For hyperspectral remotely sensed images,a subpixel mapping method of constrained spatial spectrum is proposed.Traditional subpixel mapping methods may not make full use of the rich spectral information of hyperspectral images due to straightforward using of unmixing results.The method based on constraint spatial-spectral subpixel mapping (CSSSM) is proposed,which explicitly combines the pixel abundance with the subpixel abundance through using subsampling,and derives the new subpixel abundance model by substituting the linear unmixing model.After adding sparsity and smoothness constraints to control the searching space of solutions,the subpixel abundance is obtained quickly.The reweighted 1-norm constraint is applied to the subpixel sparse abundance,and the weight is updated adaptively.Total variational （TV） regularization is used as a new constraint for a spatial priori of the subpixel abundance,and a multiplication iterative algorithm is used to search for the subpixel abundance.Finally,a winner-take-all strategy is used to determine the overall class categories.Experiments on two synthetic data sets show that the proposed method can improve the accuracy of subpixel mapping further.