This research focuses on how to perform task offloading in LEO satellites. When a task arrives at an LEO, LEO can process this task itself or offload it to another LEO. This decision is made by considering several constraints like latency, energy, and queue. Task offloading requires LEO satellites to cooperate and make decisions based on local information, which aligns with the priority research area of Trusted Autonomous Satellite Operations. Also, to enlarge the capacity of the satellite at the same time, we consider the implementation of massive MIMO in the current system. Due to the high computational complexity of using a model-based approach to solve task offloading problems, we propose using a deep reinforcement learning-based method that can directly provide actions by interacting with the environment. To gain knowledge of the environment, we proposed a GCN-based scheme to learn the channel state information, which aligns with the subtopic of Explainable AI for Satellite Operations. LEO can provide global coverage, high-throughput, low-cost service to remote users. As for in remote area, satellite communication is the only low-cost way of communication. By increasing the capacity and computation speed of LEO, intelligent agriculture in vast remote area as well as disaster prediction and early warning can be made possible by LEO communications. Therefore, this research is beneficial to many important areas of application.