Abstract
Earth observation data are images collected by satellites orbiting the earth. The growing amount of new satellites and the continuation of established missions lead to a rapid increase of data with different specifications. The Copernicus Open Access Hub for example provides access to 10 PB of data. OpenEO (https://openeo.org/, a Horizon 2020 project) developed a unified API to foster interoperability and reproducibility across cloud services while reducing complexity for users. The openEO R client serves as an entry point for R users to efficiently interact with big earth observation data on cloud platforms from within the R language. The client is available as an R package (https://github.com/Open-EO/openeo-r-client) and enables users to create efficient and scalable workflows that can be processed by different cloud services and access resources that would otherwise be hard to access using R. This presentation showcases the potential of openEO and its R client to process and harmonize different types of multidimensional raster data sources. A single workflow combines the advantages of radar, optical and meteorological data to create a time series of wet snow maps over South Tyrol. This information product can further serve as an input for hydrological forecasting or avalanche prediction.