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Papers
61,005 resultsShowing papers similar to Assessment of surface water dynamics through satellite mapping with Google Earth Engine and Sentinel-2 data in Manipur, India
ClearLong-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data
Long-term bimonthly satellite estimates of lake water surface area were generated for numerous lakes using Google Earth Engine and Landsat imagery from the 1970s to the present. The method produced a reliable time series of lake area dynamics at high spatial resolution. Monitoring lake area changes is important for understanding how water availability is shifting under climate change.
Seasonal and annual tropical river pattern change detection using machine learning
Researchers applied machine learning to Sentinel-2 satellite imagery to detect seasonal and annual changes in tropical river channel patterns in a region with strongly seasonal rainfall, successfully classifying active channel landforms including water, bare sediment, and vegetated bars. The approach provides a scalable method for monitoring dynamic tropical river systems.
Extraction of Surface Water Extent: Automated Thresholding Approaches
This paper is not relevant to microplastics — it evaluates automated thresholding algorithms applied to satellite remote sensing data for mapping surface water extent and monitoring floods and droughts.
Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data
Researchers used satellite imagery processed with machine learning methods to map the environmental conditions of Chilika Lake, a Ramsar-designated coastal lagoon in India. The study assessed the ecological state of Asia's largest brackish water lagoon, which faces threats from climate effects and anthropogenic pressures. The findings contribute to monitoring efforts for this internationally important wetland ecosystem.
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.
Impacts of Land Use Change on Water Quality Index in the Upper Ganges River near Haridwar, Uttarakhand: A GIS-Based Analysis
Researchers assessed water quality in the upper Ganges River near Haridwar using GIS-based analysis, finding that land use changes including urbanization and agriculture significantly impacted water quality parameters along a 78-kilometer stretch.
SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements
Researchers developed SNOWED, an automatically constructed dataset of satellite imagery with labeled water edges, enabling deep learning models to accurately detect and monitor shoreline changes for environmental monitoring applications.
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.
Quantifying 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.
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.
Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir
Researchers used satellite data from Landsat-8 and Sentinel-2 combined with machine learning to estimate chlorophyll-a concentrations — a measure of algae levels — in a South African reservoir. This remote sensing approach enables water managers to monitor reservoir health continuously without costly field sampling, helping detect harmful algal blooms earlier.
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.
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.
Monitoring migratory birds of India's largest shallow saline Ramsar site (Sambhar Lake) using geospatial data for wetland restoration
Researchers monitored migratory bird populations at Sambhar Lake, India's largest shallow saline Ramsar site, using geospatial data and remote sensing tools to inform wetland restoration strategies. The study documented species composition and temporal abundance patterns to establish baseline data for conservation management of this critical stopover habitat.
The Analysis of Land Use and Climate Change Impacts on Lake Victoria Basin Using Multi-Source Remote Sensing Data and Google Earth Engine (GEE)
Multi-source remote sensing data from Google Earth Engine were used to analyze land use and climate change impacts on the Lake Victoria basin, covering 30 million people across northeast African countries. The study documented changes in land cover, surface temperature, and vegetation health that collectively threaten the lake's water quality and fisheries.
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.
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.
Estimating Reed Bed Cover in Hungarian Fish Ponds Using NDVI-Based Remote Sensing Technique
Researchers demonstrated that NDVI-based remote sensing using freely available Sentinel-2 satellite imagery can accurately estimate reed bed cover in Hungarian fish ponds, providing a cost-effective tool for monitoring aquaculture pond ecosystems.
Modeling of daily groundwater level using deep learning neural networks
Researchers applied a CNN-biLSTM deep learning model to predict daily groundwater levels, finding it outperformed conventional modeling approaches by capturing both spatial patterns and temporal dependencies in the data. The method offers improved accuracy for groundwater monitoring, which is critical for managing increasingly stressed freshwater resources.
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.
Advancing hydrological monitoring using image-based techniques: challenges and opportunities
This paper is not about microplastics — it reviews image-based techniques (including remote sensing and computer vision) for hydrological monitoring of water bodies, discussing challenges and opportunities in measuring water flow, flood events, and water quality.
Application of Remote Sensing Technology in Ecological Engineering—A Case Study of Phase I Tao River Water Diversion Project
Researchers used remote sensing satellite imagery to monitor ecological restoration and erosion control during a large Chinese water diversion project. While focused on construction impacts, remote sensing methods are also being developed to detect and map microplastic pollution from satellite data.
Exploring the Influencing Factors in Identifying Soil Texture Classes Using Multitemporal Landsat-8 and Sentinel-2 Data
This remote sensing study tested whether multitemporal Landsat and Sentinel satellite data could help map soil texture across large areas, finding that time-series imagery improved predictions compared to single-date observations.
Microplastic Pollution In Agricultural Lands And Its Environmental Impact Assessed Through Remote Sensing
Researchers combined field sampling and remote sensing to assess microplastic pollution in agricultural soils across three Indian locations, finding microplastics in both surface and subsurface layers and correlating pollution levels with land use patterns detectable by satellite imagery.