The variation in the duration of snow on the Tibetan Plateau is relatively great, and the high mountainous areas around the plateau are rich in snow and ice resources. Taking full account of the terrain of the Tibetan Plateau and the snow characteristics in the mountains, the data set adopted AVHRR data to gradually realize generating data products for daily, ten-day, and monthly snow cover areas while maintaining the snow classification accuracy. These data included the daily/10-day/monthly snow cover area data for the Tibetan Plateau from 2007 to 2015, the average accuracy of which is 0.92. It can provide reliable data for snow changes during the historical periods of the Tibetan Plateau.
QIU Yubao
The long-time series data set of snow cover area on the qinghai-tibet plateau is derived from the fusion of MODIS 005 version and IMS data set, andThe cloud-free products of daily snow cover area were obtained by using interpolation de-cloud algorithm.The projection is latitude and longitude, the spatial resolution is 0.005 degrees (about 500m), and the time is a long time series from January 1, 2003 to December 31, 2014. Each file is the result of the proportion of snow cover area on that day, and the value is 0-100 (%). It is the ENVI standard file, The naming convention： ims_mts_yyyyddd.tif, where YYYY stands for year and DDD stands for Julian day (001-365/366).Files can be directly used ENVI or ARCMAP software open view. Document description: 200 snow, 100 lake ice, 25 land, 37 sea
HAO Xiaohua
The map is "1:4 Million Ice, Snow and Frozen Soil Map of China" compiled by Mr. Shi Yafeng and Mr. Meadson. The working map compiled by the map is "Chinese Pinyin Edition of the People's Republic of China", which retains the water system and mountain annotation of the map and adds some mountain annotation. The compilation of frozen soil map is based on the actual data of frozen soil survey and exploration, interpretation of remote sensing data, temperature conditions and topographic characteristics that affect the formation and distribution of frozen soil. The height of glacier snow line is expressed by isolines. Seasonal snow accumulation and seasonal icing are based on the data of 1600 meteorological observation stations and the results of many years of investigation in China. They are expressed by isoline notation and symbols. The selection of cold (periglacial) phenomena is a representative and schematic representation observed on the spot. The boundary line between permafrost and non-permafrost is mapped by calculation based on the field data, and its comprehensive degree is relatively high (Tö pfer, 1982) "China Ice and Snow Frozen Soil Map" reflects the scale, types and characteristics of distribution of glaciers, snow cover, frozen soil and periglacial, as well as its value in scientific research and the prospect of utilization and prevention in production practice. It shows our achievements in glacier and frozen soil research in the past 30 years.
SHI Yafeng, MI Desheng
This data set includes the observation data of 40 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River since January 2014. Soil moisture of 4cm, 10cm and 20cm is the basic observation of each node; 19 nodes include the observation of soil moisture and surface infrared radiation temperature; 11 nodes include the observation of soil moisture, surface infrared radiation temperature, snow depth and precipitation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification. Please refer to "waternet data document 20141206. Docx" for details
KANG Jian, LI Xin, MA Mingguo
This data set includes the observation data of 40 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River since the end of June 2013. Soil moisture of 4cm, 10cm and 20cm is the basic observation of each node; 19 nodes include the observation of soil moisture and surface infrared radiation temperature; 11 nodes include the observation of soil moisture, surface infrared radiation temperature, snow depth and precipitation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification.
KANG Jian, LI Xin, MA Mingguo
The “long-term series of daily snow depth in Eurasia” was produced using the passive microwave remote sensing data. The temporal range is 1980~2016, and the coverage is the Eurasia continent. The spatial resolutions is 0.25° and the temporal resolution is daily. A dynamic brightness temperature gradient algorithm was used to derive snow depth. In this algorithm, the spatial and temporal variations of snow characteristics were considered and the spatial and seasonal dynamic relationships between the temperature difference between 18 GHz and 36 GHz and the measured snow depth were established. The long-term sequence of satellite-borne passive microwave brightness temperature data used to derive snow depth came from three sensors (SMMR, SSM/I and SSMI/S), and there is a certain system inconsistency among them. So, the inter-sensor calibration was performed to improve the temporal consistency of these brightness temperature data before snow depth derivation. The accuracy analysis shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file includes a file header (projection mode) and a 720*332 snow depth matrix, and each snow depth represents a 0.25°*0.25° grid. For details of the data, please refer to data specification “Snow depth dataset of Eurasian (Version 1.0) (1980-2016).doc”
CHE Tao, DAI Liyun
1、 Data Description: the data includes the content of silica in snowmelt water and soil water in hulugou small watershed from May 2013 to April 2014. 2、 Sampling location: the sampling point of snowmelt water is located near 600m below No.2 meteorological station, with ground elevation of 3514.45m, longitude and latitude of 99 ° 53 ′ 20.655 ″ e, 38 ° 14 ′ 14.987 ″ n. The sampling point of soil water is located at 300m above and below the No.2 meteorological station, with the longitude and latitude of 99 ° 53 ′ 31.333 ″ E and 38 ° 13 ′ 50.637 ″ n. 3、 Measurement method: the content of silica in the sample was measured by ICP-AES. Silicon dioxide is replaced by the value of Si in the solution.
