This dataset is prepared based on the day-by-day cloud-free product of 500m snow area in China from 2000 to 2019, and the 2000-2020 MODIS China snow phenology dataset is prepared, including three directories, with the directory naming rule of 2000-2020 China XXXX dataset, in which XXXX denotes the number of days of snow accumulation, the first day of accumulation, and the last day of accumulation of the snow phenology parameter; and sub The file naming rule is NIEER_MODIS_TTT_500m_YYYYY-YYYY.tif, in which TTT represents different snow cover parameters, and YYYYY-YYYY represents hydrological year, such as NIEER_MODIS_SCD_5000m_2001-2002.tif, with a spatial resolution of 500m and a temporal resolution of 1 year. year.
Using the day-by-day cloud-free product of 500m snow cover area in China from 2000-2019 provided by the Center, according to the corresponding definitions of each parameter of snow cover climate, snow cover days (Snowcoverdays,SCD), snow cover first days (Startofsnowcover,SCS), snow cover last days (Meltofsnowcoverdays,SCM). The MODIS snow cover data set for China from 2000 to 2020 was prepared.
The dataset was divided into three catalogs according to the different parameters, and the accuracy was verified by ground station data. This dataset is intended to provide basic data for the in-depth study and accurate analysis of snow cover, animal protection, climate prediction, agricultural water resource utilization, flooding, snowstorm warning and other fields.
The remote sensing data used in the study is a day-by-day cloud-free product (NIEER-GF-MODIS-SCE) of 500m snow accumulation area in China from 2000-2019 from our center. This product is based on the high spatial resolution cloud-free Landsat-5 TM/Landsat8 OLI imagery, and improves the standard snow accumulation extraction algorithm of the MODIS product in forested and non-forested areas in China, respectively, and uses two-part de-clouding by Hidden Markov Spatio-Temporal Modeling and interpolation of microwave snow depth data, and combines with the temperature data, and the water data to prepare the long time series of China for the period of 2000-2020 Day-by-day cloud-free snowpack area products (with a resolution of 5 km) containing five bands.
The validation data snow depth data are the daily value dataset of surface climate snow accumulation information from 2000-2020 from China Meteorological Data Network (http://data.cma.cn). The recorded data mainly include meteorological station area number, latitude, longitude, elevation, month and day of the year, as well as information on snow depth, average air temperature, snow pressure, average wind speed, and maximum wind speed and direction, with an invalid value of 32700 or 32766 for snow depth.
Firstly, the snow area product was preprocessed by assigning the snow raster value (t=1,2,3) of the product to 1, and the no-snow raster (t=0,4,255) value to 0. A hydrological year was defined as September 1 to August 31 of the following year, and then the number of snow days, the first day of snow, and the last day of snow were calculated for China from 2000 to 2020 according to the definitions of the snow climate parameter on a water year by water year basis and image by image basis.
The validation correlation coefficients for the number of snow days were R2 of 0.94, RMSE of 12.09 days, and MAE of 7.60 days; for the first day of snow accumulation, R2 of 0.79, RMSE of 12.24 days, and MAE of 4.6 days; and for the last day of snow accumulation, R2 of 0.56, RMSE of 19.89 days, and MAE of 7.74 days, with a high precision.
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 using programming languages and other corresponding software. The spatial overlay analysis of multi-year data can be used to obtain the regional spatial and temporal distribution and change trend of China's snowpack from 2000 to 2020, which can be combined with the regional meteorological factors and human activities to analyze the driving force of the regional snowpack 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 |
This work is licensed under a
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CSTR:11738.11.ncdc.isnow.db2519.2022
10.12072/ncdc.I-SNOW.db0011.2021