Preprints
https://doi.org/10.5194/essd-2023-190
https://doi.org/10.5194/essd-2023-190
01 Jun 2023
 | 01 Jun 2023
Status: a revised version of this preprint is currently under review for the journal ESSD.

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

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

Abstract. Accurate, detailed, and up-to-date information on cropland extent is crucial for provisioning food security and environmental sustainability. However, because of the complexity of agricultural landscapes and lack of sufficient training samples, it remains challenging to monitor cropland dynamics at high spatial and temporal resolutions across large geographical extents, especially for places where agricultural land use is changing dramatically. Here we developed a novel cost-effective annual cropland mapping framework that integrated time-series Landsat imagery, automated training sample generation, and machine learning and change detection techniques. We implemented the proposed scheme to a cloud computing platform of Google Earth Engine and generated China’s annual cropland dataset (CACD) at a 30 m spatial resolution for the first time. Results demonstrated that our approach was capable of tracking dynamic cropland changes in different agricultural zones. The pixel-wise F1 scores for annual maps and change maps of CACD were 0.79±0.02 and 0.81, respectively. A further cross-product comparison in terms of accuracy assessment, correlations with statistics, and spatial details indicated the precision and robustness of CACD than other datasets. According to our estimation, from 1986 to 2021, China’s total cropland area expanded by 30,300 km2 (1.79 %), which underwent an increase before 2000 but a general decline between 2000–2015 and a slight recovery afterward. Cropland expansion was concentrated in the northwest while the eastern coastal region experienced substantial cropland loss. In addition, we observed 419,342 km2 (17.57 %) of croplands that were abandoned at least once during the study period. The consistent, high-resolution data of CACD can support progress toward sustainable agricultural use and food production in various research applications. The full archive of CACD is freely available at https://doi.org/10.5281/zenodo.7936885 (Tu et al., 2023a).

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

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-190', Chong Liu, 11 Jun 2023
    • AC1: 'Reply on RC1', Ying Tu, 26 Sep 2023
  • RC2: 'Comment on essd-2023-190', Anonymous Referee #2, 30 Aug 2023
    • AC2: 'Reply on RC2', Ying Tu, 26 Sep 2023
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Peng Gong, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu

Data sets

A 30 m annual cropland dataset of China from 1986 to 2021 Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Peng Gong, Yuqi Bai, Jun Yang, Le Yu, Bing Xu https://doi.org/10.5281/zenodo.7936885

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

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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. China’s total cropland area expanded by 30,300 km2 (1.79 %) during the study period, with significant expansions observed in the northwest but substantial losses along the eastern coastal region. Our fine-resolution cropland maps offer valuable information for diverse applications and decision makings in the future.
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