SUN Ziyong, CHANG Qixin
"Heihe River Basin Ecological hydrological comprehensive atlas" is supported by the key project of Heihe River Basin Ecological hydrological process integration research. It aims at data arrangement and service of Heihe River Basin Ecological hydrological process integration research. The atlas will provide researchers with a comprehensive and detailed background introduction and basic data set of Heihe River Basin. The snow day map of Heihe River Basin is one of the hydrological and water resources in the atlas, with the scale of 1:2500000, the positive axis and equal volume conic projection, and the standard latitude of 25 47 n. Data source: this map shows the distribution of annual average snow days in 10 hydrological years in the whole Heihe River Basin from August 1, 2001 to July 31, 2011. The original data comes from MODIS daily snow products modisa 1 and myd10a1 provided by the National Snow and Ice Data Center (NSIDC) of the United States, as well as the long-term series snow depth data set of China provided by the scientific data center for cold and dry regions (WESTDC).
WANG Jianhua, ZHAO Jun, WANG Xiaomin
The daily cloudless MODIS Snow area ratio data set (2000-2015) of the Qinghai Tibet Plateau is based on MODIS daily snow product - mod10a1, which is obtained by using a cloud removal algorithm based on cubic spline interpolation. The data set is projected by UTM with spatial resolution of 500m, providing daily snow cover FSC results in the Tibetan Plateau. The data set is a day-to-day document, from 24 February 2000 to 31 December 2015. Each file is the result of snow area proportion on that day, the value is 0-100%, which is envi standard file, the naming rule is: yyyddd_fsc_0.5km.img, where yyyy represents the year, DDD represents Julian day (001-365 / 366). Files can be opened and viewed directly with envi or ArcMap. The original MODIS Snow data product for cloud removal comes from the mod10a1 product processed by the National Snow and Ice Data Center (NSIDC). This data set is in the format of HDF and uses the sinusional projection. The attributes of the daily cloudless MODIS Snow area ratio data set (2000-2015) on the Qinghai Tibet Plateau consist of the spatial-temporal resolution, projection information and data format of the data set. Temporal and spatial resolution: the temporal resolution is day by day, the spatial resolution is 500m, the longitude range is 72.8 ° ~ 106.3 ° e, and the latitude is 25.0 ° ~ 40.9 ° n. Projection information: UTM projection. Data format: envi standard format. File naming rules: "yyyyddd" + ". Img", where yyyy stands for year, DDD stands for Julian day (001-365 / 366), and ". Img" is the file suffix added for easy viewing in ArcMap and other software. For example, 2000055 ﹐ FSC ﹐ 0.5km.img represents the result on the 55th day of 2000. The envi file of this data set is composed of header file and body content. The header file includes row number, column number, band number, file type, data type, data record format, projection information, etc.; take 2000055 ﹣ FSC ﹣ 0.5km.img file as an example, the header file information is as follows: ENVI Description = {envi file, created [sat APR 27 18:40:03 2013]} Samples = 5760 Lines = 3300 Bands = 1 Header offset = 0 File type = envi standard Data type = 1: represents byte type Interleave = BSQ: data record format is BSQ Sensor type = unknown Byte order = 0 Map Info = {UTM, 1.500, 1.500, - 711320.359, 4526650.881, 5.0000000000e + 002, 5.0000000000e + 002, 45, north, WGS-84, units = meters} Coordinate system string = {projcs ["UTM [u zone [45N], geocs [" GCS [WGS [1984], data ["d [WGS [1984", organization ID ["WGS [1984", 6378137.0298.257223563]], prime ["Greenwich", 0.0], unit ["degree", 0.01745532925199433]]] project ["transfer [Mercator"]] parameter ["false [easting", 500000.0], parameter ["false [easting", 500000.0], parameter [500000.0], parameter [500000.0], parameter [false [false [easting ", 500000.0], parameter], parameter [500000.0], parameter [500000.0], parameter [500000.0], parameter [false [easting", 500000.0], parameter [500000.0], parameter [500000.0], parameter [500000.0], parameter ["false_northing", 0.0], parameter ["central_meridian", 87.0], parameter ["scale" _Factor ", 0.9996], parameter [" latitude ﹣ of ﹣ origin ", 0.0], unit [" meter ", 1.0]]} Wavelength units = unknown, band names = {2000055}
TANG Zhiguang, WANG Jian
