Geospatial datacubes are the database backend of novel types of spatiotemporal decision-support systems employed in large organizations. These datacubes extend the datacube concept underlying the field of Business Intelligence (BI) into the realm of geospatial decision-support and geographic knowledge discovery. The interoperability between geospatial datacubes facilitates the reuse of their content. Such interoperability, however, faces risks of data misinterpretation related to the heterogeneity of geospatial datacubes. Although the interoperability of transactional databases has been the subject of several research works, no research dealing with the interoperability of geospatial datacubes exists. In this paper, the authors support the semantic interoperability between geospatial datacubes and propose a categorization of semantic heterogeneity problems that may occur in geospatial datacubes. Additionally, the authors propose an approach to deal with the related risks of data misinterpretation, which consists of evaluating the fitness-for-use of datacubes models, and a general framework that facilitates making appropriate decisions about such risks. The framework is based on a hierarchical top-down structure going from the most general level to the most detailed level, showing the usefulness of the proposed approach in environmental applications.