11 citations as recorded by crossref.

1. Modeling and Prediction of Environmental Factors and Chlorophyll a Abundance by Machine Learning Based on Tara Oceans Data Z. Cui et al. 10.3390/jmse10111749
2. High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning C. Galdies & R. Guerra 10.3390/w15081454
3. Variations in the summer oceanic <i>p</i>CO<sub>2</sub> and carbon sink in Prydz Bay using the self-organizing map analysis approach S. Xu et al. 10.5194/bg-16-797-2019
4. A comparative assessment of the uncertainties of global surface ocean CO<sub>2</sub> estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? L. Gregor et al. 10.5194/gmd-12-5113-2019
5. Applications of Machine Learning in Chemical and Biological Oceanography B. Sadaiappan et al. 10.1021/acsomega.2c06441
6. A Surface Ocean CO2 Reference Network, SOCONET and Associated Marine Boundary Layer CO2 Measurements R. Wanninkhof et al. 10.3389/fmars.2019.00400
7. Generalization of Parameter Selection of SVM and LS-SVM for Regression . Zeng et al. 10.3390/make1020043
8. Spatial reconstruction of long-term (2003–2020) sea surface pCO2 in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis Z. Wang et al. 10.5194/essd-15-1711-2023
9. The impact of the South-East Madagascar Bloom on the oceanic CO<sub>2</sub> sink N. Metzl et al. 10.5194/bg-19-1451-2022
10. Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data E. Jang et al. 10.3390/rs9080821
11. Reconstruction of global surface ocean <i>p</i>CO<sub>2</sub> using region-specific predictors based on a stepwise FFNN regression algorithm G. Zhong et al. 10.5194/bg-19-845-2022