The geospatial Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC; formerly GeoSPEC) pioneers an on-demand science data processing system (SDPS) producing user-customized Level 1 calibrated radiance to Level 3+ data products in anticipation for the 2017-2027 Earth Decadal Survey prioritized spaceborne global imaging spectrometer to advance the study of Surface Biology and Geology (SBG). SBG data volumes (~20 TB/day) of high dimensionality (>224 bands) would be infeasible to download and the breadth of applications of the data across dozens of disciplines presents a need to evolve the traditional NASA SDPS. ImgSPEC streamlines processing data into key SBG observables that have demonstrated algorithms at local-to-regional scales and may vary locally. As such, a traditional, monolithic SDPS could not fully exploit the information in SBG measurements. To remove this barrier to use, ImgSPEC demonstrates an on-demand SDPS prototype that improves imaging spectroscopy data discovery, access, and utility enabling shared knowledge transfer from advanced imaging spectroscopy users to less experienced users such as decision makers and the general public. We test three use cases: 1) standard data processing workflows, 2) customized variants of standard workflows, and 3) algorithm development of new workflows. We create collaborative algorithm development environments that offer services typically restricted to NASA SDPSs such as data product provenance and bulk processing. We leverage existing NASA-funded information technologies such as the hybrid on-premise/ cloud science data system (HySDS), the Multi-mission Algorithm and Analysis Platform (MAAP), ECOSIS – a crowd-sourced spectral database, and ECOSML – a crowd-sourced model database. We demonstrate ImgSPEC on the Terrestrial Ecosystem use case processing through to foliar traits and fractional cover, thus aligning with driving thrusts for the SBG Science and Applications Communities.

The ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP) is a platform designed to meet the need of the international scientific community to collaborate on the generation and analysis of increasingly massive datasets from space-based, airborne, and field observations for aboveground terrestrial carbon dynamics. The MAAP is an open-source, cloud-based platform that distinguishes itself from other science platforms by being agnostic to any science disciplines and allows scientists to write, develop, and execute their own algorithms in shared workspaces that are tailored to their specific area of research. We present an overview of the capabilities of the Pilot implementation of MAAP. Users can explore and visualize ESA and NASA data that is ingested in the MAAP data catalog, develop and test algorithms to generate new datasets and act upon existing ones, launch matured algorithms as large-scale processing jobs, and analyze the results. This allows scientists to follow the entire scientific process within the platform, from the conception of a hypothesis to the creation of scientific results in a version-controlled and reproducible environment. Users can document their process, collaborate with other users, and share algorithms and datasets. We will conclude with an outlook on the features that the next phase of development will bring to the platform.