0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Marine & Wildlife Policy & Risk Sign in to save

Investigating Detection of Floating Plastic Litter from Space Using Sentinel-2 Imagery

Remote Sensing 2020 168 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 45 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Kyriacos Themistocleous, Christiana Papoutsa, Silas Michaelides, Diofantos Hadjimitsis

Summary

Researchers tested whether Sentinel-2 satellite imagery could detect floating plastic debris on the ocean surface, using a 3 by 10 meter plastic bottle target deployed off Cyprus. A newly developed Plastic Index proved more effective than existing indices at identifying the target, offering a promising tool for large-scale ocean plastic monitoring from space.

Study Type Environmental

Plastic litter floating in the ocean is a significant problem on a global scale. This study examines whether Sentinel-2 satellite images can be used to identify plastic litter on the sea surface for monitoring, collection and disposal. A pilot study was conducted to determine if plastic targets on the sea surface can be detected using remote sensing techniques with Sentinel-2 data. A target made up of plastic water bottles with a surface measuring 3 m × 10 m was created, which was subsequently placed in the sea near the Old Port in Limassol, Cyprus. An unmanned aerial vehicle (UAV) was used to acquire multispectral aerial images of the area of interest during the same time as the Sentinel-2 satellite overpass. Spectral signatures of the water and the plastic litter after it was placed in the water were taken with an SVC HR1024 spectroradiometer. The study found that the plastic litter target was easiest to detect in the NIR wavelengths. Seven established indices for satellite image processing were examined to determine whether they can identify plastic litter in the water. Further, the authors examined two new indices, the Plastics Index (PI) and the Reversed Normalized Difference Vegetation Index (RNDVI) to be used in the processing of the satellite image. The newly developed Plastic Index (PI) was able to identify plastic objects floating on the water surface and was the most effective index in identifying the plastic litter target in the sea.

Sign in to start a discussion.

More Papers Like This

Article Tier 2

Finding Plastic Patches in Coastal Waters using Optical Satellite Data

Researchers demonstrated for the first time that floating macroplastic patches can be detected in optical data from the European Space Agency's Sentinel-2 satellites, validating detections against ground-truth observations and identifying characteristics that distinguish plastic from other floating material.

Article Tier 2

Towards Detecting Floating Objects on a Global Scale with Learned Spatial Features Using Sentinel 2

Researchers developed a machine learning approach using Sentinel-2 satellite imagery to detect floating plastic debris and marine litter on a global scale, demonstrating that learned spatial features can improve detection of large aggregations of floating objects on water surfaces.

Article Tier 2

Remotely Sensing the Source and Transport of Marine Plastic Debris in Bay Islands of Honduras (Caribbean Sea)

Researchers used high-resolution Sentinel-2 satellite imagery over Bay Islands, Honduras (2014–2019) and found that patches of floating macroplastics are detectable from space, validating satellite detections against field surveys and demonstrating potential for large-scale marine plastic monitoring.

Article Tier 2

Development of Novel Classification Algorithms for Detection of Floating Plastic Debris in Coastal Waterbodies Using Multispectral Sentinel-2 Remote Sensing Imagery

Researchers developed classification algorithms using Sentinel-2 satellite imagery to detect floating plastic debris in coastal waters near Cyprus and Greece. They tested both unsupervised and supervised methods and found that a semi-supervised fuzzy c-means approach achieved the highest accuracy for identifying plastics. The study demonstrates that remote sensing technology can be an effective tool for monitoring and mapping marine plastic pollution at scale.

Article Tier 2

Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)

Researchers tested remote sensing of floating plastic targets in a real marine environment using Sentinel-2 satellite imagery and unmanned aerial systems during the 2019 Plastic Litter Project, collecting reference spectral data to help calibrate detection algorithms. The study provided a validated dataset characterizing the spectral behavior of floating plastics to support future remote monitoring efforts.

Share this paper