The snow area proportion data set of Heihe River basin provides a time series product of snow area proportion on cloudless days from 2010 to 2012. The data uses satellite MODIS data with high temporal resolution (1 day) and spatial resolution (500m). Firstly, the automatic algorithm N-FINDR is used to select the end elements. On the basis of automatic extraction, six types of end elements including snow, vegetation, cloud, soil, rock and water are selected manually, and the spectral database is established according to the 2009 images; Based on the spectral database, the fully constrained linear unmixing method (FCLs) is used for sub-pixel decomposition to obtain the primary snow area proportion product; Finally, the cloud free products of MODIS daily snow area ratio are obtained by using the difference cloud removal algorithm
Verified by using high-resolution image Landsat TM, it has higher accuracy than the existing MODIS Snow area proportion product (mod10a1). It can provide more accurate snow parameter input for hydrology and meteorology of the basin
Data Description: 0-100 snow area proportion, 0 Non snow; Projection type: longitude and latitude projection, WGS-84 datum
Spatial resolution: 0.005 degrees
Time resolution: 1 day
Data Description: 0-100 snow area proportion, 0 Non snow
Projection type: longitude and latitude projection, WGS-84 datum
Spatial resolution: 0.005 degrees
Time resolution: 1 day
Six types of end elements including snow, vegetation, cloud, soil, rock and water were selected by artificial method, and the spectral database was established according to the 2009 images; Based on the spectral database, the fully constrained linear unmixing method (FCLs) is used for sub-pixel decomposition to obtain the primary snow area proportion product; Finally, the cloud free products of MODIS daily snow area ratio are obtained by using the difference cloud removal algorithm
Good data quality
| # | number | name | type |
| 1 | KZCX2-XB3-15 | other |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.ncdc.NIEER.2021.1751
10.12072/ncdc.nieer.db3523.2023
