As Earth-observing satellite constellations grow in size and capability, distributed onboard control offers a pathway to novel responses and time-sensitive measurements. However, deploying autonomy to satellites requires efficient computation and communication. This work addresses the challenge of scheduling observations for hundreds of satellites in a dynamic, large-scale problem with millions of variables. We present the dynamic multi-satellite constellation observation scheduling problem (DCOSP), a new formulation of dynamic distributed constraint optimization problems (DDCOP) that models integrated scheduling and execution. DCOSP features a novel optimality condition, for which we construct an exact omniscient offline algorithm. Motivated by the strong resource constraints of onboard satellite operations, we introduce a framework to incorporate metareasoning in DDCOPs that controls when agents expend resources to recompute solutions. In addition, we present the dynamic incremental neighborhood stochastic search (D-NSS) algorithm, an incomplete online decomposition-based DDCOP algorithm that repairs localized sub-problems in response to dynamic events. We demonstrate in realistic simulations that D-NSS converges to near-optimal solutions, outperforming standard DDCOP baselines in solution quality, computation time, and message volume, while our metareasoning framework successfully balances resource conservation with utility. As part of the NASA FAME mission, this work lays the foundation for the largest in-space demonstration of distributed multi-agent AI to date.