Articles | Volume 17, issue 10
https://doi.org/10.5194/essd-17-5065-2025
https://doi.org/10.5194/essd-17-5065-2025
Data description paper
 | 
01 Oct 2025
Data description paper |  | 01 Oct 2025

Global-PCG-10: a 10 m global map of plastic-covered greenhouses derived from Sentinel-2 in 2020

Bowen Niu, Quanlong Feng, Bingwen Qiu, Shuai Su, Xinmin Zhang, Rongji Cui, Xinhong Zhang, Fanli Sun, Wenhui Yan, Siyuan Zhao, Hanyu Shi, Cong Ou, Xiaolu Yan, Jianhua Gong, Gaofei Yin, Jianxi Huang, Jiantao Liu, Bingbo Gao, Xiaochuang Yao, Jianyu Yang, and Dehai Zhu

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Cited articles

Aguilar, M., Nemmaoui, A., Novelli, A., Aguilar, F., and García Lorca, A.: Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series, Remote Sens., 8, 513, https://doi.org/10.3390/rs8060513, 2016. 
Aguilar, M. A., Jiménez-Lao, R., Ladisa, C., Aguilar, F. J., and Tarantino, E.: Comparison of spectral indices extracted from Sentinel-2 images to map plastic covered greenhouses through an object-based approach, GIScience Remote Sens., 59, 822–842, https://doi.org/10.1080/15481603.2022.2071057, 2022. 
Chen, B., Feng, Q., Niu, B., Yan, F., Gao, B., Yang, J., Gong, J., and Liu, J.: Multi-modal fusion of satellite and street-view images for urban village classification based on a dual-branch deep neural network, Int. J. Appl. Earth Obs. Geoinf., 109, 102794, https://doi.org/10.1016/j.jag.2022.102794, 2022. 
Chen, D., Ma, A., Zheng, Z., and Zhong, Y.: Large-scale agricultural greenhouse extraction for remote sensing imagery based on layout attention network: A case study of China, ISPRS J. Photogram. Remote Sens., 200, 73–88, https://doi.org/10.1016/j.isprsjprs.2023.04.020, 2023. 
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We have proposed a novel framework to generate the first publicly released 10-m global plastic-covered greenhouse (PCG) map for 2020, derived from Sentinel-2. The global PCG area is about 14 259.85 km2 in 2020, concentrated between 30° N and 40° N. China has 8224.90 km2, accounting for 57.67 % of global and 83.29 % of Asian PCGs. The Global-PCG-10 map shows producer's accuracy of 85.12 % ± 0.90 % and user's accuracy of 99.82 % ± 0.11 %.
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