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Papers
61,005 resultsShowing papers similar to Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data
ClearAssessment 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.
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.
Foss4g Based High Frequency and Interoperable Lake Water-quality Monitoring System
This paper describes an open-source, high-frequency water quality monitoring system for lakes using free geospatial software tools. The system enables continuous tracking of changes including those driven by pollution, supporting better management of freshwater resources.
Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme
Researchers validated a satellite-based method for estimating water clarity (Secchi disk depth) in Chinese lakes using hyperspectral imagery. While focused on remote sensing of water quality, such tools can be applied to monitoring microplastic distribution in aquatic environments by proxy.
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.
Long-term human expansion and the environmental impacts on the coastal zone of China
This study analyzed long-term expansion of reclamation, aquaculture ponds, and urban land cover in the Circum-Bohai Coastal Zone of China from satellite data using Google Earth Engine. Human coastal expansion accelerated significantly over the study period and was associated with declining ecosystem services including carbon storage, water purification, and biodiversity habitat.
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.
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.
Regional Satellite Algorithms to Estimate Chlorophyll-a and Total Suspended Matter Concentrations in Vembanad Lake
Researchers developed regional satellite algorithms to estimate chlorophyll-a concentrations and total suspended matter in Vembanad Lake, India, using remote sensing data to monitor water quality in a highly productive but increasingly polluted coastal ecosystem. The algorithms were calibrated against in-situ measurements and found to improve the accuracy of water quality assessments compared to global ocean-color models, supporting sustainable development monitoring goals.
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.
Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake
Researchers used a combination of field measurements, weather data, and satellite imagery to estimate chlorophyll-a concentrations at different depths in a Chilean lake. They compared deep learning and statistical models and found all three approaches performed well for predicting algal levels in the freshwater ecosystem. The study advances water quality monitoring techniques that can help track environmental changes, including those potentially linked to 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.
Spatial–Temporal Wetland Landcover Changes of Poyang Lake Derived from Landsat and HJ-1A/B Data in the Dry Season from 1973–2019
Satellite imagery analysis of Poyang Lake in China from 1973 to 2019 revealed major shifts in wetland cover types driven by flooding, drought cycles, and human activity. The study is focused on remote sensing of wetland dynamics and is not directly related to microplastics research.
Assessing Shoreline Changes in Fringing Salt Marshes from Satellite Remote Sensing Data
This paper is not about microplastics; it uses satellite remote sensing (Landsat and Sentinel-2) to track historical shoreline changes in narrow salt marshes of the Aveiro lagoon in Portugal, documenting significant retreat since 2000.
Dynamic Mapping of Inland Freshwater Aquaculture Areas in Jianghan Plain, China
This study used remote sensing to dynamically map changes in freshwater aquaculture area in Jianghan Plain, China over recent decades, tracking rapid expansion driven by growing consumer demand. The spatial data provide a foundation for assessing the environmental footprint of aquaculture expansion.
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.
Analysis of Land Use Evolution of Suzhou Wetlands Based on RS and GIS
Researchers used satellite remote sensing and GIS to track changes in land use and wetland coverage in Suzhou, China over time. Understanding how wetland ecosystems change is important for assessing their capacity to filter pollutants, including microplastics carried by stormwater and runoff.
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.
The Changes in Dominant Driving Factors in the Evolution Process of Wetland in the Yellow River Delta during 2015–2022
This paper is not about microplastics; it uses satellite time-series imagery to analyze changes in wetland area and type in the Yellow River Delta between 2015 and 2022.
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.
Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model
Researchers used Landsat satellite imagery and machine learning to estimate forest carbon storage in a region of China over two decades. The study demonstrates remote sensing as a practical tool for tracking carbon stocks and the effects of land-use change.
Did the COVID-19 Lockdown-Induced Hydrological Residence Time Intensify the Primary Productivity in Lakes? Observational Results Based on Satellite Remote Sensing
Researchers investigated whether COVID-19 lockdown-induced changes in hydrological residence time affected primary productivity in lakes using satellite remote sensing, finding that reduced human activity and altered water flow patterns during lockdowns produced measurable changes in phytoplankton biomass in monitored lake systems.
Численное моделирование изменения рельефа дна водоема при наличии гравитационных волн
This study develops mathematical models to simulate changes in underwater terrain caused by wave processes, integrating remote sensing and survey data to account for incomplete environmental information. The resulting algorithms can help predict how riverbeds and lake floors change under varying climatic and geographic conditions.
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.