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 Tibetan Plateau has an average altitude of over 4000 m and is the region with the highest altitude and the largest snow cover in the middle and low latitudes of the Northern Hemisphere regions. Snow cover is the most important underlying surface of the seasonal changes on the Tibetan Plateau and an important composing element of ecological environment. Ice and snow melt water is an important water resource of the plateau and its downstream areas. At the same time, plateau snow, as an important land-surface forcing factor, is closely related to disastrous weather (such as droughts and floods) in East Asia, the South Asian monsoon and in the middle and lower reaches of the Yangtze River. It is an important indicator of short-term climate prediction and one of the most sensitive responses to global climate change. The snow depth refers to the vertical depth from the surface of the snow to the ground. It is an important parameter for snow characteristics and one of the conventional meteorological observation elements. It is the key parameter of snow water equivalent estimation, climate effect studies of snow cover, the basin water balance, the simulation and monitoring of snow-melt, and snow disaster evaluation and grading. In this data set, the Tibetan Plateau boundary was determined by adopting the natural topography as the leading factor and by comprehensive consideration of the principles of altitude, plateau and mountain integrity. The main part of the plateau is in the Tibetan Autonomous Region and Qinghai Province, with an area of 2.572 million square kilometers, accounting for 26.8% of the total land area of China. The snow depth observation data are the monthly maximum snow depth data after quality detection and quality control. There are 102 meteorological stations in the study area, most of which were built during the 1950s to 1970s. The data for some months or years for sites existing during this period were missing, and the complete observational records from 1961 to 2013 were adopted. The temporal resolution is daily, the spatial coverage is the Tibetan Plateau, and all the data were quality controlled. Accurate and detailed plateau snow depth data are of great significance for the diagnosis of climate change, the evolution of the Asian monsoon and the management of regional snow-melt water resources.
National Meteorological Information Center, Tibet Meteorological Bureau, China
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
Snow water equivalent (the product of snow depth and density) is an important factor reflecting the change in snow cover on the ground surface, and it is also an important parameter in surface hydrological models and climatic models. As the “Headwaters of Asia”, the Tibetan Plateau is the source of several major rivers, which are fed with glacier and snow meltwater. Based on the sensitivity of passive microwave radiation to snow, these monitoring data enable long-term inversion of snow water equivalents in the High Asia region. The data set includes daily snow water equivalent, monthly snow water equivalent and five-day snow water equivalent, and these data can be applied in analyses of local hydrology, animal husbandry production and other fields.
QIU Yubao
This data set includes the observation data of 25 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River from January 2015 to December 2015. 4cm and 20cm soil moisture / temperature is the basic observation of each node; some nodes also include 10cm soil moisture / temperature, surface infrared radiation temperature, snow depth and precipitation observation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification. For details, please refer to "2015 data document 20160501. Docx of water net of Babao River in the upper reaches of Heihe River"
KANG Jian, LI Xin, MA Mingguo
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 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
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
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
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
"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 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 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
The dataset of snow spectral reflectance observations was obtained in the Binggou watershed foci experimental area on Mar. 23, 2008. Flat open space was chosen for the observations and observation items included: (1) Multi-angle snow spectrum by the observation platform made by BNU for snow bidirectional reflectance properties from 10:50-13:50 BJT; (2) Snow albedo by the total radiometer for its relationship with the solar altitude from 10:00-14:36 BJT; (3) The snow spectrum by the portable ASD (Xinjiang Meteorological Administration). Two files including raw data and the preprocessed data were archived.
BAI Yunjie, HAO Xiaohua, MA Mingguo, SHU Lele, WANG Xufeng, ZHU Shijie, QU Wei, REN Jie, CHANG Cun, MA Zhongguo, JIANG Tenglong, XIAO Pengfeng , ZHANG Pu
The dataset of fresh snow properties observations was obtained at the temporary sampling plot in the Qilian county on Mar. 20, 2008. Those provide reliable data for retrieval of snow parameters from remote sensing approaches. Observation items included: (1) Snow parameters such as snow depth, snow grain size by the handheld microscope, and snow density by the snow shovel (2) Fresh snow albedo by the total radiometer (3) Fresh snow spectrum by ASD Two files including raw data and preprocessed data were archived.
