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
Cite
@article{arxiv.2012.04495,
title = {Retrieval of Case 2 Water Quality Parameters with Machine Learning},
author = {Ana B. Ruescas and Gonzalo Mateo-Garcia and Gustau Camps-Valls and Martin Hieronymi},
journal= {arXiv preprint arXiv:2012.04495},
year = {2020}
}