A method for mapping strontium isotope ratio (87Sr/86Sr) variations in bedrock and water has been recently developed for use in the interpretation of 87Sr/86Sr datasets for provenance studies. The mapping process adopted the simplifying assumption that strontium (Sr) comes exclusively from weathering of the underlying bedrock. The scope of this bedrock-only mapping method is thus limited to systems where the contributions of other sources of Sr are minimal. In this paper, we build on this 87Sr/86Sr mapping method by developing a mixing model of Sr fluxes from multiple sources to the bioavailable Sr pool. The new multiple source model includes: (1) quantitative calculations of Sr fluxes from bedrock weathering using an empirical rock weathering model; and (2) addition of sub-models calculating the contribution of Sr fluxes from atmospheric aerosols based on outputs from global climate model simulations. We compared the performance of the new multiple source model and the bedrock-only mapping method in predicting observed values from two datasets of bioavailable 87Sr/86Sr from the circum-Caribbean region (Antilles and Mesoamerica). Although the bedrock-only method performs relatively well in Mesoamerica (n = 99, MAE = 0.00011, RMSE = 0.00073), its prediction accuracy is lower for the Antillean dataset (n = 287, MAE = 0.0021, RMSE = 0.0027). In comparison, the new multiple source model, which accounts for the deposition of sea salt and mineral dust aerosols, performs comparably well in predicting the observed 87Sr/86Sr values in both datasets (MAE = 0.00040, RMSE = 0.00087 and MAE = 0.00014, RMSE = 0.0010). This study underscores the potential of using process-oriented spatial modeling to improve the predictive power of Sr isoscapes over large spatial scales and to refine sampling strategies and bioavailable Sr dataset interpretations for provenance studies.