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Papers
61,005 resultsShowing papers similar to Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme
ClearOptimization and Evaluation of Widely-Used Total Suspended Matter Concentration Retrieval Methods for ZY1-02D’s AHSI Imagery
This study evaluated methods for measuring total suspended matter concentrations in Chinese inland waters using hyperspectral satellite imagery. Remote sensing of water quality is increasingly relevant for monitoring pollution including microplastics in large water bodies.
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.
An inversion model of microplastics abundance based on satellite remote sensing: a case study in the Bohai Sea
Researchers developed a satellite-based model to estimate microplastic concentrations in China's Bohai Sea using remote sensing data. The model combined water color measurements from satellites with field sampling to predict microplastic distribution across a large area. The study suggests that remote sensing could become a practical tool for monitoring ocean microplastic pollution over wide regions without relying solely on labor-intensive field sampling.
Can Water Constituents Be Used as Proxy to Map Microplastic Dispersal Within Transitional and Coastal Waters?
Researchers tested whether measurable water quality parameters such as turbidity and chlorophyll could serve as indirect proxies for predicting microplastic distribution in coastal and transitional waters using remote sensing data. Results showed limited predictive power, suggesting that microplastic monitoring cannot reliably be inferred from water clarity measures alone.
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.
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.
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.
Classification and distribution of freshwater microplastics along the Italian Po river by hyperspectral imaging
Researchers used hyperspectral imaging to classify and map the distribution of freshwater microplastics collected from four stations along Italy's Po River, correlating microplastic categories with polymer types and morphological features.
Estimating microplastic concentrations in surface water using satellite-based turbidity measurements: a case study on the New River, VA
Researchers used satellite-derived turbidity measurements as a proxy for microplastic concentrations in the New River, Virginia, developing and validating a model that enables broader spatial and temporal monitoring of riverine microplastic pollution without intensive field sampling.
Monitoring Water Clarity Using Landsat 8 Imagery in Jiaozhou Bay, China, From 2013 to 2022
This paper is not about microplastics. It uses Landsat 8 satellite imagery to monitor water clarity changes in Jiaozhou Bay, China from 2013 to 2022, finding that rainfall and human activities are the primary drivers of transparency changes. The study focuses on remote sensing and water quality monitoring with no direct connection to microplastic pollution.
Rapid and direct detection of small microplastics in aquatic samples by a new near infrared hyperspectral imaging (NIR-HSI) method
Researchers developed a rapid near-infrared hyperspectral imaging method capable of detecting and chemically identifying small microplastics (down to a few hundred micrometers) in aquatic samples faster and with less labor than traditional spectroscopy approaches.
Water Quality Grade Identification for Lakes in Middle Reaches of Yangtze River Using Landsat-8 Data with Deep Neural Networks (DNN) Model
Researchers developed a deep neural network model applied to Landsat-8 satellite data to automatically identify water quality grades for lakes in the middle Yangtze River reaches, demonstrating that machine learning and remote sensing can provide cost-effective large-scale monitoring as an alternative to labor-intensive in situ measurements.
New Radiometric Approaches to Compute Underwater Irradiances: Potential Applications for High-Resolution and Citizen Science-Based Water Quality Monitoring Programs
This paper presents new radiometric methods for calculating underwater light attenuation, a measure used to assess water quality and clarity. Accurate water quality monitoring tools are important for tracking pollution levels, including the optical effects of microplastics suspended in water.
Detection and identification of microplastics directly in water by hyperspectral imaging
Researchers used hyperspectral imaging to identify different types of microplastics mixed together in water, demonstrating that the technique can distinguish polymer types based on their spectral signatures. This non-destructive, real-time method could improve the speed and accuracy of microplastic monitoring in water samples.
Indicative Lake Water Quality Assessment Using Remote Sensing Images-Effect of COVID-19 Lockdown
This study used remote sensing satellite images to assess lake water quality during COVID-19 lockdowns, finding that reduced human activity led to improved water quality indicators. The results illustrate how anthropogenic activities significantly degrade water quality, which is relevant context for microplastic pollution driven by human activity.
Hyperspectral Imaging as a Potential Online Detection Method of Microplastics
Researchers evaluated hyperspectral imaging (HSI) as a potential online detection method for microplastics in aquatic environments, assessing its ability to rapidly identify polymer types. The study found HSI shows strong promise for fast polymer identification, though improvements in processing speed are needed for real-time monitoring applications.
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.
Spatial and temporal distribution characteristics of microplastics in surface water of typical shallow lake in the middle and lower reaches of Yangtze River: A case study of Lake Baoan, Hubei Province
A seasonal sampling campaign at Lake Baoan, a shallow Yangtze River basin lake in China, found an average microplastic abundance of about 16 particles per litre in surface water, with fibers making up 40% of particles and polyethylene the dominant polymer. Microplastic concentrations peaked in winter and were lowest in summer, but showed no significant correlation with conventional water-quality indicators — suggesting that standard water-quality monitoring will miss microplastic contamination.
Hyperspectral imaging for identification of irregular-shaped microplastics in water
Researchers demonstrated a method using hyperspectral imaging to detect and identify ten different types of microplastics directly in water samples. By selecting fourteen specific wavelengths and computationally removing water interference, they could distinguish between plastic types without the labor-intensive sample preparation that current methods require. The technique could make routine microplastic water monitoring faster and more accessible for environmental testing.
Analysis on advances and characteristics of microplastic pollution in China’s lake ecosystems
Statistical data on microplastic pollution were compiled and analyzed for 86 lakes across China's lake ecosystems over the past five years, revealing widespread contamination with concentrations generally higher in lakes near urban and industrial areas. The review identifies China's heavily polluted eastern lake region as a priority for microplastic monitoring and management intervention.
Distribution Characteristics and Source Analysis of Microplastics in Urban Freshwater Lakes: A Case Study in Songshan Lake of Dongguan, China
Researchers found microplastics in both surface water and sediments of Songshan Lake, an urban freshwater lake in China, identifying fiber shapes as dominant and using principal component analysis to trace sources including atmospheric deposition, runoff, and recreational activities.
Urban Water Quality Assessment Based on Remote Sensing Reflectance Optical Classification
Researchers developed an urban water quality assessment method combining remote sensing reflectance optical classification with traditional water quality grading principles, enabling spatially and temporally continuous monitoring of urban water bodies.
An effective strategy for the monitoring of microplastics in complex aquatic matrices: Exploiting the potential of near infrared hyperspectral imaging (NIR-HSI)
Researchers developed a near infrared hyperspectral imaging (NIR-HSI) method for rapid monitoring of microplastics in complex marine matrices, demonstrating effective detection and polymer identification that overcomes the time and cost limitations of conventional spectroscopic analysis approaches.
Eliminating the interference of water for direct sensing of submerged plastics using hyperspectral near-infrared imager
Researchers developed a hyperspectral imaging technique — which captures light across many wavelengths invisible to the human eye — that can detect different types of plastic submerged in water by filtering out water's interference with the light signal. The method successfully identified nine common plastic types at depths up to 15 mm, offering a promising tool for detecting microplastics in aquatic environments from lab benches or aircraft.