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

Viewed

Total article views: 5,710 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,009 1,574 127 5,710 140 188
  • HTML: 4,009
  • PDF: 1,574
  • XML: 127
  • Total: 5,710
  • BibTeX: 140
  • EndNote: 188
Views and downloads (calculated since 09 Sep 2019)
Cumulative views and downloads (calculated since 09 Sep 2019)

Viewed (geographical distribution)

Total article views: 5,710 (including HTML, PDF, and XML) Thereof 5,069 with geography defined and 641 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Nov 2025
Download
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.
Read more
Share