## 4 Discussion
### PlanetScope Product  
PlanetScope imagery has been available since 2016, but it was only in 2018 that cloud-free images along the southern coast of Korea could be obtained continuously during a red tide event. Therefore, we conducted a study in 2018. The PlanetScope satellite constellation consists of groups of individual satellites. In this study, we used PlanetScope images from the three satellites. Of the 36 images, the number of satellite IDs 10x, 0 fxx, and 0 exo were 20, 12, and 4, respectively. In some cases, two satellite ID images were acquired with a slight time difference on the same date. The three satellites exhibit different relative spectral responses. Satellite IDs 0 fxx and 0 exx showed similar spectral response curves, but the satellite ID 10 xx most utilized in this study showed very different curves from those of  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Sensitivity**} & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**F-Measure**} \\ \cline{2-13}  & **LE** & **ME** & **HE** & **LE** & **ME** & **HE** & **LE** & **ME** & **HE** & **LE** & **ME** & **HE** \\ \hline RTI & 0.90 & 0.75 & 0.87 & 0.50 & 0.42 & 0.50 & 0.03 & 0.09 & 0.15 & 0.06 & 0.14 & 0.23 \\ U-Net#4 & 0.87 & 0.79 & 0.89 & 0.61 & 0.42 & 0.66 & 0.04 & 0.16 & 0.21 & 0.06 & 0.22 & 0.31 \\ \hline \end{tabular}
\end{table}
Table 6: Performance evaluation of RTI and U-Net #4 model using 36 PlanetScope images. FOMs were evaluated according to the red tide extent.  
Figure 9: Red tide areas extracted from RTI, U-Net #4, and ground-truth maps of 36 PlanetScope images.  
different satellite IDs. To determine the effect of this difference on the actual spectrum, we performed an analysis of the red tide spectrum at the same location obtained from different satellite images of the same date. The spectral shapes did not show significant differences, but minor variations in magnitude were observed. These factors could affect the spectral analysis of red tide blooms and affect the performance of our U-Net model because the deep convolutional layers in the U-Net architecture learn by extracting spectral features related to red tide waters from multi-band SR PlanetScope images. Compared to ocean sensors, terrestrial sensors such as PlanetScope have a low spectral resolution and a small band number. The algorithms for red tide detection typically use blue and green bands. This is because these wavelengths allow us to distinguish not only the surrounding waters but also the red tide waters. Therefore, the absence of these wavebands can be a major cause of the difficulty in detecting red tide blooms. In addition, because terrestrial sensors have lower SNRs than ocean color sensors, the utilization of terrestrial sensors for red tide detection leads to poor performance for red tide pixels mixed with seawater or turbid waters. The Planet SR product used in this study was corrected by combining standard atmospheric models with the use of water vapor, ozone, and aerosol data derived from MODIS [47]. This provides reliable and consistent SR scenes over PlanetScope's varied constellation of satellites. However, some limitations remain, including the effects of haze and thin cirrus clouds in each image. In fact, of the majority of the 36 images were cloud-free, while four images had cloud cover (20-50%). However, we confirmed that the degree of cloud cover did not significantly affect the performance of the U-Net model. This area also has complex optical properties that may affect the accuracy of atmospheric correction. Despite these limitations, the U-Net model showed reliable results for red tide detection. Therefore, we believe that the U-Net model will also show reliable results for other red tide events, using 36 images with varying atmospheric conditions and sea environments, even if only for nine consecutive days.