Satellite-derived shorelines (SDSs) are increasingly used to monitor beach morphology worldwide, yet their application remains poorly validated in microtidal environments strongly influenced by atmospheric forcing. In this study, the performance of CoastSat and CoastSat.slope using nine years of in situ beach profiles from six sandy beaches in Buenos Aires (Argentina) was evaluated. The analysis compares alternative sea level forcings—including global tidal predictions (FES2022), a regional barotropic model with meteorological forcing (MSAS), and wave setup from reanalysis products—and evaluates the effect of using locally trained classifiers on shoreline detection. The results show that locally trained classifiers markedly reduced RMSE values, from 9–21 m with the default classifier to 7–12 m with the locally trained one, while the MSAS model consistently outperforms FES2022 for sea level corrections across all sites. CoastSat.slope provided effective slope estimates for tidal corrections but tended to overestimate values relative to field data. Sensitivity tests confirmed that overestimation has a smaller impact on water level correction than underestimation, explaining why validation metrics improved when using CS.slope-derived slopes. These findings translate into actionable guidelines: (i) prioritize regional sea level models when nontidal variability is large; (ii) apply wave setup corrections cautiously in microtidal coasts; and (iii) use locally trained classifiers in heterogeneous or urbanized beaches. Overall, this study demonstrates that with appropriate parameterization, CoastSat is a reliable tool for shoreline monitoring in atmospherically forced, microtidal coasts, and its methodological insights are transferable to other low-energy, data-scarce regions worldwide.
Billet et al. (Fri,) studied this question.
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