Snow depth variability in the Northern Hemisphere mountains observed from space

Area of Science:

• Earth Observation
• Hydrology
• Remote Sensing

Background:

• Accurate snow depth data is crucial for managing water resources, especially in Northern Hemisphere mountains where over a billion people depend on snowmelt.
• Current large-scale remote sensing of mountain snow depth remains a significant challenge.

Purpose of the Study:

• To demonstrate the capability of Sentinel-1 satellite data for mapping mountain snow depth across the Northern Hemisphere.
• To provide a foundation for long-term monitoring of snow-water resources and their vulnerability to climate change.

Main Methods:

• Utilized an empirical change detection approach with Sentinel-1 data.
• Mapped snow depth at 1 km² resolution in Northern Hemisphere mountain ranges.
• Validated retrievals using approximately 4000 ground-based measurements and reanalysis data.

Main Results:

• Sentinel-1 successfully mapped snow depth in Northern Hemisphere mountains.
• The data captured spatial variability within and between mountain ranges.
• Inter-annual snow depth differences were accurately represented, as shown by contrasting 2017 and 2018 data in the Sierra Nevada and Alps.

Conclusions:

• Sentinel-1 offers a valuable tool for large-scale snow depth monitoring in mountains.
• The findings support the long-term assessment of mountain snow-water resources under climate change.
• Ensured continuity of Sentinel-1 data until at least 2030 provides a basis for ongoing research and resource management.