Geophysics

    

Retrieval of Case 2 Water Quality Parameters with Machine Learning

Authors: Ana B. Ruescas, Gonzalo Mateo-García, Gustau Camps-Valls, Martin Hieronymi

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.

Comments: 8 Pages.

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Submission history

[v1] 2020-12-08 08:07:44

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