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.
In this dataset, we developed a multi-level decision tree snow accumulation discrimination algorithm based on the AVHRR-CDR reflectance product for China's snow accumulation characteristics, and meanwhile, we utilized the Hidden Markov Algorithm and multi-source data fusion method to realize the complete de-cloudedness of the product, and prepared a day-by-day cloud-free snow area dataset with a spatial resolution of 5 km for the period of 1980-2020.
The dataset is stored in HDF5 file format, and each HDF5 file contains 18 data elements, including data values, data start date, latitude and longitude. Also, for a quick preview of the snow distribution, the day-by-day files contain thumbnails of the snow area, which are stored in jpg format. The dataset also contains a user manual for the convenience of users. 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 fully open and shared.
The AVHRR CDR surface reflectance is derived from the Climate Data Record (CDR) under the National Oceanic and Atmospheric Administration (NOAA) of the United States of America in hdf format with a spatial resolution of 5km.
Using high resolution Landsat TM data as the true value, training AVHRR CDR surface reflectance data, developing AVHRR CDR cloud-snow differentiation algorithm and multilevel decision tree snow accumulation discrimination algorithm, through this algorithm, based on the GEE platform, obtaining the primary product, the primary product is nulled and de-clouded by Hidden Markov de-clouded and snow depth data interpolation methods, based on the python standard product production system, and finally obtain the day-by-day cloud-free snowpack area product in the study area.
Data quality is good.
| # | number | name | type |
| 1 | 41971325 | National Natural Science Foundation of China | |
| 2 | 2019YFC1510503 | National key R & D plan |
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CSTR:11738.11.ncdc.I-SNOW.2020.10
10.12072/ncdc.I-SNOW.db0005.2020