GE Chunmei, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, LIU Yan, ZHANG Pu
The dataset of ground truth measurements for snow synchronizing with Envisat ASAR was obtained in the Binggou watershed foci experimental area on Mar. 15, 2008. The Envisat ASAR data were acquired in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:34 BJT. Observation items included: (1) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the snowfork in BG-B, BG-D, BG-E and BG-F; (2) Snow parameters including the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, snow density by the aluminum case, snow depth by the ruler, and the snow surface temperature synchronizing with ASAR in BG-H, BG-D, BG-E and BG-F; (3) The snow spectrum by the portable ASD (Xinjiang Meteorological Administration) synchronizing with ASAR in BG-H15; the major and minor axis and shape of the snow layer grain through the self-made snow sieve. Two files including raw data and the preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, HAO Xiaohua, LI Hongyi, LIANG Ji, SHU Lele, WANG Xufeng, XU Zhen, MA Mingguo, QU Wei, REN Jie, CHANG Cun, DOU Yan, MA Zhongguo, LIU Yan, ZHANG Pu
The dataset of ground truth measurements for snow synchronizing with EO-1 Hyperion and Landsat TM was obtained in the Binggou watershed foci experimental area on Mar. 17, 2008. Observation items included: (1) Snow parameters as snow depth by the ruler, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer simultaneous with the satellite in BG-A, BG-E, BG-F and BG-H. (2) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork in BG-A, BG-E and BG-H. Besides, 25-hour fixed-point continuous observation was carried out at the Binggou cold region hydrometerological station. (3) The snow spectrum by ASD (Xinjiang Meteorological Administration) (4) Snow albedo by the total radiometer Two files including raw data and preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, HAO Xiaohua, LIANG Ji, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, MA Mingguo, CHANG Cun, DOU Yan, MA Zhongguo, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu
The dataset of snow spectral reflectance observations was obtained in the Binggou watershed foci experimental area from Dec. 5 to Dec. 15, 2007 during the pre-observation period. The aims of the measurements were to verify feasibility of the predetermined observation schemes and to collect data for retrieval from remote sensing approaches. All data were acquired by ASD spectrometer from Xinjiang Meteorological Administration. Observation items included: (1) Random observations on snow spectrum in the chosen snowpack at the Binggou cold region hydrometeorological station on Dec. 5, 6 and 7, 2007 (2) Snow spectrum observations in BG-A simultaneous with MODIS and Terra MISR on Dec. 10, 2007 (3) The pure and the mixed snow pixel spectrum in BG-A on Dec. 15, 2007 (4) Multi-angle snow spectrum in the chosen snowpack in BG-A on Dec. 15, 2007 Seven subfolders including raw data and pre-processed data are named after the acquisition time, Dec. 5, 2007, Dec. 6, 2007, Dec. 7, 2007, Dec. 10, 2007, Dec. 13, 2007, Dec. 15, 2007 and Dec. 15, 2007, respectively.
ZHANG Pu, LIU Yan
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 ground truth measurements for snow was obtained, synchronizing with airborne microwave radiometers (K&Ka bands) mission in the Binggou watershed foci experimental area on Mar. 29, 2008. Those provide reliable ground data for retrieval of snow properties and parameters, especially snow depth and snow water equivalent study. Observation items include (1) snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the snowfork in BG-A; (2) snow parameters in BG-A (18 points), BG-B (20 points), BG-EF (20 points) and BG-I (20 points): snow depth by the ruler, the snow temperature (mean of two measurements) by the probe thermometer, snow grain size by the handheld microscope, snow density by the cutting ring for each snow layer, and the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer. For each snow pit, the snowpack was divided into several layers with 10-cm intervals of snow depth. Two files including raw data and pre-processed data were archived.
BAI Yanfen, BAI Yunjie, CAO Yongpan, GE Chunmei, GU Juan, HAN Xujun, HAO Xiaohua, LI Hongyi, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, CHANG Cun, DOU Yan, MA Zhongguo, JIANG Tenglong, 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
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No.1 (freeze/thaw status), No. 2 (snow parameters) and No. 3 (freeze/thaw status) quadrates of the A'rou foci experimental areas on Mar. 12, 2008. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:29 BJT. The quadrates were divided into 4×4 subsites, with each one spanning a 30×30 m2 plot. Center and corner points of each subsite were chosen for all observations except for the cutting ring measurements which only observed the center points. In No. 1 quadrate, numerous ground data were collected, the soil temperature, soil volumetric moisture, the loss tangent, soil conductivity, and the real part and the imaginary part of soil complex permittivity by the POGO soil sensor, soil volumetric moisture by ML2X, the soil volumetric moisture profile (10cm, 20cm, 30cm, 40cm, 60cm and 100cm) by PR2, the mean soil temperature from 0-5cm by the probe thermometer, soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). In No. 2 quadrate, simultaneous with ASAR, snow parameters were measured, the snow surface temperature by the thermal infrared probe, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, snow density by the aluminum case, the snow surface temperature and the snow-soil interface temperature by the thermal infrared probe, snow spectrum by ASD, and snow albedo by the total radiometer. In No. 3 quadrate soil volumetric moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity were measured by WET, the mean soil temperature from 0-5cm by the probe thermometer (5# and 7#), the surface radiative temperature by the hand-held infrared thermometer (5#), and soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). Surface roughness was detailed in the "WATER: Surface roughness dataset in the A'rou foci experimental area". Besides, GPR (Ground Penetration Radar) observations were also carried out in No. 1 quadrate of A'rou. Those provide reliable ground data for retrieval and verification of soil moisture and freeze/thaw status from active remote sensing approaches.
