The Coded Aperture Snapshot Spectral Imaging system (CASSI) is a remarkable optical imaging architecture, which senses the spectral information of a three dimensional scene by using two-dimensional coded focal plane array (FPA) projections. The projections in CASSI are localized such that each measurement contains spectral information only from a specific spatial region of the data cube. Spatial resolution in CASSI is highly dependent on the resolution the FPA detector exhibits; hence, high-resolution images require high-resolution detectors that demand high costs. To overcome this problem, in this paper is proposed an optical model for spatial superresolution imaging called SR-CASSI. Spatial super-resolution is attained as an inverse problem from a set of low-resolution coded measurements by using a compressive sensing (CS) reconstruction algorithm. This model allows the reconstruction of spatially super-resolved hyper-spectral data cubes, where the spatial resolution is significantly enhanced. Simulation results show an improvement of up to 8 dB in PSNR when the proposed model is used.