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Water quality retrievals from combined Landsat TM data and ERS-2 SAR data in the Gulf of Finland

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4 Author(s)
Yuanzhi Zhang ; Lab. of Space Technol., Helsinki Univ. of Technol., Espoo, Finland ; Pulliainen, J.T. ; Koponen, S.S. ; Hallikainen, M.T.

This paper presents the applicability of combined Landsat Thematic Mapper and European Remote Sensing 2 synthetic aperture radar (SAR) data to turbidity, Secchi disk depth, and suspended sediment concentration retrievals in the Gulf of Finland. The results show that the estimated accuracy of these water quality variables using a neural network is much higher than the accuracy using simple and multivariate regression approaches. The results also demonstrate that SAR is only a marginally helpful to improve the estimation of these three variables for the practical use in the study area. However, the method still needs to be refined in the area under study.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:41 ,  Issue: 3 )