Earth Observation Satellites (EOS) are critical for acquiring space-based information, supporting diverse applications from environmental monitoring to urban planning. The increasing demand for satellite imaging services necessitates efficient planning for complex and heterogeneous observation tasks, including point, area, and especially challenging moving targets. Traditional mission planning approaches, often relying on "single objective, single model, single algorithm" paradigms with meta-tasks and time windows, struggle to integrate these diverse requirements and face scalability issues with increasing task complexity. To address these limitations, this paper proposes a novel Grid Heat-driven Imaging Satellite Complex Task Planning Method. We introduce a Grid Heat-based Requirements Observation Model (GHROM) that unifies point, area, and moving targets into a single, time-variant geospatial heatmap, where grid cell "heat" represents observation priority. For this grid-based representation, we design a Satellite Discrete State Task Planning Model that transforms the problem into finding the highest heat gain path within a Directed Acyclic Graph (DAG) of discrete satellite states, where attitude transition constraints are embedded as graph edges. Based on this model, we develop a Grid Heat-Driven Dynamic Programming Algorithm (GMDPA). Extensive simulation experiments demonstrate the effectiveness and significant advantages of GMDPA, particularly in large-scale scenarios, showcasing its superior performance in terms of solution quality and computational efficiency compared to traditional heuristic, metaheuristic, and genetic algorithms.