Gross domestic product (GDP) is a monetary measure of the market value of all the final goods and services produced in a period of time, which has been used to determine the economic performance of a whole country or region. According to the collected the published global GDP data of 2015, a downscaling model, named support vector machine regression kriging was established for predicting 100-m GDP in thirty-four key nodes along the Belt and Road. The remote sensed night light data, land cover, vegetation and terrain indices were employed as ancillary variables in downscaling process. To solve the problem of missing data existing in the ancillary datasets, we will apply kriging and function interpolation methods to fill gaps. The aggregation and resampling were used to obtain 1-km and 500-m all ancillary variables, as well as 100-m terrain indices including elevation, slope and aspect. The adopted downscaling model contains trend and residual predictions. The support vector machine regression is used to model the relationship among GDP and its ancillary variables for obtaining GDP trends at fine scale based on scale invariant of the relationship. And then, the kriging interpolation is used to estimate GDP residuals at fine scale. In the downscaling process, the mentioned downscaling model was firstly employed in 1-km and 500-m data for obtaining 500-m GDP predictions; and it was again used in 500-m and 100-m data for achieving 100-m GDP predictions. The 100-m GDP predictions in constant 2011 international US dollars would provide high spatial resolution data for risk assessments.
GE Yong, LING Feng
Vegetation photosynthesis is a key component of carbon cycle in terrestrial ecosystem. Simulating photosynthesis activities on different spatial and temporal scales is helpful to solve the problem of land carbon budget, and it is also an important way to accurately predict the direction of future climate change and an important prerequisite for scientific understanding of the supporting capacity of terrestrial ecosystem for sustainable development of human society. At present, although a variety of algorithms and products for estimating the total primary productivity (GPP) of ecosystems have been relatively mature, there are still great differences and uncertainties in the global GPP products of long time series, especially the trend of their temporal variation. Sunlight induced chlorophyll fluorescence (SIF) remote sensing is a new type of remote sensing technology developed rapidly in recent years. The close relationship between SIF and photosynthetic process makes it an effective probe to indicate the changes of vegetation photosynthesis and a powerful means to monitor GPP. A new vegetation index (Nirv) based on remote sensing data, namely the product of normalized vegetation index (NDVI) and near-infrared reflectance, is highly related to remote sensing SIF products; based on mechanism derivation, model simulation and analysis of remote sensing data, Nirv can be used as an alternative product of SIF to estimate global GPP. Therefore, on the basis of analyzing the feasibility of Nirv as SIF and GPP probe, this data set generates the global high-resolution long-time series GP data from 1982 to 2018 based on the AVHRR data of remote sensing and hundreds of flux stations around the world, and analyzes the temporal and spatial variation trend of global GPP. The resolution is month, 0.05 degree, and the data unit is gcm-2 The annual average global GPP is about 128.3 ± 4.0 PG Cyr − 1, and the root mean square error (RMSE) of the data is 1.95 gcm-2 D-1. The data set can be used to study global climate change and carbon cycle.
WANG Songhan, ZHANG Yongguang
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)
National Snow and Ice Data Center（NSIDC）
This dataset is based on the long sequence (1981-2013)normalized difference vegetation index product(Version 3) of the latest NOAA Global Inventory Monitoring and Modeling System (GIMMS). First, the NDVI data products were re-sampled from the spatial resolution of 1/12 degree to 0.5 degree, then the time series of every year was smoothed by the double-logistic method, and the smoothed curvature was calculated. The maximum curvature of spring was selected as the returning green stage of the vegetation in Spring. This data can be used to analyze the temporal and spatial characteristics of the Holarctic vegetation phenology in Spring.
XU Xiyan
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
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
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 data set is the average wind speed of the Central Asia including three temperate deserts, the Karakum, Kyzylkum and Muyunkun Deserts, and one of the world's largest arid zones. The data was obtained by GLDAS global three-hour assimilation data extraction calculation. The data is in tif format. The space and time resolutions are 0.25° and 3 hours respectively. The time is from 01, January, 2017 to 31, December, 2017. The data set uses the the Geodetic coordinate system. We can use the data to calculate the sand flux. It can be used for the investigation of the Desert oil and gas field, and oasis cities.
GAO Xin
Snow cover dataset is produced by snow and cloud identification method based on optical instrument observation data, covering the time from 1989 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground under clear sky or transparent thin cloud is covered by snow. The input data sources include AVHRR L1 data of NOAA and MetOp serials of satellites, and L1 data corresponding to AVHRR channels taken from TERRA/MODIS. Decision Tree algorithm (DT) with dynamic thresholds is employed independent of cloud mask and its cloud detection emphasizes on reserving snow, particularly under transparency cirrus. It considers a variety of methods for different situations, such as ice-cloud over the water-cloud, snow in forest and sand, thin snow or melting snow, etc. Besides those, setting dynamic threshold based on land-surface type, DEM and season variation, deleting false snow in low latitude forest covered by heavy aerosol or soot, referring to maximum monthly snowlines and minimum snow surface brightness temperature, and optimizing discrimination program, these techniques all contribute to DT. DT discriminates most snow and cloud under normal circumstances, but underestimates snow on the Qinghai-Tibet Plateau in October. Daily product achieves about 95% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.
