# Google Earth Engine for Monitoring Marine Mucilage: Izmit Bay in Spring Bay in Spring 2021
## 6 Conclusions  
In this study, five popular water indices were employed to extract non-water items (i.e., mucilage aggregates) to unroll the extent of the natural disaster of the extensive mucilage outbreak witnessed in spring 2021 in the Sea of Marmara. The results of this study revealed some important conclusions about the effectiveness of water indices for mucilage mapping and GEE performance in terms of coding and processing performance. Firstly, differences in the thematic maps for the detection of mucilage on the GEE platform showed varying performances of the considered water indices. Among the considered water indices, the AFAI index was superior, followed by the NDTI and MI indices. Performances of NDWI and NDWI\({}_{\text{Gao}}\) indices were inferior as they detected more features in the water, particularly the existence of algal blooms. It should be stated that the NDWI index detected more pixels as mucilage than the others. Secondly, temporal variation of mucilage in Izmit Bay was also analyzed through areal estimations. According to the findings, after starting to bloom on 14 May, the mucilage event reached its peak point on 24 May, then started to disappear day by day. Considering that the 13 June image included two sections with clouds and shadows, the mucilage-covered water surface was about 15 km\({}^{2}\) (about 5 km\({}^{2}\) was disregarded). Furthermore, mucilage formations covered about 2 km\({}^{2}\) on 13 and 18 July. Although mucilage slightly increased on the image taken on 28 July, it was not detected in the following images, that is, no mucilage was detected afterward. Another finding in this study is that mucilage and algal blooms show higher reflectance in the green band compared to blue and red bands due to the chlorophyll concentration. This could be the main reason that the available water indices considering green bands in their formulas detect areas covered with algae concentrations as mucilage formations. Because of this, thematic maps of AFAI were the best ones representing the mucilage-covered areas and to some extent distinguishing areas covered with algal blooms. On the other hand, generated mucilage maps showed variation in the amount of mucilage aggregates on the sea surface, which validates the high sensitivity of phytoplankton to atmospheric and environmental conditions. With the developed JavaScript code, mucilage aggregates on the sea surface together with other objects (algal bloom, platform, etc.) can be extracted in a very short time in the cloud environment of the GEE without downloading any large-sized imagery. It should be also pointed out that the basic level of hardware is sufficient to perform the operations and run the code, and no remote sensing software is required for pre-processing the imagery or applying the indices and other estimations and reporting procedures. It provides the opportunity to work in different computer environments at any time. This study introduces a novel methodology including all steps for extracting mucilage-covered areas from Sentinel-2 imagery. The written code can be easily adapted to produce the thematic maps of other water indices and conduct other image processing tools or methods. The analysis of Sentinel-2 images with water indices on the GEE cloud computing platform can offer new opportunities to users at different experience levels for providing cost-effective and timely production of mucilage maps. The GEE platform provided convenience at all stages of the application with its ready-to-use tools and flexibility in developing customized codes. The results of this study convey the weakness of the considered water indices for mapping mucilage-covered areas as they did not coincide with each other, nor with the results of [60], who employed image classification with machine learning algorithms.