66 citations as recorded by crossref.

1. Satellite retrieval of oceanic particulate organic carbon: Towards an accurate and seamless dataset for the global ocean Z. Zhang et al. https://doi.org/10.1016/j.scitotenv.2024.176910
2. Spatial and temporal variability of SST and ocean color in the Gulf of Maine based on cloud-free SST and chlorophyll reconstructions in 2003–2012 Y. Li & R. He https://doi.org/10.1016/j.rse.2014.01.019
3. Filtering remotely sensed chlorophyll concentrations in the Red Sea using a space–time covariance model and a Kalman filter D. Dreano et al. https://doi.org/10.1016/j.spasta.2015.04.002
4. Environmental and climactic effects of chlorophyll-a variability around Iceland using reconstructed satellite data fields N. McGinty et al. https://doi.org/10.1016/j.jmarsys.2016.06.005
5. Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al. https://doi.org/10.3390/jmse11020340
6. An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea Surface Temperature Data B. Ping et al. https://doi.org/10.1371/journal.pone.0155928
7. A satellite‐based mobile warning system to reduce interactions with an endangered species M. Breece et al. https://doi.org/10.1002/eap.2358
8. Specifying the equatorial ionosphere using CINDI on C/NOFS, COSMIC, and data interpolating empirical orthogonal functions R. Stoneback et al. https://doi.org/10.1002/jgra.50596
9. Probabilistic relationships between wind and surface water circulation patterns in the SE Bay of Biscay L. Solabarrieta et al. https://doi.org/10.1007/s10236-015-0871-5
10. The global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2 K. Qin et al. https://doi.org/10.5194/essd-16-5287-2024
11. 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. https://doi.org/10.3390/rs12030480
12. GEMS逐小时对流层NO2产品数据质量评价与提升：以华东地区为例 高. Gao Hongrui et al. https://doi.org/10.3788/AOS240826
13. 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
14. 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
15. Analysis of high frequency geostationary ocean colour data using DINEOF A. Alvera-Azcárate et al. https://doi.org/10.1016/j.ecss.2015.03.026
16. Mapping large-area tidal flats without the dependence on tidal elevations: A case study of Southern China C. Zhao et al. https://doi.org/10.1016/j.isprsjprs.2019.11.022
17. Persistent upwelling in the Mid-Atlantic Bight detected using gap-filled, high-resolution satellite SST S. Murphy et al. https://doi.org/10.1016/j.rse.2021.112487
18. Initial skill assessment of the California Harmful Algae Risk Mapping (C-HARM) system C. Anderson et al. https://doi.org/10.1016/j.hal.2016.08.006
19. Variational interpolation of high-frequency radar surface currents using DIVAnd A. Barth et al. https://doi.org/10.1007/s10236-020-01432-x
20. Using 14 Years of Satellite Data to Describe the Hydrodynamic Circulation of the Patras and Corinth Gulfs B. Caterina & A. Hubert-Ferrari https://doi.org/10.3390/jmse13030623
21. Interpolation of the gaps in current maps generated by high-frequency radar D. Saha et al. https://doi.org/10.1080/01431161.2016.1230281
22. 卫星观测港口CO2排放间接估算：以日照港为例 陆. Lu Lingxiao et al. https://doi.org/10.3788/AOS250851
23. Decline Trends of Chlorophyll-a in the Yellow and Bohai Seas over 2005–2024 from Remote Sensing Reconstruction Y. Tian et al. https://doi.org/10.3390/jmse14010061
24. Assimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the Southern Ocean A. Barth et al. https://doi.org/10.1016/j.ocemod.2015.07.011
25. Seamless monitoring of marine heatwaves: capability of gap-filled, geostationary satellite sea surface temperature data S. Lee & M. Park https://doi.org/10.1080/15481603.2024.2447746
26. Efficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Sea D. Dreano et al. https://doi.org/10.1007/s10236-017-1065-0
27. Reconstruction of daily chlorophyll-a concentrations in the transit of severe tropical cyclone Hudhud using the ExDINEOF method Z. Wang et al. https://doi.org/10.3389/fmars.2023.1230116
28. Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis E. Olmedo et al. https://doi.org/10.3390/rs10030485
29. Reconstruction of Ocean Color Data Using Machine Learning Techniques in Polar Regions: Focusing on Off Cape Hallett, Ross Sea J. Park et al. https://doi.org/10.3390/rs11111366
30. Multiscale neural assimilation scheme for high-resolution sea surface temperature reconstruction from satellite observations M. Beauchamp et al. https://doi.org/10.1038/s41598-025-23682-9
31. Correlation between concentrations of chlorophyll-a and satellite derived climatic factors in the Persian Gulf M. Moradi & N. Moradi https://doi.org/10.1016/j.marpolbul.2020.111728
32. Analysis of 23 Years of Daily Cloud-Free Chlorophyll and Suspended Particulate Matter in the Greater North Sea A. Alvera-Azcárate et al. https://doi.org/10.3389/fmars.2021.707632