ZHENG Zhaojun, CHU Duo
The dataset is the land cover of Qing-Tibet Plateau in 2014. The data format is a TIFF file, spatial resolution is 300 meters, including crop land, grassland, forest land, urban land, and so on. The dataset offers a geographic fundation for studying the interaction between urbanization and ecological reservation of Qing-Tibet Plateau. This land cover data is a product of CCI-LC project conducted by European Space Agency. The coordinate reference system of the dataset is a geographic coordinate system based on the World Geodetic System 84 reference ellipsoid. There are 22 major classes of land covers. The data were generated using multiple satellite data sources, including MERIS FR/RR, AVHRR, SPOT-VGT, PROBA-V. Validation analysis shows the overall accuracy of the dataset is more than 70%, but it varies with locations and land cover types.
DU Yunyan
This data set is a three-level classification map of Eurasian grassland remote sensing in 2009. The data is in TIF grid format, with a spatial resolution of 1km. The three-level grassland is classified as: temperate meadow grassland, temperate typical grassland, temperate desertification grassland, temperate grassland desertification, and temperate desert. The data is processed according to the ESA global cover 2009 Product global cover map, combined with the historical meteorological data (precipitation, annual accumulated temperature, humidity coefficient, evaporation) and DEM data of ECMWF website. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
TANG Jiakui
This data set is a spatiotemporal variation map of temperate grassland types in Eurasia - three level classification of Inner Mongolia region of China (2009). The data is in TIF grid format with a spatial resolution of 1km. The data is processed on the basis of the existing grass type map of Inner Mongolia grassland. The grassland type map of Inner Mongolia grassland is based on the field survey data, neimengqi County as the unit, the grassland type classification system, on the basis of prediction, the field sample data, remote sensing image and other information data are superposed, and the local historical grassland survey data and relevant data are referred to, and the field plot is modified. We select 2000-2009 historical meteorological data, further analyze and modify the satellite data, and carry out spatial interpolation calculation. The classification of temperate grassland in Inner Mongolia was obtained. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
TANG Jiakui
This data set is the annual maximum value data (ndvi-am) of the normalized vegetation index of the Qinghai Tibet Plateau from 2000 to 2018. The data is in grid TIFF format, with a spatial resolution of 250m and a grid data value range of [- 1,1]. It can be used to study the change of vegetation coverage, grassland degradation and other ecological environment changes in the Qinghai Tibet Plateau, and can also provide data support for the study of urbanization and ecological environment interaction stress. The data is calculated based on the land level 2 standard data product of MODIS medium resolution sensor mod13 series (https://modis.gsfc.nasa.gov/data/dataprod/mod13.php). The level 2 Product data is a specific application data product generated after processing the original MODIS original data set. Ndvi-am data is processed by calculating the annual maximum value of NDVI of each pixel based on the normalized vegetation index data.
DU Yunyan, YI Jiawei
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
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
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
This data set is the land use data of the key areas of Qilian mountain in 2018, spatial resolution 2m. This data set is based on the data of climate, altitude, topography, and land cover type of the Qilian mountain. Through the high-resolution remote sensing images to interprets the surface cover types. For the land types that cannot be reflected by the images, collect relevant data in the field, check and correct the land use types. At the same time, the maps and attribute information are uniformly entered and edited to form land use data in the Qilian Mountain area in 2018.
QI Yuan, ZHANG Jinlong, YAN Changzhen, DUAN Hanchen, JIA Yongjuan
This dataset is blended by two other sets of data, snow cover dataset based on optical instrument remote sensing with 1km spatial resolution on the Qinghai-Tibet Plateau (1989-2018) produced by National Satellite Meteorological Center, and near-real-time SSM/I-SSMIS 25km EASE-grid daily global ice concentration and snow extent (NISE, 1995-2018) provided by National Snow and Ice Data Center (NSIDC, U.S.A). It covers the time from 1995 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground is covered by snow. The input data sources include daily snow cover products generated by NOAA/AVHRR, MetOp/AVHRR, and alternative to AVHRR taken from TERRA/MODIS corresponding observation, and snow extent information of NISE derived from observation by SSM/I or SSMIS of DMSP satellites. The processing method of data collection is as following: first, taking 1km snow cover product from optical instruments as initial value, and fully trusting its snow and clear sky without snow information; then, under the aid of sea-land template with relatively high resolution, replacing the pixels or grids where is cloud coverage, no decision, or lack of satellite observation, by NISE's effective terrestrial identification results. For some water and land boundaries, there still may be a small amount of cloud coverage or no observation data area that can’t be replaced due to the low spatial resolution of NISE product. Blended daily snow cover product achieves about 91% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.
ZHENG Zhaojun, CAO Guangzhen
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