The use of hyperspectral data for evaluation of water quality parameters in the River Sava

The water quality monitoring through in situ sampling is costly and time consuming process that results in sparse point data difficult to achieve continuous water quality maps. Recent developments in remote sensing technology, especially in optical remote sensing, provide a significant potential to complement and enhance classical laboratory measurements. In this study we have assessed single band, first derivative and band ratio models for retrieving concentrations of chlorophyll a, turbidity and total suspended solids (TSS) from hyperspectral reflectance data collected along the River Sava. The spectral band ratio model showed the best correlation with Chl-a (R745/R418, R2 = 0.72) and TSS (R373/R396 , R2 = 0.78), while the first derivative model had the best correlation with turbidity values (R2 = 0.87). These results represent a promising first step in the initiative to develop a methodology for the water quality monitoring of the River Sava using remotely sensed data originating from various airborne and satellite hyperspectral sensors.