33. Impact of HF radar current gap-filling methodologies on the Lagrangian assessment of coastal dynamics I. Hernández-Carrasco et al. https://doi.org/10.5194/os-14-827-2018
34. Coastal current convergence structures in the Bay of Biscay from optimized high-frequency radar and satellite data S. Bertin et al. https://doi.org/10.1016/j.scitotenv.2024.174372
35. Generation of super-resolution gap-free ocean colour satellite products using data-interpolating empirical orthogonal functions (DINEOF) A. Alvera-Azcárate et al. https://doi.org/10.5194/os-21-787-2025
36. Empirical Forecasting of HF-Radar Velocity Using Genetic Algorithms A. Orfila et al. https://doi.org/10.1109/TGRS.2014.2366294
37. Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan D. Putra & P. Hsu https://doi.org/10.3390/ijgi13050162
38. Observation based climatology Martian atmospheric waves perturbation Datasets J. Zhang et al. https://doi.org/10.1038/s41597-022-01909-y
39. 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
40. Gap Filling of the CALYPSO HF Radar Sea Surface Current Data through Past Measurements and Satellite Wind Observations A. Gauci et al. https://doi.org/10.1155/2016/2605198
41. Mapping of sediment plume of the Cochin estuary, Southwest India, using satellite observations V. Abhishek et al. https://doi.org/10.1016/j.rsma.2025.104567
42. 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
43. 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
44. Validation of HF radar sea surface currents in the Malta-Sicily Channel F. Capodici et al. https://doi.org/10.1016/j.rse.2019.02.026
45. Analysis of SMOS sea surface salinity data using DINEOF A. Alvera-Azcárate et al. https://doi.org/10.1016/j.rse.2016.02.044
46. Comparison of Cloud-Filling Algorithms for Marine Satellite Data A. Stock et al. https://doi.org/10.3390/rs12203313
47. 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
48. Substantially underestimated global health risks of current ozone pollution Y. Wang et al. https://doi.org/10.1038/s41467-024-55450-0
49. Daily scale wintertime sea surface temperature and IPC-Navidad variability in the southern Bay of Biscay from 1981 to 2010 G. Esnaola et al. https://doi.org/10.5194/os-9-655-2013
50. Physics-informed neural data assimilation for high-resolution coastal SPM reconstruction from model and satellite data W. Chen et al. https://doi.org/10.1016/j.apor.2025.104871
51. Evaluation of surface Lagrangian transport barriers in the Gulf of Trieste F. Enrile et al. https://doi.org/10.1016/j.csr.2018.04.016
52. Filling in missing sea-surface temperature satellite data over the Eastern Mediterranean Sea using the DINEOF algorithm A. Nikolaidis et al. https://doi.org/10.2478/s13533-012-0148-1
53. 3D-DINEOF: Extension of Decomposition Dimensions of Data Interpolating Empirical Orthogonal Functions M. Yu et al. https://doi.org/10.1109/TGRS.2026.3668373
54. Tidal currents in the Malta – Sicily Channel from high-frequency radar observations S. Cosoli et al. https://doi.org/10.1016/j.csr.2015.08.030
55. Reconstruction of Daily MODIS/Aqua Chlorophyll-a Concentration in Turbid Estuarine Waters Based on Attention U-NET H. Ye et al. https://doi.org/10.3390/rs15030546
56. Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal W. Zhou et al. https://doi.org/10.3390/rs9090959
57. 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
58. Mesoscale eddies in the Black Sea: Characteristics and kinematic properties in a high-resolution ocean model E. Sadighrad et al. https://doi.org/10.1016/j.jmarsys.2021.103613
59. Space-time deep hybrid boosting learning for investigating day-night hourly seamless air temperature distribution from FY-4A over China Q. Su et al. https://doi.org/10.1016/j.jhydrol.2025.134245
60. Data reconstruction of daily MODIS chlorophyll-a concentration and spatio-temporal variations in the Northwestern Pacific M. Xing et al. https://doi.org/10.1016/j.scitotenv.2022.156981
61. Reconstruction of Satellite Chlorophyll-a Concentration in the Southwestern East Sea using Imputation Method J. Park et al. https://doi.org/10.22761/DJ2021.3.4.002
62. Reconstructing spatial–temporal continuous MODIS land surface temperature using the DINEOF method W. Zhou et al. https://doi.org/10.1117/1.JRS.11.046016
63. Reconstruction of chlorophyll-a satellite data in Bohai and Yellow sea based on DINCAE method X. Luo et al. https://doi.org/10.1080/01431161.2022.2090872
64. Spatial and temporal variability of chlorophyll-a concentrations in the coastal Gulf of Alaska, 1998–2011, using cloud-free reconstructions of SeaWiFS and MODIS-Aqua data J. Waite & F. Mueter https://doi.org/10.1016/j.pocean.2013.07.006
65. 4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry M. Beauchamp et al. https://doi.org/10.5194/gmd-16-2119-2023
66. Half a century of satellite remote sensing of sea-surface temperature P. Minnett et al. https://doi.org/10.1016/j.rse.2019.111366