Hyperspectral rapid detection of bacterial content and water quality parameters in coastal bathing waters

Abstract

This study addresses the significant issue of water pollution in coastal areas, exacerbated by extensive residential and tourist activities. Monitoring bathing water quality is crucial, necessitating efficient observation and new analysis methods. This research presents a methodology using drone-based hyperspectral remote sensing and radiative transfer modelling (RTM) in shallow waters to measure chlorophyll, turbidity and bacterial content with a spatial resolution of 10 cm. The bacterial concentration is determined by incorporating the absorption spectra into the model. Results indicate the methodology's efficacy in differentiating between various coastal water types in the Canary Islands through water quality parameter accuracy tests. Promising bacterial content results were observed, with RTM-derived values compared with in situ colony forming unit measurements. While innovative, further development is required. Nonetheless, the methodology shows potential for rapid pollution detection and early warning systems, significantly reducing detection time compared to conventional methods.