Peter Zellner

The **Massive Open Online Course - Earth Observation Open Data Science (MOOC EOODS)** teaches the concepts of data cubes, cloud platforms, and open science in the context of Earth Observation (EO). It aims at Earth Science students, researchers, and Data Scientists who want to increase their technical capabilities onto the newest standards in EO cloud computing. The course is designed as a MOOC that explains the concepts of cloud native EO and open science by applying them to a typical EO workflow from data discovery, data processing up to sharing the results in an open and FAIR way. The [EO College platform](https://eo-college.org/welcome) hosts the course and hands-on exercises are carried out directly on European EO cloud platforms, such as [Euro Data Cube](https://eurodatacube.com/) or [openEO Platform](https://openeo.cloud/), using open science tools like the [Open Science Data Catalogue](https://opensciencedata.esa.int/) and [STAC](https://stacspec.org/en) to embed the relevance of the learned concepts into real-world applications. The MOOC is an open learning experience relying on a mixture of animated lecture content and hands-on exercises created together with community renowned experts. After finishing the course, the participants will understand the concepts of cloud native EO, be capable of independently using cloud platforms to approach EO related research questions and be confident in how to share research by adhering to the concepts of open science. The MOOC is valuable for the EO community and open science as there is currently no learning resource available where the concepts of cloud native computing and open science in EO are taught jointly to bridge the gap towards the recent cloud native advancements in EO. The course is open to everybody, thus serving as teaching material for a wide range of purposes including universities and industry, maximizing the outreach to potential participants. Our talk will give an overview of the MOOC at the current status. Furthermore, we encourage review, feedback on its content and discussion.

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Producing wet snow maps by harmonizing different types of multidimensional raster data over South Tyrol

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.