Abstract
Due to the recent anomalous climatic conditions, the mean summer temperature in Korea for the current year reached 25.6°C, marking the highest temperature recorded since 1973. Concurrently, the incidence of large-scale green algal blooms has escalated beyond the typical frequency in the four principal river systems. The Ministry of Environment has instituted a water quality monitoring network aimed at gathering data pertinent to water quality assessment and regulatory measures; however, the reliance on discrete point data imposes constraints on comprehending the spatial distribution and proliferation of green algae. Techniques for remote monitoring of green algae utilizing satellite imagery may serve as an alternative or complementary resource to conventional point-based water quality assessments. In the present study, we devised and critically assessed an algorithm for the remote estimation of freshwater chlorophyll-a concentrations through the analysis of Sentinel-2 satellite data. Given the challenges associated with field data collection, the GLORIA dataset was employed, and three variations of the CatBoost algorithm were developed based on different input variables, with their effectiveness concerning Sentinel-2 satellite data subjected to evaluation. Among the three algorithms, the variant that was trained utilizing variables such as band ratio and spectral shape was contrasted with 76 field measurements of surface chlorophyll-a, demonstrating a commendable accuracy with an R value of 0.79, root mean square error of 10.1 mg/m<sup>3</sup> when applied to the GLORIA dataset. The chlorophyll-a quantification algorithm predicated on Sentinel-2 satellite data, which was developed in this study, holds the potential to be utilized effectively for collaborative management and responsive measures regarding green algae by providing objective insights into the prevalence of green algae across extensive areas, temporal trends, and localized hotspots.
Published Version
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