## 4 Included Application Cases
The bands with 10 m and 20 m spatial resolution were included in the analysis. The authors analyzed the correlations between the selected bands and the data from the sensors. Their results indicate that bands 4 and 12 are the ones with the best correlation. A mathematical model to estimate soil moisture based on both bands was proposed and verified using images for other days. The proposed model's mean absolute error (MAE) was 8.4%. Another example is shown in [20], a study by [PERSON] et al., in which the authors proposed a tool for automated coastline extraction. In this case, the authors used the WorldView-2 multispectral imagery, with a single multispectral, 2 m spatial resolution and panchromatic, 0.5 m spatial resolution image combined with a stereo-pair-derived digital surface model. The images were acquired on 28 th October 2016 from the Iz-Rava island group. With the acquired images, indexes were calculated and, using established thresholds, the coastline was extracted. On the other hand, based on the stereo-pair-derived digital surface model, the coastline was extracted according to the elevation. The extracted coastlines were compared with other accurate methods such as UAVs with a centimeter-level accuracy reference. The average deviation of the coastline extraction was 0.73 m.  
We found more papers that use remote sensing in the marine area. Even though remote sensing is majorly applied onshore, mainly since light reflection in offshore areas is limited and most bands cannot provide useful information, there are still several examples of remote sensing in marine areas, such as [20]. [PERSON] et al. [21] used remote sensing to study a variable, chlorophyll-a concentrations, with high spatial variability in a large area. In this case, the selected satellite was the Landsat 8 OLI. The authors proposed the use of a machine learning approach called the gradient-boosting decision tree to predict the chlorophyll-a concentrations in coastal waters. Data from 2013 to 2020 were used. The data from the chlorophyll-a concentrations were obtained from 13 automatic monitoring stations. Bands 1 to 7 from the satellite were included in the analyses. The model with better performance has as input features B4, B3 + B4, B3, B1 \(-\) B4, B2 + B4, B1 + B4, and B2 \(-\) B4. The R2 of the model is higher than 0.77, and the MAE is below 1 ug/L. Another example of remote sensing applied in the coastal area is found in [22]. In this paper, [PERSON] et al. analyzed the effect of an extreme sea storm event on the Golf of Naples, Italy. They focused on the event of the 28 th of December, 2020, in which the coastal seawall, road edges, and touristic structures suffered damage due to the waves. Climatologic data from seven weather stations were included in their study, comprising onshore and offshore stations. Meteorological maps from EUMETSAT from the 26 th of December to 28 th of December were used in their study. Their results suggested that the sole option for having an integrated approach that has the correct base of information is to include all the physical and anthropic components of the coastal system. The application of remote sensing in urban areas can also provide useful tools and approaches. Remote sensing and GIS in the urban area can be applied to different problems, such as monitoring the variation in a specific land cover or even for planning where to build new structures. Regarding monitoring changes in an urban area, [PERSON] [23] evaluated the vegetation cover in the vicinity of archaeological sites. The authors selected Sentinel 1 and Sentinel 2 as image sources. Crowdsourced OpenStreetMap geodata supplemented the images from the satellites. The authors calculated the normalized difference vegetation index (NDVI) and the radar vegetation index (RVI) from the Sentinel images. The results of the NDVI, RVI, and their combination for the analyzed area were compared with the Google Earth red-green-blue free high-resolution optical images.