13 citations as recorded by crossref.

1. Variations in the summer oceanic pCO2 and carbon sink in Prydz Bay using the self-organizing map analysis approach S. Xu et al. 10.5194/bg-16-797-2019
2. A comparative assessment of the uncertainties of global surface ocean CO2 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
3. A Surface Ocean CO2 Reference Network, SOCONET and Associated Marine Boundary Layer CO2 Measurements R. Wanninkhof et al. 10.3389/fmars.2019.00400
4. 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
5. A Review of Machine Learning Applications in Ocean Color Remote Sensing Z. Zhang et al. 10.3390/rs17101776
6. The impact of the South-East Madagascar Bloom on the oceanic CO2 sink N. Metzl et al. 10.5194/bg-19-1451-2022
7. Performance Evaluation of Gradient Descent Optimizers in Estuarine Turbidity Estimation with Multilayer Perceptron and Sentinel-2 Imagery N. Ndou & N. Nontongana 10.3390/hydrology11100164
8. Reconstruction of global surface ocean pCO2 using region-specific predictors based on a stepwise FFNN regression algorithm G. Zhong et al. 10.5194/bg-19-845-2022
9. 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
10. High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning C. Galdies & R. Guerra 10.3390/w15081454
11. Applications of Machine Learning in Chemical and Biological Oceanography B. Sadaiappan et al. 10.1021/acsomega.2c06441
12. Generalization of Parameter Selection of SVM and LS-SVM for Regression J. Zeng et al. 10.3390/make1020043
13. 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