# Google Earth Engine for Monitoring Marine Mucilage: Izmit Bay in Spring Bay in Spring 2021
## 5 Discussion
For the delineation of mucilage-covered areas, validation of the results is a major concern since floating gelatinous materials move fast on the sea surface, making the in situ collection of ground reference data an impossible task. To be exact, the problem is not similar to the ground reference data collection for land use and land cover classification (e.g., for vegetation types). The analyst is faced with either measuring the locations of high mucilage covered areas accurately (e.g., using a GPS) on the sea surface at the same time as the satellite acquires the image or collecting mucilage-covered areas from the satellite image using the spectral reflectance information when it is available through spectral signature analysis of pixels. The first option is not likely to be feasible for a study analyzing dynamic or moving objects on the water surface. For such cases, the best option is to collect pixels as samples using spectral signatures of the features when a spectral library is available. In the spectral library approach, fieldwork is not required at the time of image acquisition since preliminary spectral information is available [58]. As underlined by [59], knowing the spectral characteristics of a cover or species allows the use of different classification methods, based on the identification of those specific characteristics previously identified in the signatures. The possibility of identifying these characteristics is maximized when detailed spectral information is available. For instance, supervised image classification for detecting mucilage-covered areas was conducted by [60] for Izmit Bay by collecting sample pixels using spectral signatures of clear water and mucilage. Their results showed that whilst mucilage covered an area of 15.13 km\({}^{2}\) on 19 May, it covered an area of 20.13 km\({}^{2}\) on 24 May. On the other hand, Ref. [61] employed supervised classification on the GEE platform for mucilage mapping in the Sea of Marmara using visually selected water and mucilage pixels in that they used histogram analysis for the collected samples. It was reported that thematic maps with very high accuracy (over 98%) were produced. Since the satellite programs have different visiting frequencies, it is difficult to find cloud-free images to study the suddenly appearing natural phenomenon of mucilage bloom. As it is well-known that mucilage events occur in spring or fall terms with the fast-changing weather temperatures, it is likely to have cloudy or rainy days that hinder the usability of optical satellite images. Therefore, to eliminate the effects of clouds and their shadows from the images, either a preprocessing stage or a new water index suppressing the disruptive effects is required to produce reliable and valid thematic maps and areal estimations. In the current study, the performance of the AFAI index was more successful than the other indices for the areas covered with clouds and shadows. In fact, the thematic maps produced with the AFAI index revealed its effectiveness in disregarding the shadow effects on the water pixels. After visual interpretation of the produced water index maps, the AFAI index produced the clearest maps of mucilage-covered areas, followed by the MI index, which is developed for the purpose of mucilage mapping. NDTI index produced similar results to the MI index. However, the NDWI index was not found to be effective for extracting mucilage formations as it was mainly identifying other features of water, especially algal bloom concentrations. This finding suggests the development of more sophisticated indices or analysis tools for mucilage mapping.