Articles | Volume 12, issue 3
Earth Syst. Sci. Data, 12, 1913–1928, 2020
https://doi.org/10.5194/essd-12-1913-2020
Earth Syst. Sci. Data, 12, 1913–1928, 2020
https://doi.org/10.5194/essd-12-1913-2020

Data description paper 28 Aug 2020

Data description paper | 28 Aug 2020

A cultivated planet in 2010 – Part 1: The global synergy cropland map

Miao Lu et al.

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Cited articles

Bey, A., Diaz, A. S.-P., Maniatis, D., Marchi, G., Mollicone, D., Ricci, S., Bastin, J.-F., Moore, R., Federici, S., Rezende, M., Patriarca, C., Turia, R., Gamoga, G., Abe, H., Kaidong, E., and Miceli, G.: Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation, Remote Sensing, 8, 807, https://doi.org/10.3390/rs8100807, 2016. 
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Brown, M. E. and Brickley, E. B.: Evaluating the use of remote sensing data in the US Agency for International Development Famine Early Warning Systems Network, J. Appl. Remote Sens., 6, 0635111, https://doi.org/10.1117/1.Jrs.6.063511, 2012. 
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Chen, D., Lu, M., Zhou, Q., Xiao, J., Ru, Y., Wei, Y., and Wu, W.: Comparison of Two Synergy Approaches for Hybrid Cropland Mapping, Remote Sensing, 11, 213, https://doi.org/10.3390/rs11030213, 2019. 
Short summary
Global cropland distribution is critical for agricultural monitoring and food security. We propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of cropland area, which is independent of training samples. The synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics.