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Papers
61,005 resultsShowing papers similar to Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)
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.
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.
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.
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.
Hyperspectral ultraviolet to shortwave infrared characteristics of marine-harvested, washed-ashore and virgin plastics
Researchers characterized the hyperspectral optical properties of marine-harvested plastic litter across ultraviolet to shortwave infrared wavelengths, generating spectral signatures needed to support remote sensing detection of floating plastic debris. The spectral library produced contributes to developing satellite and airborne monitoring systems for large-scale ocean plastic surveillance.
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.
Assessment of marine litter through remote sensing: recent approaches and future goals
This review classified remote sensing approaches for detecting marine litter — including satellite, aircraft, and drone platforms with optical, infrared, and radar sensors — finding that few studies had reached operational status and that detecting small or submerged litter remains a major technical challenge.
Towards the Spectral Mapping of Plastic Debris on Beaches
This paper reviews the use of remote sensing (satellite and aerial imaging) to detect and map plastic debris on beaches. Advances in spectral imaging could allow large-scale automated monitoring of coastal plastic pollution, which is currently labor-intensive and limited in coverage.
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.
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.
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.
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.
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.
Quantifying Marine Plastic Debris in a Beach Environment Using Spectral Analysis
Researchers analyzed shortwave infrared reflectance spectra of weathered marine plastic debris on sandy beaches, finding that polymer type significantly influences detection capability at sub-pixel surface covers relevant to remote sensing applications.
Hyperspectral reflectance of pristine, ocean weathered and biofouled plastics from a dry to wet and submerged state
Researchers built an open-access hyperspectral library covering pristine, ocean-weathered, and artificially biofouled plastics measured from dry through submerged states, filling a gap in reference data needed for satellite and drone-based plastic pollution monitoring. The library is particularly valuable because biofouling alters a plastic's optical signature and makes remote identification much harder, so having reference spectra for fouled materials improves algorithm accuracy for detecting plastic debris in real-world ocean environments.
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.
Abundance of Plastic-Litter in Hyperspectral Imagery Using Spectral Unmixing in Coastal Environment
This study tested whether hyperspectral satellite or aerial imagery combined with spectral unmixing algorithms can detect and map microplastic litter in coastal environments. Results showed the approach can identify plastic fragments smaller than a pixel by analyzing mixed spectral signals, offering a scalable monitoring tool. Remote sensing methods like this could greatly reduce the cost and labor of tracking coastal plastic pollution at large spatial scales.
Developing Beach Litter Monitoring System Based on Reflectance Characteristics and its Abundance
Researchers developed a beach litter monitoring system using optical reflectance characteristics of plastic debris, training a remote sensing model to detect and classify litter items on sandy beach surfaces. The system demonstrated accurate detection of common plastic litter types and offers a scalable, automated alternative to manual beach surveys.
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.
Aerial Remote Sensing of Aquatic Microplastic Pollution: The State of the Science and How to Move It Forward
A systematic literature review of aerial remote sensing for aquatic microplastic detection identified three main approaches — spectral characteristics, floating debris imaging, and AI-based analysis — all still largely experimental rather than operational.
Uncovering Plastic Litter Spectral Signatures: A Comparative Study of Hyperspectral Band Selection Algorithms
This paper is not primarily about microplastics; it focuses on hyperspectral band-selection algorithms to identify the optical spectral signatures of plastic litter under water, primarily as a remote-sensing detection methodology. While relevant to plastic pollution monitoring, it does not assess microplastic abundance, distribution, or ecological/health effects.
Hyperspectral longwave infrared reflectance spectra of naturally dried algae, anthropogenic plastics, sands and shells
Researchers measured the light reflectance spectra of common beach litter materials — plastics, algae, sand, and shells — to develop reference data for identifying plastic pollution in remote sensing imagery. This spectral library is a key step toward using satellites and drones to detect and map plastic pollution on beaches and coastlines at large scale.
Advanced Classification of Marine Pollutants Using Sentinel-2 Multispectral Thermal Imaging and Vision Transformer for Enhanced Water Quality Assessment
This study used satellite multispectral imaging from the Sentinel-2 platform combined with a Vision Transformer machine learning model to automatically classify different types of marine pollutants — including plastics, algae, and oil — from aerial imagery. The AI-based approach significantly outperformed traditional classification methods and could detect plastic debris patches across large ocean areas. Automated large-scale detection of marine plastic pollution from satellites could transform the way we monitor and respond to ocean plastic contamination.
Targeting Plastics: Machine Learning Applied to Litter Detection in Aerial Multispectral Images
Researchers applied machine learning to aerial multispectral images for automated detection of plastic litter in natural areas, demonstrating that combining spectral data with classification algorithms can effectively identify and monitor plastic waste pollution.