Articles | Volume 13, issue 10
Data description paper
21 Oct 2021
Data description paper |  | 21 Oct 2021

GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery

Miao Zhang, Bingfang Wu, Hongwei Zeng, Guojin He, Chong Liu, Shiqi Tao, Qi Zhang, Mohsen Nabil, Fuyou Tian, José Bofana, Awetahegn Niguse Beyene, Abdelrazek Elnashar, Nana Yan, Zhengdong Wang, and Yiliang Liu

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Cited articles

Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., and Parsian, S.: Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review, IEEE J. Sel. Top. Appl., 13, 5326–5350,, 2020. 
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and Wood, E.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Scientific Data, 5, 180214,, 2018. 
Becker, M. and Johnson, D. E.: Cropping intensity effects on upland rice yield and sustainability in West Africa, Nutr. Cycl. Agroecosys., 59, 107–117,, 2001. 
Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., and Friedl, M. A.: Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery, Remote Sens. Environ., 240, 111685,, 2020. 
Challinor, A. J., Parkes, B., and Ramirez-Villegas, J.: Crop yield response to climate change varies with cropping intensity, Glob. Change Biol., 21, 1679–1688,, 2015. 

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Short summary
Cropping intensity (CI) is essential for agricultural land use management, but fine-resolution global CI is not available. We used multiple satellite data on Google Earth Engine to develop a first 30 m resolution global CI (GCI30). GCI30 performed well, with an overall accuracy of 92 %. GCI30 not only exhibited high agreement with existing CI products but also provided many spatial details. GCI30 can facilitate research on sustained cropland intensification to improve food production.