This dataset includes the monthly air temperature at 2 m in the Qilian Mountain area on the Qinghai-Tibetan Plateau during 1980 to 2013. The dataset was obtained from the ERA-interim reanalysis product. The ERA-interim system includes a 4-dimensional variational analysis (4D-Var). The quality of the data has been improved using the bias correction of satellite data. The spatial resolution of the dataset is 0.125°. The dataset includes the grid data of the air temperature in the Qilian Mountain area during the past 30 years, and provides a basic data for the studies such as climatic change, ecosystem succession, and earth system models.
WU Xiaodong
The data include soil organic matter data of Tibetan Plateau , with a spatial resolution of 1km*1km and a time coverage of 1979-1985.The data source is the soil carbon content generated from the second soil census data.Soil organic matter mainly comes from plants, animals and microbial residues, among which higher plants are the main sources.The organisms that first appeared in the parent material of primitive soils were microorganisms.With the evolution of organisms and the development of soil forming process, animal and plant residues and their secretions become the basic sources of soil organic matter.The data is of great significance for analyzing the ecological environment of Tibetan Plateau
FANG Huajun
It includes monthly data of precipitation, evaporation, water reserve change and soil water change of Tarim River. Precipitation data comes from ECMWF. Evaporation data is calculated by energy model based on Penman formula, water reserve data is retrieved by grace gravity satellite data, GLDAS data is obtained by land surface process model simulation of Noah in the United States, and NDVI data is from MODIS data products. The resolution of precipitation and evaporation is 0.5 ° * 0.5 °, and the resolution of water storage and soil water change data is 1 ° * 1 °. The data provide reference for water resource management and decision-making. Vegetation data can provide basic data for ecological change assessment.
XU Min
The sand drift potential data sets of Central Asia in 2017 is in tif format. It covers five countries in Central Asia, including Uzbekistan, Tajikistan, Kyrgyzstan, Kazakhstan and Turkmenistan. The sand drift potential is absolutely drift potential, that is, the sum of the flux in all directions, regardless of the direction of the potential. The data was obtained by GLDAS global three-hour assimilation data extraction calculation. The temporal resolution is month, the spatial resolution is 0.25°, and the time range is 2017. This data set can be used as an important reference data for sand storm disaster assessment.
GAO Xin
The Qinghai Tibet Plateau belongs to the plateau mountain climate. The temperature and its seasonal variation have been one of the hot spots in the global climate change research. The data includes the temperature data of Qinghai Tibet Plateau, with spatial resolution of 1km * 1km, temporal resolution of month and year, and time coverage of 2000, 2005, 2010 and 2015. The data are obtained by Kring interpolation on the data of national weather station in Qinghai Tibet Plateau. The data can be used to analyze the temporal and spatial distribution of air temperature in the Qinghai Tibet Plateau. In addition, the data can also be used to analyze the law of temperature change with time in the Qinghai Tibet Plateau, which is of great significance to the study of the ecological environment of the Qinghai Tibet Plateau.
FANG Huajun
Based on the night light data from remote sensing, the research group used the method of Elvidge in 2009 and 2012 to reverse the incidence of poverty in the countries along the belt and the road. This data is comparable with Gini coefficient published by the World Bank and has the following four prominent advantages: (1) Computing units can be adjusted according to administrative boundaries, reflecting poverty disparities on the sub-regional scale of large countries that are difficult to achieve using statistic data; (2) The spatial Gini coefficient estimated based on night light data is less affected by subjective factors such as survey process, and is comparatively small. Objectively, and the comparability between countries is strong, which overcomes the difficult problem of unification between statistical calibers; (3) The survey and summary cycle limits the update speed at national and large sub-regional scales, while the method based on night light data estimation is convenient to update. (4) Night light data have many years of continuous interannual data from 1992 to 2017, which overcomes the difficulty of obtaining long time series indicators of poverty, such as the gap between the rich and the poor. In view of the above four outstanding advantages, the set of data can better support the research work and provide scientific data for finding out the basic situation of poverty along the "The Belt and the Road".
