77 citations as recorded by crossref.

1. Applications of Machine Learning in Chemical and Biological Oceanography B. Sadaiappan et al. https://doi.org/10.1021/acsomega.2c06441
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4. A rare and extensive summer bloom enhanced by ocean eddies in the oligotrophic western North Pacific Subtropical Gyre C. Chow et al. https://doi.org/10.1038/s41598-017-06584-3
5. Remote sensing the sea surface CO2 of the Baltic Sea using the SOMLO methodology G. Parard et al. https://doi.org/10.5194/bg-12-3369-2015
6. Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation G. Laruelle et al. https://doi.org/10.5194/bg-14-4545-2017
7. Spatiotemporal variation in summer net community production in the Amundsen Sea Polynya: A self-organizing map analysis approach K. Park et al. https://doi.org/10.1016/j.csr.2019.07.001
8. Estimating summer sea surface pCO2 on a river-dominated continental shelf using a satellite-based semi-mechanistic model C. Le et al. https://doi.org/10.1016/j.rse.2019.02.023
9. Decadal vision in oceanography 2021: Air―sea boundary Y. Iwamoto et al. https://doi.org/10.5928/kaiyou.30.5_199
10. Estimating marine carbon uptake in the northeast Pacific using a neural network approach P. Duke et al. https://doi.org/10.5194/bg-20-3919-2023
11. Seasonal variability in the inorganic ocean carbon cycle in the Northwest Pacific evaluated using a biogeochemical and carbon model coupled with an operational ocean model M. Ishizu et al. https://doi.org/10.1007/s10584-020-02779-2
12. Spatiotemporal weighted neural network reveals surface seawater pCO2 distributions and underlying environmental mechanisms in the North Pacific Ocean Y. Liu et al. https://doi.org/10.1016/j.jag.2024.104120
13. Spatiotemporal characteristics of seasonal to multidecadal variability of pCO2 and air‐sea CO2 fluxes in the equatorial Pacific Ocean V. Valsala et al. https://doi.org/10.1002/2014JC010212
14. Arctic Ocean CO2 uptake: an improved multiyear estimate of the air–sea CO2 flux incorporating chlorophyll a concentrations S. Yasunaka et al. https://doi.org/10.5194/bg-15-1643-2018
15. A novel ratio algorithm for estimation of pCO2 fields in the Northern Indian ocean region using satellite oceanographic data I. Shaik et al. https://doi.org/10.1016/j.measurement.2025.118212
16. A monthly surface pCO2 product for the California Current Large Marine Ecosystem J. Sharp et al. https://doi.org/10.5194/essd-14-2081-2022
17. Observation system simulation experiments in the Atlantic Ocean for enhanced surface ocean pCO2 reconstructions A. Denvil-Sommer et al. https://doi.org/10.5194/os-17-1011-2021
18. Nutrient and dissolved inorganic carbon variability in the North Pacific S. Yasunaka et al. https://doi.org/10.1007/s10872-020-00561-7
19. Satellite-derived CO2 flux in the surface seawater of the Austral Ocean south of Australia M. Benallal et al. https://doi.org/10.1080/01431161.2017.1286054
20. Rapid change of the Arctic climate system and its global influences - Overview of GRENE Arctic climate change research project (2011–2016) T. Yamanouchi & K. Takata https://doi.org/10.1016/j.polar.2020.100548
21. Detecting Regional Modes of Variability in Observation‐Based Surface Ocean pCO2 P. Landschützer et al. https://doi.org/10.1029/2018GL081756
22. Reconstruction of global surface ocean pCO2 using region-specific predictors based on a stepwise FFNN regression algorithm G. Zhong et al. https://doi.org/10.5194/bg-19-845-2022
23. A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT) D. Bakker et al. https://doi.org/10.5194/essd-8-383-2016
24. Trends in pCO2 and sea–air CO2 flux over the global open oceans for the last two decades Y. Iida et al. https://doi.org/10.1007/s10872-015-0306-4
25. Satellite estimation of coastal pCO2 and air-sea flux of carbon dioxide in the northern Gulf of Mexico S. Lohrenz et al. https://doi.org/10.1016/j.rse.2017.12.039
26. A review of datasets and methods for deriving spatiotemporal distributions of atmospheric CO2 C. He et al. https://doi.org/10.1016/j.jenvman.2022.116101
27. LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean pCO2 over the global ocean A. Denvil-Sommer et al. https://doi.org/10.5194/gmd-12-2091-2019
28. Effect of seasonal change in gas transfer coefficient on air–sea CO2 flux in the western North Pacific X. Xiong et al. https://doi.org/10.1007/s10872-015-0313-5
29. Data-based estimates of the ocean carbon sink variability – first results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM) C. Rödenbeck et al. https://doi.org/10.5194/bg-12-7251-2015
30. Remote estimation of surface pCO2 on the West Florida Shelf S. Chen et al. https://doi.org/10.1016/j.csr.2016.09.004
31. Contribution of Biological Effects to Carbonate‐System Variations and the Air–Water CO2 Flux in Urbanized Bays in Japan T. Tokoro et al. https://doi.org/10.1029/2020JC016974
32. Spatio-temporal variability of surface water pCO2 and nutrients in the tropical Pacific from 1981 to 2015 S. Yasunaka et al. https://doi.org/10.1016/j.dsr2.2019.104680
33. Decadal vision in oceanography 2021: Tropical oceans T. Doi et al. https://doi.org/10.5928/kaiyou.30.5_105
34. Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost H. Sun et al. https://doi.org/10.3390/rs13142805
35. Estimating spatial and temporal variation in ocean surface pCO2 in the Gulf of Mexico using remote sensing and machine learning techniques Z. Fu et al. https://doi.org/10.1016/j.scitotenv.2020.140965
36. Mapping of the air–sea CO2 flux in the Arctic Ocean and its adjacent seas: Basin-wide distribution and seasonal to interannual variability S. Yasunaka et al. https://doi.org/10.1016/j.polar.2016.03.006
37. Using satellite data to estimate partial pressure of CO2 in the Baltic Sea G. Parard et al. https://doi.org/10.1002/2015JG003064
38. Accelerating CO2 Outgassing in the Equatorial Pacific from Satellite Remote Sensing Y. Shang et al. https://doi.org/10.3390/rs17020247
39. The Distribution of pCO2W and Air-Sea CO2 Fluxes Using FFNN at the Continental Shelf Areas of the Arctic Ocean I. Wrobel-Niedzwiecka et al. https://doi.org/10.3390/rs14020312
40. Remote Estimation of Sea Surface Nitrate in the California Current System From Satellite Ocean Color Measurements X. Yu et al. https://doi.org/10.1109/TGRS.2021.3095099
41. Technical note: Evaluation of three machine learning models for surface ocean CO2 mapping J. Zeng et al. https://doi.org/10.5194/os-13-303-2017
42. The Ocean Carbon States Database: a proof-of-concept application of cluster analysis in the ocean carbon cycle R. Latto & A. Romanou https://doi.org/10.5194/essd-10-609-2018
43. Revisiting historical trends in the Eastern Boundary Upwelling Systems with a machine learning method D. Bustos et al. https://doi.org/10.3389/fmars.2024.1446766
44. Air–sea CO2 flux in the Pacific Ocean for the period 1990–2009 M. Ishii et al. https://doi.org/10.5194/bg-11-709-2014
45. Trends and drivers in global surface ocean pH over the past 3 decades S. Lauvset et al. https://doi.org/10.5194/bg-12-1285-2015
46. Impact of Alaska atmospheric blocking on the carbon flux in the Northeast Pacific Ocean H. Wang et al. https://doi.org/10.1016/j.marenvres.2024.106770
47. Surface ocean CO2 concentration and air-sea flux estimate by machine learning with modelled variable trends J. Zeng et al. https://doi.org/10.3389/fmars.2022.989233
48. A machine learning approach to estimate surface ocean pCO2 from satellite measurements S. Chen et al. https://doi.org/10.1016/j.rse.2019.04.019
49. Quantifying the Atmospheric CO2 Forcing Effect on Surface Ocean pCO2 in the North Pacific Subtropical Gyre in the Past Two Decades S. Chen et al. https://doi.org/10.3389/fmars.2021.636881
50. Reconstruction of monthly fCO2 distribution in the Ross Sea, Antarctica during 1998 -2018 using machine learning technique and observational data sets A. Mo et al. https://doi.org/10.22761/DJ2022.4.3.003
51. An update to the Surface Ocean CO2 Atlas (SOCAT version 2) D. Bakker et al. https://doi.org/10.5194/essd-6-69-2014
52. Data-based estimates of interannual sea–air CO2 flux variations 1957–2020 and their relation to environmental drivers C. Rödenbeck et al. https://doi.org/10.5194/bg-19-2627-2022
