## 1 Introduction
Owing to their high spectral resolution and signal-to-noise ratio (SNR), ocean color sensors are excellent for detecting red tide patterns, even in conditions mixed with seawater, as well as the presence or absence of red tide blooms. However, ocean color sensors cannot provide accurate red tide information in coastal areas because of their low spatial resolution (300-500 m) and coastal masking caused by the uncertainty of atmospheric correction in coastal areas where turbidity is high [25; 26]. Because the gaps between aquaculture cages in coastal areas are fewer than tens of meters, ocean color sensors have limitations with regards to the detection of red tide blooms around these structures. Terrestrial sensors with a high spatial resolution can overcome the limitations of ocean color sensors. Some studies have employed terrestrial sensors, such as Landsat Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI), and Sentinel-2 MultiSpectral Instrument (MSI) for red tide detection. [PERSON] et al. [27] proposed a simple red tide monitoring method using Sentinel-2 MSI for the sustainable management of small lakes. They used a band combination based on the spectral features of a red tide bloom. [PERSON] and [PERSON] [28] monitored red tide blooms in coastal areas using the spectral features of Sentinel-2 MSI. [PERSON] et al. [25] attempted spatial and spectral-based image fusion of GOCI and Landsat OLI to improve the accuracy of red tide detection. Landsat OLI was chosen to compensate for the low spatial resolution of GOCI, and as a result, it was possible to obtain reasonable red tide information in both coastal and offshore areas. However, some red tide patterns suffered from severe noise caused by sea currents and ship wakes that appeared mainly in images with a high spatial resolution. In addition, [PERSON] et al. [26] investigated the synergistic effect of GOCI, Sentinel-3 Ocean and Land Colour Instrument (OLCI), Landsat ETM+, OLI, and Sentinel-2 MSI data to identify _M. polykrikoides_ blooms in coastal areas. They concluded that the red tide accuracy increased with multi-sensor data rather than with single-sensor data. In addition, they found the red tide patterns near the coastal area on Sentinel-3 OLCI images, but not on Landsat OLI images because terrestrial sensors have a lower spectral resolution and SNR than ocean color sensors. The results of these studies show the limitations of the conventional analyses on remote sensing data, even with a high spatial resolution, to extract red tide information. To overcome these limitations, machine learning and deep learning approaches have been used. They have been actively applied to earth system sciences to study the atmosphere, land surface, and ocean [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39]. In particular, machine learning has become a popular approach in geoscientific classification, anomaly detection, and regression. Furthermore, deep learning is used in geoscience to better understand the spatial and temporal structures of data. These data have features that would generally be problematic for traditional machine learning to extract [40]. However, several studies have reported success in applying deep learning to objectively extract spatial features to define and classify various conditions without using either subjective human annotation or a threshold-based method. Recently, some studies have applied machine learning and deep learning for satellite-based red tide detection. [PERSON] et al. [41] concluded that machine learning techniques such as random forest and support vector machine, outperformed the previous threshold-based methods using MODIS images [8; 17]. [PERSON] et al. [42] developed a red tide detection scheme based on a multilayer feed-forward neural network from Landsat OLI images. The detection results showed good agreement with the red tide map from in situ measurements compared to previous red tide indices developed by [PERSON] and [PERSON] [20], [PERSON] et al. [21], and [PERSON] et al. [43]. [PERSON] et al.