Data overview: This set of data mainly includes six prefecture level cities and 16 counties (Ganzhou District, Gaotai County, Shandan County, Minle County, Linze County, Sunan Yugu Autonomous County, Jinta County, Subei Mongolian Autonomous County, Suzhou District, Yumen City, Jiayuguan City, Yongchang County, Qilian County, Alxa Left Banner, Ejina Banner, Alxa Right Banner) in Heihe River Basin ）The 12 social and economic data are: GDP, output value of primary industry, output value of secondary industry, output value of tertiary industry, per capita GDP, per capita disposable income of urban residents, per capita net income of rural residents, fixed asset investment, total retail sales of social consumer goods, fiscal revenue, fiscal expenditure, and total grain output (including all kinds of work) Output of the product). It is divided into county level and township level. The data period is 2000-2009.
2020-10-12
The dataset of LAS (Large Aperture Scintillometer: BLS450, made in Germany) observations was obtained at the A'rou freeze/thaw observation station from Mar. 11 to Jul. 11, 2008. The transmitter (E100°28′16.4″, N38°03′24.3″, 11.2m) and the receiver (E100°27′25.9″, N38°02′18.1″, 11.5m) were 2390m away from each other and the operating altitude was 9.5m. The observation item was the atmospheric refractive index structural parameters (Cn2). The transmitting frequency was 5HZ and the data were output per minute. The processed data were archived in a 30 minutes cycle. The data were named after WATER_LAS_A'rou_yyyymmdd-yyyymmdd.csv (yyyymmdd-yyyymmdd for observation time). The missing data were marked "None".
2020-10-09
The monthly average vegetation index data of Heihe River Basin is based on MODIS 1 km and 250 m NDVI products. From 250 m products, the grid value of Heihe River Basin is proposed as precision control, and the 1 km product is modified by HASM method. The monthly average vegetation index of Heihe River Basin from 2001 to 2011 was obtained by fusing multi-source NDVI data using HASM method. Resolution: 1km * 1km The average precipitation data set of Heihe River Basin adopts the data information of 21 meteorological conventional observation stations in Heihe River Basin and its surrounding areas and 13 national reference stations around Heihe River basin provided by Heihe planning data management center. The daily precipitation data of each station from 1961 to 2010 is calculated. If the coefficient of variation is greater than 100%, the daily precipitation distribution trend can be obtained by using the geographic weighted regression to calculate the relationship between the station and the geographical terrain factors; if the coefficient of variation is less than or equal to 100%, the relationship between the station precipitation value and the geographical terrain factors (longitude, latitude, elevation) is calculated by ordinary least square regression, and the daily precipitation score is obtained HASM (high accuracy surface modeling method) was used to fit and modify the residual error after removing the trend. Finally, the trend surface results and residual correction results are added to get the annual average precipitation distribution of Heihe River Basin from 1961 to 2010. Time resolution: annual average precipitation from 1961 to 2010. Spatial resolution: 500M.
2020-10-09
According to the global soil map. Net standard, the 0-1m soil depth is divided into 5 layers: 0-5cm, 5-15cm, 15-30cm, 30-60cm and 60-100cm. According to the principle of soil landscape model, the spatial distribution data products of soil organic carbon content in different layers are produced by using the digital soil mapping method. The source data of this data set comes from the soil profile data integrated by the major research plan integration project of Heihe River Basin (soil data integration and soil information product generation of Heihe River Basin, 91325301). Scope: Heihe River Basin; Projection: WGS · 1984 · Albers; Spatial resolution: 100M; Data format: TIFF; Dataset content: hh_soc_layer1.tif: 0-5cm soil organic carbon content; hh_soc_layer2.tif: 5-15cm soil organic carbon content; hh_soc_layer3.tif: 15-30cm soil organic carbon content; hh_soc_layer4.tif: 30-60cm soil organic carbon content; hh_soc_layer5.tif: 60-100cm soil organic carbon content;
2020-09-30
The ground sample data was collected by LAI-2000 canopy analyzer, and the collection area was located in Dayekou, Wuxing Village (2012) and other areas. The main measure of vegetation is corn. The LAI value of the corn was obtained using the LAI2000, and the observation was repeated twice in a pattern of “one up and four down”. The leaf area of each leaf of the corn plant was obtained using CD202, and a total of three corns were collected.
