Articles | Volume 13, issue 11
Earth Syst. Sci. Data, 13, 5403–5421, 2021
https://doi.org/10.5194/essd-13-5403-2021
Earth Syst. Sci. Data, 13, 5403–5421, 2021
https://doi.org/10.5194/essd-13-5403-2021
Data description paper
19 Nov 2021
Data description paper | 19 Nov 2021

A 1 km global cropland dataset from 10 000 BCE to 2100 CE

Bowen Cao et al.

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Cited articles

Bargiel, D.: A new method for crop classification combining time series of radar images and crop phenology information, Remote Sens. Environ., 198, 369–383, https://doi.org/10.1016/j.rse.2017.06.022, 2017. 
Biradar, C. M. and Xiao, X.: Quantifying the area and spatial distribution of double- and triple-cropping croplands in India with multi-temporal MODIS imagery in 2005, Int. J. Remote Sens., 32, 367–386, https://doi.org/10.1080/01431160903464179, 2011. 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Cao, B., Yu, L., Li, X., Chen, M., Li, X., Hao, P., and Gong, P.: A 1 km global cropland dataset from 10 000 BCE to 2100 CE, Zenodo [data set], https://doi.org/10.5281/zenodo.5105689, 2021a. 
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Short summary
In the study, the first 1 km global cropland proportion dataset for 10 000 BCE–2100 CE was produced through the harmonization and downscaling framework. The mapping result coincides well with widely used datasets at present. With improved spatial resolution, our maps can better capture the cropland distribution details and spatial heterogeneity. The dataset will be valuable for long-term simulations and precise analyses. The framework can be extended to specific regions or other land use types.