We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Remote estimation of plastic distribution in California agriculture using Sentinel-2 imagery: a case study
AI summary Read the abstract
Researchers used satellite images and ground-based light measurements to detect plastic mulch films used in California farm fields. Their computer classification method distinguished plastic from soil and crops with over 98% accuracy, and tracked plastic coverage across a farming region from 2023 to 2025 without needing prior knowledge of individual field practices.
Global application of agricultural plastic mulch is increasing. While these films offer benefits for crop production, they also are a large source of plastic waste and pollution in both terrestrial and aquatic systems. The current scale of global mulch application and its deleterious impacts are unclear. Current estimates of agricultural plastic use remain limited by field-specific growing patterns and data limitations. Satellite spectroscopy may offer a means to reduce this uncertainty and model agricultural plastic use on large spatial and temporal scales, though temporally variable crop canopy growth and spectrally diverse plastic targets complicate this approach. To build toward improved plasticulture estimates, this study presents an interpretable, spectral-based classification of bulk plastic use in a California agricultural site using Sentinel-2 imagery. In situ reflectance measurements of soil and white, green, and black plastic mulch endmembers were collected using a Spectra Vista Corporation HR-1024i Spectroradiometer from 350–2500 nm. A combination of field observations and high spatial resolution NearMap imagery (n = 416 spectra) were compiled and divided into 60% training and 40% testing data using a stratified sampling approach. Informed by field reflectance data, mixture analyses, and Sentinel-2 reflectance imagery, an empirical decision tree classification was parameterized to classify dark and bright plastic debris from soil and crop (accuracy = 0.982, kappa coefficient = 0.973, macro-averaged F1-score = 0.967). Modeled agricultural plastic distributions were then retrieved through a time series of Sentinel-2 imagery over Santa Maria, CA, from April 2023 to January 2025, elucidating patterns in plastic application and crop canopy growth without the need for prior knowledge of field growing and management patterns. These results are encouraging for the utility of optical remote sensing in modeling plastic use patterns and identifying contamination hotspots throughout California and, potentially, similarly mulched agroecological systems.
More Papers Like This
Large Scale Agricultural Plastic Mulch Detecting and Monitoring with Multi-Source Remote Sensing Data: A Case Study in Xinjiang, China
AI summary Read the abstract
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.
Time Series approach to map areas of Agricultural Plastic Waste generation
AI summary Read the abstract
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.
Remote sensing detection of plastic-mulched farmland using a temporal approach in machine learning: case study in tomato crops
AI summary Read the abstract
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.
New Workflow of Plastic-Mulched Farmland Mapping using Multi-Temporal Sentinel-2 data
AI summary Read the abstract
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.
Large scale detection of plastic covered crops using multispectral and SAR satellite data
AI summary Read the abstract
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.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.