Snow duration on the Tibetan Plateau changes relatively quickly, and the mountainous areas around the plateau are characterized by abundant snow and ice resources and active atmospheric convection. Optical remote sensing is often affected by clouds. Snow cover monitoring needs to consider the cloud-removal problem on a daily time scale. Taking full account of the terrain of the Tibetan Plateau and the characteristics of snow on the mountains, this data set adopted a combination of various cloud-removing processes and steps to gradually remove the daily snow cover by maintaining the cloud-classify accuracy of the snow cover. In addition, a step-by-step comprehensive classification algorithm was formed, and the “MODIS daily cloud-free snow cover product over the Tibetan Plateau (2002-2015)” was completed. Two snow seasons from October 1, 2009, to April 30, 2011, were selected as test data for algorithm research and accuracy verification, and the snow depth data provided by 145 ground stations in the study area were used as a ground reference. The results showed that in the plateau region, when the snow depth exceeds 3 cm, the total classification accuracy of the cloud-free snow cover products is 96.6%, and the snow cover classification accuracy is 89.0%. The whole algorithm procedure, based on WGS84 projected MODIS snow products (MOD10A1 and MYD10A1) with medium resolution, results in a small loss of cloud-removal accuracy, which made the data highly reliable.
QIU Yubao
This dataset is the snow cover dataset based on the MODIS fractional snow cover mapping algorithm Coupled Regional Approach (CRA). The CRA algorithm mainly consists of three parts. (1) First, the N-FINDR (Volume Iterative Approach) and OSP (Orthogonal Subspace Projection) are used to automatically extract the endmember according to the settings (extracting 30 end endmembers). (2) On the basis of automatic extraction, combined with the IGBG land cover type map, six types of endmembers of snow, vegetation, cloud, soil, rock and water are selected by the manual screening method, and an annual spectrum database is established according to the 2009 image. There are 3 spectra in the early, middle and late months and 36 spectra a year. (3) The established spectral database is used as a priori knowledge, and based on prior knowledge, the fully constrained linear unmixing method (FCLS) for subpixel decomposition is used to obtain the fractional snow cover products. The NDSI ratio algorithm with improved topographic effect is used to obtain the snow cover area, the spatiotemporal data are then interpolated, and, finally, the multisource data fusion with the AMSR-E microwave snow depth product is undertaken. The dataset adopts a latitude and longitude (Geographic) projection method. The datum is WGS84, and the spatial resolution is 0.005°. It provides the daily cloudless snow cover area map of the Tibetan Plateau from 2008 to 2010. The data set is stored by year and consists of 3 folders from 2008 to 2010. Each folder contains the classification results of the daily snow cover of the current year. It is a tif file with the naming rule YYYY***.tif, in which YYYY represents the year (2008-2010), and *** represents the day (001~365/ 366). It can be opened directly with ARCGIS or ENVI.
HAO Xiaohua
Due to the short snow duration and thin snow layer on the Tibetan Plateau, dynamic monitoring data for daily fractional snow cover are urgently needed in order to better understand water cycling and other processes. This data set is based on MODIS Snow Cover Daily L3 Global 500 m Grid data and includes the Normalized Difference Snow Index (NDSI) data product generated from MODIS/Terra data (MOD10A1) and MODIS/Aqua data (MYD10A1). The data are in the .hdf format. The projection method is sinusoidal map projection. Combining the advantages of 90 m SRTM terrain data and fractional snow cover estimation algorithms under multiple cloud coverage types, the fractional snow cover under different cloud coverage conditions can be re-estimated to meet the production requirements of the daily less cloud (< 10%) data products in High Asia. On the basis of this method, the MODIS daily fractional snow cover data set over High Asia (2002-2016) was constructed. By taking the binary snow product under cloudless conditions as a reference, the spatial and temporal comparisons between snow distribution and snow coverage show that the spatio-temporal characteristics of the product and the binary products are highly consistent. Taking the winter of 2013 as an example, when the fractional snow cover is greater than 50%, the correlation can reach 0.8628. This data set provides daily fractional snow cover data for use in studying snow dynamics, the climate and environment, hydrology, energy balance, and disaster assessment in High Asia.
