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We propose a Bayesian hierarchical model to address the challenge of spatialmisalignment in spatio-temporal data obtained from in situ and satellitesources. The model is fit using the INLA-SPDE approach, which providesefficient computation. Our methodology combines the different data sources in a"fusion"" model via the construction of projection matrices in both spatial andtemporal domains. Through simulation studies, we demonstrate that the fusionmodel has superior performance in prediction accuracy across space and timecompared to standalone "in situ" and "satellite" models based on only in situor satellite data, respectively. The fusion model also generally outperformsthe standalone models in terms of parameter inference. Such a modeling approachis motivated by environmental problems, and our specific focus is on theanalysis and prediction of harmful algae bloom (HAB) events, where theconvention is to conduct separate analyses based on either in situ samples orsatellite images. A real data analysis shows that the proposed model is anecessary step towards a unified characterization of bloom dynamics andidentifying the key drivers of HAB events.

Spatio-temporal data fusion for the analysis of in situ and remote sensing data using the INLA-SPDE approach