Long-term plastic greenhouse mapping based on automatic sample generation and multi-temporal noise correction: A case study of Huang-Huai-Hai Plain

Plastic greenhouses (PGs), as a typical form of facility agriculture, play a crucial role in stabilizing agricultural production and increasing crop yields, but their rapid expansion has raised environmental concerns. Accurate long-term PGs monitoring is therefore essential for scientific agricultural regulation and environmental sustainability. However, most existing studies have focused on local regions or single-year mapping, and long-term PGs mapping remains limited. Moreover, acquiring multi-year high-quality training samples and developing effective classification algorithms remain major challenges for reliable PGs extraction. To address these issues, we propose a novel PGs mapping framework that integrates automatic sample generation with multi-temporal noise correction (MTNC), and utilizes Landsat time-series images to efficiently and accurately map multi-year PGs distribution in the Huang-Huai-Hai Plain. Specifically, high-quality training samples were automatically generated from multi-source land use/land cover and PGs products through spatial rules and sample migration, followed by preliminary classification with Random Forest. The initial predictions were then refined through the MTNC strategy, and the optimized labels were subsequently employed to train a segmentation network for robust PGs extraction. Accuracy assessments on two independent validation datasets demonstrate that the final PGs maps achieve overall accuracies above 90% and Kappa coefficients greater than 0.8 across all years. And cross-comparisons with existing PGs products at multiple spatial resolutions show a high level of spatial consistency ( R 2 = 0.91 with PGs-10 and 0.74 with PGs-3), further confirming the reliability of the proposed framework and the high quality of the final products. • A novel cost-efficient framework for long-term PGs mapping. • Automatic training sample generation via existing products and sample migration. • Multi-temporal noise correction to refine coarse initial results. • High-quality PGs maps with validated accuracy above 90%.