91 citations as recorded by crossref.

1. Seasonal to interannual phytoplankton response to physical processes in the Mediterranean Sea from satellite observations G. Volpe et al. https://doi.org/10.1016/j.rse.2011.09.020
2. An Improved DINEOF Algorithm Based on Optimized Validation Points Selection Method Z. Yang et al. https://doi.org/10.3390/w15030392
3. 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
4. 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
5. Reconstruction of sea surface temperature by means of DINEOF: a case study during the fishing season in the Bay of Biscay U. Ganzedo et al. https://doi.org/10.1080/01431160903491420
6. Integrated spatio-temporal DINEOF reconstruction under extreme cloud-induced data loss: application to MODIS chlorophyll-a in the eastern Indian Ocean N. Nurhayati et al. https://doi.org/10.1080/01431161.2026.2716394
7. Spatio-temporal dynamics of phytoplankton and primary production in Lake Tanganyika using a MODIS based bio-optical time series N. Bergamino et al. https://doi.org/10.1016/j.rse.2009.11.013
8. A recommender system-inspired cloud data filling scheme for satellite-based coastal land use classification R. Wang https://doi.org/10.1016/j.jag.2022.102770
9. Imprint of the Pacific Walker Circulation in Global Precipitation δ18O G. Falster et al. https://doi.org/10.1175/JCLI-D-21-0190.1
10. On the Reconstruction of Missing Sea Surface Temperature Data from Himawari-8 in Adjacent Waters of Taiwan Using DINEOF Conducted with 25-h Data Y. Yang et al. https://doi.org/10.3390/rs14122818
11. East China Sea ecosystem under multiple stressors: Heterogeneous responses in the sea surface chlorophyll-a C. Kong et al. https://doi.org/10.1016/j.dsr.2019.103078
12. 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
13. 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
14. Using a Combination of High-Frequency Coastal Radar Dataset and Satellite Imagery to Study the Patterns Involved in the Coastal Countercurrent Events in the Gulf of Cadiz C. Fanelli et al. https://doi.org/10.3390/rs16040687
15. Correction of inertial oscillations by assimilation of HF radar data in a model of the Ligurian Sea L. Vandenbulcke et al. https://doi.org/10.1007/s10236-016-1012-5
16. MDINEOF: A scheme to recover land surface temperatures under cloudy-sky conditions by incorporating radiation fluxes C. Shi et al. https://doi.org/10.1016/j.rse.2024.114208
17. 3D-DINEOF: Extension of Decomposition Dimensions of Data Interpolating Empirical Orthogonal Functions M. Yu et al. https://doi.org/10.1109/TGRS.2026.3668373
18. Research on Sea Surface temperature Reconstruction from long-term MODIS data Y. Zhou et al. https://doi.org/10.1088/1755-1315/631/1/012032
19. Monitoring Black Sea environmental changes from space: New products for altimetry, ocean colour and salinity. Potentialities and requirements for a dedicated in-situ observing system M. Grégoire et al. https://doi.org/10.3389/fmars.2022.998970
20. Gappy Spectral Proper Orthogonal Decomposition A. Nekkanti & O. Schmidt https://doi.org/10.2139/ssrn.4159544
21. Summer Discrepancies between 2 m Air Temperature and Landsat LST in Suceava City, Northeastern Romania D. Mihăilă et al. https://doi.org/10.3390/rs16162967
22. A re-analysis of Black Sea surface temperature B. Buongiorno Nardelli et al. https://doi.org/10.1016/j.jmarsys.2009.07.001
23. Prediction of Sea Surface Chlorophyll-a Concentrations Based on Deep Learning and Time-Series Remote Sensing Data L. Yao et al. https://doi.org/10.3390/rs15184486
24. Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey L. Ćatipović et al. https://doi.org/10.3390/jmse11020340
25. 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
26. Approximate and Efficient Methods to Assess Error Fields in Spatial Gridding with Data Interpolating Variational Analysis (DIVA) J. Beckers et al. https://doi.org/10.1175/JTECH-D-13-00130.1
27. divand-1.0: n-dimensional variational data analysis for ocean observations A. Barth et al. https://doi.org/10.5194/gmd-7-225-2014
28. Southward re‐distribution of tropical tuna fisheries activity can be explained by technological and management change I. Rubio et al. https://doi.org/10.1111/faf.12443
29. Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF A. Alvera-Azcárate et al. https://doi.org/10.5194/os-5-475-2009
30. 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
31. Spatio-temporal variability of surface chlorophyll a in the Yellow Sea and the East China Sea based on reconstructions of satellite data of 2001–2020 W. Xie et al. https://doi.org/10.1007/s00343-023-2335-y
32. Seasonal variability of the Agulhas Front’s position and intensity: Insights from satellite-derived sea surface temperature and chlorophyll-a concentration data Z. Yang et al. https://doi.org/10.1016/j.aosl.2025.100715
33. Outlier detection in satellite data using spatial coherence A. Alvera-Azcárate et al. https://doi.org/10.1016/j.rse.2011.12.009
34. Reconstruction and analysis of long-term satellite-derived sea surface temperature for the South China Sea H. Huynh et al. https://doi.org/10.1007/s10872-016-0365-1
35. Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region A. Hilborn & M. Costa https://doi.org/10.3390/rs10091449
36. Spatio-temporal stability and abnormality of chlorophyll-a in the Northern South China Sea during 2002–2012 from MODIS images using wavelet analysis M. Liu et al. https://doi.org/10.1016/j.csr.2013.12.010
37. An improved methodology for filling missing values in spatiotemporal climate data set A. Sorjamaa et al. https://doi.org/10.1007/s10596-009-9132-3
38. Ocean Front Detection From Instant Remote Sensing SST Images Y. Yang et al. https://doi.org/10.1109/LGRS.2016.2618941
39. Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China C. Liu et al. https://doi.org/10.1371/journal.pone.0220058
40. 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
41. Cloud-free sea surface temperature and colour reconstruction for the Gulf of Mexico: 2003–2009 Y. Zhao & R. He https://doi.org/10.1080/01431161.2012.666638
42. Gappy spectral proper orthogonal decomposition A. Nekkanti & O. Schmidt https://doi.org/10.1016/j.jcp.2023.111950
43. Cloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology D. Sirjacobs et al. https://doi.org/10.1016/j.seares.2010.08.002
44. New reconstruction and forecasting algorithm for TEC data J. Wang et al. https://doi.org/10.1088/1674-1056/23/9/099401
45. 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
46. Variational interpolation of high-frequency radar surface currents using DIVAnd A. Barth et al. https://doi.org/10.1007/s10236-020-01432-x
47. 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
48. 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
49. 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
50. Effects of natural and anthropogenic emissions on the composition and toxicity of aerosols in the marine atmosphere S. Song et al. https://doi.org/10.1016/j.scitotenv.2021.150928
51. A T-DINEOF model for multiple oceanic variables reconstruction B. Ping et al. https://doi.org/10.5194/os-22-2101-2026
52. An Empirical Orthogonal Function Reanalysis of the Northern Polar External and Induced Magnetic Field During Solar Cycle 23 R. Shore et al. https://doi.org/10.1002/2017JA024420
53. Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks H. Ren et al. https://doi.org/10.5194/hess-26-1727-2022
54. Globally coherent water cycle response to temperature change during the past two millennia B. Konecky et al. https://doi.org/10.1038/s41561-023-01291-3
55. DINEOF reconstruction for creating long-term cloud-free sea surface temperature data records: A Case study in Lombok Strait, Indonesia Y. Andrawina et al. https://doi.org/10.1088/1755-1315/893/1/012069
56. 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
57. A geospatial web service for small pelagic fish spatial distribution modeling and mapping with remote sensing S. Spondylidis et al. https://doi.org/10.1016/j.rsase.2024.101322
58. 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
59. Spatio-Temporal Dynamics and Environmental Drivers of Surface Chlorophyll-a in the Gulf of Guinea (2003–2022) L. Tchaewo et al. https://doi.org/10.3390/rs18162717
60. 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
61. 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
62. A method to generate fully multi-scale optimal interpolation by combining efficient single process analyses, illustrated by a DINEOF analysis spiced with a local optimal interpolation J. Beckers et al. https://doi.org/10.5194/os-10-845-2014
