Data store alternatives for the Multi-Mission Algorithm and Analysis Platform (MAAP)

Abstract:

In an era of continually advancing technologies, huge amounts of Earth observation data are being collected from a variety of sources or sensors with different types of formats and quality. As a result, storing, processing and sharing those are challenging as the data volume exponentially grows. The Multi-Mission Algorithm and Analysis Platform (MAAP), a cloud-based collaborative system between the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA), has been launched to support global terrestrial carbon dynamics research by bringing together relevant data, algorithms and computing capabilities in order to more easily share and process data. In the future, the MAAP will support several high data volume satellite missions including the NASA-ISRO SAR mission (NISAR) and ESA’s Biomass mission. These satellite data, in combination with other heterogeneous data collected from airborne and field campaigns, require new and innovative solutions for both managing and utilizing data. Therefore, improving the data store component in the MAAP is critical to meet those requirements. In the light of this goal, our study, first, provides an overview of data store implementations used by the Earth observation community, the techniques used to create these stores, and the systems in which those approaches are applied. Then, an evaluation of the various implementation options is presented leveraging MAAP’s criteria of identifying the most balanced data store solution for efficiently storing and accessing Earth observation data and for allowing efficient exploration and analysis with minimal additional user effort.