This dataset is based on the 5km AVHRR day-by-day cloud-free snow accumulation area product from 1980 to 2020, and the long time series snow accumulation and climate dataset in China is prepared. The dataset is divided into three directories according to different climatological parameters, namely, the number of snow days, the first day of snow accumulation, and the last day of snow accumulation, and each directory contains 40 subfiles for hydrological year-by-year snow accumulation and climatological parameters.
The naming rule of the directory is 1980-2020 China XXXX dataset, in which XXXX indicates the number of snow days, the first day of snow, and the last day of snow; and the naming rule of the subfiles is NIEER_AVHRR_TTT_5000m_YYYYY-YYYY.tif, in which TTT indicates the different snow parameters (SCD is the number of snow days, SCS is the first day of snow, SCM is the first day of snow, and SCM is the first day of snow. SCD is the number of snow days, SCS is beginning date of snow cover, and SCM is ending date of snow cover), and YYYY-YYYYY is the hydrological year.
This dataset can be used to analyze the spatial and temporal changes of snowpack in China, and provide basic data for climate research, hydrological management, ecological environment, humanities and economy, and other scientific researches, engineering construction and social services.
The remote sensing data used in this paper is the 5 km AVHRR day-by-day cloud-free snow area product for China from 1980 to 2020 from our center. The product utilizes NOAA CDR AVHRR version 4 reflectance data as input, and the snow accumulation area product (NIEER-GF-AVHRR-SCE) is finally obtained by cloud discrimination algorithm, snow discrimination algorithm and vacancy interpolation algorithm.
The validation data used in this paper were obtained from the dataset of daily surface climatic snow information from 1980 to 2020 observed by ground stations of the China Meteorological Administration (http://data.cma.cn), where snow depths were measured manually at the weather station at 8 o'clock Beijing time when the area of the field of view was more than 50% using a meter scale. Snow depths were accurate to 1 cm, and snow depths less than 1 cm were recorded as no snow, while missing measurement information was also recorded. Ultimately, the 362 weather stations with snow records were used to assess the accuracy of the snowpack phenology dataset.
Using the day-by-day cloud-free AVHRR product as the input value, the product is first preprocessed. In the first step, the snow raster value (t) of the product is assigned to 1 and the no-snow raster value is assigned to 0 to obtain an intermediate data with only two values; in the second step, the number of days of snow accumulation in China, beginning date of snow cover, and ending date of snow cover for each hydrological year are calculated according to Eq.
The validation correlation coefficients R for the number of snow days, the first day of snow accumulation, and the last day of snow accumulation were 0.80, 0.76, and 0.94, respectively, the RMSE was 22.78, 17.87, and 16.39, and the MAE was 13.26, 7.51, and 7.76, respectively, which were of high accuracy.
The data files are in GeoTIFF format, which can be directly viewed and applied by GIS and remote sensing software such as ENVI, GRASS, ArcGIS, etc., or compiled and read, calculated and analyzed by corresponding software such as programming language.
The spatial superposition analysis of multi-year data can obtain the regional spatial and temporal distribution of snow cover and the age trend of snow cover in China from 1980 to 2020, which can be combined with the regional meteorological factors, human activities, etc. to analyze the driving force of the regional snow cover changes, so as to provide information services for production and disaster early warning.
| # | number | name | type |
| 1 | 41971325 | National Natural Science Foundation of China | |
| 2 | 42171391 | National Natural Science Foundation of China | |
| 3 | 2019YFC1510503 | National key R & D plan |
This work is licensed under a
Creative
Commons Attribution 4.0 International License.
CSTR:11738.11.ncdc.isnow.db2518.2022
10.12072/ncdc.I-SNOW.db0010.2021