## 1 Introduction
Band ratios such as the Normalized Difference Water Index (NDWI), which exploits the high absorption of water in the near-infrared (NIR) wavelength, are commonly used to distinguish water from land in multispectral satellite images [32]. [PERSON] et al. (2019) [33] compared several methods to extract the waterline from water index images using different thresholding and line extraction methods. The best results were achieved using a combination of an 'optimal threshold' (e.g., Otsu) and sub-pixel waterline extraction based on a marching squares algorithm, which provided more accurate results than a traditional whole-pixel approach [33]. Similar results were also published by [PERSON] et al. (2012) [34] and [PERSON] et al. (2019) [35], revealing high accuracies using sub-pixel waterline extraction methods. When assessing coastline dynamics, the definition of a representative coastline position poses an additional challenge [8], as the position of the coastline is not only subject to long-term changes but also to short-term fluctuations, e.g., due to the tidal range [8; 15]. Previous satellite-based coastline detection studies either neglected the effect of tidal impact [36], created composites from all available images over a period of time to average out the effect of tidal variation [29], or utilized tidal modeling [37; 38; 39; 33] based on sea level data with the tidal data. The recent development of cloud-computing services, such as the freely accessible Google Earth Engine (GEE), allows for parallel computation of big analysis-ready datasets and has accelerated the potential of satellite-based large-scale time series analysis [40]. The studies of [PERSON] et al. (2018) [29] and [PERSON] et al. (2018) [22] presented global coastline change analysis based on Landsat data, exploiting the capacities of GEE. However, the existing global coastline datasets are limited to sandy beaches [29] and raster-based land loss and gain information [22] and are therefore only suitable to a limited extent for detailed local analyses. The coastal zone of Vietnam is especially vulnerable to coastline change with over 50% of the major cities, which contain 31% of the national population, being located on the coast [9]. Urbanization and the need for natural resources accelerate the pressure on the coastal zone [17]. At the same time, hazard risk information is fragmented and incomplete [26]. The studies that have previously addressed coastal erosion and accretion in Vietnam are regionally limited to the Mekong Delta [17; 19; 20; 21; 41], the Red River Delta [42; 43] or other local hotspots, such as Cua Dai Beach [44; 45]. Remote sensing, bathymetry, and field surveys have been used to discuss a variety of possible drivers of coastline change, such as natural erosion through waves and currents [45], upstream river dam construction [17], sand mining [19; 20], coastal engineering [44], sea level rise [26; 42; 46], land subsidence [47], and vegetation clearing [21; 41] in these areas. However, these findings are difficult to assemble into an overarching picture due to a lack of standardization and overlapping time periods. No complete national assessment of Vietnam's coastline change has been conducted to date. This research aims at developing a detailed nationwide analysis of annual coastline dynamics in Vietnam covering a period of 35 years (1986-2021). The analysis concentrates on: (i) Development of a GEE-based fully automated approach using only publicly and globally available datasets and tools; (ii) generation of annual coastline composites representing the mean-high water level; (iii) analysis of coastline dynamics along shore-perpendicular transects; and (iv) analysis of erosion and accretion hotspots, including a discussion on potential drivers of change at the most salient observed hotspots.