Long-term plastic greenhouse mapping based on automatic sample generation and multi-temporal noise correction: A case study of Huang-Huai-Hai Plain
文献类型：期刊论文
| 作者 | Zhang, Xiaoping3,4; Cheng, Bo2,3,4; Huang, Peng3,4; Liang, Chenbin5; Zhao, Min1,4; Wang, Guizhou3,4; He, Qinxue3,4; Gan, Yaocan3,4 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2026-02-01 |
| 卷号 | 146页码:17 |
| 关键词 | Long-term PGs mapping Automatic sample generation Multi-temporal noise correction |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2026.105123 |
| 英文摘要 | 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. |
| 资助项目 | CAS Key Technology Talent Program, National Key R&D Program of China[2024YFF0808304] ; Natural Science Basic Research Program of Shaanxi[2025JC-YBQN-395] |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001676246400001 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42855]  |
| 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, Inst Comp Technol, Beijing 100190, Peoples R China 2.Hainan Aerosp Informat Res Inst, Key Lab Earth Observat Hainan Prov, Wenchang 571399, Peoples R China 3.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 10094, Peoples R China 4.Univ Chinese Acad Sci, Beijing 10049, Peoples R China 5.Xidian Univ, Hangzhou Inst Technol, Hangzhou 311200, 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. |
入库方式： OAI收割
来源：计算技术研究所
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