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Papers
61,005 resultsShowing papers similar to Investigating Detection of Floating Plastic Litter from Space Using Sentinel-2 Imagery
ClearFinding 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.
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.
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.
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.
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.
Proof of concept for a new sensor to monitor marine litter from space
Researchers analyzed 300,000 satellite images of the Mediterranean Sea to track floating marine litter over time, finding that heavy rainfall events drive most litter inputs from land while coastal currents and wind determine how it spreads. The study demonstrates that satellites can reliably map pollution hotspots and detect seasonal trends, making space-based monitoring a practical new tool for managing ocean plastic pollution.
Advancing floating macroplastic detection from space using hyperspectral imagery
Researchers evaluated the use of hyperspectral satellite and airborne imagery to detect floating plastic debris in rivers and oceans, addressing major challenges related to plastic spectral properties in field conditions. Remote sensing tools for plastic detection are important for large-scale monitoring of the macro-scale plastic that eventually becomes microplastics.
Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation data
Researchers used free Copernicus Earth observation satellite data and machine learning to detect waste plastic in marine and terrestrial environments at a large scale. The classifier was trained on Sentinel-1 and Sentinel-2 data and performed well for detecting larger plastic accumulations. Satellite-based detection could enable continuous, wide-area monitoring of plastic pollution at a fraction of the cost of ground surveys.
Indoor spectroradiometric characterization of plastic litters commonly polluting the Mediterranean Sea: toward the application of multispectral imagery
Researchers used a laboratory spectrometer to measure the light reflectance of common plastic types found in the Mediterranean Sea as a step toward developing remote sensing methods to detect marine plastic pollution from satellites or aircraft. Aerial monitoring of plastic pollution could revolutionize our ability to track and manage large-scale ocean plastic contamination.
Global‐Scale Detection of Plastic From Space With the EMIT Imaging Spectrometer
NASA's EMIT imaging spectrometer aboard the International Space Station was used to detect plastic accumulation on land surfaces globally, producing the first satellite-scale plastic mapping at high spectral resolution. The results revealed plastic hotspots in coastal zones and near waste facilities in multiple countries, demonstrating the potential for space-based plastic pollution monitoring.
Automatic Detection and Identification of Floating Marine Debris Using Multispectral Satellite Imagery
Researchers developed a machine learning approach using Sentinel-2 satellite imagery and extreme gradient boosting to automatically detect and distinguish floating plastic debris from other marine materials like driftwood and seaweed.
Evaluating Microplastic Pollution Along the Dubai Coast: An Empirical Model Combining On-Site Sampling and Sentinel-2 Remote Sensing Data
Researchers collected coastal water samples from Dubai and combined laboratory spectral measurements with Sentinel-2 satellite imagery to build a model that estimates microplastic concentrations from space. The model achieved an R² of 87% and was used to map microplastic pollution trends along the Dubai coast from 2018 to 2021. This remote-sensing approach demonstrates a scalable method for monitoring coastal microplastic pollution over large areas without intensive fieldwork.
Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics
Researchers developed a reflectance model for how sunlight interacts with floating plastic debris on the ocean surface, accounting for plastic color, transparency, and shape, as a foundational step toward a hyperspectral remote sensing algorithm capable of detecting marine macroplastics from aircraft or satellite.
Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment
This study evaluated the potential of optical spaceborne sensors to differentiate plastic types in the environment, assessing whether satellite remote sensing can be used to map and monitor plastic pollution in terrestrial and aquatic ecosystems at scale.
Spectral Discrimination of Pumice Rafts in Optical MSI Imagery
Remote sensing using multispectral satellite imagery was used to detect and track pumice rafts in the ocean, demonstrating a method that could also help monitor floating plastic debris distribution at sea.
On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring
This review evaluates the current state of satellite and aerial remote sensing for detecting marine plastic debris, noting that while progress has been made using optical and hyperspectral imaging, significant challenges remain including low detection resolution for small particles, confusion with other floating materials, and the need for better machine learning algorithms. The paper is relevant to the microplastics field as large-scale monitoring tools are needed to track plastic pollution distribution and inform cleanup and policy efforts, though direct detection of microplastics (<5 mm) from orbit remains largely out of reach with current technology.
Large-scale detection of marine debris in coastal areas with Sentinel-2
Researchers built a deep learning model to detect floating marine debris in coastal areas using satellite imagery from the Sentinel-2 program. The system achieved strong detection accuracy across multiple test sites and can monitor large stretches of coastline regularly. The tool could help environmental agencies track and respond to marine plastic pollution at a scale that manual surveys cannot match.
Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation Data
Researchers developed a machine learning classifier using free Copernicus satellite data to detect plastic waste — including greenhouses, tyres, and waste sites — in both aquatic and terrestrial environments, achieving high accuracy and enabling low-cost large-scale plastic pollution mapping.
Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery
Researchers tested experimental hyperspectral airborne imagery to detect floating macroplastics in rivers and the ocean, demonstrating that combining spectral and spatial features improves detection accuracy over single-band approaches.
Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes
Hyperspectral PRISMA satellite images were evaluated for their ability to detect marine plastic litter using pansharpening and plastic spectral indices. Results showed that improving the spatial resolution of hyperspectral data through pansharpening enhances the discrimination of plastic objects at the ocean surface.
A Combination of Machine Learning Algorithms for Marine Plastic Litter Detection Exploiting Hyperspectral PRISMA Data
Researchers applied a combination of machine learning algorithms to hyperspectral satellite imagery from the PRISMA satellite to detect marine plastic litter along coastlines and ocean surfaces. The multi-algorithm approach improved detection accuracy over single-model methods and demonstrated the potential for satellite-based monitoring of ocean plastic pollution at scale.
Monitoring of Plastic Islands in River Environment Using Sentinel-1 SAR Data
Researchers developed a method using Sentinel-1 SAR satellite data to detect and monitor plastic islands in river environments, demonstrating the potential of radar remote sensing to track plastic debris accumulation following major rain events.
Detecting Microplastics Pollution in World Oceans Using Sar Remote Sensing
This study explored whether satellite synthetic aperture radar (SAR) imaging could detect ocean plastic pollution from space, finding that plastic-covered water patches have distinct radar signatures. Remote sensing from satellites could dramatically expand monitoring coverage for ocean microplastic accumulation zones.
Examining the Feasibility of Passive Satellite Remote Sensing of Ocean Microplastics With New High-Resolution Multiple Scattering Simulations
Researchers examined the feasibility of detecting ocean microplastics using passive satellite remote sensing by combining in situ data analysis with Mie scattering calculations and advanced multiple scattering simulations, evaluating whether spectral signatures of microplastic particles are detectable against the ocean surface optical background.