41 citations as recorded by crossref.

1. AI for atmosphere–ocean sciences: advancements, challenges and ways forward J. Luo et al.
2. Application of Synthetic DINCAE–BME Spatiotemporal Interpolation Framework to Reconstruct Chlorophyll–a from Satellite Observations in the Arabian Sea X. Yan et al.
3. Enhanced Reconstruction of Satellite-Derived Monthly Chlorophyll a Concentration With Fourier Transform Convolutional-LSTM S. Chen et al.
4. Estimation of daytime all-sky sea surface temperature from Himawari-8 based on multilayer stacking machine learning H. He et al.
5. CRITER 1.0: a coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data M. Zupančič Muc et al.
6. Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al.
7. Applications of deep learning in physical oceanography: a comprehensive review Q. Zhao et al.
8. Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies and Opportunities H. Yang et al.
9. Reconstructing long-term global satellite-based soil moisture data using deep learning method Y. Hu et al.
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.
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.
12. DINCoDE: A Data Interpolation Network With a Collaborative Dual Encoder for Reconstructing Missing Sea Surface Temperature Data X. Yang et al.
13. Self-Supervised Spatiotemporal Imputation Model for Highly Sparse Chl-a Data via Fusing Multisource Satellite Data S. Wang et al.
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.
15. Evaluation of Chlorophyll-a estimation using Sentinel 3 based on various algorithms in southern coastal Vietnam N. Binh et al.
16. Deep learning-based gap filling for near real-time seamless daily global sea surface salinity using satellite observations E. Jang et al.
17. DAT-Net: Filling of missing temperature values of meteorological stations by data augmentation attention neural network X. Guo et al.
18. Dynamic masking for chlorophyll-a reconstruction in the Bohai and Yellow Sea: dataset generation and trend analysis J. Wang et al.
19. Robust daily satellite sea surface salinity reconstruction using deep learning in low-salinity coastal regions S. Jung et al.
20. Deep learning for sea surface temperature reconstruction under cloud occlusion A. Asperti et al.
21. Assessment of gap-filling techniques applied to satellite phytoplankton composition products for the Atlantic Ocean E. Mehdipour et al.
22. Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al.
23. Physically-aware deep learning for reconstructing gap-free sea surface temperature in the South China Sea C. Su et al.
24. A deep learning approach for coastal downscaling: The northern Adriatic Sea case-study F. Adobbati et al.
25. HIDRA-D: deep-learning model for dense sea level forecasting using sparse altimetry and tide gauge data M. Rus et al.
26. 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.
27. Evaluation of Gap-Filling Methods for Inland Water Color Remote Sensing Data: A Case Study in Lake Taihu Y. Si et al.
28. MAESSTRO: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion E. Goh et al.
29. LLM4HRS: LLM-Based Spatiotemporal Imputation Model for Highly Sparse Remote Sensing Data S. Wang et al.
30. A gap-filling method for satellite-derived chlorophyll-a time series based on neighborhood spatiotemporal information G. Zhou et al.
31. Remote sensing of sea surface salinity: challenges and research directions Y. Kim et al.
32. PARAN: A novel physics-assisted reconstruction adversarial network using geostationary satellite data to reconstruct hourly sea surface temperatures S. Jung et al.
33. A General Convolutional Neural Network to Reconstruct Remotely Sensed Chlorophyll-a Concentration X. Zhang & M. Zhou
34. Potential error underestimation of cross-validation in missing value reconstruction in ocean satellite data M. Yu et al.
35. End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations J. Vient et al.
36. Observation-Only Deep Learning for Gappy Satellite-Derived Ocean Color Data Using 4DVarNet C. Dorffer et al.
37. Generalization Performance of Neural Mapping Schemes for the Space–Time Interpolation of Satellite-Derived Ocean Color Datasets T. Nguyen et al.
38. Space–time regression and interpolation of metocean measurements: A focus on satellite data for the offshore energy sector L. Gambarelli et al.
39. Resilient Anomaly Detection in Ocean Drifters with Unsupervised Learning, Deep Learning Models, and Energy-Efficient Recovery C. Guo et al.
40. Empirical Function Method: A Precise Approach for Filling Data Gaps in Satellite Sea Surface Temperature Imagery G. Zheng & X. Li
41. A Review of Machine Learning Applications in Ocean Color Remote Sensing Z. Zhang et al.