Coded apertures with random patterns are extensively used in compressive spectral imagers to sample the incident scene in the image plane. Random samplings, however, are inadequate to capture the structural characteristics of the underlying signal due to the sparsity and structure nature of sensing matrices in spectral imagers. This paper proposes a new approach for super-resolution compressive spectral imaging via adaptive coding. In this method, coded apertures are optimally designed based on a two-tone adaptive compressive sensing (CS) framework to improve the reconstruction resolution and accuracy of the hyperspectral imager. A liquid crystal tunable filter (LCTF) is used to scan the incident scene in the spectral domain to successively select different spectral channels. The output of the LCTF is modulated by the adaptive coded aperture patterns and then projected onto a low-resolution detector array. The coded aperture patterns are implemented by a digital micromirror device (DMD) with higher resolution than that of the detector. Due to the strong correlation across the spectra, the recovered images from previous spectral channels can be used as a priori information to design the adaptive coded apertures for sensing subsequent spectral channels. In particular, the coded apertures are constructed from the a priori spectral images via a two-tone hard thresholding operation that respectively extracts the structural characteristics of bright and dark regions in the underlying scenes. Super-resolution image reconstruction within a spectral channel can be recovered from a few snapshots of low-resolution measurements. Since no additional side information of the spectral scene is needed, the proposed method does not increase the system complexity. Based on the mutual-coherence criterion, the proposed adaptive CS framework is proved theoretically to promote the sensing efficiency of the spectral images. Simulations and experiments are provided to demonstrate and assess the proposed adaptive coding method. Finally, the underlying concepts are extended to a multi-channel method to compress the hyperspectral data cube in the spatial and spectral domains simultaneously.