Reaction-diffusion models are widely used to study spatially-extended chemical reaction systems. The input parameters on which these models are predicated are experimentally derived. In order to understand how the dynamics of a reaction-diffusion model are affected by changes in input parameters, efficient methods for computing parametric sensitivities are required. In this talk, we focus on compartment-based stochastic models of spatially-extended chemical reaction systems, which partition the computational domain into voxels. Parametric sensitivities are often calculated using Monte Carlo techniques that are typically computationally expensive; however, variance reduction techniques can decrease the number of Monte Carlo simulations required. By exploiting the characteristic dynamics of spatially-extended reaction networks, we are able to adapt existing finite difference schemes to robustly estimate parametric sensitivities in a spatially-extended network. Our methods are tested for functionality and reliability in a range of different scenarios.