This data is daily downscaled 500m snow depth data for Xinjiang. Its production is mainly based on the random forest algorithm, using the Chinese long-term series snow depth dataset, terrain data, spatial position data, snow cover days, etc. to prepare a daily snow depth dataset with a spatial resolution of 500m from 2010 to 2020. This dataset is stored in TIF file format, with the naming convention of "SNDP+year+DOY. tif" and DOY being the product of year and day. This dataset aims to provide effective data support for in-depth research and accurate analysis of snow cover, climate change, and snow disaster warning.
The 'China Long term Snow Depth Dataset' is sourced from the National Qinghai Tibet Plateau Science Data Center; The MOD13A1 normalized vegetation index data is sourced from Earthdata; SRTM terrain data comes from a geographic spatial data cloud platform, including altitude, slope, aspect, and surface roughness data.
This dataset is based on data from Xinjiang meteorological stations, China's long-term series snow depth dataset, longitude, latitude, SRTM altitude, slope, aspect, surface roughness, MOD13A1 normalized vegetation index data, and snow cover days data. A snow depth downscaling model based on random forest algorithm is established to produce 500m downscaled snow depth products.
The original data for this dataset is a long-term series snow depth dataset from China. The accuracy of the snow depth dataset before and after downscaling was evaluated and compared using snow depth measurement data from 50 measured stations in Xinjiang from 2013 to 2020. The results showed that the accuracy of the snow depth data was improved after downscaling, with an average coefficient of determination R2 of 0.61, a comprehensive evaluation index KEG 'of 0.64, a root mean square error RMSE of 4.59cm, an average absolute error MAE of 1.36cm, and a correlation coefficient of 0.81.
| # | number | name | type |
| 1 | 2020D09 | Spatio-temporal differentiation and influence mechanism of remote sensing snow cover parameters in the middle Tianshan Mountains based on snow cover parameters and influence factor data sets | other |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.NIEER.DB2841.2023
10.12072/ncdc.nieer.db2841.2023
