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A record-breaking agglomeration of Sargassum was packed along the northern Jeju coast in Korea in 2021, and laborers suffered from removing them from the beach. If remote sensing can be used to detect the locations at which Sargassum accumulated in a timely and accurate manner, we could remove them before their arrival and reduce the damage caused by Sargassum. This study aims to detect Sargassum distribution on the coast of Jeju Island using the Geostationary KOMPSAT 2B (GK2B) Geostationary Ocean Color Imager-II (GOCI-II) imagery that was launched in February 2020, with measurements available since October 2020. For this, we used GOCI-II imagery during the first 6 months and machine learning models including Fine Tree, a Fine Gaussian support vector machine (SVM), and Gentle adaptive boosting (GentleBoost). We trained the models with the GOCI-II Rayleigh-corrected reflectance (RhoC) image and a ground truth map extracted from high-resolution images as input and output, respectively. Qualitative and quantitative assessments were carried out using the three machine learning models and traditional methods such as Sargassum indexes. We found that GentleBoost showed a lower false positive (6.2%) and a high F-measure level (0.82), and a more appropriate Sargassum distribution compared to other methods. The application of the machine learning model to GOCI-II images in various atmospheric conditions is therefore considered successful for mapping Sargassum extent quickly, enabling reduction of laborers’ efforts to remove them.

Sargassum Detection Using Machine Learning Models: A Case Study with the First 6 Months of GOCI-II Imagery