Normalized difference vegetation index (NDVI) is widely utilized to examine vegetation coverage and estimate crop yield. To obtain a high-resolution (HR) NDVI, fusion techniques, which first generates a HR multispectral (MS) image by fusing a low-resolution (LR) MS image and a HR panchromatic image, and then calculates the HR NDVI based on the fused HR MS image, are utilized in previous studies. A HR vegetation index calculated on the basis of HR panchromatic image could provide HR spatial resolution, and this vegetation index has a spatial structure that is similar to that of NDVI. Therefore, this similarity is investigated to construct a novel method called FusionNDVI to improve the fusion performance in this study. The fusion problem is formulated to minimize a least square fitting error term and a nonlocal gradient sparsity regularization term. The fitting term is used to limit the difference between the fused HR NDVI and the LR NDVI, whereas the regularizer enforces a similar nonlocal spatial structure in the fused NDVI and the HR vegetation index. An efficient solving algorithm based on the augmented Lagrangian method of multipliers is derived. The superiority of the proposed FusionNDVI method over the state-of-the-art ones is verified via simulations.