QIU Yubao
The dataset of snow properties measured by the Snowfork was obtained in the Binggou watershed foci experimental area from Dec. 5-16 2007, during the pre-observation period. The aims of the measurements were to verify applicability of the instruments and to acquire snow parameters for simultaneous airborne, satellite-borne and ground-based remote sensing experiments and other control experiments. Observation items included: (1) physical quantities by direct observations: resonant frequency, the rate of attenuation and 3db bandwidth (2) physical quantities by indirect observations: snow density, snow complex permittivity (the real part and the imaginary part), snow volumetric moisture and snow gravimetric moisture. Five files including raw data and processed data are kept, data by the Snowfork on Dec 5, data by BG-A MODIS on Dec 6 and 7, data in BG-B, BG-C, BG-D and BG-E on Dec 10, and data in BG-D with the microwave radiometer on Dec 14 and 16.
HAO Xiaohua, LIANG Ji
The dataset of ground truth measurements for snow synchronizing with the airborne PHI mission was obtained in the Binggou watershed foci experimental area on Mar. 24, 2008. Observation items included: (1) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork in BG-A. (2) Snow parameters as the snow surface temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, and snow density by the aluminum case in BG-A1, BG-A2, BG-B, BG-D, BG-E and BG-F5 (three sampling units each) from 11:11-12:35 (BJT) with the airplane overpass. 64 points were selected by four groups. (3) Snow albedo by the total radiometer in BG-A. (4) The snow spectrum by ASD (Xinjiang Meteorological Administration) in BG-A11 Two files including raw data and preprocessed data were archived.
GE Chunmei, GU Juan, HAO Xiaohua, LI Hongyi, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Jianhua, WANG Xufeng, WU Yueru, XU Zhen, ZHU Shijie, LIANG Xingtao, LIU Zhigang, QU Wei, REN Jie, FANG Li, LI Hua, CHANG Cun, DOU Yan, MA Zhongguo, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu
The dataset of snow properties measured by the Snowfork was obtained in the Binggou watershed foci experimental area from Mar. 10 to 30, 2008, in cooperation with simultaneous airborne, satellite-borne and ground-based remote sensing experiments and other control experiments. Observation items included (1) physical quantities by direct observations: resonant frequency, the rate of attenuation and 3db bandwidth; (2) physical quantities by indirect observations: snow density, snow complex permittivity (the real part and the imaginary part), snow volumetric moisture and snow gravimetric moisture. 13 files are archived, and the user guide of the sampling plot and observation background is included too.
HAO Xiaohua, LIANG Ji, LI Zhe
First, Data Description The data includes stable hydrogen and oxygen isotope data of snow melt water, river water and soil water from July 2013 to April 2014. Second, Sampling Sites The snowmelt water sampling point is located in the middle of the third area, with a latitude and longitude of 99°53′28.004′′E, 38°13′25.781′′N, and the number of acquisitions is 3 times; The river water sampling point is located at the exit of the Hulugou Basin, with a latitude and longitude of 99°52′47.7′′E, 38°16′11′′N, and the sampling frequency is once a week; The soil water sampling point is located in the middle and lower part of the Hongnigou catchment area, with a sampling depth of 90cm and 180cm underground, and a latitude and longitude of 99°52'25.98′′E, 38°15′36.11′′N. Third, Testing Method The samples were measured by L2130-i ultra-high precision liquid water and water vapor isotope analyzer.
CHANG Qixin, SUN Ziyong
This data set provides daily snow thickness distribution data of China from October 24, 1978 to December 31, 2012, with a spatial resolution of 25km.The original data used for the inversion of the snow depth data set came from SMMR (1978-1987), SSM/I (1987-2008) and amsr-e (2002-2012) daily passive microwave bright temperature data processed by the national snow and ice data center (NSIDC).As the three sensors are mounted on different platforms, there is a certain system inconsistency in the obtained data.The time consistency of bright temperature data is improved by cross calibration of bright temperature of different sensors.Then, based on Chang algorithm, Dr. Che tao is used to carry out snow depth inversion.Refer to the data description document for specific inversion methods.
