Kristen D. Splinter Jesus Palomar-Vazquez Josep E. Pardo-Pascual Jaime Almonacid-Caballer Carlos Cabezas-Rabadan Etienne Kras Arjen Luijendijk Floris Kalkoen Luis P. Almeida Daniel Pais Antonio Henrique da Fontoura Klein Yongjing Mao Daniel Harris Bruno Castelle Daniel D. Buscombe Sean Vitousek Kilian Vos 2023 <div id="Abs1-section" class="c-article-section"><div id="Abs1-content" class="c-article-section__content"><p>Satellite remote sensing is becoming a widely used monitoring technique in coastal sciences. Yet, no benchmarking studies exist that compare the performance of popular satellite-derived shoreline mapping algorithms against standardized sets of inputs and validation data. Here we present a new benchmarking framework to evaluate the accuracy of shoreline change observations extracted from publicly available satellite imagery (Landsat and Sentinel-2). Accuracy and precision of five established shoreline mapping algorithms are evaluated at four sandy beaches with varying geologic and oceanographic conditions. Comparisons against long-term in situ beach surveys reveal that all algorithms provide horizontal accuracy on the order of 10 m at microtidal sites. However, accuracy deteriorates as the tidal range increases, to more than 20 m for a high-energy macrotidal beach (Truc Vert, France) with complex foreshore morphology. The goal of this open-source, collaborative benchmarking framework is to identify areas of improvement for present algorithms, while providing a stepping stone for testing future developments, and ensuring reproducibility of methods across various research groups and applications.</p></div></div> application/pdf 10.1038/s43247-023-01001-2 en Nature Benchmarking satellite-derived shoreline mapping algorithms article