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Articles | Volume 14, issue 6
https://doi.org/10.5194/tc-14-1763-2020
https://doi.org/10.5194/tc-14-1763-2020
Research article
 | 
03 Jun 2020
Research article |  | 03 Jun 2020

Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach

Jianwei Yang, Lingmei Jiang, Kari Luojus, Jinmei Pan, Juha Lemmetyinen, Matias Takala, and Shengli Wu

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Cited articles

Armstrong, R., Knowles, K., Brodzik, M., and Hardman, M.: DMSP SSM/I-SSMIS Pathfinder Daily EASE-Grid Brightness Temperatures, Version 2. Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center, https://doi.org/10.5067/3EX2U1DV3434, 1994. 
Bair, E. H., Abreu Calfa, A., Rittger, K., and Dozier, J.: Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan, The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, 2018. 
Basang, D., Barthel, K., and Olseth, J. A.: Satellite and Ground Observations of Snow Cover in Tibet during 2001–2015, Remote Sens., 9, 1201, https://doi.org/10.3390/rs9111201, 2017. 
Belgiu, M. and Lucian, D.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm. Remote Sens., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016. 
Biau, G. Ã. Š. and Scornet, E.: A random forest guided tour, TEST, 25, 197–227, https://doi.org/10.1007/s11749-016-0481-7, 2016. 
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Short summary
There are many challenges for accurate snow depth estimation using passive microwave data....
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