In most places on the planet vegetation thrives: it is known as “greening Earth”. However in certain regions, especially in the Arctic, there are areas exhibiting a browning trend. This phenomenon is well known but not fully understood yet, and grasping its impact on local ecosystems requires involvement of scientists from different disciplines, including social sciences and humanities, as well as local populations. Here we focus on the Troms and Finnmark counties in northern Norway to assess the extent of the problem and any link with local environmental conditions as well as potential impacts. We have chosen to adopt an open and collaborative process and take advantage of the services offered by RELIANCE on the European Open Science Cloud (EOSC). RELIANCE delivers a suite of innovative and interconnected services that extend the capabilities of the European Open Science Cloud (EOSC) to support the management of the research lifecycle within Earth Science Communities and Copernicus Users. The RELIANCE project has delivered 3 complementary  technologies: Research Objects (ROs), Data Cubes and AI-based Text Mining. RoHub is a Research Object management platform that implements these 3 technologies and enables researchers to collaboratively manage, share and preserve their research work. We will show how we are using these technologies along with EGI notebooks to work open and share an executable Jupyter Notebook that is fully reproducible and reusable. We use a number of Python libraries from the Pangeo software stack such as Xarray, Dask and Zarr. Our Jupyter Notebook is bundled with its computational environment, datacubes and related bibliographic resources in an executable Research Object. We believe that this approach can significantly speed up the research process and can drive it to more exploitable results. Up to now, we have used indices derived from satellite data (in particular Sentinel-2) to assess how the vegetation cover in Troms and Finnmark counties has changed. To go a bit further we are investigating how to relate such information to relevant local parameters obtained from meteorological reanalysis data (ERA5 and ERA5-land from ECMWF). That should give a good basis for training an Artificial Intelligence algorithm and testing it, with the objective of getting an idea about the possibility of “predicting” what is likely to happen in the near future with certain types of vegetation like mosses and lichens which are essential for local populations and animals.