The Cryosphere
The Cryosphere
TC
Articles | Volume 17, issue 1
Articles | Volume 17, issue 1
https://doi.org/10.5194/tc-17-33-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-33-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 1
Research article
 | 
09 Jan 2023
Research article |  | 09 Jan 2023

Towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data

Huadong Wang, Xueliang Zhang, Pengfeng Xiao, Tao Che, Zhaojun Zheng, Liyun Dai, and Wenbo Luan

Viewed

Total article views: 2,240 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,629 528 83 2,240 79 71
  • HTML: 1,629
  • PDF: 528
  • XML: 83
  • Total: 2,240
  • BibTeX: 79
  • EndNote: 71
Views and downloads (calculated since 24 Mar 2022)
Cumulative views and downloads (calculated since 24 Mar 2022)

Viewed (geographical distribution)

Total article views: 2,240 (including HTML, PDF, and XML) Thereof 2,181 with geography defined and 59 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary
The geographically and temporally weighted neural network (GTWNN) model is constructed for estimating large-scale daily snow density by integrating satellite, ground, and reanalysis data, which addresses the importance of spatiotemporal heterogeneity and a nonlinear relationship between snow density and impact variables, as well as allows us to understand the spatiotemporal pattern and heterogeneity of snow density in different snow periods and snow cover regions in China from 2013 to 2020.
Read more