Abstract
Snow monitoring plays a crucial role in water resource management. The increasing availability of remote sensing data offers significant advantages but also introduces challenges related to data accessibility, processing, and storage. For operational use, scalable workflows are essential to ensure global applicability.
Leveraging a cloud-based platform such as the Copernicus Data Space Ecosystem (CDSE) enables efficient data processing directly where the data are stored, without data download. Our workflows are built using the openEO API, which provides a standardized interface for accessing and processing large Earth observation datasets worldwide.
In this demonstration, we will showcase key applications for snow monitoring. Specifically, we will explore snow and ice cover classification, snow cover fraction downscaling, wet snow detection, and snow albedo estimation. The session will illustrate how different sensors and methodologies can be leveraged to achieve reliable outputs while demonstrating the power and scalability of cloud computing platforms. A particular focus will be placed on how our workflow leverages cloud scalability to reconstruct long-term time series at high spatial resolution—crucial for monitoring snow over large areas and extended periods.
This demo is suited for researchers, practitioners, and decision-makers interested in snow monitoring, as well as those looking to integrate openEO-based workflows into their environmental data processing pipelines. Participants will gain insights into how cloud-based infrastructures streamline large-scale Earth observation analysis.