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Papers
61,005 resultsShowing papers similar to Seasonal and annual tropical river pattern change detection using machine learning
ClearQuantifying the Geomorphic Effect of Floods Using Satellite Observations of River Mobility
This paper is not about microplastics; it uses satellite imagery and machine learning to study how flood magnitude, duration, and hydrograph shape determine lateral erosion and channel change in rivers.
Flux to Flow: a Clearer View of Earth’s Water Cycle Via Neural Networks and Satellite Data
This dissertation developed neural network methods to enhance the spatial resolution of satellite measurements of Earth's water cycle, enabling finer-scale monitoring of hydrological processes such as precipitation, evaporation, and runoff across diverse environments.
Insights into suspended sediment and microplastic budget of a lowland river: integrating in-situ measurements, Sentinel-2 imagery, and machine learning
Researchers combined river measurements, satellite imagery, and machine learning to track how much microplastic the Tisza River in Hungary carries downstream each day. They found that flood events spike microplastic transport five-fold, meaning a small number of high-water days drive the majority of plastic particles reaching the sea.
Assessment of surface water dynamics through satellite mapping with Google Earth Engine and Sentinel-2 data in Manipur, India
Researchers used Google Earth Engine and Sentinel-2 satellite imagery to map seasonal surface water dynamics in Manipur, India, accurately tracking the extent and timing of water body changes across the region to support watershed planning.
Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery
Researchers used satellite imagery and machine learning to detect and map litter accumulation in the Tisza River, Hungary, finding that dams are major hotspots and that flood events drive litter transport. While models performed well in controlled tests, real-world accuracy was moderate, highlighting the challenge of using satellite data to monitor riverine plastic pollution at scale. This matters because rivers are a primary pathway for plastic and microplastic debris reaching the ocean.
The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed
This paper is not about microplastics; it uses satellite rainfall data, HEC-RAS flood modeling, and artificial neural networks to predict flood inundation heights in the Majalaya Watershed of Indonesia.
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.
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.
Quantification of floating riverine macro-debris transport using an image processing approach
A new image-based algorithm was developed to measure how much floating debris is moving across a river surface, using color detection and template matching. This tool could help track macro-debris transport in rivers, which is the primary pathway for plastic litter reaching the ocean.
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.
Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy
Researchers developed satellite-based models using neural network algorithms to estimate riverine microplastic concentrations, using suspended sediment concentration as a proxy, offering a cost-effective approach for broad-scale freshwater microplastic 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.
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.
Coastal Marine Debris Detection and Density Mapping With Very High Resolution Satellite Imagery
Researchers used high-resolution satellite imagery combined with machine learning to detect and map coastal marine debris density in southern Japan, finding that satellite-based methods can estimate debris amounts and types on beaches with reasonable accuracy.
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.
Remote sensing detection of plastic-mulched farmland using a temporal approach in machine learning: case study in tomato crops
Researchers tested machine learning classifiers on Sentinel-2 satellite time-series images to map plastic-mulched farmlands, achieving 99.7% accuracy using a multilayer perceptron model and demonstrating that a 3-image composite series reduces confusion with background vegetation — producing the first plastic mulch map for Latin America.
Enhancing discharge estimation from SWOT satellite data in a tropical tidal river environment
Researchers developed a methodology to improve river discharge estimates from the SWOT satellite mission in tidally influenced river environments in southern Vietnam. They found that measurement errors from the satellite could be reduced by optimizing the size of river segments analyzed. While not directly related to microplastics, the study advances remote sensing tools that can help monitor coastal water dynamics relevant to understanding pollutant transport in estuarine systems.
Enhanced spatiotemporal mapping of urban wetland microplastics: An interpretable CNN-GRU approach using satellite imagery and limited samples
Researchers built an interpretable CNN-GRU deep learning model combining satellite remote sensing with limited in-situ measurements to map microplastic distribution in urban wetlands with enhanced spatiotemporal resolution, enabling more comprehensive monitoring with less field sampling.
Quantifying geomorphically effective floods using satellite observations of river mobility
This study used satellite imagery to measure how floods reshape river channels by tracking river mobility over time. Researchers found that cumulative flood power — combining magnitude and duration — better predicts channel change than either factor alone. The findings improve understanding of how rivers respond to extreme flood events.
An Efficient Data Driven-Based Model for Prediction of the Total Sediment Load in Rivers
A data-driven machine learning model was developed to predict total sediment load in rivers using readily available hydrological and morphological variables, outperforming conventional empirical sediment transport equations in accuracy. The model provides a practical tool for river management applications where comprehensive physical measurements are unavailable.
Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage
Researchers developed a deep learning approach to classify mesoscale oceanic eddy signatures in sea surface temperature satellite images, overcoming the challenges posed by complex eddy structures and cloud coverage that corrupts large fractions of imagery.
Evaluation of microplastic pollution in urban lentic ecosystem using remote sensing, GIS, and Support Vector Machine (SVM): relevance for environmental and ecological risk
Researchers assessed microplastic pollution in 24 urban ponds and lakes in Kolkata, India, finding significantly higher concentrations during the post-monsoon season, with fibers making up about 59% of all particles. They developed machine learning and remote sensing models that achieved up to 98% accuracy in identifying water bodies and predicting microplastic levels from satellite imagery. The study demonstrates that combining field sampling with remote sensing technology can enable large-scale monitoring of urban microplastic pollution.
Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models
Researchers used Landsat 8 satellite imagery and machine learning to assess the spatial extent and recovery trajectory of oil spill damage to coastal vegetation and wetlands in Nigeria, demonstrating that remote sensing combined with AI models can track long-term ecosystem recovery.
Water Quality Monitoring And Ground Water Level Prediction Using Machine Learning
Researchers applied machine learning techniques to water quality monitoring and groundwater level prediction, demonstrating the potential of data-driven approaches for environmental sensing and resource management.