The trend of building larger and more complex imaging satellite constellations leads to the challenge in managing multiple acquisition requests of the Earth surface. Optimally planning these acquisitions is an intractable optimization problem, and heuristic algorithms are used today for finding sub-optimal solutions. Recently, quantum algorithms have been considered for this purpose, due to the potential breakthroughs that they can bring in optimization, expecting either a speedup or an increase in the solution quality. Hybrid quantum-classical methods have been considered as a short-term solution for taking advantage of small quantum machines. In this paper, we propose reverse quantum annealing as a method for improving the acquisition plan obtained by a classical optimizer. We investigate the benefits of the method with different annealing schedules and different problem sizes. The obtained results provide guidelines on designing a larger hybrid quantum-classical framework based on reverse quantum annealing for this application.