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Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning
Bo Ping1, Yunshan Meng2, Fenzhen Su3
1School of Earth System Science, Institute of Surface-Earth System Science, Tianjin University, Tianjin, 300072, China.
Marine Environmental Research
|June 5, 2024
Summary
Machine learning, specifically random forest, effectively estimates subsurface oceanic dissolved oxygen (DO) using surface data. Sea surface temperature is a key predictor, outperforming salinity and chlorophyll-a for DO retrieval.
Area of Science:
- Oceanography
- Marine Biogeochemistry
- Machine Learning Applications
Background:
- Oceanic dissolved oxygen (DO) is vital for marine ecosystems and biogeochemical cycles.
- Satellite observations are limited in providing subsurface DO data due to restricted depth.
- Estimating subsurface DO from surface parameters is crucial for oceanographic research.
Purpose of the Study:
- To analyze the potential of machine learning (ML) methods for retrieving subsurface oceanic dissolved oxygen (DO).
- To compare the performance of Support Vector Regression (SVR), Random Forest (RF) regression, and Extreme Gradient Boosting (XGBoosting) regression for DO estimation.
- To identify key surface oceanic parameters influencing DO retrieval accuracy.
Main Methods:
- Employed machine learning regression techniques: Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoosting).
- Utilized surface oceanic parameters (Sea Surface Temperature, Sea Surface Salinity, Sea Surface Chlorophyll-a) to predict subsurface DO.
- Evaluated model performance using determination coefficients (R²) and root mean square error (RMSE) across various depths.
Main Results:
- The Random Forest (RF) method generally exhibited superior performance in subsurface DO retrieval compared to SVR and XGBoosting.
- DO estimation accuracy varied with depth, initially decreasing then improving, with the poorest performance at 600 dbar.
- Sea Surface Temperature (SST) was identified as a more significant predictor for DO retrieval than Sea Surface Salinity (SSS) and Sea Surface Chlorophyll-a (SCHL).
Conclusions:
- Machine learning, particularly RF, offers a viable approach for estimating subsurface oceanic DO from surface measurements.
- RF method demonstrated higher retrieval accuracy above 700 dbar compared to the PISCES model, with notable differences in the equatorial deep ocean.
- The study highlights the importance of SST in predicting subsurface DO and the depth-dependent accuracy of ML models.
Keywords:
Chlorophyll-a concentration (chlor-a)Dissolved oxygen (DO)Machine learning (ML)Sea surface salinity (SSS)Sea surface temperature (SST)More Related Videos
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