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Capturing the history of operations and activities during a computational workflow is significantly important for Earth Observation (EO). The data provenance helps to collect the metadata that records the lineage of data products, providing information about how data are generated, transferred, manipulated, by whom all these operations are performed and through which processes, parameters, and datasets. This paper presents an approach to improve those aspects, by integrating the data provenance library yProv4WFs within openEO, a platform to let users connect to Earth Observation cloud back-ends in a simple and unified way. In addition, it is demonstrated how the integration of data provenance concepts across EO processing chains enables researchers and stakeholders to better understand the flow, the dependencies, and the transformations involved in analytical workflows.

This work presents the results of an algorithm for the retrieval of snow surface albedo and grain size by exploiting Sentinel-3 OLCI data. The algorithm is a hybrid approach based on spectral indices and radiative transfer theory and it was tested and intercompared with ground data from 6 field stations and other sensors in the European Alps for the period 2017-2023. The results indicate that for albedo the algorithm agrees well with ground measurements showing an unbiased Root Mean Square Error (ubRMSE) between 0.05 and 0.13 and a correlation coefficient ranging from 0.63 to 0.78 depending on the locations. For grain size, even though a general underestimation is found, the estimates reflect well the typical grain size metamorphosis from winter to spring. To further test the algorithm, these results from Sentinel-3 data were also confronted with ECOSTRESS thermal data specifically useful to show the metamorphosis of the grain size. For albedo, the algorithm was further applied to PRISMA hyperspectral images, showing consistent results with the values obtained by multispectral Sentinel-3 imagery.

The usage of Sentinel-1 Single Look Complex (SLC) and its derived interferometric products was limited due to the complexity of the workflows and computational resources required to access, download, process, store, and exploit them. We propose SAR2Cube as a framework based on open source software capable to efficiently store Sentinel-1 Single Look Complex (SLC) images into data cubes and produce on-the-fly Interferometric Synthetic Aperture Radar (InSAR) products, also providing a benchmark with the official European Space Agency (ESA) SNAP processing toolbox.

Due to the success of the Copernicus program and the general awareness towards satellite Earth Observation (EO) data, a growing number of cloud-based EO services are now available to users for processing and analyzing the available EO data. From a user perspective this is currently creating confusion due to the large number of available services and the lack of comparability between the offers. In addition, offers based on proprietary APIs without open source components have an inherent risk of vendor lock-in. The openEO standard has been developed to address this, and enabled creating openEO platform (see https://openeo.cloud), a federation of openEO backends, where the combined offering supersedes the capabilities of the individual parts.

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). The course is designed to bridge the gap towards the recent cloud native advancements in EO by offering a MOOC as an integrated and open learning experience relying on a mixture of animated lecture content and hands-on exercises hosted on the EO themed e-learning platform EO College.