## 1 Introduction
On the other hand, Landsat missions, especially Landsat 8 and 9, offer a moderate spatial resolution (30 m) while providing unmatched temporal depth, with an archive extending back to 1972 and a revisit time of 16 days. The long historical continuity of Landsat data is essential for analyzing multi-decadal shoreline change, while Sentinel-2 enhances spatial detail and short-term variability. Various methods have been employed to fuse the datasets, including pixel-level stacking, temporal harmonization, and machine learning-based fusion. The synergetic action results in surpassing cloudy periods, filling gaps, cross-validating the results, and enhancing spatiotemporal resolutions, as studies have shown, for instance, the improvement of shoreline delineation accuracy and tracing seasonal trends in short term and long-term monitoring periods [17]. The open-access data policy that facilitates both missions is cost-effective and scalable, and their datasets offer replicable shoreline monitoring across diverse and harsh coastal environments [18]. The need for further improvement in coastal monitoring has been progressed and enhanced by integrating satellite data as core technologies with Geographic Information Systems (GIS), Unmanned Aerial Vehicles (UAVs), and Artificial Intelligence (AI). GIS is a powerful means of carrying out advanced spatial analysis. It allows researchers to combine satellite images with many environmental and human aspects. In this way, they can obtain a detailed, three-dimensional vision of essential problems such as shoreline variability, sediment movements, and erosive pattern dynamics [14]. Tools such as the Digital Shoreline Analysis System (DSAS)--developed by the USGS--extend GIS functionality to quantify shoreline change over time [19]. DSA provides essential shoreline change metrics like EPR (End Point Rate), NSM (Net Shoreline Movement), and LRR (Linear Regression Rate) and can be integrated with machine learning algorithms to process large volumes of satellite data efficiently. AI has been posited to be a powerful tool in coastal monitoring, aiding shoreline segmentation, water delineation, erosion prediction modeling, and pattern recognition. Techniques such as Machine Learning (ML) and Deep Learning (DL) are increasingly applied in coastal erosion studies for prediction models and data analysis [20; 21]. For example, WaterNet, an advanced deep-learning model, has achieved over 99% accuracy in shoreline segmentation using satellite imagery [20]. UAVs complement satellite-based monitoring by capturing high-resolution multispectral imagery and generating 3D models of areas inaccessible by conventional means [22]. These UAV datasets are essential for the in situ validation of satellite results and to produce detailed Digital Elevation Models (DEMs), which improve erosion impact terrain assessments. Combined, these technologies form a multi-scale, integrated framework for dynamic shoreline monitoring [23]. Given the persistent effects of climate change and escalating human activity on coastlines, there is a pressing necessity to implement adaptive coastal management strategies based on resilience and technology integration. In changing coastal environments with rapid transformation rates, conventional approaches are not enough. Satellite-based monitoring coupled with UAVs, AI, and GIS is a feasible and scalable approach to simultaneous high-frequency and high-accuracy shoreline monitoring. This review discusses the effectiveness of missions on Sentinel and Landsat and their integration with state-of-the-art tools in support of multi-scale coastal erosion tracking and resilience planning. This review carefully examines literature from 2021 onward to highlight recent advancements in Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAV), and high-frequency satellite observations enabled by Sentinel-2 and Landsat 8/9. Although it is not all-encompassing, this focus ensures relevance to the emerging tools and methodologies utilized in shoreline monitoring.