Snowpack is an important component of the cryosphere, and the extent of snow cover affects the energy balance of the earth's atmosphere, which in turn affects climate and environmental change. Snowpack area is one of the important snowpack parameters, which is an important input to hydrological and climate models.
This dataset addresses the characteristics of snow accumulation in China, based on the MODIS reflectance product MOD/MYD09GA, and develops a multi-index combined snow discrimination algorithm under different land cover types to improve the accuracy of snow area in forested and mountainous areas, and at the same time realizes complete de-cloudedness of the product by using the Hidden Markov Algorithm and multi-source data fusion methods to prepare the cloud-free day-by-day snow accumulation data for the period of 2000-2020 at the spatial resolution of The day-by-day cloud-free snow accumulation area dataset with a spatial resolution of 500 m from 2000 to 2020 is prepared.
The dataset is stored in HDF5 file format, and each HDF5 file contains 18 data elements, including data values (0=land, 1=image-identified snow, 2=declouded interpolated snow, 3=snow-depth interpolated snow, 4=water, 255=fill value), data start date, latitude, longitude, and so on. Also for a quick preview of the snow distribution, the day-by-day file contains thumbnails of the snow area, stored in jpg format. In addition, this dataset contains a user manual. This dataset will be continuously supplemented and improved based on real-time satellite remote sensing data and algorithm updates (currently until December 2020), and will be adopted for full open sharing.
MODIS day-by-day surface reflectance products MOD09GA,MYD09GA from the National Aeronautics and Space Administration (NASA), the data format is hdf format, the spatial resolution is 500m.
Using Landsat-8 OIL data as the true value, combined with MODIS land cover classification product MCD12Q1, based on MODIS albedo products MOD09GA and MYD09GA, the snow accumulation decision tree classification algorithms under different conditions of surface cover types were obtained, and based on the GEE platform, the primary products were obtained.
The primary products were de-clouded by Hidden Markov Algorithm and snow depth data interpolation method, and the running program was developed based on python language to finally obtain the day-by-day cloud-free snow area products in the study area.
The data quality is good.
| # | number | name | type |
| 1 | 2019YFC1510503 | National key R & D plan | |
| 2 | 41971325 | National Natural Science Foundation of China |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.ncdc.I-SNOW.2020.9
10.12072/ncdc.I-SNOW.db0001.2020
