19 citations as recorded by crossref.

1. Mid-Term Simultaneous Spatiotemporal Prediction of Sea Surface Height Anomaly and Sea Surface Temperature Using Satellite Data in the South China Sea Q. Shao et al. https://doi.org/10.1109/LGRS.2020.3042179
2. Gaps Filling in HF Radar Sea Surface Current Data Using Complex Empirical Orthogonal Functions S. Kolukula et al. https://doi.org/10.1007/s00024-020-02613-x
3. Short-Term Prediction of Global Sea Surface Temperature Using Deep Learning Networks T. Xu et al. https://doi.org/10.3390/jmse11071352
4. Sea Surface Temperature Prediction Using ConvLSTM-Based Model with Deformable Attention B. Shi et al. https://doi.org/10.3390/rs16224126
5. Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies and Opportunities H. Yang et al. https://doi.org/10.1145/3748259
6. DP-BICNN: A Bidirectional Information Compensation Neural Network Coupled With Data-Driven and Physical Information for Sea Surface Temperature Prediction X. Liu et al. https://doi.org/10.1109/TGRS.2025.3560717
7. DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations A. Barth et al. https://doi.org/10.5194/gmd-13-1609-2020
8. Prediction of daily sea surface temperature using artificial neural networks S. Aparna et al. https://doi.org/10.1080/01431161.2018.1454623
9. VMD-SSTM: A sea surface temperature forecast model based on multi-scale information in the Western Indian Ocean S. Liu et al. https://doi.org/10.1016/j.ocemod.2026.102768
10. High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension S. Jung et al. https://doi.org/10.3390/rs14030575
11. 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
12. Effect of Sparse Representation of Time Series Data on Learning Rate of Time-Delay Neural Networks M. Kalantari Khandani & W. Mikhael https://doi.org/10.1007/s00034-020-01610-8
13. Forecasting of Mesoscale Eddies in the Kuroshio Extension Based on Temporal Modes-Enhanced Neural Network H. Wang et al. https://doi.org/10.3390/jmse11112201
14. Accurate reconstruction of satellite-derived SST under cloud and cloud-free areas using a physically-informed machine learning approach C. Young et al. https://doi.org/10.1016/j.rse.2024.114339
15. Prediction of Sea Surface Temperature in the South China Sea by Artificial Neural Networks L. Wei et al. https://doi.org/10.1109/LGRS.2019.2926992
16. A study on the use of imputation methods for experimentation with Radial Basis Function Network classifiers handling missing attribute values: The good synergy between RBFNs and EventCovering method J. Luengo et al. https://doi.org/10.1016/j.neunet.2009.11.014
17. Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling H. Lee & K. Kang https://doi.org/10.1155/2015/935868
18. Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al. https://doi.org/10.3390/jmse11020340
19. Enhancing observations data: A machine-learning approach to fill gaps in the moored buoy data S. Kolukula & M. PLN https://doi.org/10.1016/j.rineng.2025.104708