Harmful algal bloom (HAB) caused by Magalefidinium polykrikoides becomes frequent in Korean coastal waters during the mid-1990s and is now annual events on the southern coast of Korea. HAB often leads to high rates of fish mortality and subsequent economic losses in aquaculture. In addition, non-harmful algal blooms (non-HABs) caused by the dinoflagellate Alexandrium sp., Mesodinium rubrum, and the diatom Skeletonema sp. occur simultaneously in time and space. Because HAB and non-HABs are difficult to discriminate using multi-band satellite data, most previous studies have attempted only detection or qualitative classification with limited data. In contrast, in this current study, we aimed to quantitatively discriminate M. polykrikoides bloom associated HAB from non-HABs around the southern coast of Korea using a convolutional neural network (CNN) model with Sentinel-3 Ocean and Land Colour Instrument (OLCI) imagery with a spatial resolution of 300 m and 16 spectral bands for the first time. To identify the effect of non-HAB patches on the performance of the CNN model, five CNN models were trained with OLCI images as input and ground-truth HAB maps as output data. The appropriate figure-of-merits values (FOMs) with sensitivity of 0.53, precision of 0.92, and F-measure of 0.67 were reasonable when the CNN model trained using the dataset with the highest ratio of non-HABs patches was applied to HAB images. Even when non-HAB images were applied to the models, the CNN model exhibited the lowest error pixel count. Therefore, we confirmed that the CNN model, which can discriminate red tide blooms with subtle differences between the spectrum bands and spatial characteristics, helps solve the complexity and ambiguity in discriminating HAB from non-HABs.

1990 年代中期，由Magalefidinium polykrikoides引起的有害藻华 (HAB)在韩国沿海水域频繁发生，现在是韩国南部海岸的年度事件。HAB 经常导致水产养殖中的鱼类死亡率高和随后的经济损失。此外，由甲藻Alexandrium sp.、Mesodinium rubrum和硅藻Skeletonema sp.引起的无害藻华（非 HABs） 。在时间和空间上同时发生。由于使用多波段卫星数据很难区分 HAB 和非 HAB，因此以前的大多数研究仅尝试使用有限的数据进行检测或定性分类。相比之下，在目前的这项研究中，我们旨在定量区分M. polykrikoides首次使用卷积神经网络 (CNN) 模型与 Sentinel-3 海洋和陆地颜色仪器 (OLCI) 图像与空间分辨率为 300 m 和 16 个光谱带的韩国南部海岸周围的非 HAB 相关的 HAB . 为了确定非 HAB 补丁对 CNN 模型性能的影响，五个 CNN 模型使用 OLCI 图像作为输入，ground-truth HAB 图作为输出数据进行了训练。当使用非 HAB 补丁比例最高的数据集训练的 CNN 模型应用于 HAB 图像时，具有 0.53 的灵敏度、0.92 的精度和 0.67 的 F 度量的适当品质因数值 (FOM) 是合理的. 即使将非 HAB 图像应用于模型，CNN 模型也显示出最低的错误像素数。因此，我们确认了 CNN 模型，