63. 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
64. Satellite‑derived chlorophyll-a concentration variability in the Hainan eastern upwelling region: a 20-year analysis (2003–2022) H. Xu et al. https://doi.org/10.1016/j.asr.2026.06.061
65. 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
66. The Civitavecchia Coastal Environment Monitoring System (C-CEMS): a new tool to analyze the conflicts between coastal pressures and sensitivity areas S. Bonamano et al. https://doi.org/10.5194/os-12-87-2016
67. Variability of the phytoplankton biomass in the southwestern Bay of Bengal from 2003 to 2018 Z. Mo et al. https://doi.org/10.1016/j.ecolind.2023.110879
68. Research on different time-scale prediction models for the total electron content . Sheng Zheng https://doi.org/10.7498/aps.61.219401
69. Spatiotemporal variability of surface particulate organic carbon distributions in the Yellow Sea: multi-temporal scale analysis and environmental controls Y. Sheng et al. https://doi.org/10.1007/s00343-026-5404-1
70. Application and Analysis of XCO2 Data from OCO Satellite Using a Synthetic DINEOF–BME Spatiotemporal Interpolation Framework Y. Jiang et al. https://doi.org/10.3390/rs14174422
71. 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
72. The effect of the solar wind on the ionosphere R. Shore https://doi.org/10.1093/astrogeo/aty273
73. Investigating frontal variability from repeated glider transects in the Ligurian Current (North West Mediterranean Sea) L. Piterbarg et al. https://doi.org/10.1016/j.jmarsys.2013.08.003
74. Modeling environmental influence on Atlantic bluefin tuna bycatch by Mexican longliners in the Gulf of Mexico A. Abad‐Uribarren et al. https://doi.org/10.1111/fog.12446
75. Model sensitivity across scales: a case study of simulating an offshore low-level jet P. Hawbecker et al. https://doi.org/10.5194/wes-11-51-2026
76. 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
77. Assessing the impact of the lower Sesan 2 dam on SPM and POC concentrations in the 3 S–Mekong Confluence via high-spatial resolution satellite imagery J. Masson et al. https://doi.org/10.1007/s11368-026-04494-4
78. Drought Extent and Severity on Arable Lands in Romania Derived from Normalized Difference Drought Index (2001–2020) R. Dobri et al. https://doi.org/10.3390/rs13081478
79. 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
80. Forced changes in the Pacific Walker circulation over the past millennium G. Falster et al. https://doi.org/10.1038/s41586-023-06447-0
81. Assessing the DINEOF Algorithm for Spatiotemporal Reconstruction of Multisource Remote Sensing Data in Inland Lakes: A Case Study of Lake Dianchi P. Chen et al. https://doi.org/10.1109/JSTARS.2026.3715829
82. Comparative study of the variability of the phytoplankton biomass in two upwelling zones of the western Arabian Sea from 2003 to 2020 H. Xu et al. https://doi.org/10.1016/j.marpolbul.2024.117522
83. Reconstruction of MODIS total suspended matter time series maps by DINEOF and validation with autonomous platform data B. Nechad et al. https://doi.org/10.1007/s10236-011-0425-4
84. 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
85. Long-term and regional variability of phytoplankton biomass and its physical oceanographic parameters in the Yellow Sea, China X. Lu et al. https://doi.org/10.1016/j.ecss.2021.107497
86. Analysis of estuarine marine heatwaves in an upwelling system: The Ría de Arousa as a case study M. Des et al. https://doi.org/10.1016/j.gloplacha.2025.104776
87. A regional ionospheric TEC mapping technique over China and adjacent areas: GNSS data processing and DINEOF analysis A. Ercha et al. https://doi.org/10.1007/s11432-015-5399-2
88. A machine learning approach for spatiotemporal imputation of MODIS chlorophyll-a H. Mohebzadeh et al. https://doi.org/10.1080/01431161.2021.1957513
89. 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
90. Interannual variability of dimethylsulfide in the Yellow Sea S. Wang et al. https://doi.org/10.1007/s00343-020-0480-0
91. Identifying the key factors influencing spatial and temporal variations of regional coastal fishing activities R. Chen et al. https://doi.org/10.1016/j.ocecoaman.2023.106940