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RESEARCH PRODUCT
Retrieval of Case 2 Water Quality Parameters with Machine Learning
Gustau Camps-vallsAna B. RuescasGonxalo Mateo-garciaMartin Hieronymisubject
FOS: Computer and information sciencesComputer Science - Machine Learning010504 meteorology & atmospheric sciences0211 other engineering and technologiesFOS: Physical sciences02 engineering and technologyMachine learningcomputer.software_genre01 natural sciencesData modelingMachine Learning (cs.LG)Physics - Geophysicssymbols.namesakeRadiative transferGaussian process021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsArtificial neural networkbusiness.industry6. Clean waterRandom forestGeophysics (physics.geo-ph)Support vector machineColored dissolved organic matterKernel (statistics)Physics - Data Analysis Statistics and ProbabilitysymbolsArtificial intelligencebusinesscomputerData Analysis Statistics and Probability (physics.data-an)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
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-07-01 |