The dataset of ground truth measurements synchronizing with Terra MISR and MODIS was obtained in sampling plot BG-A of the Binggou watershed foci experimental area on Dec. 10 and Dec. 11, 2007 during the pre-observation period. Observation items included: (1) Snow parameters including the snow surface temperature, the snow-soil interface temperature, the land surface temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, snow depth by the ruler and the snow grain size by the handheld microscope. (2) Snow density in "WATER: Dataset of snow density measurements in the Binggou watershed foci experimental area on Dec. 6 and Dec. 10, 2007 during the pre-observation period" (3) Snow properties in "WATER: Dataset of snow properties measured by the Snowfork in the Binggou watershed foci experimental area during the pre-observation period" Raw data and pre-processed data including snow parameters synchronizing with Terra MISR and MODIS and the temperature synchronizing with MODIS were archived herein.
LI Xin, WANG Jian, MA Mingguo, Wang Weizhen, CHE Tao, HAO Xiaohua, LI Hongyi, LIANG Ji, BAI Yunjie, WANG Xufeng, WU Yueru, WANG Yang, LUO Lihui, ZHANG Pu, LIU Yan
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
This data is a simulated output data set of 5km monthly hydrological data obtained by establishing the WEB-DHM distributed hydrological model of the source regions of Yangtze River and Yellow River, using temperature, precipitation and pressure as input data, and GAME-TIBET data as verification data. The dataset includes grid runoff and evaporation (if the evaporation is less than 0, it means deposition; if the runoff is less than 0, it means that the precipitation in the month is less than evaporation). This data is a model based on the WEB-DHM distributed hydrological model, and established by using temperature, and precipitation (from itp-forcing and CMA) as input data, GLASS, MODIA, AVHRR as vegetation data, and SOILGRID and FAO as soil parameters. And by the calibration and verification of runoff，soil temperature and soil humidity, the 5 km monthly grid runoff and evaporation in the source regions of Yangtze River and Yellow River from 1998 to 2017 was obtained. If asc can't open normally in arcmap, please delete the blacks space of the top 5 lines of the asc file.
WANG Lei
The dataset of snow density measurements was obtained in the Binggou watershed foci experimental area on Dec. 6 and Dec. 10, 2007 during the pre-observation period, to survey the snow layer and acquire the snow density for retrieval and modeling from remote sensing approaches. Observation items included: (1) Snow layer density: measured by snow shovel weighing method. Each 10cm was a unit. (2) Snow density, snow depth, snow temperature, snow-soil interface temperature, and snow grain size in BG-A. Measured were carried out in BG-A on Dec. 6, 2007, and in BG-B, BG-C and BG-D on Dec. 10, 2007. The dataset includes raw data and processed data plus GPS and calibration data for the snow shovel.
HAO Xiaohua, LIANG Ji, WANG Xufeng
The data set is the meteorological and observational data of hulugou shrub experimental area in the upper reaches of Heihe River, including meteorological data, albedo data and evapotranspiration data under shrubs. 1. Meteorological data: Qilian station longitude: 99 ° 52 ′ E; latitude: 38 ° 15 ′ n; altitude: 3232.3m, scale meteorological data from January 1, 2012 to December 31, 2013. Observation items include: temperature, humidity, vapor pressure, net radiation, four component radiation, etc. The data are daily scale data, and the calculation period is 0:00-24:00 2. Albedo: daily surface albedo data from January 1, 2012 to July 3, 2014, including snow and non snow periods. The measuring instrument is the radiation instrument on the 10m gradient tower in hulugou watershed. Among them, the data from August 4 to October 2, 2012 was missing due to instrument circuit problems, and the rest data quality was good 3. Evapotranspiration: surface evapotranspiration data of Four Typical Shrub Communities in hulugou watershed. The observation period is from July 18 to August 5, 2014, which is the daily scale data. The data include precipitation data, evaporation and infiltration data observed by lysimeter. The data set can be used to analyze the evapotranspiration data of alpine shrubs and forests. The evapotranspiration of grassland under canopy was measured by a small lysimeter with a diameter of 25 cm and a depth of 30 cm. Two lysimeters were set up in each shrub plot, and one lysimeter was set for each shrub in transplanting experiment. The undisturbed undisturbed soil column with the same height as the barrel is placed in the inner bucket, and the outer bucket is buried in the soil. During the embedding, the outer bucket shall be 0.5-1.0 cm higher than the ground, and the outer edge of the inner barrel shall be designed with a rainproof board about 2.0 cm wide to prevent surface runoff from entering the lysimeter. Lysimeter was set up in the nearby meteorological stations to measure grassland evapotranspiration, and a small lysimeter with an inner diameter of 25 cm and a depth of 30 cm was also set up in the sample plot of Picea crassifolia forest to measure the evaporation under the forest. All lysimeters are weighed at 20:00 every day (the electronic balance has a sensing capacity of 1.0 g, which is equivalent to 0.013 mm evaporation). Wind proof treatment should be taken to ensure the accuracy of measurement. Data processing method: evapotranspiration is mainly calculated by mass conservation in lysimeter method. According to the design principle of lysimeter lysimeter, evapotranspiration is mainly determined by the quality difference in two consecutive days. Since it is weighed every day, it is calculated by water balance.
