Articles | Volume 16, issue 5
https://doi.org/10.5194/essd-16-2297-2024
https://doi.org/10.5194/essd-16-2297-2024
Data description paper
 | 
06 May 2024
Data description paper |  | 06 May 2024

A 30 m annual cropland dataset of China from 1986 to 2021

Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu

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Cited articles

Belgiu, M. and Csillik, O.: Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis, Remote Sens. Environ, 204, 509–523, https://doi.org/10.1016/j.rse.2017.10.005, 2018. 
Boryan, C., Yang, Z., Mueller, R., and Craig, M.: Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program, Geocarto Int., 26, 341–358, https://doi.org/10.1080/10106049.2011.562309, 2011. 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. 
Bryan, B. A., Gao, L., Ye, Y., Sun, X., Connor, J. D., Crossman, N. D., Stafford-Smith, M., Wu, J., He, C., Yu, D., Liu, Z., Li, A., Huang, Q., Ren, H., Deng, X., Zheng, H., Niu, J., Han, G., and Hou, X.: China's response to a national land-system sustainability emergency, Nature, 559, 193–204, https://doi.org/10.1038/s41586-018-0280-2, 2018. 
Canny, J.: A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8, 679–698, https://doi.org/10.1109/TPAMI.1986.4767851, 1986. 
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Short summary
We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
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