Building a Reinforcement learning Model for Estimating Reliable Algae Concentration: Widely Applicable Correction Factors

Algal blooms by eutrophication are regarded as a serious issue in many regions including Korea’s Four Major Rivers. Accurately measuring water Chlorophyll-a (Chl-a) is essential to propose effective solutions for addressing this problem. However, it is very hard to obtain water quality data for all desired regions through direct measurement. By utilizing remote sensing to collect a large amount of data from various water bodies, an accurate and rapid model to estimate Chl-a concentration can be developed, playing a crucial role in addressing the algae problem.

This study utilized Sentinel-3 OLCI (Ocean and Land Color Instrument) data with a spatial resolution of approximately 300 meters. Bio-optical algorithms were applied to estimate Chl-a concentration. Bio-optical algorithms vary in types depending on the parameters used, such as radiance, reflectance, and Inherent optical properties (IOPs). In this study, IOPs were utilized to use the inherent properties of water. Accurate IOPs estimation is important because the coefficients of IOPs estimation algorithms are influenced by regional and temporal variability. The Bottom of Atmosphere (BOA) reflectance, derived from radiance data of OLCI EFR using the C2RCC processor, was utilized to estimate IOPs. Based on the derived IOPs, bio-optical algorithms were applied to estimate Chl-a concentration. After that, reinforcement learning was employed to refine the IOPs estimation process, dynamically adjusting coefficients to improve Chl-a concentration accuracy across varying conditions. Observed Chl-a data from the Water Environment Information System were used for model training and validation. Therefore, this study aims to estimate and map algal concentrations across Korea’s Four Major Rivers. Reflectance-based NDWI was calculated to delineate inland water bodies, and the reflectance data were incorporated into the Chl-a reinforcement learning model developed in this study to generate detailed spatial maps. This study is expected to contribute to solving green algae problems and water quality management by enabling more accurate and rapid Chl-a concentration estimation as it is not swayed by regional and temporal variations.