ZHANG Qian
The “Eco-Hydrology Integrated Atlas of the Heihe River Basin ” was supported by the major program: Synthetic Research on the Eco-hydrological Process of the Heihe River Basin. It provided data collation and service for Synthetic Research on the Eco-hydrological Process of the Heihe River Basin. The Atlas will provide researchers with a comprehensive and detailed introduction of the background and basic data sets of the Heihe River Basin. Eco-Hydrology Integrated Atlas of the Heihe River Basin: Remote Sensing Mosaicing of the Heihe River Basin, scale 1:2500000, positive-axis equivalence conical projection, standard parallel: north latitude 25 47 Data source: Landsat TM Mosaic Image of the Heihe River Basin in 2010, Heihe River Basin Boundary,River Network Dataset of the Heihe River Basin, The Resident Site Distribution Data of the Heihe River Basin, etc.
WANG Jianhua, ZHAO Jun
The SRTM sensor has two bands, namely C-band and X-band. The SRTM we are using now comes from the C-band. The publicly released SRTM digital elevation products include DEM data at three different resolutions: * SRTM1 covers only the continental United States, with a spatial resolution of 1s; * SRTM3 data covers the world with a spatial resolution of 3s. This is the most widely used dataset. The elevation reference of SRTM3 is the geoid of EGM96 and the horizontal reference is WGS84. The nominal absolute elevation accuracy is ± 16m, and the absolute plane accuracy is ± 20m. * SRTM30 data also covers the world, with a resolution of 30s. There are multiple versions of SRTM data. The early SRTM data was completed by NASA's "JPL" (Jet Propulsion Laboratory) ground data processing system (GDPS). The data is called SRTM3- 1. The National Geospatial Intelligence Agency has further processed the data, and the lack of data has been significantly improved. The data is called SRTM3-2. This dataset is mainly the fourth version of SRTM terrain data obtained by CIAT (International Center for Tropical Agriculture) using a new interpolation algorithm. This method better fills the SRTM 90 data hole. The interpolation algorithm comes from Reuter et al. (2007). The data of SRTM is organized as follows: every 5 latitude and longitude grids is divided into a file, which are divided into 24 rows (-60 to 60 degrees) and 72 columns (-180 to 180 degrees). The file naming rule is srtm_XX_YY.zip, where XX indicates the number of columns (01-72), and YY indicates the number of rows (01-24). The resolution of the data is 90 m. Data use: SRTM data uses a 16-bit value to represent the elevation value (-/ + / 32767 meters), the maximum positive elevation is 9000 meters, and the negative elevation (12,000 meters below sea level). -32767 standard for empty data.
CGIAR-CSI
The “Eco-Hydro Integrated Atlas of Heihe River Basin” is supported by the Synthetic Research on the Eco-hydrological Process of the Heihe River Basin– a key project to provide data collation and service for the Heihe River Basin eco-hydrological process integration study. This atlas will provide researchers with a comprehensive and detailed introduction to the Heihe River Basin background and basic data sets. The 1:100,000 topographic framing index of the Heihe River Basin is one of the basic geographs of the atlas, with a scale of 1:2500000, Lambert conformal conic projection, and a standard latitude: north latitude 25 47 . Data source: 1:100000 topographic map index data, Heihe River boundary.
ZHAO Jun, WANG Jianhua
China long-sequence surface freeze-thaw dataset——decision tree algorithm (1987-2009), is derived from the decision tree classification using passive microwave remote sensing SSM / I brightness temperature data. This data set uses the EASE-Grid projection method (equal cut cylindrical projection, standard latitude is ± 30 °), with a spatial resolution of 25.067525km, and provides daily classification results of the surface freeze-thaw state of the main part of mainland China. The data set is stored by year and consists of 23 folders, from 1987 to 2009. Each folder contains the day-to-day surface freeze-thaw classification results for the current year. It is an ASCII file with the naming rule: SSMI-frozenYYYY ***. Txt, where YYYY represents the year and *** represents the Julian date (001 ~ 365 / 366). The freeze-thaw classification result txt file can be opened and viewed directly with a text program, and can also be opened with ArcView + Spatial Analyst extension module or Arcinfo's Asciigrid command. The original frozen and thawed surface data was derived from daily passive microwave data processed by the National Snow and Ice Data Center (NSIDC) since 1987. This data set uses EASE-Grid (equivalent area expandable earth grid) as a standard format . China's surface freeze-thaw long-term sequence data set-The decision tree algorithm (1987-2009) attributes consist of the spatial-temporal resolution, projection information, and data format