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

doi:10.1016/j.jag.2026.105123

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	Long-term plastic greenhouse mapping based on automatic sample generation and multi-temporal noise correction: A case study of Huang-Huai-Hai Plain
	Zhang, Xiaoping 1,2; Cheng, Bo 1,2,5; Huang, Peng 1,2; Liang, Chenbin 3; Zhao, Min 2,4; Wang, Guizhou 1,2; He, Qinxue 1,2; Gan, Yaocan 1,2
	2026-02-01
发表期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	1569-8432
卷号	146 页码:17
摘要	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 (R2 = 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.
关键词	Long-term PGs mapping Automatic sample generation Multi-temporal noise correction
DOI	10.1016/j.jag.2026.105123
收录类别	SCI
语种	英语
WOS研究方向	Physical Geography ; Remote Sensing
WOS类目	Geography, Physical ; Remote Sensing
WOS记录号	WOS:001676246400001
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42856
专题	中国科学院计算技术研究所
通讯作者	Cheng, Bo; Liang, Chenbin
作者单位	1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 10094, Peoples R China 2.Univ Chinese Acad Sci, Beijing 10049, Peoples R China 3.Xidian Univ, Hangzhou Inst Technol, Hangzhou 311200, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 5.Hainan Aerosp Informat Res Inst, Key Lab Earth Observat Hainan Prov, Wenchang 571399, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Xiaoping,Cheng, Bo,Huang, Peng,et al. Long-term plastic greenhouse mapping based on automatic sample generation and multi-temporal noise correction: A case study of Huang-Huai-Hai Plain[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2026,146:17.
APA	Zhang, Xiaoping.,Cheng, Bo.,Huang, Peng.,Liang, Chenbin.,Zhao, Min.,...&Gan, Yaocan.(2026).Long-term plastic greenhouse mapping based on automatic sample generation and multi-temporal noise correction: A case study of Huang-Huai-Hai Plain.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,146,17.
MLA	Zhang, Xiaoping,et al."Long-term plastic greenhouse mapping based on automatic sample generation and multi-temporal noise correction: A case study of Huang-Huai-Hai Plain".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 146(2026):17.