2020-09-15
The data was directly clipped from China's 1:100,000 land-use data.China 1:100000 data of land use is a major application in the Chinese Academy of Sciences "five-year" project "the national resources and environment remote sensing macroscopic investigation and study of dynamic organized 19 Chinese Academy of Sciences institute of remote sensing science and technology team, by means of satellite remote sensing, in three years based on Landsat MSS, TM and ETM remote sensing data established China 1:100000 images and vector of land use database.A hierarchical land cover classification system was adopted for the land use data of heihe basin of 1:100,000, and the whole basin was divided into 6 primary categories (arable land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 26 secondary categories.The data type is vector polygon, which is stored in Shape format.There are two types of data projection: WGS84/ALBERS;Data coverage covers the new heihe watershed boundary (lack of outer Mongolia data).
2020-09-07
ET (ET) monitoring is crucial to agricultural water resource management, regional water resource utilization planning and socio-economic sustainable development.The limitations of traditional ET monitoring methods mainly lie in that they cannot observe a large area at the same time and can only be limited to observation points. Therefore, the cost of personnel and equipment is relatively high, and they can neither provide surface ET data, nor provide ET data of different land use types and crop types. Quantitative monitoring of ET can be achieved by using remote sensing. The characteristics of remote sensing information are that it can not only reflect the macroscopic structure characteristics of the earth surface, but also reflect the microscopic local differences. Version 2.0 (second edition) of the surface evapotranspiration data set of the heihe river basin from 2000 to 2013 is based on multi-source remote sensing data and the latest ETWatch model is adopted to estimate the raster image data. Its temporal resolution is monthly scale and the spatial resolution is 1km scale. The data covers the whole basin in millimeters.Data types include monthly, quarterly, and annual data. The projection information of the data is as follows: Albers equal-area cone projection, Central longitude: 110 degrees, First secant: 25 degrees, Second secant: 47 degrees, Coordinates by west: 4000000 meter. File naming rules are as follows: Monthly cumulative ET value file name: heihe-1km_2013m01_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2013 represents the year of 2013, m01 represents the month of January, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Name of quarterly cumulative ET value file: heihe-1km_2013s01_eta.tif Heihe refers to heihe river basin, 1km refers to the resolution of 1km, 2013 refers to 2013, s01 refers to january-march, is the first quarter, eta refers to the actual evapotranspiration data, and tif refers to the data in tif format. Annual cumulative value file name: heihe-1km_2013y_eta.tif Among them, heihe represents heihe river basin, 1km represents the resolution of 1km, 2013 represents the year of 2013, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format.
2020-08-26
The forest hydrology experimental area of Heihe River integrated remote sensing experiment includes the dense observation area of Dayekou basin and the dense observation area of Pailugou basin. Due to the concentrated distribution of the fixed sample plots in the drainage ditch basin, these sample plots lack of representativeness to the forest of the whole dayokou basin, so in June 2008, 43 temporary forest sample plots were set up in the whole dayokou basin. The data set is the ground observation data of the 43 temporary plots. In addition to the measurement and recording of stand status and site factors, Lai was also observed. The instruments used to measure each wood in the sample plot are mainly tape, DBH, flower pole, tree measuring instrument and compass. The DBH, tree height, height under branch, crown width in cross slope direction, crown width along slope direction and single tree growth were measured for each tree. WGS84 latitude and longitude coordinates of the center point of the sample plot were measured with different hand-held GPS, and the positioning error was about 5-30m. Other observation factors include: Forest Farm, slope direction, slope position, slope, soil thickness, canopy density, etc. The implementation time of these temporary sample plots is from 2 to 30 June 2008. The data set can provide ground data for the development of remote sensing inversion algorithm of forest structure parameters.
2020-08-20
The data set mainly includes observation data of each tree in the super site, and the observation time is from June 2, 2008 to June 10, 2008. The super site is set around the Dayekou Guantan Forest Station. Since the size of the super site is 100m×100m, in order to facilitate the forest structure parameter survey, the super site is divided into 16 sub-sample sites, and tally forest measurement is performed in units of sub-samples. The tally forest measurement factors include: diameter, tree height, height under branch, crown width in transversal slope direction, crown width in up and down slope direction, and tindividual tree growth status. The measuring instruments are mainly: tape, diameter scale, laser altimeter, ultrasonic altimeter, range pole and compass. The data set also records the center point latitude and longitude coordinates of 16 sub-samples (measured by Z-MAX DGPS). The data set can be used for verification of remote sensing forest structure parameter extraction algorithm. The data set, together with other observation data of the super site, can be used for reconstruction of forest 3D scenes, establishment of active and passive remote sensing mechanism models, and simulation of remote sensing images,etc.