CHE Tao, LI Xin, DAI Liyun
The parameter inversion study project of soil moisture and snow water equivalent on the Tibetan Plateau in the past 20 years is part of the key research plan of Environmental and Ecological Science for West China of the National Natural Science Foundation of China. The person in charge is Jiancheng Shi, a researcher at the Institute of Remote Sensing Applications of the Chinese Academy of Sciences. The project ran from January 2004 to December 2007. The data collection of the project: the Monthly MODIS Snow Cover Product of Tibetan Plateau (2001-2005). Based on the image data acquired by MODIS, combined with ASTER image data, the data set carried out snow cover area classification and change analysis at a subpixel level on the Tibetan Plateau. The research mainly focused on studying the subpixel snow cover area classification algorithm, including the statistical regression method and the mixed-pixel decomposition method using the normalized snow index. In the mixed-pixel decomposition, a linear mixed model was adopted, and snow and non-snow end members were automatically extracted using the normalized snow index and the normalized vegetation index. On the basis of the subpixel snow cover area classification algorithm, the snow cover area variation on the Tibetan Plateau was analyzed. Using the method of establishing a decision tree, clouds and snow were detected, cloud-removal was performed, and the subpixel of the Tibetan Plateau was formed by synthesis and mosaicking of the time series images. The snow cover area classification database analyzes and describes the spatial distribution and variation characteristics of the snow cover area of the Tibetan Plateau.
SHI Jiancheng, XU Lina
The dataset of ground-based RPG-8CH-DP microwave radiometers (6.925H/V, 18.7H/V and 36.5H/V) and ground truth observations for snow was obtained in the Binggou watershed foci experimental area on Mar. 24 (time-continuous from 11:42 to 17:28 BJT) and Mar. 25, 2008 (short-time multi-angle observations). A gentle slope of 10° was chosen as the observation site, where there was firn snow and the snow layer and the ice layer appeared alternately. The radiometer beam was set from -20° to -55°, with the steplength 5°. Observation items included: (1) The brightness temperature by the microwave radiometer in .BRT and .txt (the ASCII format). Each row in .txt was listed by year, month, date, hour, minute, second, 6.925GHz (h), 6.925GHz (v), 10.65GHz (h), 10.65GHz (v) , 18.7GHz (h), 18.7GHz (v), 36.5GHz (h), 36.5GHz (v), the elevation angle, and the azimuth angle. Values for 6.925GHz and 10.65GHz were zero due to the absence of these two radiometers. (2) Snow parameters including the snow profile temperature by the probe thermometer and the handheld infrared thermometer, the snow grain size by the handheld microscope, snow moisture, snow density, and snow permittivity by the snow fork. Five subfolders are archived, including the brightness temperature and the profiles of liquid water content, the snow grain size, snow density and the snow temperature.
CHANG Sheng, PENG Danqing, ZHANG Yongpan, ZHANG Zhiyu, ZHAO Shaojie, ZHENG Yue, ZHANG Zhiyu
The dataset of ground truth measurements for snow synchronizing with the airborne microwave radiometers (K&Ka bands) mission was obtained in the Binggou watershed foci experimental area on Mar. 30, 2008. Those provide reliable data for retrieval of snow parameters and properties, especially for dry and wet snow identification. Observation items included: (1) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the snowfork in BG-A; (2) Snow parameters including snow depth, the snow surface temperature synchronizing with the airborne microwave radiometers (K&Ka bands), the snow layer temperature, the snow grain size and snow density in BG-A (10 points), BG-B (6 points), BG-F (12 points), BG-H (21 points) and BG-I (20 points); For each snow pit, the snowpack was divided into several layers with 10-cm intervals of snow depth. The layer depth (by the ruler), the snow grain size (by the handheld microscope), snow density (by the cutting ring) and the snow temperature (by the probe thermometer) were obtained at each snow pit. Two files including raw data and the preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, GU Juan, HAO Xiaohua, LI Hongyi, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Jianhua, WANG Xufeng, WU Yueru, XU Zhen, ZHU Shijie, LI Hua, CHANG Cun, MA Zhongguo, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu, CHE Tao
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