The dateset of GPR (Ground Penetration Radar) observations was obtained in the A'rou foci experimental area from Mar. 10 to Jun. 19, 2008. Those provide reliable dataset for retrieval of soil moisture and frozen depth from GPR observations. Observation items, sites and time were as follows: (1) GPR in No. 1 quadrate of A'rou on Mar. 10, 2008 (2) GPR+TDR in No. 2 and 3 quadrates of A'rou on Mar. 11, 2008 (3) GPR in No. 1 quadrate of A'rou on Mar. 12, 2008 (4) GPR in No. 2 quadrate of A'rou on Mar. 14, 2008 (5) GPR +TDR in No. 1 quadrate of A'rou on Mar. 15, 2008 (6) GPR +TDR in L6 of A'rou on Mar. 16, 2008 (7) GPR +TDR in L6 of A'rou on Mar. 17, 2008 (8) GPR +TDR in L6 of A'rou on Mar. 18, 2008 (9) GPR +TDR in L6 of A'rou on Mar. 19, 2008 (10) GPR in L6 of A'rou on Mar. 20, 2008 (11) GPR +TDR in No. 3 quadrate of A'rou on Mar. 21, 2008 (12) GPR in No. 1 and 3 quadrates of A'rou on May. 31, 2008 (13) GPR in No. 1 quadrate of A'rou on Jun. 20, 2008
0 2019-09-13
The dataset of ground truth measurement synchronizing with ALOS PALSAR was obtained in the Linze grassland foci experimental area on Jun. 27, 2008. The data were in FBD mode and HH/HV polarization combinations, and the overpass time was approximately at 23:41 BJT. Observations were carried out in the reed plot A, the saline plot B, the alfalfa plot D and the barley plot E, which were divided into 6×6 subsites, with each one spanning a 120×120 m2 plot. Soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring and the mean soil temperature from 0-5cm by the probe thermometer were measured in A and B; the soil temperature, soil moisture, the loss tangent, soil conductivity, the real part and the imaginary part of soil complex permittivity by the POGO soil sensor, the mean soil temperature from 0-5cm by the probe thermometer, and soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring in D and E. Data were archived in Excel file. See WATER: Dataset of setting of the sampling plots and stripes in the foci experimental area of Linze station for more information.
0 2019-05-23
On 25 August 2012, a Leica ALS70 airborne laser scanner boarded on the Y-12 aircraft was used to obtain LiDAR DSM point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 5200 m with the point cloud density 1 point per square meter. Aerial LiDAR-DSM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
0 2019-09-15
The Karuola Glacier of Tibet is located at the junction of Langkazi County, the Shannan Area of the Tibetan Autonomous Region and Jiangzi County of the Shigatse Region. Latitude: 28°54'23.30′′～28°56'50.95′′N, Longitude 90°11′42.21′′～90°09′26.23′′E. It is a continental glacier with an average elevation of 5042 meters. It is the north-south spreading part of the Ningjingangsang peak. Based on the integration of the first glacier inventory data of China from the Cold and Arid Regions Environmental and Engineering Research Institute，Chinese Academy of Sciences, the 1:100,000 inventory data of the Yarlu Zangbu River Basin Glacier from the Sharing Platform for the Earth Systematic Science Data, and Google Earth remote sensing image and field survey data, the dataset was obtained with the help of ArcGIS, ENVI and other software by the following steps: first, the research and development of the data was achieved by band combination, research area clipping, manual visual interpretation and other techniques, and the accuracy of the obtained data was then verified. This dataset includes a total of 25 statistics of vector and area data of Tibet’s Karuola Glacier. It recorded the changes at the borders of Karuola Glacier in the past 45 years and could be used as reference data for the study of glacier and climate changes on the Tibetan Plateau.
0 2020-04-28
This data set contains meteorological observation data of meteorological elements from January 1, 2015 to December 31, 2015 on the haihewen meteorological observation network in yaokou station.The station is located in da dong shu pass, qilian county, qinghai province.The latitude and longitude of the observation point are 100.2421E, 38.0142N, and 4148m above sea level.Data including two observation points, all in pass observatory, located about 10 m, a set of continuous observation in 2015 (30 min output), another set for September 18, 2015 in 10 m high pass new stations (10 min), specific include: air temperature, relative humidity sensors at 5 m, toward the north (two sets of observation, 10 min and 30 min output);The barometer is installed in an anti-skid box on the ground (two groups of observation, 10min and 30min output respectively);The inverted bucket rain gauge is installed at 10m;The wind speed and direction sensor is set at 10m, facing due north (two groups, respectively 10min and 30min output);The four-component radiometer consists of two observation points, one of which is installed at the 6m position of the weather tower, facing due south (10min output), and the other is installed on a support 1.5m above the ground (30min output).The two infrared thermometers are installed at the position of 6m, facing south, and the probe is facing vertically downward.The soil temperature probe was buried at 0cm on the surface and 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm underground (the two groups were observed and output for 10min and 30min respectively).The soil moisture probes were buried in the ground at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm (the two groups were observed and output for 10min and 30min respectively).The soil heat flux plates were buried 6cm underground (observed in two groups for 10min (3 heat flux plates) and 30min (2 heat flux plates) respectively). Observation items are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:C), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watts/m2), soil temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: Celsius), soil moisture (Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_40cm, Ms_80cm, Ms_120cm, Ms_160cm) (unit: volume water content, percentage). Processing and quality control of observation data :(1) ensure 144 or 48 data per day (every 10min or 30min). If data is missing, it will be marked by -6999;The four-component long-wave radiation output of 30min was lost between 1.1-4.1 in 2015 due to sensor problems.The 30min observation data was missing between 5.24 and 7.12 due to collector problems.(2) eliminate the moments with duplicate records;(3) data that is obviously beyond the physical meaning or the range of the instrument is deleted;(4) the part marked by red letter in the data is the data in question;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: 2015-9-10-10:30;(6) the naming rule is: AWS+ site name. Please refer to Liu et al. (2018) for hydrometeorological network or site information, and Liu et al. (2011) for observation data processing.
