Supporting Open Science through Cloud-Based and Interoperable APIs for Data Access and Visualization: The Multi-Mission Algorithm and Analysis Platform (MAAP) Data System

The aboveground biomass research community utilizes a diverse set of heterogeneous data for developing accurate estimation models. Data from field projects and airborne campaigns provide valuable in-situ data for calibration and validation while satellite remote sensing observations are required to create global biomass estimates. Furthermore, the computational requirement for global estimation requires high performance or scalable, dynamic infrastructures. Innovative technological solutions lowering the barrier from data to scientific results are needed as both the volume of available data for refining biomass models and the scientific need for rapid processing increases. The Joint ESA-NASA Multi-Mission Algorithm and Analysis Platforms (MAAP) is one such cloud-based solution that offers solutions to efficiently discovering, accessing, and processing at scale disparate data. This presentation will demonstrate the key components of the MAAP data system including the accessibility of data through interoperable APIs as well as subsetting and visualization of cloud-optimized data including lidar point clouds and raster datasets. The data team strives to provide a high level of service for all datasets. Datasets must be processed and curated into these additional layers of service for subsetted data access and scalable visualization. The data team prioritizes this curation for datasets which have been identified as the highest value (such as the ICESat-2 ATL08 point cloud dataset and the SRTM Digital Elevation Model) and or the most challenging to use by the MAAP user working group. This presentation will discuss the curation and management efforts required to support these services and user engagement efforts promoting open science to lowering the barrier to analysis.