# Google Earth Engine for Monitoring Marine Mucilage: Izmit Bay in Spring Bay in Spring 2021
## 5 Discussion  
The literature review revealed that almost all indices related to water were designed either to delineate surface water bodies, such as rivers, lakes, and reservoirs from satellite images (e.g., [51; 52; 53; 54]), or to determine the quality of the water in lakes or rivers (e.g., [55; 56]). Except for the MI index, no index was proposed to specifically extract areas covered with mucilage formations. The results of this study revealed that the performance of MI, originally developed for MODIS imagery, was inferior, especially when compared to the results of AFAI. Since the spectral reflectances of algal blooms and mucilage are similar in green bands, water indices using green bands fail to discriminate between mucilage and algal blooms. This can cause a major problem since mucilage occurs in water conditions that are suitable for algal blooms. In some cases, it is reported that they follow each other in their occurrences. Figure 8: Wind speed and direction information for Izmit Bay between 17 May 2021 and 31 May 2021 (source: www.meteoblue.com, accessed on 15 June 2022). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**AFAI**} & \multicolumn{2}{c}{**MI**} & \multicolumn{2}{c}{**NDTI**} & \multicolumn{2}{c}{**NDWI**} & \multicolumn{2}{c}{**NDWI\({}_{\text{Gao}}\)**} \\ \cline{2-11}
**Image** & \begin{tabular}{c} **Area** \\ **(km\({}^{2}\))** \\ \end{tabular} & \begin{tabular}{c} **Ratio** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Area** \\ **(km\({}^{2}\))** \\ \end{tabular} & \begin{tabular}{c} **Ratio** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Area** \\ **(km\({}^{2}\))** \\ \end{tabular} & \begin{tabular}{c} **Ratio** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Area** \\ **(km\({}^{2}\))** \\ \end{tabular} & \begin{tabular}{c} **Ratio** \\ **(km\({}^{2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Area** \\ **(km\({}^{2}\))** \\ \end{tabular} &
\begin{tabular}{c} **Ratio** \\ **(\%)** \\ \end{tabular} \\ \hline
14 May & 6.05 & 2.11 & 6.39 & 2.22 & 5.66 & 1.97 & 8.86 & 3.08 & 4.82 & 1.68 \\
19 May & 8.45 & 2.94 & 10.78 & 3.75 & 11.21 & 3.90 & 11.71 & 4.08 & 10.43 & 3.63 \\
24 May & 32.12 & 11.18 & 34.93 & 12.75 & 36.07 & 12.55 & 39.22 & 13.65 & 25.27 & 8.79 \\
13 June * & 21.99 & 7.65 & 21.92 & 7.63 & 21.75 & 7.57 & 22.30 & 7.76 & 13.03 & 4.53 \\
13 July & 2.45 & 0.85 & 3.72 & 1.30 & 5.08 & 1.77 & 3.20 & 1.11 & 2.13 & 0.74 \\
18 July & 2.06 & 0.72 & 3.32 & 1.16 & 4.76 & 1.66 & 2.78 & 0.97 & 1.95 & 0.68 \\
28 July & 6.06 & 2.11 & 8.53 & 2.97 & 10.63 & 3.70 & 7.58 & 2.64 & 4.41 & 1.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mucilage-covered areas estimated from index maps. * Note that all images except for the image taken on 13 June were cloud-free images. Finding an optimal threshold is a crucial step to identify algal blooms in clear water bodies. In general, visual interpretation has been usually conducted in many scenarios [46]. Similarly, the major problem encountered was determining the threshold level to decide on high-density mucilage in the determination of mucilage-covered water surfaces using water indices. Although a fixed threshold level of 0.5 was set to estimate mucilage aggregates on the water surface, it is best to design a daily in situ measurement on the study area to calibrate the optimal threshold value. As it was noticed that each index extracts different features on the water surface, the threshold optimization or determination process must be performed for each method separately. Whilst [57] investigated the NDWIs to determine the best performing index and to establish appropriate thresholds for clearly identifying water features, Ref. [46] suggested different threshold levels for Landsat (0.01-0.02) and MODIS (0.05-0.12) in the application of AFAI instead of a single threshold on all images.