BAI Yanfen, CAO Yongpan, GE Chunmei, GU Juan, HAN Xujun, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Jianhua, WANG Xufeng, WU Yueru, XU Zhen, QU Wei, CHANG Cun, DOU Yan, MA Zhongguo, YU Meiyan, ZHAO Jin, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu, PATRICK Klenk, YUAN Xiaolong
The dataset of ground-based microwave scatterometer and snow parameter observations was obtained in the Binggou watershed experimental area on Mar. 16, 2008. Observation items included: (1) Snow backscattering coefficient by the scatterometer (2) Snow parameters as the snow surface temperature by the probe thermometer, snow grain size by the handheld microscope, snow density by the snow shovel, the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer in BG-I. (3) The snow spectrum by the portable ASD (Xinjiang Meteorological Administration) at the Dadongshu mountain pass; the major and minor axis and shape of the snow layer grain through the snow sieve. (4) Snow albedo by the total radiometer from 10:29 to 15:00 (5) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork at the Dadongshu mountain pass Two subfolders including raw data and preprocessed data were archived.
LIU Zengcan, LIU Zengcan, QIN Wei, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, MA Mingguo, CHANG Cun, DOU Yan, MA Zhongguo, ZHANG Pu, JIANG Tenglong
The dataset of ground truth measurements for snow synchronizing with MODIS was obtained in the Binggou watershed foci experimental area on Mar. 14, 2008. Those provide reliable data for snow-cover extent mapping and the retrieval of the snow surface temperature from MODIS remote sensing approaches. Observation items included: (1) Snow parameters including the snow surface temperature, the snow-soil interface temperature, the land surface (ground surface) temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, snow depth by the ruler, snow density by the snow shovel, the snow grain size by the handheld microscope and the snow surface temperature synchronizing with MODIS. (2) Snow albedo by the total radiometer in BG-A from 11:10-13:24 on Mar. 14, 2008. (3) The snow spectrum by the portable ASD (Xinjiang Meteorological Administration) synchronizing with MODIS in BG-A and BG-I. Two files including raw data and the preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, GU Juan, HAO Xiaohua, LI Hongyi, LIANG Ji, SHU Lele, WANG Xufeng, XU Zhen, MA Mingguo, CHANG Cun, DOU Yan, MA Zhongguo, LIU Yan, ZHANG Pu
The dataset of ground truth measurements for snow synchronizing with MODIS was obtained in the Binggou watershed foci experimental area on Mar. 19, 2008. Those provide reliable data for retrieval and verification of the snow temperature through airborne and satellite-borne remote sensing approaches. Observation items included: (1) Snow parameters, such as snow depth by the ruler (five measurements at random each point), the snow surface temperature by the infrared thermometer (several measurements at random), the snow layer temperature by the probe thermometer (10cm as an interval and two times each point), the snow grain size by the handheld microscope (10cm as an interval and three times each point) in BG-B from 12:40-13:00 (BJT) with the satellite overpass on Mar. 19, 2008. 64 points were selected by four groups. (2) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork in BG-A,automatically in coordination with ASD. (3) The snow spectrum by the portable ASD. (4) Snow albedo by the portable radiometer in BG-A. Two files including raw data and preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, HAO Xiaohua, LIANG Ji, MA Mingguo, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, DOU Yan, LIU Yan, ZHANG Pu
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 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 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 dataset of intensive snow parameter measurements was obtained in the Binggou watershed foci experimental area on Mar. 11, 2008. Those provide reliable data for retrieval of snow parameters from remote sensing approaches. Observation items included the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, snow density by the aluminum case, the snow surface temperature by the handheld infrared thermometer, and the snow-soil interface temperature by the handheld infrared thermometer in three plots in BG-Z. 4 points were selected and measured 4 times in each plot. Two files including raw data and preprocessed data (3 subfolders enclosed) on snow properties were archived; besides, profile pictures of each point were also included.
