43 citations as recorded by crossref.

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3. 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
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6. Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al. https://doi.org/10.3390/jmse11020340
7. Applications of deep learning in physical oceanography: a comprehensive review Q. Zhao et al. https://doi.org/10.3389/fmars.2024.1396322
8. Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies and Opportunities H. Yang et al. https://doi.org/10.1145/3748259
9. Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al. https://doi.org/10.3389/feart.2023.1130853
10. 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
11. 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. https://doi.org/10.1029/2022MS003589
12. DINCoDE: A Data Interpolation Network With a Collaborative Dual Encoder for Reconstructing Missing Sea Surface Temperature Data X. Yang et al. https://doi.org/10.1109/TGRS.2025.3638341
13. Self-Supervised Spatiotemporal Imputation Model for Highly Sparse Chl-a Data via Fusing Multisource Satellite Data S. Wang et al. https://doi.org/10.1109/TGRS.2024.3440912
14. 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
15. Evaluation of Chlorophyll-a estimation using Sentinel 3 based on various algorithms in southern coastal Vietnam N. Binh et al. https://doi.org/10.1016/j.jag.2022.102951
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18. Dynamic masking for chlorophyll-a reconstruction in the Bohai and Yellow Sea: dataset generation and trend analysis J. Wang et al. https://doi.org/10.1088/2515-7620/ae5766
19. Robust daily satellite sea surface salinity reconstruction using deep learning in low-salinity coastal regions S. Jung et al. https://doi.org/10.1016/j.marpolbul.2025.118462
20. Deep learning for sea surface temperature reconstruction under cloud occlusion A. Asperti et al. https://doi.org/10.1016/j.apor.2026.105038
21. 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
22. Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. https://doi.org/10.5194/gmd-16-5895-2023
23. Physically-aware deep learning for reconstructing gap-free sea surface temperature in the South China Sea C. Su et al. https://doi.org/10.1016/j.isprsjprs.2026.04.016
24. Comparative Evaluation of Machine Learning Models for Satellite Chlorophyll-a Gap Reconstruction in the Chesapeake Bay R. Chidananda et al. https://doi.org/10.3390/rs18111736
25. 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
26. 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
27. 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. https://doi.org/10.1109/TGRS.2026.3668244
28. Evaluation of Gap-Filling Methods for Inland Water Color Remote Sensing Data: A Case Study in Lake Taihu Y. Si et al. https://doi.org/10.3390/rs17233843
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33. An Evolutionary Process-Embedded Spatiotemporal Interpolation Method for Marine Environmental Fields Z. Ma et al. https://doi.org/10.3390/rs18111809
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35. A General Convolutional Neural Network to Reconstruct Remotely Sensed Chlorophyll-a Concentration X. Zhang & M. Zhou https://doi.org/10.3390/jmse11040810
36. 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
37. End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations J. Vient et al. https://doi.org/10.3390/rs14164024
38. Observation-Only Deep Learning for Gappy Satellite-Derived Ocean Color Data Using 4DVarNet C. Dorffer et al. https://doi.org/10.1109/TGRS.2025.3624465
39. Generalization Performance of Neural Mapping Schemes for the Space–Time Interpolation of Satellite-Derived Ocean Color Datasets T. Nguyen et al. https://doi.org/10.1109/JSTARS.2025.3622311
40. Space–time regression and interpolation of metocean measurements: A focus on satellite data for the offshore energy sector L. Gambarelli et al. https://doi.org/10.1016/j.apor.2026.104997
41. Resilient Anomaly Detection in Ocean Drifters with Unsupervised Learning, Deep Learning Models, and Energy-Efficient Recovery C. Guo et al. https://doi.org/10.3390/oceans7010005
42. Empirical Function Method: A Precise Approach for Filling Data Gaps in Satellite Sea Surface Temperature Imagery G. Zheng & X. Li https://doi.org/10.1109/TGRS.2023.3335940
43. A Review of Machine Learning Applications in Ocean Color Remote Sensing Z. Zhang et al. https://doi.org/10.3390/rs17101776