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S2Coast-2023: The first global 10-meter resolution coastline dataset derived from enhanced Sentinel-2 composite imagery using Google Earth Engine

  • Yuanqiang Duan
  • , Arturo Sanchez-Azofeifa
  • , Chunpeng Chen*
  • , Bo Tian*
  • , Xing Li
  • , Dhritiraj Sengupta
  • , Yunxuan Zhou
  • *Corresponding author for this work
  • East China Normal University
  • University of Alberta
  • Aerospace Information Technology University
  • Hong Kong Polytechnic University
  • Institute of Eco-Chongming (IEC)
  • Jiangsu Normal University
  • Plymouth Marine Laboratory

Research output: Contribution to journalArticlepeer-review

Abstract

Coastlines serve as dynamic interfaces between terrestrial and marine ecosystems. While advances in satellite remote sensing have promoted coastline monitoring, no comprehensive global coastline dataset derived from Sentinel-2 imagery has been produced, despite its 10-m resolution and frequent revisit capabilities. To address this, we propose S2Coast, a knowledge-based framework based on the Google Earth Engine (GEE) platform, designed to automatically detect the unified High Water Line (HWL) from annually composited Sentinel-2 imagery, termed HWLSentinel-2. This method integrates multi-temporal observational information, spectral characteristics, and spatial features to delineate the stable extent of high seawater submergence captured in cloud-free satellite images over a year. The boundaries in the resultant “Land-Water” binarization image represent HWLSentinel-2, derived through integrated threshold segmentation for three decision layers. Subsequently, raster-to-vector conversion and optimization steps were performed. Following the sequential execution of 12,275 sub-tasks, the resultant S2Coast-2023 dataset includes approximately 2.17 million kilometers of coastline for 2023, covering all continents and most islands larger than 100 m2, excluding Antarctica and remote polar islands. Global validation based on 1146 samples demonstrates that the developed tool, S2Coast, exhibits robust stability and universality, with average 88 % of sampled coastline segments falling within a 10-m buffer when comparing repeatedly generated coastlines from consecutive years (2021 to 2023). For positional accuracy, using 532 coastline samples with very high resolution image-based OpenStreetMap coastlines as reference data reveal an average positional deviation of −1.10 m (95 % CI: −2.06 to −0.15 m) and an average root mean square error (RMSE) of 17.40 m (95 % CI: 16.23 to 18.65 m). As the first global coastline dataset with 10-m resolution and a unified coastline indicator, it will serve as a crucial foundational resource for future coastal research.

Original languageEnglish
Article number115186
JournalRemote Sensing of Environment
Volume334
DOIs
StatePublished - 1 Mar 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 14 - Life Below Water
    SDG 14 Life Below Water
  2. SDG 15 - Life on Land
    SDG 15 Life on Land

Keywords

  • Global coastline mapping
  • Google earth engine (GEE)
  • Imagery compositing
  • Knowledge-based classification
  • Multi-feature fusion
  • Senitinel-2 imagery

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