MA Mingguo, BAI Yanfen, BAI Yunjie, GE Chunmei, GU Juan, HAO Xiaohua, LI Hongyi, LI Zhe, LIANG Ji, SHU Lele, WANG Jianhua, WANG Xufeng, WU Yueru, XU Zhen, FANG Li, LI Hua, CHANG Cun, DOU Yan, MA Zhongguo, LIU Yan, ZHANG Pu, MA Hongwei, YAN Yeqing, YUAN Xiaolong
The dataset of ground truth measurements for snow synchronizing with EO-1 Hyperion was obtained in the Binggou watershed foci experimental area on Mar. 22, 2008. Those provide reliable data for retrieval of snow parameters from remote sensing approaches. Observation items included: (1) snow surface emissivity by the portable emissivity determinator near the Binggou cold region hydrometerological station; (2) snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the snowfork in BG-A from 11:20-13:53 (BJT) on Mar. 2, 2008; (3) snow parameters in BG-A, BG-B, BG-C, BG-D, BG-E and BG-F, and variables including the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, snow density by the aluminum case and the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer simultaneous with the satellite; (4) the land surface infrared temperature in BG-D, BG-E, BG-B and BG-F during the airborne mission; (5) fresh snow albedo by the total radiometer east to A2; (6) snow spectrum by the portable ASD from Xinjiang Meteorological Administration and Nanjing University, GPS recordings enclosed. Two files including raw data and preprocessed data were archived.
BAI Yanfen, BAI Yunjie, CAO Yongpan, GE Chunmei, GU Juan, HAN Xujun, HAO Xiaohua, HUANG Chunlin, LIANG Ji, SHU Lele, WANG Xufeng, WU Lizong, XU Zhen, ZHU Shijie, MA Mingguo, FANG Li, LI Hua, CHANG Cun, DOU Yan, MA Zhongguo, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu, MA Hongwei, SUN Jicheng
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 snow depth measured by the elevation-graduated snow sticks was obtained in the Binggou watershed foci experimental area from Nov. 11 to 16, 2007, during the pre-observation period. 51 snow-stakes (2m long) were arranged according to different topographic landscapes, such as the flat, ubac, tailo and partial shade, and the length above the ground was recorded. From Mar. 2 to Apr. 6, 2008, the intensive observation period, ten measurements (Mar. 2, Mar. 4, Mar. 9, Mar. 16, Mar. 19, Mar. 21, Mar. 23, Mar. 29, Apr. 1 and Apr. 6) were carried out both manually and additionally by the telescope for the snow depth around the snow-stakes. Two files including raw data and preprocessed snow depth data were archived. Those provide reliable data for snow spatial heterogeneity study and snow accumulation and melt monitoring in the Binggou watershed.
BAI Yunjie, HAO Xiaohua, LI Hongyi
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 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
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
Chinese Cryospheric Information System is a comprehensive information system for the management and analysis of Chinese Cryospheric data. The establishment of Chinese Cryospheric Information System is to meet the needs of earth system science, to provide parameters and validation data for the development of response and feedback model of frozen soil, glacier and snow cover to global change under GIS framework; on the other hand, it is to systemically sort out and rescue valuable cryospheric data, to provide a scientific, efficient and safe management and division for it Analysis tools. The basic datasets of the Tibet Plateau mainly takes the Tibetan Plateau as the research region, ranging from longitude 70 -- 105 ° east and latitude 20 -- 40 ° north, containing the following types of data: 1. Cryosphere data. Includes: Permafrost type (Frozengd), (Fromap); Snow depth distribution (Snowdpt) Quatgla (Quatgla) 2. Natural environment and resources. Includes: Terrain: elevation, elevation zoning, slope, slope direction (DEM); Hydrology: surface water (Stram_line), (Lake); Basic geology: Quatgeo, Hydrogeo; Surface properties: Vegetat; 4. Climate data: temperature, surface temperature, and precipitation. 3. Socio-economic resources (Stations) : distribution of meteorological Stations on the Tibetan Plateau and it surrounding areas. 4. Response model of plateau permafrost to global change (named "Fgmodel"): permafrost distribution data in 2009, 2049 and 2099 were projected. Please refer to the following documents (in Chinese): "Design of Chinese Cryospheric Information System.doc", "Datasheet of Chinese Cryospheric Information System.DOC", "Database of the Tibetan Plateau.DOC" and "Database of the Tibetan Plateau 2.DOC".
LI Xin
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
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