Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data

Abstract

We study the use of airborne and simulated satellite remote sensing data for classification of three water quality variables: Secchi depth, turbidity, and chlorophyll a. An extensive airborne spectrometer and ground truth data set obtained in four lake water quality measurement campaigns in southern Finland during 1996–1998 was used in the analysis. The class limits for the water quality variables were obtained from two operational classification standards. When remote sensing data is used, a combination of them proved to be the most suitable. The feasibility of the system for operational use was tested by training and testing the retrieval algorithms with separate data sets. In this case, the classification accuracy is 90% for three Secchi depth classes, 79% for five turbidity classes, and 78% for five chlorophyll a classes. When Airborne Imaging Spectrometer for Applications (AISA) data was spectrally averaged corresponding to Envisat Medium Resolution Imaging Spectrometer (MERIS) channels, the classification accuracy was about the same as in the case of the original AISA channels.