Using Geoweaver to Make Snow Mapping Workflow FAIR

This paper replicates a snow depth estimation workflow in Geoweaver based on data from NASA National Snow and Ice Data Center (NSIDC), Airborne Snow Observatory (ASO), and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets to make it more FAIRable for the community. The workflow searches and retrieves spatiotemporally coincident data from various data sources, preprocesses the data, and trains/tests it using a linear regression model and a deep learning model. The trained model will be able to estimate snow depth based on new remote sensed observations. Our experiments show that Geoweaver can significantly improve the capacity of sharing and synchronizing workflow among team members or individuals from other teams. Using the simple one-stop import/export button, scientists can share not only source code, but also all the history of activity and issues during their work so the people on the receiving end can see those struggles and avoid them.