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Papers
61,005 resultsShowing papers similar to Estimating Reed Bed Cover in Hungarian Fish Ponds Using NDVI-Based Remote Sensing Technique
ClearChlorophyll-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.
Use of the Sentinel-2 and Landsat-8 Satellites for Water Quality Monitoring: An Early Warning Tool in the Mar Menor Coastal Lagoon
Researchers used Sentinel-2 and Landsat-8 satellites to monitor water quality during the 2021 ecological crisis in Mar Menor, a large coastal lagoon in the Western Mediterranean. The satellite-based methods accurately measured chlorophyll-a and turbidity with low error margins, enabling identification of eutrophication hotspots. The study demonstrates that satellite remote sensing can serve as a cost-effective early warning tool for monitoring water quality in coastal environments.
Standardized Fuzzy Comprehensive Evaluation Biological Index Method for Ecosystem Health Evaluation in Large Waters of Aquaculture Type
Researchers developed a standardized fuzzy comprehensive evaluation biological index method for assessing ecosystem health in large aquaculture-type water bodies, providing a more nuanced tool for monitoring river and lake ecosystem conditions under increasing human environmental pressure.
A review of remote sensing in coastal aquaculture: data, geographic hotspots, methods, and challenges
This review synthesises remote sensing methods for monitoring coastal aquaculture, covering satellite and aerial data sources, identifying geographic hotspots of aquaculture expansion, and evaluating current and emerging techniques for assessing environmental impacts such as plastic debris from nets, cages, and buoys.
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.
A review of remote sensing in coastal aquaculture: data, geographic hotspots, methods, and challenges
This review examines remote sensing applications in coastal aquaculture, synthesising data sources, geographic hotspots, and methodological advances that allow satellite and aerial imagery to monitor aquaculture facility extent, water quality, and environmental impacts including plastic debris from aquaculture infrastructure.
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.
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.
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.
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.
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.
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.
The use of remote sensing for monitoring Posidonia oceanica and Marine Protected Areas: A systemic review
This systematic review examines how remote sensing technologies have been used to monitor Posidonia oceanica seagrass meadows and marine protected areas in the Mediterranean. Healthy seagrass beds are ecologically significant as they can trap microplastics in their sediments and are sensitive to pollution stress.
Determine stormwater pond geometrics and hydraulics using remote sensing technologies: A comparison between airborne-LiDAR and UAV-photogrammetry field validation against RTK-GNSS
Researchers compared UAV-photogrammetry and airborne-LiDAR against RTK-GNSS ground truth for measuring stormwater pond geometry and hydraulics across six ponds. UAV-photogrammetry outperformed infrared airborne-LiDAR for wet ponds, while correction methods for vegetation penetration improved dry pond performance, establishing UAV photogrammetry as the preferred cost-effective approach for pond monitoring.
Large scale detection of plastic covered crops using multispectral and SAR satellite data
Researchers used satellite imagery combining optical and radar data to detect large-scale plastic covering of agricultural crops across wide geographic areas. The remote sensing approach could help monitor plasticulture practices and track the potential for plastic debris to enter nearby ecosystems.
Identification for the species of aquatic higher plants in the Taihu Lake basin based on hyperspectral remote sensing
Researchers developed a hyperspectral remote sensing method using a C4.5 decision tree algorithm to identify and map eight aquatic higher plant species in the Taihu Lake basin, addressing the challenge of distinguishing species with small spectral differences against dynamic water optical backgrounds. The approach enables large-scale, fine-resolution monitoring of aquatic plant distribution as an indicator of ecosystem health.
Finding Plastic Patches in Coastal Waters using Optical Satellite Data
Researchers demonstrated for the first time that floating macroplastic patches can be detected in optical data from the European Space Agency's Sentinel-2 satellites, validating detections against ground-truth observations and identifying characteristics that distinguish plastic from other floating material.
Monitoring Water Diversity and Water Quality with Remote Sensing and Traits
This study defines five characteristics of water diversity and quality that can be monitored using remote sensing technology, from local waterbodies to continental scales. Researchers demonstrate how satellite and aerial sensing methods can track changes in water traits, structure, and biological communities more efficiently than traditional in-person sampling. The approach is particularly relevant for detecting pollution impacts, including emerging contaminants, across large and dynamic aquatic ecosystems.
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.
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.
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.
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.
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.
Assessment of Physicochemical Parameters by Remote Sensing of Bacalar Lagoon, Yucatán Peninsula, Mexico
Researchers used Landsat 8 and Sentinel 2 remote sensing data to assess physicochemical water quality parameters in Bacalar Lagoon, Mexico, which has shifted from oligotrophic to eutrophic conditions due to anthropogenic pollution. Statistical models developed from correlations between satellite reflectance and in situ measurements successfully predicted electrical conductivity, salinity, turbidity, and total dissolved solids.