53. A Review of Machine Learning Applications in Ocean Color Remote Sensing Z. Zhang et al. https://doi.org/10.3390/rs17101776
54. Estimation of fire-induced carbon emissions from Equatorial Asia in 2015 using in situ aircraft and ship observations Y. Niwa et al. https://doi.org/10.5194/acp-21-9455-2021
55. A Multiparametric Nonlinear Regression Approach for the Estimation of Global Surface Ocean pCO2 Using Satellite Oceanographic Data K. Krishna et al. https://doi.org/10.1109/JSTARS.2020.3026363
56. Global ocean surface pCO2 retrieval and the influence of mesoscale eddies on its performance Y. Wang et al. https://doi.org/10.1016/j.scitotenv.2025.179856
57. Satellite-derived CO2fugacity in surface seawater of the tropical Atlantic Ocean using a feedforward neural network H. Moussa et al. https://doi.org/10.1080/01431161.2015.1131872
58. Long-term surface pCO₂ trends from observations and models J. Tjiputra et al. https://doi.org/10.3402/tellusb.v66.23083
59. Spatiotemporal reconstruction of global ocean surface pCO2 based on optimized random forest H. Wu et al. https://doi.org/10.1016/j.scitotenv.2023.169209
60. Estimation of sea surface pCO2 and air–sea CO2 flux in the East China Sea using in-situ and satellite data over the period 2000–2016 J. Liu et al. https://doi.org/10.1016/j.csr.2022.104879
61. Decomposing tropical Pacific air-sea CO2 flux anomalies during marine heatwaves and cold spells: thermal versus non-thermal drivers Z. Meng & Y. Cai https://doi.org/10.3389/fmars.2026.1767039
62. Recent increase in oceanic carbon uptake driven by weaker upper-ocean overturning T. DeVries et al. https://doi.org/10.1038/nature21068
63. Knowledge discovery through higher education census data S. Machado de CAMPOS et al. https://doi.org/10.1016/j.techfore.2019.119742
64. Carbon Air–Sea Flux in the Arctic Ocean from CALIPSO from 2007 to 2020 S. Zhang et al. https://doi.org/10.3390/rs14246196
65. Research ReportDiurnal global ocean surface pCO2 and air–sea CO2 flux reconstructed from spaceborne LiDAR data S. Zhang et al. https://doi.org/10.1093/pnasnexus/pgad432
66. Assessment of Air–Sea CO2 Flux in Global Surface Oceans Using Satellite Data I. Shaik et al. https://doi.org/10.1007/s11220-025-00676-5
67. Variability of pCO2 and FCO2 in the Mexican Pacific during 25 years L. Coronado-Álvarez et al. https://doi.org/10.1016/j.jmarsys.2022.103853
68. Reconstruction and spatiotemporal analysis of global surface ocean pCO2 considering sea area characteristics H. Wu et al. https://doi.org/10.5194/bg-23-967-2026
69. Variations in the summer oceanic pCO2 and carbon sink in Prydz Bay using the self-organizing map analysis approach S. Xu et al. https://doi.org/10.5194/bg-16-797-2019
70. Estimating surface pCO2 in the northern Gulf of Mexico: Which remote sensing model to use? S. Chen et al. https://doi.org/10.1016/j.csr.2017.10.013
71. A Global Surface Ocean fCO2 Climatology Based on a Feed-Forward Neural Network J. Zeng et al. https://doi.org/10.1175/JTECH-D-13-00137.1
72. Research on Atlantic surface pCO2 reconstruction based on machine learning J. Liu et al. https://doi.org/10.1016/j.ecoinf.2025.103094
73. Statistical Analysis of Spatiotemporal Variations of Air‐Sea CO2 Fluxes in the Kuroshio Region T. Tokoro et al. https://doi.org/10.1029/2023JC019762
74. Mapping of sea surface nutrients in the North Pacific: Basin-wide distribution and seasonal to interannual variability S. Yasunaka et al. https://doi.org/10.1002/2014JC010318
75. Biological organic carbon export estimated from the annual carbon budget observed in the surface waters of the western subarctic and subtropical North Pacific Ocean from 2004 to 2013 M. Wakita et al. https://doi.org/10.1007/s10872-016-0379-8
76. A novel sea surface pCO2-product for the global coastal ocean resolving trends over 1982–2020 A. Roobaert et al. https://doi.org/10.5194/essd-16-421-2024
77. Remote Sensing Estimations of the Seawater Partial Pressure of CO₂ Using Sea Surface Roughness Derived From Synthetic Aperture Radar Y. Wang et al. https://doi.org/10.1109/TGRS.2024.3379984