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
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
Data Overview: Zhangye's channels are divided into five levels: dry, branch, bucket, agricultural and Mao channels, of which the agricultural channels are generally unlined. Mao channels are field projects, so the three levels of dry, branch and bucket channels and a small part of agricultural channels are mainly collected. The irrigation canal system data includes 2 main canals (involving multiple irrigation districts), 157 main canals (within a single irrigation district), 782 branch canals and 5315 dou canals, with a total length of 8, 745.0km. Data acquisition process: remote sensing interpretation and GPS field measurement are adopted for data acquisition of irrigation canal system. Direct GPS acquisition channel is the most effective method, but the workload of GPS acquisition channel is too large, and we only verify the measurement in some irrigation areas. The main method is to first collect the manual maps of irrigation districts drawn by each water pipe. Most of these maps have no location, only some irrigation districts such as Daman and Shangsan have been located based on topographic maps, and some irrigation districts in Gaotai County have used GPS to locate some channels. Referring to the schematic diagram of the irrigation district, channel spatial positioning is carried out based on Quikbird, ASTER, TM remote sensing images and 1: 50000 topographic maps. For the main canal and branch canal, due to the obvious linear features on remote sensing images and the general signs on topographic maps, it can be located more accurately. For Douqu, areas with high-resolution images can be located more accurately, while other areas can only be roughly located according to fuzzy linear features of images and prompt information of irrigation district staff, with low positioning accuracy. Each water management office simultaneously provides channel attribute data, which is one-to-one corresponding to spatial data. After the first draft of the channel distribution map is completed, it is submitted twice to the personnel familiar with the channel distribution of each water pipe for correction. The first time is mainly to eliminate duplication and leak, and the second time is mainly to correct the position and perfect the attribute data. Description of data content: The fields in the attribute table include code, district and county name, irrigation area name, channel whole process, channel name, channel type, location, total length, lined, design flow, design farmland, design forest and grass, real irrigation farmland, real irrigation forest and grass, water right area, and remarks. Code example: G06G02Z15D01, where the first letter represents the county name, the 2nd and 3rd numbers represent the county (district) number, the 4th to 6th characters represent the trunk canal code, the 7th to 9th characters represent the branch canal code, and the 10th to 12th characters represent the dou canal code.
2020-06-08
ASAR (Advanced Synthetic Aperture Radar) is a Synthetic Aperture Radar sensor mounted on ENVISAT satellite. It operates in c-band with a wavelength of 5.6 cm and features multi-polarization, variable observation Angle and wide-range imaging. Heihe river basin of ENVISAT ASAR remote sensing data sets mainly through central Europe "dragon plan" project, the data to the Image mode, cross polarization (Alternating Polarisation) model with wide is given priority to, the spatial resolution of 30 meters. ENVISAT ASAR data 404 scenes are currently available in heihe river basin, including 82 scenes in APP mode, 7 scenes in IMP mode and 315 scenes in WSM mode. The acquisition time is: APP can choose the polarization mode, the time range is from 2007-08-15 to 2007-12-23, 2008-01-02 to 2008-12-20, 2009-02-15 to 2009-09-06; IMP imaging mode, time range from 2009-06-19 to 2009-07-12; WSM wide format, time range from 2005-12-05 to 2005-12-31,2006-01-06 to 2006-12-31, 2007-01-01 to 2007-12-30, 2008-01-01 to 2008-12-28, 2009-03-13 to 2009-05-22. Product level is L1B, without geometric correction, is amplitude data.
