Submodular Functions are a special class of Set Functions, which generalize several Information Theoretic quantities such as Entropy and Mutual Information [1]. Submodular functions have subgradients and subdifferentials [2] and admit polynomial time algorithms for minimization, both of which are fundamental characteristics of convex functions. Submodular functions also show signs similar to concavity. Submodular function maximization, though NP hard, admits constant factor approximation guarantees and concave functions composed with modular functions are submodular. In this paper, we try to provide a more complete picture on the relationship between submodularity with concavity. We characterize the superdifferentials and polyhedra associated with upper bounds and provide optimality conditions for submodular maximization using the superdifferentials.