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Papers
61,005 resultsShowing papers similar to Finding Plastic Patches in Coastal Waters using Optical Satellite Data
ClearInvestigating Detection of Floating Plastic Litter from Space Using Sentinel-2 Imagery
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Large scale detection of plastic covered crops using multispectral and SAR satellite data
Researchers used satellite imagery combining optical and radar data to detect large-scale plastic covering of agricultural crops across wide geographic areas. The remote sensing approach could help monitor plasticulture practices and track the potential for plastic debris to enter nearby ecosystems.
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.
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.
MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data
Researchers created MARIDA, the first benchmark dataset using Sentinel-2 satellite imagery for machine learning-based marine debris detection, distinguishing plastic debris from co-existing features like algae, ships, and various water types across global locations.
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.
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.
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.
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.
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.
Satellite sensors as an emerging technique for monitoring macro- and microplastics in aquatic ecosystems
This review assessed the emerging use of satellite remote sensing technologies for monitoring macro- and microplastic pollution in aquatic ecosystems, evaluating current capabilities and limitations of different satellite sensors for detecting waterborne plastic debris.
Plastic film residues on cropland: monitoring soil contamination through optical remote sensing
Researchers used optical remote sensing to monitor plastic film residues on agricultural cropland, demonstrating that satellite-based methods can detect surface plastic contamination across large areas. The study provides a scalable approach for tracking agricultural plastic residues — a major secondary microplastic source in soils — without the labor intensity of field sampling.
Application of Remote Sensing for the Detection and Monitoring of Microplastics in the Coastal Zone of the Colombian Caribbean
Researchers explored using remote sensing technology, including Sentinel-2 satellite imagery and machine learning algorithms, to detect and monitor microplastic pollution along the Colombian Caribbean coast. The study found that combining multispectral satellite data with computational models shows promise for systematic, large-scale monitoring of coastal microplastic contamination in regions where ground-level surveillance remains limited.
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.