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Papers
61,005 resultsShowing papers similar to Exploring the Influencing Factors in Identifying Soil Texture Classes Using Multitemporal Landsat-8 and Sentinel-2 Data
ClearGeospatial Artificial Intelligence (GeoAI) and Satellite Imagery Fusion for Soil Physical Property Predicting
Researchers combined satellite imagery with geospatial AI to predict soil physical properties including clay, sand, and silt content using 317 soil samples from Iran, outperforming traditional approaches. The method supports precision agriculture and land resource management with high spatial resolution predictions.
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.
Spatial prediction of physical and chemical properties of soil using optical satellite imagery: a state-of-the-art hybridization of deep learning algorithm
Not relevant to microplastics — this study uses deep learning models combining satellite imagery and topographic data to predict soil chemical properties (pH, organic carbon, phosphorus, potassium) across a region of Iran, with no connection to microplastic pollution.
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.
AI and machine learning for soil analysis: an assessment of sustainable agricultural practices
Researchers reviewed how artificial intelligence and machine learning tools can improve the accuracy and speed of measuring soil water content and texture compared to traditional statistical methods. Better soil analysis is critical for smart irrigation and sustainable farming, especially as climate variability makes conventional tools less reliable.
New Workflow of Plastic-Mulched Farmland Mapping using Multi-Temporal Sentinel-2 data
Researchers used multi-temporal satellite imagery to map plastic-mulched farmland in China, providing a tool for monitoring the environmental risk of agricultural plastic use. Plastic mulch is a significant source of microplastic contamination in agricultural soils when film residues break down over time.
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.
Estimation of Pb and Cd Content in Soil Using Sentinel-2A Multispectral Images Based on Ensemble Learning
This paper is not relevant to microplastics research — it develops machine learning models using Sentinel-2 satellite imagery to estimate lead and cadmium concentrations in soil near a mining area in China.
A New Method for Detecting Plastic-Mulched Land Using GF-2 Imagery
Researchers developed a new remote sensing method for detecting plastic-mulched agricultural land using GF-2 satellite imagery by introducing a K-T transform component specifically enhanced for plastic identification. The method was combined with texture metrics and spectral bands in an object-oriented classification approach. The study demonstrates improved accuracy in mapping plastic mulch coverage, which is important for monitoring agricultural plastic waste that contributes to soil microplastic pollution.
VIRS based detection in combination with machine learning for mapping soil pollution
Researchers reviewed visible and infrared reflectance spectroscopy combined with machine learning — including neural networks and random forest algorithms — as a low-cost, remote method for detecting and mapping soil contamination across proximal, airborne, and satellite sensor platforms.
Spatial Distribution and Geostatistical Prediction of Microplastic Abundance in a Micro-Watershed with Tropical Soils in Southeastern Brazil
Researchers used geostatistical methods to predict and spatially map microplastic abundance in agricultural soil across a micro-watershed with tropical soils in southeastern Brazil. The study found heterogeneous spatial distribution patterns influenced by land use and topography, demonstrating that kriging-based interpolation can produce reliable continuous maps for environmental risk assessment.
A novel way to rapidly monitor microplastics in soil by hyperspectral imaging technology and chemometrics
Hyperspectral imaging combined with chemometrics was demonstrated as a novel way to rapidly detect and map multiple types of microplastics in soil samples, identifying particles of different polymer types based on their spectral signatures. The approach could enable faster and more spatially detailed monitoring of microplastic contamination in agricultural and environmental soils.
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.
The potentiality of GIS for assessing soil pollution – A review
Not relevant to microplastics — this review examines how Geographic Information Systems (GIS) can be applied to assess and map soil pollution from heavy metals, pesticides, and other contaminants, with no substantive microplastics content.
Plastic film residues on cropland: monitoring soil contamination through optical remote sensing
Researchers used optical remote sensing to monitor plastic film residues on agricultural cropland, demonstrating that satellite-based methods can detect surface plastic contamination across large areas. The study provides a scalable approach for tracking agricultural plastic residues — a major secondary microplastic source in soils — without the labor intensity of field sampling.
Application of machine learning in assessing spatial distribution patterns of soil microplastics: a case study of the Bang Pakong Watershed, Thailand
Machine learning models were applied to predict spatial distribution patterns of microplastics in soils across a Thai watershed, identifying land use types and proximity to water bodies as key factors driving contamination levels.
Time Series approach to map areas of Agricultural Plastic Waste generation
Researchers applied a time-series remote sensing approach to map the spatial distribution of agricultural plastic waste generation across extensive agricultural landscapes, using satellite imagery to detect plastic-mulched farmlands and other agri-plastics to address the lack of comprehensive plasticulture data needed for effective waste management and land-use policy.
Spatiotemporal Analysis of the Impacts of Climate Change on UAE Mangroves
Researchers analyzed spatiotemporal changes in UAE mangrove ecosystems using remote sensing, finding that climate variables such as land surface temperature and salinity significantly influenced mangrove growth and distribution patterns over time.
Evaluating Microplastic Pollution Along the Dubai Coast: An Empirical Model Combining On-Site Sampling and Sentinel-2 Remote Sensing Data
Researchers collected coastal water samples from Dubai and combined laboratory spectral measurements with Sentinel-2 satellite imagery to build a model that estimates microplastic concentrations from space. The model achieved an R² of 87% and was used to map microplastic pollution trends along the Dubai coast from 2018 to 2021. This remote-sensing approach demonstrates a scalable method for monitoring coastal microplastic pollution over large areas without intensive fieldwork.
Critical evaluation of hyperspectral imaging technology for detection and quantification of microplastics in soil
Researchers evaluated whether hyperspectral imaging technology can reliably detect and quantify microplastics in soil under varying real-world conditions. They found that near-infrared imaging generally works well but is significantly affected by factors like soil moisture, microplastic color, and particle size. The study recommends sorting microplastics by size before analysis and further research into moisture effects, providing the first comprehensive evaluation of this emerging detection technology for soil monitoring.
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.
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.
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.
Soil Types and Degradation Pathways in Saudi Arabia: A Geospatial Approach for Sustainable Land Management
This paper is not about microplastics; it uses geospatial mapping to classify soil types and land degradation pathways across Saudi Arabia for sustainable land management purposes, without addressing plastic pollution.