100 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.
2. DINFNN: Data Inpainting Fourier Neural Network for Cloud-Induced Extensive Missing Area in Sea Surface Temperature Z. Zuo et al.
3. Robust daily satellite sea surface salinity reconstruction using deep learning in low-salinity coastal regions S. Jung et al.
4. 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
5. Can deep learning beat numerical weather prediction? M. Schultz et al.
6. Can the Structure Similarity of Training Patches Affect the Sea Surface Temperature Deep Learning Super-Resolution? B. Ping et al.
7. Dynamic masking for chlorophyll-a reconstruction in the Bohai and Yellow Sea: dataset generation and trend analysis J. Wang et al.
8. Bridging observations, theory and numerical simulation of the ocean using machine learning M. Sonnewald et al.
9. Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review S. Cheng et al.
10. PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks G. Pietropolli et al.
11. HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures M. Rus et al.
12. A General Convolutional Neural Network to Reconstruct Remotely Sensed Chlorophyll-a Concentration X. Zhang & M. Zhou
13. Deep learning for ocean temperature forecasting: a survey X. Zhao et al.
14. CRITER 1.0: a coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data M. Zupančič Muc et al.
15. Inpainting of cloud-occlusion sea surface temperature image from a novel generative network using multi-scale physical constraints Y. Diao et al.
16. Reconstruction of the Basin‐Wide Sea‐Level Variability in the North Sea Using Coastal Data and Generative Adversarial Networks Z. Zhang et al.
17. Predicting subsurface thermohaline structure from remote sensing data based on long short-term memory neural networks H. Su et al.
18. Reconstruction of Daily MODIS/Aqua Chlorophyll-a Concentration in Turbid Estuarine Waters Based on Attention U-NET H. Ye et al.
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.
20. Validation and Calibration of Significant Wave Height and Wind Speed Retrievals from HY2B Altimeter Based on Deep Learning J. Wang et al.
21. Deep learning-based gap filling for near real-time seamless daily global sea surface salinity using satellite observations E. Jang et al.
22. Accurate reconstruction of satellite-derived SST under cloud and cloud-free areas using a physically-informed machine learning approach C. Young et al.
23. LLM4HRS: LLM-Based Spatiotemporal Imputation Model for Highly Sparse Remote Sensing Data S. Wang et al.
24. Comparison of Cloud-Filling Algorithms for Marine Satellite Data A. Stock et al.
25. Self-Supervised Spatiotemporal Imputation Model for Highly Sparse Chl-a Data via Fusing Multisource Satellite Data S. Wang et al.
26. Ocean Currents Reconstruction from a Combination of Altimeter and Ocean Colour Data: A Feasibility Study D. Ciani et al.
27. DualSeq: A Novel Ensemble Method for Predictive Analysis of Sea Surface Temperature Using Remote Sensing Data L. Chaudhary et al.
28. Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method C. Gu et al.
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.
30. Gap-Filling of Highly Incomplete Daily Chlorophyll-a Remote Sensing Time-Series Data Over the Eastern China Seas via Spatiotemporal-Periodicity Aware Tensor Completion G. Zhou et al.
31. Spatiotemporal Fusion Network Based on Improved Transformer for Inverting Subsurface Thermohaline Structure J. Mu et al.
32. Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022 A. Wang & H. Su
33. Deep‐learning‐based harmonization and super‐resolution of near‐surface air temperature from CMIP6 models (1850–2100) X. Wei et al.
34. Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies R. Fablet et al.
35. STA-GAN: A Spatio-Temporal Attention Generative Adversarial Network for Missing Value Imputation in Satellite Data S. Wang et al.
36. A deep learning approach for coastal downscaling: The northern Adriatic Sea case-study F. Adobbati et al.
37. Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms B. Buongiorno Nardelli et al.
38. Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan D. Putra & P. Hsu
39. PARAN: A novel physics-assisted reconstruction adversarial network using geostationary satellite data to reconstruct hourly sea surface temperatures S. Jung et al.
40. 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.
41. Potential error underestimation of cross-validation in missing value reconstruction in ocean satellite data M. Yu et al.
42. Multimodal learning–based reconstruction of high-resolution spatial wind speed fields M. Zambra et al.
43. Mitigating Masked Pixels in a Climate-Critical Ocean Dataset A. Agabin et al.
44. Prediction of Dominant Ocean Parameters for Sustainable Marine Environment D. Menaka & S. Gauni
45. A machine learning approach for spatiotemporal imputation of MODIS chlorophyll-a H. Mohebzadeh et al.
46. 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.
47. Physics-informed neural data assimilation for high-resolution coastal SPM reconstruction from model and satellite data W. Chen et al.
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.
49. CARE-SST: context-aware reconstruction diffusion model for sea surface temperature M. Choo et al.
50. 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
51. Enhanced Reconstruction of Satellite-Derived Monthly Chlorophyll a Concentration With Fourier Transform Convolutional-LSTM S. Chen et al.