of the data set. Spatio-temporal resolution: the time resolution is day by day, the spatial resolution is 25.067525km, the longitude range is 60 ° ~ 140 ° E, and the latitude is 15 ° ~ 55 ° N. Projection information: Global equal-area cylindrical EASE-Grid projection. For more information about EASE-Grid projection, see the description of this projection in data preparation. Data format: The data set consists of 23 folders from 1987 to 2009. Each folder contains the results of the day-to-day surface freeze-thaw classification of the year, and is stored as a txt file on a daily basis. File naming rules: For example, SMI-frozen1994001.txt represents the surface freeze-thaw classification results on the first day of 1994. The ASCII file of the data set is composed of a header file and a body content. The header file consists of 6 lines of description information such as the number of rows, the number of columns, the coordinates of the lower left point of the x-axis, the coordinates of the lower left point of the y-axis, the grid size, and the value of the data-less area. Array, with columns as the priority. The values are integers, from 1 to 4, 1 for frozen, 2 for melting, 3 for desert, and 4 for precipitation. Because the space described by all ASCII files in this data set is nationwide, the header files of these files are unchanged. The header files are extracted as follows (where xllcenter, yllcenter and cellsize are in m): ncols 308 nrows 166 xllcorner 5778060 yllcorner 1880060 cellsize 25067.525 nodata_value 0 All ASCII files in this data set can be opened directly with a text program such as Notepad. Except for the header file, the main content is a numerical representation of the surface freeze-thaw state: 1 for frozen, 2 for melting, 3 for desert, and 4 for precipitation. If you want to display it with an icon, we recommend using ArcView + 3D or Spatial Analyst extension module to read it. During the reading process, a grid format file will be generated. The displayed grid file is the graphic representation of the ASCII code file. Reading method: [1] Add 3D or Spatial Analyst extension module in ArcView software, and then create a new View; [2] Activate View, click the File menu, select the Import Data Source option, the Import Data Source selection box pops up, select ASCII Raster in Select import file type: in this box, and a dialog box for selecting the source ASCII file automatically pops up Find any ASCII file in the data set and press OK; [3] Type the name of the Grid file in the Output Grid dialog box (a meaningful file name is recommended for later viewing), and click the path where the Grid file is stored, press Ok again, and then press Yes (to select an integer) Data), Yes (call the generated grid file into the current view). The generated file can be edited according to the Grid file standard. This completes the process of displaying the ASCII file as a Grid file. [4] During batch processing, you can use ARCINFO's ASCIIGRID command to write an AML file, and then use the Run command to complete in the Grid module: Usage: ASCIIGRID <in_ascii_file> <out_grid> {INT | FLOAT}
LI Xin
The data set is from February 24, 2000 to December 31, 2004, with a resolution of 0.05 degrees, MODIS data, and the data format is .hdf. It can be opened with HDFView. The data quality is good. The missing dates are as follows: 2000 1 -54 132 219-230 303 2001 111 167-182 2002 079-086 099 105 2003 123 324 351-358 2004 219 349 The number after the year is the nth day of the year Pixel values are as follows: 0: Snow-free land 1-100: Percent snow in cell 111: Night 252: Antarctica 253: Data not mapped 254: Open water (ocean) 255: Fill An example of file naming is as follows: Example: "MOD10C1.A2003121.004.2003142152431.hdf" Where: MOD = MODIS / Terra 2003 = Year of data acquisition 121 = Julian date of data acquisition (day 121) 004 = Version of data type (Version 4) 2003 = Year of production (2003) 142 = Julian date of production (day 142) 152431 = Hour / minute / second of production in GMT (15:24:31) The corner coordinates are: Corner Coordinates: Upper Left (70.0000000, 54.0000000) Lower Left (70.0000000, 3.0000000) Upper Right (138.0000000, 54.0000000) Lower Right (138.0000000, 3.0000000) Among them, Upper Left is the upper left corner, Lower Left is the lower left corner, Upper Right is the upper right corner, and Lower Right is the lower right corner. The number of data rows and columns is 1360, 1020 Geographical latitude and longitude coordinates, the specific information is as follows: Coordinate System is: GEOGCS ["Unknown datum based upon the Clarke 1866 ellipsoid", DATUM ["Not specified (based on Clarke 1866 spheroid)", SPHEROID ["Clarke 1866", 6378206.4,294.9786982139006, AUTHORITY ["EPSG", "7008"]]], PRIMEM ["Greenwich", 0], UNIT ["degree", 0.0174532925199433]] Origin = (70.000000000000000, 54.000000000000000)
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