Retrieval of Case 2 Water Quality Parameters with Machine Learning
Water quality parameters are derived applying several machine learning regression methods on the Case2eXtreme dataset (C2X). The used data are based on Hydrolight in-water radiative transfer simulations at Sentinel-3 OLCI wavebands, and the application is done exclusively for absorbing waters with h...
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| description | Water quality parameters are derived applying several machine learning regression methods on the Case2eXtreme dataset (C2X). The used data are based on Hydrolight in-water radiative transfer simulations at Sentinel-3 OLCI wavebands, and the application is done exclusively for absorbing waters with high concentrations of coloured dissolved organic matter (CDOM). The regression approaches are: regularized linear, random forest, Kernel ridge, Gaussian process and support vector regressors. The validation is made with and an independent simulation dataset. A comparison with the OLCI Neural Network Swarm (ONSS) is made as well. The best approached is applied to a sample scene and compared with the standard OLCI product delivered by EUMETSAT/ESA |
| doi_str_mv | 10.48550/arxiv.2012.04495 |
| format | Article |
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| subjects | Computer Science - Learning Datasets Dissolved organic matter Gaussian process Machine learning Neural networks Parameters Physics - Data Analysis, Statistics and Probability Physics - Geophysics Radiative transfer Water quality |
| title | Retrieval of Case 2 Water Quality Parameters with Machine Learning |
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