71. Satellite observations estimating the effects of river discharge and wind-driven upwelling on phytoplankton dynamics in the Chesapeake Bay N. Nezlin et al. https://doi.org/10.1002/ieam.4597
72. 3D-DINEOF: Extension of Decomposition Dimensions of Data Interpolating Empirical Orthogonal Functions M. Yu et al. https://doi.org/10.1109/TGRS.2026.3668373
73. Monitoring of Hydrological Resources in Surface Water Change by Satellite Altimetry W. Li et al. https://doi.org/10.3390/rs14194904
74. Inversion of the three-dimensional temperature structure of mesoscale eddies in the Northwest Pacific based on deep learning F. Yu et al. https://doi.org/10.1007/s13131-021-1841-z
75. Downscaling of ocean fields by fusion of heterogeneous observations using Deep Learning algorithms S. Thiria et al. https://doi.org/10.1016/j.ocemod.2023.102174
76. The current and future warming of Lake Titicaca D. Dinh et al. https://doi.org/10.1016/j.jglr.2026.102752
77. HIDRA-D: deep-learning model for dense sea level forecasting using sparse altimetry and tide gauge data M. Rus et al. https://doi.org/10.5194/gmd-19-2177-2026
78. A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data Z. Li et al. https://doi.org/10.3390/rs16101745
79. MAESSTRO: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion E. Goh et al. https://doi.org/10.5194/os-20-1309-2024
80. Reconstruction of Three‐Dimensional Temperature and Salinity Fields From Satellite Observations L. Meng et al. https://doi.org/10.1029/2021JC017605
81. Remote sensing of sea surface salinity: challenges and research directions Y. Kim et al. https://doi.org/10.1080/15481603.2023.2166377
82. Assessment of gap-filling techniques applied to satellite phytoplankton composition products for the Atlantic Ocean E. Mehdipour et al. https://doi.org/10.5194/gmd-19-1619-2026
83. Data-Driven Interpolation of Sea Surface Suspended Concentrations Derived from Ocean Colour Remote Sensing Data J. Vient et al. https://doi.org/10.3390/rs13173537
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95. Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment T. Zhang et al. https://doi.org/10.3390/fishes9030081
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