The acquisition of the spatio-temporal characteristics of a sound field over a large volume of space is experimentally challenging, as a large number of transducers is required to sample the sound field. Sound field reconstruction methods are a resourceful approach, as they enable the interpolation and extrapolation of the sound field from a limited number of observed data. In this study we examine the spatio-temporal and spatio-spectral reconstruction of the sound field in a room from distributed measurements of the sound pressure. Specifically, a variational Gaussian process regression model is formulated, using time-domain anisotropic kernels to reconstruct the direct sound and early reflections, and frequency-domain isotropic kernels for reconstructing the late reverberant field. The proposed methodology is compared experimentally to classical regression models based on plane wave decompositions, which are widely used in sound field reconstruction in enclosures due to their simplicity and accuracy.