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Papers
61,005 resultsShowing papers similar to Innovative software for analysing satellite data and methane emissions using radiative transfer model
ClearEstimating 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.
Software for evaluating the threat of plastic pollution to global agriculture
Researchers developed software to assess the long-term impacts of plasticulture — plastic film use in agriculture — on crop productivity and greenhouse gas emissions, synthesizing evidence on how accumulated macro- and microplastics in farmland soils threaten food and climate security.
Soil Organic Carbon Estimation via Remote Sensing and Machine Learning Techniques: Global Topic Modeling and Research Trend Exploration
Researchers used advanced topic modeling and bibliometric analysis to map global research trends in estimating soil organic carbon using remote sensing and machine learning. They identified key research clusters including satellite imagery analysis, deep learning methods, and regional carbon mapping efforts. The study provides a roadmap for future research priorities in monitoring soil carbon stocks, which is critical for understanding climate change.
Quantifying the environmental impact of pollutant plumes from coastal rivers with remote sensing and river basin modelling
Researchers combined satellite remote sensing with river basin modeling to track pollution plumes from four coastal rivers in Italy, measuring their size, timing, and pollutant loads. The method can estimate how much contamination comes from rainfall runoff versus wastewater discharge, helping managers better understand and address coastal pollution threats.
Software for evaluating the threat of plastic pollution to global agriculture
Researchers synthesized data on long-term plasticulture use to assess its impacts on crop productivity and greenhouse gas emissions, developing software tools to evaluate the compounding threat of macro- and microplastic accumulation in global agricultural soils on food and climate security.
A Combination of Machine Learning Algorithms for Marine Plastic Litter Detection Exploiting Hyperspectral PRISMA Data
Researchers applied a combination of machine learning algorithms to hyperspectral satellite imagery from the PRISMA satellite to detect marine plastic litter along coastlines and ocean surfaces. The multi-algorithm approach improved detection accuracy over single-model methods and demonstrated the potential for satellite-based monitoring of ocean plastic pollution at scale.
Unveiling the research landscape of planetscope data in addressing earth-environmental issues: a bibliometric analysis
This bibliometric analysis examined scientific publications using PlanetScope satellite imagery from 2017 to 2023, analyzing 582 documents to map research trends and application areas. The study found growing use of high-resolution PlanetScope data for land use classification, agriculture, and environmental monitoring, with machine learning increasingly applied to enhance analysis.
Integrated Analytical Approach: An Added Value in Environmental Diagnostics
Researchers demonstrated the value of an integrated multi-technique analytical approach for environmental diagnostics, showing through three marine case studies that combining multiple survey methods yields a more complete and accurate picture of anthropogenic environmental impacts than any single method alone.
Extraction the Spatial Distribution of Mangroves in the Same Month Based on Images Reconstructed with the FSDAF Model
Researchers applied the FSDAF spatiotemporal fusion model to reconstruct cloud-free satellite images for the same target month, enabling accurate extraction of mangrove spatial distributions in coastal wetlands despite the persistent cloud cover that limits image availability in mangrove-growing regions. The approach demonstrated improved accuracy in mapping mangrove extent compared to methods relying on mosaicked images spanning several months.
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.
Large Scale Agricultural Plastic Mulch Detecting and Monitoring with Multi-Source Remote Sensing Data: A Case Study in Xinjiang, China
Satellite imagery was used to monitor plastic mulch film coverage across large agricultural areas in China, mapping both spatial extent and temporal changes. Accurately tracking plastic mulch use is important because agricultural film residues are a major source of microplastic contamination in farmland soils.
Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review
This systematic review of over 15,000 papers identified 103 coastal phenomena and 39 parameters that can now be accurately mapped and monitored using remote sensing data. The authors validated 91% of retrieved parameters, demonstrating that satellite and aerial remote sensing has become a comprehensive tool for tracking coastal environmental changes including pollution and habitat degradation.
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.
Earth Observations for Monitoring Marine Coastal Hazards and Their Drivers
Researchers reviewed the use of Earth observation technologies for monitoring coastal hazards including pollution, sea-level changes, and extreme weather events. The study highlights how satellite-based monitoring and forecasting systems are increasingly important for managing risks to densely populated coastal zones, including emerging threats from marine pollution such as microplastics.
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.
The Detection and Tracking of Ocean Surface Roughness Supression by Ocean Pollutans Via Surfactants
Researchers investigated how ocean pollutants and surfactants suppress ocean surface roughness and demonstrated that GNSS-R satellites can detect these anomalies as apparent errors in wind speed estimation. By combining GNSS-R data with hyperspectral imagery and other satellite products, the study shows it is possible to distinguish between different pollutant types and generate individual pollutant maps.
Global Atmospheric Microplastics Emissions Estimated Using Constrained Bayesian Inverse Modeling
Scientists used advanced computer modeling to estimate how much microplastic pollution is being released into the air around the world. These tiny plastic particles can travel through the atmosphere and end up in the air we breathe, on our food, and in water sources. This research helps us better understand how much microplastic pollution humans are exposed to, which is important for assessing potential health risks from breathing or consuming these particles.
Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
Researchers developed a convolutional neural network-based algorithm to automatically detect and quantify floating marine macro-litter in aerial images, training it on 3,723 images and integrating it into a web application for practical monitoring use.
Atmospheric microplastic measurements reconciliation with emission estimates: A Lagrangian approach
Researchers used a Lagrangian atmospheric transport model to reconcile discrepancies between field measurements of atmospheric microplastic concentrations and global emission estimates, finding that measurement variability and gaps in emission source characterization are primary drivers of the mismatch.
Detection of Vegetation Spectral Signatures in Hyperspectral Images using Artificial Neural Networks
This study developed a computer program that can identify plants and vegetation in detailed satellite images by analyzing how they reflect different colors of light. The technology successfully detected about 42% of an area as vegetation in a test neighborhood, which was more accurate than older methods. This could help scientists better monitor environmental changes like deforestation or urban green spaces that affect air quality and human health.
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.
Continuous Monitoring of Forests in Wetland Ecosystems with Remote Sensing and Probability Sampling
This paper is not about microplastics; it develops a remote-sensing statistical method for monitoring above-ground biomass in wetland forest areas to improve carbon accounting.
Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation Data
Researchers developed a machine learning classifier using free Copernicus satellite data to detect plastic waste — including greenhouses, tyres, and waste sites — in both aquatic and terrestrial environments, achieving high accuracy and enabling low-cost large-scale plastic pollution mapping.
Comment on egusphere-2024-2839
Researchers developed an improved backtrajectory analysis protocol using the FLEXPART particle dispersion model validated against WRF-Chem tracer simulations, demonstrating that commonly used backtrajectory methods are unreliable for identifying atmospheric emission sources in polar regions and showing that the updated protocol correctly identifies known sources including methane sulfonic acid and black carbon.