0 2020-04-10
This dataset includes passive microwave remote sensing brightness temperatures data for longitude and latitude projections and 0.25 degree resolution from 2002 to 2008 in China. 1. Data processing process: NSIDC produces AMSR-E gridded brightness temperature data by interpolating AMSR-E data (6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz) to the output grids from swath space using an Inverse Distance Squared (ID2) method. 2. Data format: Brightness temperature files: two-byte unsigned integers, little-endian byte order Time files: two-byte signed integers, little-endian byte order 3. Data naming: ID2rx-AMSRE-aayyyydddp.vnn.ccc (China-ID2r1-AMSRE-D.252002170A.v03.06V) ID2 Inverse Distance Squared r1 Resolution 1 swath input data AMSRE Identifies this an AMSR-E file D.25 Identifies this as a quarter degree file yyyy Four-digit year ddd Three-digit day of year p Pass direction (A = ascending, D = descending) vnn Gridded data version number (for example, v01, v02, v03) ccc AMSR-E channel indicator: numeric frequency (06, 10, 18, 23, 36, or 89) followed by polarization (H or V) 4. Cutting range: Corner Coordinates: Upper Left (60.0000000, 55.0000000) (60d 0'0.00 "E, 55d 0'0.00" N) Lower Left (60.0000000, 15.0000000) (60d 0'0.00 "E, 15d 0'0.00" N) Upper Right (140.0000000, 55.0000000) (140d 0'0.00 "E, 55d 0'0.00" N) Lower Right (140.0000000, 15.0000000) (140d 0'0.00 "E, 15d 0'0.00" N) Center (100.0000000, 35.0000000) (100d 0'0.00 "E, 35d 0'0.00" N) Origin = (60.000000000000000, 55.000000000000000) 5. Data projection: GEOGCS ["WGS 84", DATUM ["WGS_1984", SPHEROID ["WGS 84", 6378137,298.257223563, AUTHORITY ["EPSG", "7030"]], TOWGS84 [0,0,0,0,0,0,0], AUTHORITY ["EPSG", "6326"]], PRIMEM ["Greenwich", 0, AUTHORITY ["EPSG", "8901"]], UNIT ["degree", 0.0174532925199433, AUTHORITY ["EPSG", "9108"]], AUTHORITY ["EPSG", "4326"]]
0 2020-10-12
Data overview: This set of data mainly includes perennial River, seasonal river, river trunk, surface main channel, surface branch channel and other water system conditions in the Heihe River Basin. The data base year is 2009. Data preparation process: obtained from 1:100000 topographic map and 2009 TM remote sensing image digitization. Data content description: the data mainly has three important attributes, namely, grade, GB and name. The river classification is based on the Strahler classification method, and the final level of the main stream reaches seven levels. River coding is based on the national basic geographic information element dictionary. The standard of basic geographic information element data dictionary is adopted.
0 2020-06-05
In 2000, the population grid data of Heihe River Basin was generated based on 1:100000 land use data and population statistics data of each county in 2000. Using principal component analysis and factor analysis, four factors are extracted from 11 regionalization indexes, and the Heihe River Basin is divided into four population distribution characteristic regions by using factor scores for hierarchical clustering. The linear regression model between rural residential land, cultivated land area and rural population is established based on the population statistical data of each county in 2000. The total population of each district and county is controlled. The population coefficient is adjusted according to the principle of different population distribution characteristics. The cultivated land population distribution coefficient is modified in the middle green continent, and the grassland population distribution is increased in the upstream mountainous area and the downstream desert oasis area Coefficient. The spatial distribution of urban population density in river basin is simulated by using the exponential model. Based on the above methods, the population spatial distribution results of 25m grid in Heihe River Basin and the data of 1km grid on scale are finally obtained. At the township level, the accuracy of the results of population spatialization is verified, and compared with the population data of Heihe River Basin estimated by the existing databases (GPW 1995, UNEP / grid1995, landscan 2002 and cn2000pop). The results show that the methods and models used in this study can obtain more accurate spatial distribution data of population in the basin.
0 2020-02-24
This data originates from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2015. This data set includes 1:250,000 natural place names (AANP) in Sanjiangyuan area, including traffic element names, memorial sites and historic sites, mountain names, water system names, marine geographical names, natural geographical names, etc. Natural Place Name Data (AANP) Attribute Item Names and Definitions: Attribute Item Description Fill in Example NAME Name Ramsay Laboniwa PINYIN Chinese Pinyin Lamusailabaoniwa CLASS Toponymic Classification Code HB
0 2019-09-15
The No. 5 hydrological section is located at Ban Bridge (100.276° E, 39.259° N, 1398 m) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. The dataset contains observations recorded by the No.5 hydrological section from 19 June, 2012, to 10 August, 2012. The width of this section is 270 meters. The water level was measured using an HOBO pressure range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
0 2019-09-13
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