Melón‐Nava (2024) Recent Patterns and Trends of Snow Cover (2000–2023) in the Cantabrian Mountains (Spain) from Satellite Imagery Using Google Earth Engine

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Identification

• Journal: Remote Sensing
• Year: 2024
• Date: 2024-09-26
• Authors: Adrián Melón‐Nava
• DOI: 10.3390/rs16193592

Research Groups

Not specified

Short Summary

This study utilizes multi-sensor satellite data and Google Earth Engine to monitor daily snow cover in the Cantabrian Mountains, revealing a significant long-term decline in snow-cover days.

Objective

• To extract and analyze metrics regarding snow cover extent, duration, frequency, and trends in the Cantabrian Mountains.

Study Configuration

• Spatial Scale: Cantabrian Mountains (with specific focus on altitudes between 1000 m and 2000 m a.s.l.).
• Temporal Scale: 23 years.

Methodology and Data

• Models used: Google Earth Engine (GEE) for data processing.
• Data sources: Sentinel-2, Landsat (5–8), and MODIS satellite imagery.

Main Results

• Observed an overall decrease in snow-cover days (SCDs) of $-0.26$ days per year, with a more pronounced decrease of $-0.92$ days per year in areas showing significant trends.
• Identified the most significant decreases in snow cover within the 1000–2000 m a.s.l. altitude range.
• Determined that the Snow-Cover Fraction (SCF) exhibits high interannual variability, with peak values occurring between late January and early February.
• Noted limitations in data accuracy within forested areas, steep slopes, and regions with persistent cloud cover.

Contributions

• Demonstrates the effectiveness of integrating multi-sensor satellite data via GEE for long-term snow monitoring in regions characterized by sparse ground-based observation networks.

Funding

Not specified

Citation

@article{MelónNava2024Recent,
  author = {Melón‐Nava, Adrián},
  title = {Recent Patterns and Trends of Snow Cover (2000–2023) in the Cantabrian Mountains (Spain) from Satellite Imagery Using Google Earth Engine},
  journal = {Remote Sensing},
  year = {2024},
  doi = {10.3390/rs16193592},
  url = {https://doi.org/10.3390/rs16193592}
}