Variability of remote sensing spectral indices in Boreal lake basins
Hakala, T., Pölönen, I., Honkavaara, E., Näsi, R., Hakala, T., & Lindfors, A. (2018). Variability of remote sensing spectral indices in Boreal lake basins. In F. Remondino, I. Toschi, & T. Fuse (Eds.), ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020” (pp. 411-417). International Society for Photogrammetry and Remote Sensing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2. https://doi.org/10.5194/isprs-archives-xlii-2-411-2018
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesAuthors
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2018Copyright
© Authors 2018.
Remotely sensed hyperspectral data has widely been used to determine water quality parameters in oceanic waters. However in
freshwater basins the dependence between the hyperspectral data and the parameters is more complicated. In this work some ideas are
presented concerning the study of this dependence. The data used in this study were collected from the lake Hiidenvesi in southern
Finland. The hyperspectral data consists of reflectances in 36 bands in the wavelength area 508...878 nm and the separately measured
water quality parameters are turbidity, blue-green algae, chlorophyll, pH and dissolved oxygen. Hyperspectral data was used as bare
band reflectances, but also in the form of two simple spectral indices: ratio A/B and difference A-B, where A and B go through all
the bands. The correlations of the indices with the parameters were presented visually as 1- or 2-dimensional arrays. To examine the
significance on the results of different variables, the data was classified in two different ways: the natural basins and the values of the
water quality parameters. It was noticed that the variability of the correlation arrays was particularly strong among different basins in
both the magnitude of correlation and the best performing indices. Further studies are needed to clarify which features of the basins are
of most importance in predicting the shapes of the correlation arrays.
...


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