In this paper, the concept of "meta-metadata" is introduced as an abstraction of metadata as metadata is an abstraction of the data. The problem of finding coincident data from satellites is examined using metadata and metametadata. The meta-metadata approach is shown to be more generally applicable and supports a more heterogeneous system. Finally, the implementation of a prototype system for performing coincidence search is described.The challenges and opportunities for archives are evolving. The number and scale of archives are increasing, as is the interoperability of archives through the use of standards and Internet connectivity. Archivists are challenged because the demands on archives are increasing faster than the technology; however, there is great opportunity because of the large and diverse volume of data online. One way to reduce the challenge and to take advantage of the opportunity is through the introduction of meta-metadata. Metadata reduces the demands on an archive by providing information about archive holdings in a more compact form than is available through the data, and it may support the discovery of related data in an archive. Meta-metadata reduces the demands on archives by representing the metadata in a more compact form, and it may support the discovery of related data across multiple archives. The ability to search across multiple archives becomes valuable as the variety of data online allows combining data in ways not envisioned when the data were collected.The problem of finding coincident data from satellites can be exemplified by two questions: (1) When does a specific instrument onboard a specific spacecraft view a particular wheat field in Kansas?, and (2) When do two specific instruments on two spacecraft both view a particular wheat field in Kansas within half an hour of each other? The National Aeronautics and Space Administration's (NASA's) Earth Observing System (EOS) Data and Information System (EOSDIS) collects spacecraft data from multiple spacecraft and supports the interdisciplinary use of those data. In some cases, however, the metadata provided with the data does not facilitate coincidence searches. EOSDIS metadata for spacecraft data ignore the inherent relationship between the space and time dimensions of the data. For data granules with large temporal extent, ignoring the spacetime relationship leads to spatial metadata with little or no information content. The lack of information in the metadata leads to coincidence queries that are all hits or all misses. To perfom space-time coincidence queries on such metadata can be a resource-prohibitive endeavor because of the large numbers of false-positive data granule hits. Once the relevant data granules are found in the database by eliminating the false-positive hits, it is frequently beneficial to subset those data granules to obtain the data of interest only. To spatially subset data that is organized by time of data collection, space must be related to time.The natural way to relate space and time for spacecraft da...