River plastic hotspot detection from space

Abstract

Plastic pollution threatens terrestrial and aquatic ecosystems, and rivers play a central role in transporting and retaining plastics across landscapes. Effective mitigation requires scalable methods to identify riverine plastic accumulation hotspots. Here, we present a semi-automated, cloud-based pipeline that integrates satellite remote sensing and machine learning to detect river plastic hotspots. High-resolution PlanetScope imagery is used to annotate training regions, which are transferred to Sentinel-2 multispectral data to train Random Forest classifiers within Google Earth Engine. The approach is evaluated across three contrasting river systems—the Citarum (Indonesia), Motagua (Guatemala), and Odaw (Ghana)—to assess transferability under diverse environmental conditions. Intra-river transfer achieves up to 99.5% accuracy, while optimized inter-river transfer yields a plastic F1-score of 79%, outperforming previously reported results of 69%. By providing an open-access Google Earth Engine application, this work enables reproducible, large-scale monitoring of riverine plastic pollution and supports the development of global, satellite-based assessment strategies.