2020-06-08
The geomorphic data of Heihe River are from the geomorphic Atlas of the people's Republic of China (1:1 million). This data is based on remote sensing image and other multi-source data integration and update. The main data used and referenced include: 1) remote sensing image data: TM and 2000's around 1990's nationwide About ETM image; 2) historical geomorphic map: 15 published 1 million geomorphic maps, two sets of 1:4 million geomorphic maps in China, 500000 or 1 million geomorphic sketches in all provinces and cities in China; 3) basic geographic data: 1:250000 basic geographic data and 250000 DEM data in China; 4) geological data: 1:500000 geological map in China; 5) relevant thematic maps: land use map, vegetation map and land resource map And so on. The interpretation method adopts the human-computer interaction method based on ArcGIS, and is carried out according to the interpretation sequence of hierarchical classification: the first layer: plain and mountain; the second layer: basic geomorphic types (28); the third layer: 10 genetic types; the fourth layer: secondary genetic types; the fifth layer: morphological difference classification types; the sixth layer: secondary morphological difference classification types; the seventh layer: slope, slope The eighth layer is the type of geomorphic material determined by material composition or lithology; the ninth layer is the combination of 1-7 layers of map spots. There are 441 geomorphic types and codes. Data fields include: fenfu (view frame number), name (attribute), class (code), sname (administrative division).
2020-06-08
This data mainly includes the distribution of city, county, township and village level residential areas in the Heihe River Basin, and the data base year is 2009. The data is based on the existing data of residential areas in Heihe River Basin, the latest Google electronic map and the atlas of Gansu Province. There are two main attributes of the data, i.e. residential area classification and total name. The residential area classification is classified according to level 1 - City, level 2 - County, level 3 - Township and level 4 - village.
2020-06-05
SRTM (Shuttle Radar Topography Mission) is by NASA and the national geospatial intelligence agency (NGA) cooperation to build the global 3 d graphics data project.In February 2000, the SRTM system mounted on the U.S. space shuttle endeavour collected radar image data between latitude 60 ° north and latitude 57 ° south, and acquired radar image data covering more than 80% of the world's land surface.After more than two years of processing, the digital terrain elevation model was made. This data set including the heihe river basin SRTM points picture and Mosaic two kinds of data, and the points of the graph is SRTM version 4 data by the CGIAR - CSI (international centre for tropical agriculture, http://srtm.csi.cgiar.org/) treatment, compared with the previous version has greatly improved, including: 1) use a lot of interpolation algorithm, 2) use more auxiliary DEM data to fill the blank spots and blank area, 3) compared with the third version of the data and migration half a yuan.The Mosaic map is obtained by splicing on the basis of sub-map. The sub-charts include srtm_56_04,srtm_56_05,srtm_57_04 and srtm_57_054. The data are 16 bit values representing the elevation value (-/+/32767 m). The maximum positive elevation is 9000 m and the maximum negative elevation is 12,000 m below sea level.Null data is identified by -32767.Divide the file into 24 rows (-60 to 60 degrees) and 72 columns (-180 to 180 degrees) per 5 latitude and longitude squares.
2020-06-05
Data overview: this set of data mainly includes the spatial distribution of major roads in the heihe river basin, the attributes include road classification and road coding, and the data base year is 2010. Data preparation process: this set of data is based on the topographic map, remote sensing image and the latest road traffic map updated by the transportation department of gansu province in 2009. Data description: there are two important attributes of the data, namely, road classification and road code. The road classification is divided into national road, provincial road, county road, township road and private road. The road code is defined in accordance with the highway grade code of the traffic department.
2020-06-05
Based on the data information provided by the data management center of Heihe project, the daily humidity data of 21 regular meteorological observation stations in Heihe River Basin and its surrounding areas and 13 national reference stations around Heihe River were collected and calculated. The spatial stability analysis is carried out to calculate the coefficient of variation. If the coefficient of variation is greater than 100%, the geographical weighted regression is used to calculate the relationship between the station and the geographical terrain factors, and the monthly humidity distribution trend is obtained; if the coefficient of variation is less than or equal to 100%, the common least square regression is used to calculate the relationship between the station humidity value and the geographical terrain factors (latitude, longitude, elevation, slope, aspect, etc.) The residual after removing the trend was fitted and corrected by HASM (high accuracy surface modeling method). Finally, the monthly average humidity distribution of the Heihe River Basin in 1961-2010 is obtained by adding the trend surface results and the residual correction results. Time resolution: monthly average humidity for many years from 1961 to 2010. Spatial resolution: 500M.
2020-03-28
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