52. Evolving a Bayesian network model with information flow for time series interpolation of multiple ocean variables M. Li et al.
53. Estimation of daytime all-sky sea surface temperature from Himawari-8 based on multilayer stacking machine learning H. He et al.
54. Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies and Opportunities H. Yang et al.
55. SVIFNN: Robust Inpainting Fourier Neural Network for SST Scientific Visualization Image Leveraging Significant Stability and Nonsignificant Anomalies Z. Zuo et al.
56. A daily reconstructed chlorophyll-a dataset in the South China Sea from MODIS using OI-SwinUnet H. Ye et al.
57. Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al.
58. Super-resolution of subsurface temperature field from remote sensing observations based on machine learning H. Su et al.
59. Machine learning for the physics of climate A. Bracco et al.
60. 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.
61. Seamless finer-resolution soil moisture from the synergistic merging of the FengYun-3 satellite series D. Hagan et al.
62. Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives M. Buzzicotti
63. Hybrid machine learning data assimilation for marine biogeochemistry I. Higgs et al.
64. CLOINet: ocean state reconstructions through remote-sensing, in-situ sparse observations and deep learning E. Cutolo et al.
65. Deep learning for sea surface temperature reconstruction under cloud occlusion A. Asperti et al.
66. Attention-enhanced deep learning model for reconstruction and downscaling of thermocline depth in the tropical Indian Ocean Z. Feng et al.
67. Investigating ocean surface responses to typhoons using reconstructed satellite data C. Ji et al.
68. Satellite observations estimating the effects of river discharge and wind-driven upwelling on phytoplankton dynamics in the Chesapeake Bay N. Nezlin et al.
69. 3D-DINEOF: Extension of Decomposition Dimensions of Data Interpolating Empirical Orthogonal Functions M. Yu et al.
70. Monitoring of Hydrological Resources in Surface Water Change by Satellite Altimetry W. Li et al.
71. Inversion of the three-dimensional temperature structure of mesoscale eddies in the Northwest Pacific based on deep learning F. Yu et al.
72. Downscaling of ocean fields by fusion of heterogeneous observations using Deep Learning algorithms S. Thiria et al.
73. The current and future warming of Lake Titicaca D. Dinh et al.
74. HIDRA-D: deep-learning model for dense sea level forecasting using sparse altimetry and tide gauge data M. Rus et al.
75. 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.
76. MAESSTRO: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion E. Goh et al.
77. Reconstruction of Three‐Dimensional Temperature and Salinity Fields From Satellite Observations L. Meng et al.
78. Remote sensing of sea surface salinity: challenges and research directions Y. Kim et al.
79. Assessment of gap-filling techniques applied to satellite phytoplankton composition products for the Atlantic Ocean E. Mehdipour et al.
80. Data-Driven Interpolation of Sea Surface Suspended Concentrations Derived from Ocean Colour Remote Sensing Data J. Vient et al.
81. High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension S. Jung et al.
82. Space–time regression and interpolation of metocean measurements: A focus on satellite data for the offshore energy sector L. Gambarelli et al.
83. A spatiotemporal attention-augmented ConvLSTM model for ocean remote sensing reflectance prediction G. Zhou et al.
84. Projection of extreme temperature events in megacity Beijing C. Meng et al.
85. Evaluation of Gap-Filling Methods for Inland Water Color Remote Sensing Data: A Case Study in Lake Taihu Y. Si et al.
86. Data reconstruction of daily MODIS chlorophyll-a concentration and spatio-temporal variations in the Northwestern Pacific M. Xing et al.
87. Predicting particle catchment areas of deep-ocean sediment traps using machine learning T. Picard et al.
88. A gap-filling method for satellite-derived chlorophyll-a time series based on neighborhood spatiotemporal information G. Zhou et al.
89. Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al.
90. Sea Surface Temperature Image Completion Method Based on Multiscale Fourier Fusion Neural Operator X. Chen et al.
91. Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment T. Zhang et al.
92. Spatial Densification of Coastal Sea Surface Temperature and Chlorophyll via Bayesian Kriging A. Vassilis & K. Vassilia
93. DINCoDE: A Data Interpolation Network With a Collaborative Dual Encoder for Reconstructing Missing Sea Surface Temperature Data X. Yang et al.
94. End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations J. Vient et al.
95. 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.
96. Empirical Function Method: A Precise Approach for Filling Data Gaps in Satellite Sea Surface Temperature Imagery G. Zheng & X. Li
97. Joint Interpolation and Representation Learning for Irregularly Sampled Satellite-Derived Geophysical Fields R. Fablet et al.
98. 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
99. CCGAN as a Tool for Satellite-Derived Chlorophyll a Concentration Gap Reconstruction L. Ćatipović et al.
100. Resilient Anomaly Detection in Ocean Drifters with Unsupervised Learning, Deep Learning Models, and Energy-Efficient Recovery C. Guo et al.