RETRIEVAL OF CASE 2 WATER QUALITY PARAMETERS WITH MACHINE LEARNING

被引：0

作者：

Ruescas, Ana B. ^{[1
]}

Mateo-Garcia, Gonzalo ^{[1
]}

Camps-Valls, Gustau ^{[1
]}

Hieronymi, Martin ^{[2
]}

机构：

[1] Univ Valencia, IPL, Valencia, Spain

[2] Helmholtz Zentrum Geesthacht, Inst Coastal Res, Geesthacht, Germany

来源：

IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2018年

基金：

欧洲研究理事会;

关键词：

Remote Sensing; Water Quality Parameters; Case 2 Absorbing Waters; Machine Learning Regression;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：124 / 127

页数：4

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