The Cryosphere
The Cryosphere
TC
Articles | Volume 14, issue 6
Articles | Volume 14, issue 6
https://doi.org/10.5194/tc-14-1763-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-14-1763-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 6
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

Model code and software

randomForest: Breiman and Cutler's Random Forests for Classification and Regression L. Breiman, A. Cutler, A. Liaw, and M. Wiener https://CRAN.R-project.org/package=randomForest

DMSP SSM/I-SSMIS Pathfinder Daily EASE-Grid Brightness Temperatures, Version 2 R. Armstrong, K. Knowles, M. Brodzik, and M. Hardman https://doi.org/10.5067/3EX2U1DV3434

RF\_based\_Longterm\_SnowDepth\_China.rar J. Yang and L. Jiang https://doi.org/10.6084/m9.figshare.11988027

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Short summary
There are many challenges for accurate snow depth estimation using passive microwave data. Machine learning (ML) techniques are deemed to be powerful tools for establishing nonlinear relations between independent variables and a given target variable. In this study, we investigate the potential capability of the random forest (RF) model on snow depth estimation at temporal and spatial scales. The result indicates that the fitted RF algorithms perform better on temporal than spatial scales.
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