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The Bayesian hierarchical model has the outstanding capacity to combine prior knowledge with observations, as well as to generate rich spatiotemporal patterns. However, this advanced method has limited use in remotely sensed lattice data applications due to the large computational burden and unreasonable statistical inferences based on statistical units with a fixed scale. This paper presents a multiscale spatial homogeneous subdivision method and develops a Bayesian space-time hierarchical model (BSTHM) for remotely sensed lattice data. This can solve the above-mentioned limitations by constructing multiscale spatial homogeneous units with good statistical properties. The quantitative criteria for subdivision are provided. The outcome of the BSTHM is not only more reasonable in theory but also much easier to interpret; meanwhile, the computational efficiency is also considerably improved. This novel …