Detecting turbid plumes from satellite remote sensing: State-of-art thresholds and the novel PLUMES algorithm

Turbid coastal plumes carry sediments, nutrients, and pollutants. Satellite remote sensing is an effective tool for studying water quality parameters in these turbid plumes while covering a wide range of hydrological and meteorological conditions. However, determining boundaries of turbid coastal plumes poses a challenge. Traditionally, thresholds are the approach of choice for plume detection as they are simple to implement and offer fast processing (especially important for large datasets). However, thresholds are site-specific and need to be re-adjusted for different datasets or when meteorological and hydrodynamical conditions differ. This study compares state-of-the-art threshold approaches with a novel algorithm (PLUMES) for detecting turbid coastal plumes from satellite remote sensing, tested for Patos Lagoon, Brazil. PLUMES is a semi-supervised, and spatially explicit algorithm, and does not assume a unique plume boundary. Results show that the thresholds and PLUMES approach each provide advantages and limitations. Compared with thresholds, the PLUMES algorithm can differentiate both low or high turbidity plumes from the ambient background waters and limits detection of coastal resuspension while automatically retrieving metrics of detected plumes (e.g., area, mean intensity, core location). The study highlights the potential of the PLUMES algorithm for detecting turbid coastal plumes from satellite remote sensing products, which can have significantly positive implications for coastal management. However, PLUMES, despite its demonstrated effectiveness in this study, has not yet been applied to other study sites.

Automated summary

Turbid coastal plumes, carrying sediments, nutrients, and pollutants, pose a challenge in terms of detecting their boundaries. This study compares traditional threshold approaches with a novel algorithm (PLUMES) for detecting turbid coastal plumes from satellite remote sensing. Results show that PLUMES has advantages over thresholds, as it can differentiate different turbidity levels and automatically retrieve metrics of detected plumes. However, PLUMES has only been tested in one study site and needs further application in different areas.