2020-08-20
The data set contains data from January 1, 2017 to December 31, 2017 from the meteorological element gradient observation system of alu superstation, upstream of the heihe hydrometeorological observation network.The station is located in caoban village, aru township, qilian county, qinghai province.The longitude and latitude of the observation point are 100.4643e, 38.0473n and 3033m above sea level.The air temperature, relative humidity and wind speed sensors are located at 1m, 2m, 5m, 10m, 15m and 25m respectively, with a total of six layers facing due north.The wind direction sensor is located at 10m, facing due north;The barometer is installed at 2m;The tilting rain gauge is installed on the 28m observation tower of super aru station;The four-component radiometer is installed at 5m, facing due south;Two infrared thermometers are installed at 5m, facing due south, and the probe facing vertically downward.The photosynthetic effective radiometer is installed at 5m, facing due south, and the probe facing vertically upward.Part of the soil sensor is buried at 2m in the south direction of the tower body, and the soil heat flow plate (self-correcting formal) (3 pieces) are all buried at 6cm underground.The mean soil temperature sensor TCAV is buried 2cm and 4cm underground.The soil temperature probe is buried at the surface of 0cm and underground of 2cm, 4cm, 6cm, 10cm, 15cm, 20cm, 30cm, 40cm, 60cm, 80cm, 120cm, 160cm, 200cm, 240cm, 280cm and 320cm, among which the 4cm and 10cm layers have three repeats.The soil water sensor is buried underground 2cm, 4cm, 6cm, 10cm, 15cm, 20cm, 30cm, 40cm, 60cm, 80cm, 120cm, 160cm, 200cm, 240cm, 280cm and 320cm respectively, among which the 4cm and 10cm layers have three duplexes. The observations included the following: air temperature and humidity (Ta_1 m, Ta_2 m, Ta_5 m, Ta_10 m, Ta_15 m and Ta_25 m; RH_1 m, RH_2 m, RH_5 m, RH_10 m, RH_15 m and RH_25 m) (℃ and %, respectively), wind speed (Ws_1 m, Ws_2 m, Ws_5 m, Ws_10 m, Ws_15 m and Ws_25 m) (m/s), wind direction (WD_2 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/(s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm_1, Ts_4 cm_2, Ts_4 cm_3, Ts_6 cm, Ts_10 cm_1, Ts_10 cm_2, Ts_10 cm_3, Ts_15 cm, Ts_20 cm, Ts_30 cm, Ts_40 cm, Ts_60 cm, Ts_80 cm, Ts_120 cm, Ts_160 cm, Ts_200 cm, Ts_240 cm, Ts_280 cm and Ts_320 cm) (℃), and soil moisture (Ms_2 cm, Ms_4 cm_1, Ms_4 cm_2, Ms_4 cm_3, Ms_6 cm, Ms_10 cm_1, Ms_10 cm_2, Ms_10 cm_3, Ms_15 cm, Ms_20 cm, Ms_30 cm, Ms_40 cm, Ms_60 cm, Ms_80 cm, Ms_120 cm, Ms_160 cm, Ms_200 cm, Ms_240 cm, Ms_280 cm and Ms_320 cm) (%, volumetric water content). Processing and quality control of observed data :(1) ensure 144 pieces of data every day (every 10min), and mark by -6999 in case of data missing;The soil heat flux G1 was between 2017.1.1-2.28 and 2017.8.8-8.23, while the soil heat flux G3 was between 4.16-7.6. Due to sensor problems, data was missing.(2) excluding the time with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letter in the data is the data in question;(5) date and time have the same format, and date and time are in the same column.For example, the time is: 2017-6-10:10:30;(6) the naming rule is: AWS+ site name. For information of hydrometeorological network or station, please refer to Liu et al. (2018), and for observation data processing, please refer to Liu et al. (2011).
2020-08-17
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