## 2 Materials and Methods
### Red Tide Detection Methods  
We performed visual inspection and spectral analysis using the 3B SR PlanetScope images. The optical spectra of various types were extracted, and then, we applied the conventional RTI to PlanetScope images. RTI was developed by [PERSON] et al. [48] for the detection of red tide blooms from unmanned aerial vehicle (UAV) images. It utilizes the optical features of the red tide waters. The water-leaving radiance of clear seawater and red tide water are significantly different in the blue and green wavelength bands [49]. However, the turbidity of seawater makes it difficult to differentiate the spectral characteristics of red tide waters from highly turbid seawater. Because the waters on the southern coast of Korea have complex optical properties, the reflectance of red tide waters is largely different from \begin{table}
\begin{tabular}{c c} \hline
**Mission Characteristics** & **Sun-Synchronous Orbit** \\ \hline Orbit altitude & 475 km \\ Equator crossing time & 9:30–11:30 am (local solar time) \\  & Blue: 455–515 nm \\  & Green: 500–590 nm \\  & Red: 590–670 nm \\  & NIR: 780–860 nm \\ Spatial resolution & 3 m \\  & Product size & 24 \(\times\) 7 km\({}^{2}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Constellation overview of PlanetScope. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Date**} & \multicolumn{8}{c}{**July 2018**} & \multicolumn{8}{c}{**August 2018**} \\ \cline{2-11}  & **24** & **25** & **26** & **27** & **28** & **29** & **30** & **31** & **1** & **2** \\ \hline Number of available images & 4 & 2 & 2 & 3 & 0 & 6 & 2 & 4 & 8 & 5 \\ \hline Total & & & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: PlanetScope imagery available during _M. polykrikoides_ bloom event in the southern coast of Korea, 2018.  
that of highly turbid waters in the red wavelength band [19]. The RTI was calculated using the reflectance in the red, green, and blue wavelength bands as follows [48]:  
\[\text{Red~{}Tide~{}Index}=\frac{\left(R_{green~{}band}~{}-~{}R_{blue~{}band}\right) }{\left(R_{green~{}band}~{}+~{}R_{blue~{}band}\right)}+\frac{\left(R_{red~{}band}~ {}-~{}R_{blue~{}band}\right)}{\left(R_{red~{}band}~{}+~{}R_{blue~{}band}\right)} \tag{1}\]  
Then, a threshold was applied to differentiate red tide waters from seawater. Originally, it was flagged as red tide water when the RTI value was > 0.2. However, the threshold is dependent on sensors, and even images acquired by the same sensor can have different optimal thresholds. We compared the statistics of the RTI distributions of non-red tide and red tide pixels, and then determined the optimal thresholds to differentiate the two RTI distributions of each image. Next, a deep CNN, U-Net, was trained with PlanetScope imagery and the corresponding ground-truth red tide maps were used as both input and output. A total of 36 image pairs were used to develop the U-Net model to learn the spectral features of red tides from PlanetScope images. The deep convolutional layers in a CNN extract spectral features related to red tide waters from multi-band images. We adopted the U-Net CNN architecture for red tide detection through deep learning pixel-wise spectral features from the PlanetScope images. U-Net is a \"U\"-shaped CNN with encoder and decoder structures, and it is widely applied to pixel-wise classification or object segmentation [50]. U-Net can be trained end-to-end from very few images and has been found to outperform the previous best method. Moreover, in this study, the network was fast and had a reasonable training time. Figure 2 shows the percentage of red tide pixels in each image from 24 July to 2 August 2018. The portion of red tide pixels among the 36 images was 2.56%, and non-red tide pixels significantly outnumbered red tide pixels. In addition, the size of the entire image (for example, 4620 \(\times\) 9004 pixels on 29 July 2018) was too large to pass through the U-Net model.