# Multi-site assessment of microtidal wave-dominated active beach state and morpho-sedimentary parameters using optical satellite imagery

## TL;DR

This paper introduces a new satellite-based method to assess beach states and their morphological properties across many locations, enabling large-scale coastal monitoring.

## Contribution

A novel remotely observable metric, X_b, is introduced for classifying beach states and linking them to sediment and morphology parameters using satellite imagery.

## Key findings

- The metric X_b successfully classifies beach states into five active states with transferable thresholds.
- X_b reveals temporal beach state evolution, including occurrence, residence time, and transitions with seasonal patterns.
- X_b correlates with beach-face slope and sediment grain size, enabling satellite-based coastal monitoring at large scales.

## Abstract

Traditionally, beach states are defined from visual observations, in-situ measurements and/or video imagery, which limits their application to a handful of well-instrumented sites. In this work, we propose a different approach by focusing on a remotely observable quantity: the cross-shore distance between the offshore wave-breaking and the shoreline position, denoted \documentclass[12pt]{minimal}
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\begin{document}$$X_b$$\end{document}. This metric defines the active beach state, capturing where waves dissipate energy relative to the underlying morphology. Using 10 years of Sentinel-2 imagery, \documentclass[12pt]{minimal}
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\begin{document}$$X_b$$\end{document} is evaluated across 30 wave-dominated microtidal sandy beaches spanning reflective to fully dissipative conditions. The metric reproduces the structure of classical beach state frameworks and enables classification into five active states (R, LTT, TBR/RBB, LBT, and D) using transferable thresholds. \documentclass[12pt]{minimal}
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\begin{document}$$X_b$$\end{document} is continuous, thus it also reveals how beach state evolve through time, allowing quantification of state occurrence, residence time, and transitions, with seasonal variability consistent with independent classifications at well-studied sites. Furthermore, using empirical relationships, we demonstrate that \documentclass[12pt]{minimal}
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\begin{document}$$X_b$$\end{document} carries first-order information about beach-face slope (\documentclass[12pt]{minimal}
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\begin{document}$$\tan \beta$$\end{document}) and sediment grain size (\documentclass[12pt]{minimal}
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\begin{document}$$D_{50}$$\end{document}), opening a pathway toward systematic satellite-based monitoring of coastal morphodynamics at regional to global scales.