Nonnegative matrix factorization (NMF) has been widely used in hyperspectral unmixing (HU) in recent years since it can simultaneously estimate endmember and abundance matrices. However, most existing NMF unmixing methods only impose geometric or statistical unilateral prior on endmember or abundance matrix, meanwhile ignore the synergistic effect of both priors. To overcome this problem, in this paper, we propose a novel geometric and statistical hybrid method, called the weighted Ly₂ sparse total variation regularized and volume prior constrained NMF (wL_1/2TVVC- NMF).The proposed approach integrates the endmember volume, abundance sparsity and piecewise smoothness into the unified NMF unmixing framework, and imposes the two kinds of prior information to the matrix factors simultaneously. It not only captures the sparsity and smoothness of abundance map, but also enhances the endmember identification accuracy, and improves the stability of results and noise robustness. The optimization model is simply solved by the variable splitting and augmented Lagrangian algorithm. Several experiments were conducted to demonstrate the performance of proposed method.