SONG Yaoxuan, LIU Zhangwen
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
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
The “Long-term series of daily global snow depth” was produced using the passive microwave remote sensing data. The temporal range is 1980~2018, and the coverage is the global land. The spatial resolutions is 25,067.53 m 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 is a 1383*586 snow depth matrix, and each snow depth represents a 25,067.53m* 25,067.53m grid. The projection of this data is EASE-Grid, and following is the file header which describes the projection detail. File header: ncols 1383 nrows 586 xllcorner -17334193.54 yllcorner -7344787.75 cellsize 25,067.53 NODATA_value -1
CHE Tao, LI Xin, DAI Liyun
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
High Asia is very sensitive to climate change, and is a hot area of global change research. The changes of temperature and precipitation will be reflected in the freezing and thawing time of ice and snow. Satellite microwave remote sensing can provide continuous monitoring ability of ice and snow surface state in time and space. When a small part of ice and snow begins to melt, micro liquid water will also be reflected in active and passive microwave remote sensing signals. In the microwave band, the dielectric constant of ice and liquid water is very different, so it provides a basic theory for the microwave remote sensing monitoring of ice and snow melting. In the case of passive microwave, when ice and snow begin to melt and liquid water appears, its absorption and emissivity increase rapidly, so its emissivity, brightness temperature and backscatter coefficient will also change rapidly. This data set is the initial time of ice and snow melting in the high Asia region retrieved by using the satellite microwave radiometer and scatterometer observations from 1979 to 2018. The passive microwave remote sensing data are SMMR on satellite (1979-1987) and SSM / i-ssmis radiometer on DMSP (1988 present). The active microwave remote sensing data is the QuikSCAT satellite scatterometer (2000-2009).
Xiong Chuan, SHI Jiancheng, YAO Ruzhen, LEI Yonghui, PAN Jinmei
The dataset was produced based on MODIS data. Parameters and algorithm were revised to be suitable for the land cover type in the Three-River-Source Regions. By using the Markov de-cloud algorithm, SSM/I snow water equivalent data was fused to the result. Finally, high accuracy daily de-cloud snow cover data was produced. The data value is 0(no snow) or 1(snow). The spatial resolution is 500m, the time period is from 2000-2-24 to 2019-12-31. Data format is geotiff, Arcmap or python+GDAL were recommended to open and process the data.
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 dataset was derived from long-term daily snow depth in China based on the boundary of the three-river-source area. The snow depth ranges from 0 to 100 cm, and the temporal coverage is from January 1 1980 to December 31 2020. The spatial and temporal resolutions are 0.25o and daily, respectively. Snow depth was produced from satellite passive microwave remote sensing data which came from three different sensors that are SMMR, SSM/I and SSMI/S. Considering the systematic bias among these sensors, the inter-sensor calibrations were performed to obtain temporal consistent passive microwave remote sensing data. And the long-term daily snow depth in China were produced from this consistent data based on the spectral gradient method.For header file information, refer to the data set header.txt.
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
"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
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
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
High Mountain Asia is the third largest cryosphere on earth other than the Antarctic and Arctic regions. The large amounts of glaciers and snow over the High Mountain Asia play an important role not only on global water cycle but also on water resources and ecology of the arid regions of central Asia. The snowline, as the lower boundary of the snow covered area at the end of melting season, its altitude changes can directly reflect the changes in snow and glaciers. The snowline altitude provides a possibility to rapidly obtain a proxy for their equilibrium line altitude (ELA) which in turn is an indicator for the glacier mass balance. In this dataset, the daily MODIS snow cover products from 2001 to 2019 are used as the main data source. The cloud removal of the daily MODIS snow cover products was firstly carried out based on the developed cubic spline interpolation cloud-removel method, and snow covered days (SCD) are extracted using the cloud-removed MODIS snow cover products. In addition, the MODIS SCD threshold for estimating perennial snow cover is calibrated using the observed data of glacier annual mass balance and Landsat data at the end of melting season. The altitude value of the snowline at the end of melting season is determined by combining the perennial snow cover area and the hypsometric (area-elevation) curve. Finally, the 30km gridded dataset of snowline altitude in the High Mountain Asia during 2001-2019 is generated. This dataset can provide data support for the study of cryosphere and climate change over the High Mountain Asia.
TANG Zhiguang, DENG Gang, WANG Xiaoru
This data includes the distribution along the height of the blowing snow flux collected during the wind-blown snow event at the big winter tree pass observation station (longitude 100 degrees 14 minutes 28 seconds east and latitude 38 degrees 00 minutes 58 seconds north) on December 17, 2013 at solstice on July 9, 2014.
HUANG Ning, WANG Zhengshi
This data is 2002.07.04-2010.12.31 MODIS daily cloudless snow products in the Tibetan Plateau. Due to the snow and cloud reflection characteristics, the use of optical remote sensing to monitor snow is severely disturbed by the weather. This product is based on the most commonly used cloud removal algorithm, using the MODIS daily snow product and passive microwave data AMSR-E snow water equivalent product, and the daily cloudless snow product in the Tibetan Plateau is developed. The accuracy is relatively high. This product has important value for real-time monitoring of snow cover dynamic changes on the Tibetan Plateau. Projection method: Albers Conical Equal Area Datum: D_Krasovsky_1940 Spatial resolution: 500 m Data format: tif Naming rules: maYYMMDD.tif, where ma represents the data name; YY represents the year (01 represents 2001, 02 represents 2002 ...); MM represents the month (01 represents January, 02 represents February ...); DD represents the day (01 Means 1st, 02 means 2nd ...).
HUANG Xiaodong
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