Subpixel mapping (SPM) is a technique to predict spatial locations of land cover classes within mixed pixels in remotely sensed imagery. The two-step approach first estimates fraction images by spectral unmixing and then inputs fraction images into an SPM algorithm to generate the final subpixel land cover map. A shortcoming of this approach is that the information about the credibility of fraction images is not considered. In this letter, we proposed a general framework of SPM which is directly applied to original coarse resolution remotely sensed imagery by integrating spectral and spatial information. Based on the proposed framework, the linear unmixing model and the maximal spatial dependence model were combined to construct a novel SPM model aiming to minimize the least squares error of spectral signature and make the subpixel land cover map spatially smooth, simultaneously. By applying to an Airborne Visible/Infrared Imaging Spectrometer hyperspectral image, the proposed model was evaluated both visually and quantitatively by comparing it with hard classification and the two-step SPM approach. The results showed that the regularization parameter, which balances the influence of spectral and spatial terms, plays an important role on the solution. The L-curve approach was a reasonable method to select the regularization parameter, with which an increased accuracy of the proposed model was obtained.