52 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. 10.3390/jmse11040743
2. Iterative spatial leave-one-out cross-validation and gap-filling based data augmentation for supervised learning applications in marine remote sensing A. Stock & A. Subramaniam 10.1080/15481603.2022.2107113
3. Can deep learning beat numerical weather prediction? M. Schultz et al. 10.1098/rsta.2020.0097
4. Can the Structure Similarity of Training Patches Affect the Sea Surface Temperature Deep Learning Super-Resolution? B. Ping et al. 10.3390/rs13183568
5. Evolving a Bayesian network model with information flow for time series interpolation of multiple ocean variables M. Li et al. 10.1007/s13131-021-1734-1
6. Bridging observations, theory and numerical simulation of the ocean using machine learning M. Sonnewald et al. 10.1088/1748-9326/ac0eb0
7. Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review S. Cheng et al. 10.1109/JAS.2023.123537
8. Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al. 10.3390/jmse11020340
9. A General Convolutional Neural Network to Reconstruct Remotely Sensed Chlorophyll-a Concentration X. Zhang & M. Zhou 10.3390/jmse11040810
10. Super-resolution of subsurface temperature field from remote sensing observations based on machine learning H. Su et al. 10.1016/j.jag.2021.102440
11. Reconstruction of the Basin‐Wide Sea‐Level Variability in the North Sea Using Coastal Data and Generative Adversarial Networks Z. Zhang et al. 10.1029/2020JC016402
12. Predicting subsurface thermohaline structure from remote sensing data based on long short-term memory neural networks H. Su et al. 10.1016/j.rse.2021.112465
13. Reconstruction of Daily MODIS/Aqua Chlorophyll-a Concentration in Turbid Estuarine Waters Based on Attention U-NET H. Ye et al. 10.3390/rs15030546
14. Validation and Calibration of Significant Wave Height and Wind Speed Retrievals from HY2B Altimeter Based on Deep Learning J. Wang et al. 10.3390/rs12172858
15. Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives M. Buzzicotti 10.1209/0295-5075/acc88c
16. CLOINet: ocean state reconstructions through remote-sensing, in-situ sparse observations and deep learning E. Cutolo et al. 10.3389/fmars.2024.1151868
17. Comparison of Cloud-Filling Algorithms for Marine Satellite Data A. Stock et al. 10.3390/rs12203313
18. Investigating ocean surface responses to typhoons using reconstructed satellite data C. Ji et al. 10.1016/j.jag.2021.102474
19. Satellite observations estimating the effects of river discharge and wind‐driven upwelling on phytoplankton dynamics in the Chesapeake Bay N. Nezlin et al. 10.1002/ieam.4597
20. Monitoring of Hydrological Resources in Surface Water Change by Satellite Altimetry W. Li et al. 10.3390/rs14194904
21. Ocean Currents Reconstruction from a Combination of Altimeter and Ocean Colour Data: A Feasibility Study D. Ciani et al. 10.3390/rs13122389
22. Inversion of the three-dimensional temperature structure of mesoscale eddies in the Northwest Pacific based on deep learning F. Yu et al. 10.1007/s13131-021-1841-z
23. Downscaling of ocean fields by fusion of heterogeneous observations using Deep Learning algorithms S. Thiria et al. 10.1016/j.ocemod.2023.102174
24. Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method C. Gu et al. 10.3390/s22155600
25. Reconstruction of Three‐Dimensional Temperature and Salinity Fields From Satellite Observations L. Meng et al. 10.1029/2021JC017605
26. Remote sensing of sea surface salinity: challenges and research directions Y. Kim et al. 10.1080/15481603.2023.2166377
27. Data-Driven Interpolation of Sea Surface Suspended Concentrations Derived from Ocean Colour Remote Sensing Data J. Vient et al. 10.3390/rs13173537
28. High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension S. Jung et al. 10.3390/rs14030575
29. A spatiotemporal attention-augmented ConvLSTM model for ocean remote sensing reflectance prediction G. Zhou et al. 10.1016/j.jag.2024.103815
30. Data reconstruction of daily MODIS chlorophyll-a concentration and spatio-temporal variations in the Northwestern Pacific M. Xing et al. 10.1016/j.scitotenv.2022.156981
31. Deep‐learning‐based harmonization and super‐resolution of near‐surface air temperature from CMIP6 models (1850–2100) X. Wei et al. 10.1002/joc.7926
32. Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies R. Fablet et al. 10.1109/TGRS.2023.3268006
33. A gap-filling method for satellite-derived chlorophyll-a time series based on neighborhood spatiotemporal information G. Zhou et al. 10.1016/j.jag.2024.103724
34. STA-GAN: A Spatio-Temporal Attention Generative Adversarial Network for Missing Value Imputation in Satellite Data S. Wang et al. 10.3390/rs15010088
35. Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al. 10.3389/feart.2023.1130853
36. Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms B. Buongiorno Nardelli et al. 10.3390/rs14051159
37. Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment T. Zhang et al. 10.3390/fishes9030081
38. 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. 10.1109/TGRS.2021.3067646
39. End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations J. Vient et al. 10.3390/rs14164024
40. Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies S. Martin et al. 10.1029/2022MS003589
41. Empirical Function Method: A Precise Approach for Filling Data Gaps in Satellite Sea Surface Temperature Imagery G. Zheng & X. Li 10.1109/TGRS.2023.3335940
42. Joint Interpolation and Representation Learning for Irregularly Sampled Satellite-Derived Geophysical Fields R. Fablet et al. 10.3389/fams.2021.655224
43. Prediction of Dominant Ocean Parameters for Sustainable Marine Environment D. Menaka & S. Gauni 10.1109/ACCESS.2021.3122237
44. A machine learning approach for spatiotemporal imputation of MODIS chlorophyll-a H. Mohebzadeh et al. 10.1080/01431161.2021.1957513
45. 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. 10.1029/2022MS003424
46. Micro-Climate Computed Machine and Deep Learning Models for Prediction of Surface Water Temperature Using Satellite Data in Mundan Water Reservoir S. Mukonza & J. Chiang 10.3390/w14182935
47. CCGAN as a Tool for Satellite-Derived Chlorophyll a Concentration Gap Reconstruction L. Ćatipović et al. 10.3390/jmse11091814
48. 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. 10.3389/fmars.2021.724913
49. 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 10.3390/environments10100170
50. Reconstruction of chlorophyll-a satellite data in Bohai and Yellow sea based on DINCAE method X. Luo et al. 10.1080/01431161.2022.2090872
51. Application of DINCAE to Reconstruct the Gaps in Chlorophyll-a Satellite Observations in the South China Sea and West Philippine Sea Z. Han et al. 10.3390/rs12030480
52. 4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry M. Beauchamp et al. 10.5194/gmd-16-2119-2023