Satellite Validation of Citizen Science Marine Pollution Data: Multi-Site Correlation Analysis of Sentinel-2 Floating Debris Index and EyeSea Ground-Truth Reports

Satellite Validation of Citizen Science Marine Pollution Data: Multi-Site Correlation Analysis of Sentinel-2 Floating Debris Index and EyeSea Ground-Truth Reports

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Authors

Marius Catalin Suteu 

Abstract

We present the first multi-site correlation analysis between the Sentinel-2 Floating Debris Index (FDI) and community-reported marine pollution data from the EyeSea citizen science platform. Across three geographically diverse coastal sites—Vasai-Virar (India), Santa Cruz, Galápagos (Ecuador), and Puerto Montt (Chile)—8,123 geotagged beach pollution reports were analysed against 91 Sentinel-2 L2A scenes spanning January 2023 to March 2026. Statistically significant temporal correlations were found at all three sites, with the strongest at Santa Cruz (ρ = +0.773, p < 0.001). After seasonal decomposition, the residual correlation at Vasai-Virar reached ρ = +0.861 (p = 0.001), demonstrating that anomalous debris events are captured by both satellite and citizen scientists independently. These results establish citizen science marine pollution data as a scientifically verifiable ground-truth source for satellite remote sensing validation.

DOI

https://doi.org/10.31223/X5FV1F

Subjects

Environmental Indicators and Impact Assessment, Environmental Monitoring, Natural Resources and Conservation

Keywords

marine debris, floating debris index, sentinel-2, citizen science, groud-truth validation, remote sensing, marine pollution, seasonal decomposition, costal monitoring

Dates

Published: 2026-03-27 16:06

Last Updated: 2026-03-27 16:06

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
The first author is CTO of EyeSea, the platform that collected the ground-truth data used in this study. The satellite data (Sentinel-2) were independently acquired from ESA Copernicus via the Element84 Earth Search API. All statistical analyses were performed using open-source tools and the complete code and data are publicly available at https://doi.org/10.5281/zenodo.19251731.

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