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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Bibliographic Details
Main Authors: Ruescas, Ana B., Mateo-Garcia, Gonzalo, Camps-Valls, Gustau, Hieronymi, Martin
Format: Conference Proceeding
Language:English
Subjects:
Case 2 Absorbing Waters
Data models
Estimation
Ground penetrating radar
Machine learning
Machine Learning Regression
Numerical models
Oceans
Remote Sensing
Water Quality Parameters
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Summary: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.
ISSN:2153-7003
DOI:10.1109/IGARSS.2018.8518810