Open-Earth-Monitor Global Workshop 2026

Accurate snow monitoring requires high spatial and temporal resolution to capture rapid processes such as melt and accumulation. However, current satellite missions present inherent trade-offs: optical sensors such as Sentinel-2 provide high spatial resolution (tens of meters) but limited revisit times, while sensors like MODIS offer daily observations at coarser spatial resolution (∼500 m). In addition, different sensors retrieve complementary snow properties, including snow cover extent from optical data and wet/dry snow conditions from SAR observations.

To overcome these limitations, multi-mission data integration is essential. Furthermore, robust estimation of Snow Water Equivalent (SWE) requires the coupling of remote sensing observations with physically-based or conceptual snow models driven by meteorological forcing. The increasing volume and complexity of such datasets demand scalable, cloud-based processing solutions, particularly for large-scale applications.

In this contribution, we present a scalable workflow for large-scale snow water equivalent (SWE) estimation, aimed at generating daily high-resolution (50 m) SWE data across extensive regions, such as for example the extratropical Andes within the SNOWCOP project and South Tyrol within the Open-Earth-Monitor project. The workflow explores alternative cloud-based processing strategies, including (i) data access through Copernicus Data Space Ecosystem or other STAC APIs combined with containerized processing environments (Docker), enabling flexible and reproducible workflows without systematic local data download, and (ii) data-proximate processing using openEO. These complementary approaches allow us to evaluate trade-offs between flexibility, scalability, and computational efficiency for multi-source data fusion and large-scale snow monitoring applications.