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

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

doi:https://doi.org/10.5194/tc-17-33-2023

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

Data sets

Snow survey dataset from measurement routes in the typical regions of China during 2017-2021 T. Che https://doi.org/10.12072/ncdc.I-SNOW.db0007.2021

EarthExplorer USGS https://earthexplorer.usgs.gov/

ERA5-Land hourly data from 1981 to present Muñoz-Sabater https://doi.org/10.24381/cds.e2161bac

MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 M. Friedl and D. Sulla-Menashe https://doi.org/10.5067/MODIS/MCD12Q1.006

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.