Functional Data Analysis (FDA) has emerged as a powerful framework for datasets that exhibit continuous variation over specified intervals. Unlike traditional methods, FDA treats data as functions, providing a more comprehensive understanding of their behavior and variability. This approach is particularly beneficial for exploring observations collected over time or space. The Spatial Functional Data Analysis (SFDA) incorporates spatial locations as an additional dimension. This integration enhances the ability to model complex associations between curves in the functional domain. Despite progress in analyzing multivariate spatial data, limited attention has been given to SFDA. This study addresses this gap by proposing a Bayesian Gaussian functional approach based on B-splines. The proposal also addresses challenges posed by irregularly spaced observations in the functional domain, a feature rarely considered in the literature. A simulation study is developed to evaluate performance. Finally, results are explored using a relatively novel real dataset from monitoring stations of temperature in Mexico City.