This paper presents a novel approach for anomaly detection base on computing and utilizing descriptive spectral signatures. The goal of the work is to distinguish between contaminated and normal water areas within a region of investigation. A site-independent approach was developed by considering descriptive spectral signatures characterising normal sweat lake water as reference spectral features. Thereafter, it was possible to detect and determine the distribution of industrial outlet plumes which usually have spectral characteristics that deviate from the surrounding unaffected normal waters. The method was evaluated on airborne hyperspectral remotely-sensed image-data acquired over the region of Norrsundet, Sweden. In this region, areas of different water types were found, such as riverine sweet water, coastal salt seawater, as well as waste water discharged from paper-pulp industries. The work aimed at identifying these types of waters and their distributions. The needed reference descriptive spectral signatures of uncontaminated normal water were generated by using a dataset consisting of laboratory measurements of chlorophyll-a and phaeophytine-a concentrations and the corresponding field reflectance spectra collected at 22 sampling stations in Lake Erken, Sweden. The final results, showing the locations and distributions of contaminated and normal water areas, are in full agreement with field observations in the investigated region.