Articles | Volume 16, issue 7
https://doi.org/10.5194/essd-16-3213-2024
https://doi.org/10.5194/essd-16-3213-2024
Data description paper
 | 
10 Jul 2024
Data description paper |  | 10 Jul 2024

ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021

Qinghang Mei, Zhao Zhang, Jichong Han, Jie Song, Jinwei Dong, Huaqing Wu, Jialu Xu, and Fulu Tao

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Cited articles

Ahmed, M., Seraj, R., and Islam, S. M. S.: The k-means Algorithm: A Comprehensive Survey and Performance Evaluation, Electronics, 9, 1295, https://doi.org/10.3390/electronics9081295, 2020. 
Arthur, D. and Vassilvitskii, S.: k-means++: The advantages of careful seeding, Stanford InfoLab Technical Report, No. 2006-13, Stanford University, http://ilpubs.stanford.edu:8090/778/ (last access: 3 July 2024), 2006. 
Bach, F. and Jordan, M.: Learning Spectral Clustering, in: Advances in Neural Information Processing Systems, Proceedings of the 16th International Conference on Neural Information Processing Systems (NIPS 2003), Vancouver, Canada, 9–12 December 2003, MIT Press, https://proceedings.neurips.cc/paper_files/paper/2003/file/d04863f100d59b3eb688a11f95b0ae60-Paper.pdf (last access: 3 July 2024), 2003. 
Chabalala, Y., Adam, E., and Ali, K. A.: Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes, Remote Sens., 14, 2621, https://doi.org/10.3390/rs14112621, 2022. 
Chen, D., Huang, J., and Jackson, T. J.: Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands, Remote Sens. Environ., 98, 225–236, https://doi.org/10.1016/j.rse.2005.07.008, 2005. 
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Short summary
In order to make up for the lack of long-term soybean planting area maps in China, we firstly generated a dataset of soybean planting area with a spatial resolution of 10 m for major producing areas in China from 2017 to 2021 (ChinaSoyArea10m). Compared with existing datasets, ChinaSoyArea10m has higher consistency with census data and further improvement in spatial details. The dataset can provide reliable support for subsequent studies on yield monitoring and food security.
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