Articles | Volume 18, issue 3
https://doi.org/10.5194/essd-18-1995-2026
https://doi.org/10.5194/essd-18-1995-2026
Data description article
 | 
17 Mar 2026
Data description article |  | 17 Mar 2026

ChinaAI-FSC: a comprehensive AI-ready MODIS fractional snow cover dataset for China (2000–2022)

Jinliang Hou, Mingkai Zhang, Xiaohua Hao, Jifu Guo, Peng Dou, Ying Zhang, and Chunlin Huang

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Cited articles

Azizi, A. H., Akhtar, F., Kusche, J., Tischbein, B., Borgemeister, C., and Oluoch, W. A.: Machine learning-based estimation of fractional snow cover in the Hindukush Mountains using MODIS and Landsat data, J. Hydrol., 638, 131579, https://doi.org/10.1016/j.jhydrol.2024.131579, 2024. 
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005. 
Chander, G., Hewison, T. J., Fox, N., Wu, X., Xiong, X., and Blackwell, W. J.: Overview of intercalibration of satellite instruments, IEEE T. Geosci. Remote, 51, 1056–1080, https://doi.org/10.1109/TGRS.2012.2228654, 2013. 
Chen, J., Zhu, X., Vogelmann, J. E., Gao, F., and Jin, S.: A simple and effective method for filling gaps in Landsat ETM+ SLC-off images, Remote Sens. Environ., 115, 1053–1064, https://doi.org/10.1016/j.rse.2010.12.010, 2011. 
Christensen, T.: What is AI-Ready Open Data?, presented 22 October 2020, NOAA NESDIS/STAR, U. S. Department of Commerce, National Oceanic and Atmospheric Administration, https://www.star.nesdis.noaa.gov/star/documents/meetings/2020AI/presentations/202010/20201022_Christensen.pdf (last access: 25 October 2025), 2020. 
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Short summary
ChinaAI-FSC provides the first large-scale, artificial intelligence (AI)-ready fractional snow cover dataset for China, covering 2000–2022. It integrates observations from the Moderate Resolution Imaging Spectroradiometer, Landsat, and Sentinel-2 satellites and is carefully processed to enable training and evaluation of AI models and large-scale snow mapping. This dataset improves snow monitoring accuracy and supports reproducible research on climate and hydrological processes.
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