Applying spectral unmixing to determine surface water parameters in mining environment

Water has been traditionally monitored by in situ measurements taking point samples at regular intervals. From an optical perspective, in addition to pure water itself, the optical properties of surface waters are mainly influenced by three constituents: phytoplankton, suspended sediment, and coloured dissolved organic matter (CDOM). Although imaging spectroscopy can serve as a modern method to monitor polluted surface waters, only a limited number of studies have been published on this topic. In our study, we tested the feasibility of mapping the properties of surface waters affected by long-term mining activities using airborne multi-flight-line HyMap hyperspectral (HS) datasets. An approach using fundamental water image end-members to map relative abundances of selected parameters of surface waters (dissolved Fe, dissolved organic carbon DOC, non-dissolved particles) was tested and ground truth (eight monitored ponds) was then used to validate the results of spectral mapping. Although the detected end-members did not implicitly have to be absolutely pure, they represented the most extreme water types within the studied area. Correlations between the studied water parameters and three fractional images were detected (dissolved Fe: R2=0.74, undissolved particles: R2=0.57, DOC: R2=0.42); these images were further used to create semi-automatic maps.