On July 23, 1972, the United States launched the world's first resource satellite "Landsat 1" , and Landsat 2 and Landsat 3 were launched in the following 10 years. These three satellites were the first generation of resource satellites. They were equipped withreturn-beam vidicon cameras and multi-spectral scanners (MSS) with 3 and 4 spectral segments respectively, a resolution of 79m and a width of 185Km. There are 28 scenes of MSS data in Heihe River Basin currently which were obtained on the following dates: 1972-10-14, 1972-10-30, 1973-01-10, 1973-01-31, 1973-02-16, 1973-06-04, 1973. -10-07, 1973-10-28 (2 scenes), 1973-12-22, 1974-01-05, 1975-10-07, 1975-10-09, 1976-07-04, 1976-10-18 , 1976-11-07, 1976-11-27, 1976-12-30, 1977-01-19, 1977-02-07, 1977-04-20, 1977-05-06 (2 scenes), 1977-05 -08, 1977-06-10, 1977-06-29, 1977-07-18, 1978-10-09. Ortho rectification was performed on the images.
LP DAAC User Services
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.
ZHAO Jun
Railway distribution map is the basic data in the mapping process. In order to facilitate the use of users, we compiled the railway data set of Heihe River basin according to the railway data set distributed by the National Basic Geographic Information Center, the atlas of Gansu Province compiled by the Gansu Provincial map Geographic Information Center, the sky map and Guge map published by the China Surveying and Mapping Bureau. This data basically reflects the distribution of Railways around the Heihe River basin around 2010. The national standard of data classification and coding of national basic geographic information system - Classification and code of basic land information data (GB / T 13923-92) is adopted for railway coding, and the code is five digit code (National Basic Geographic Information Center 2010).
National Basic Geographic Information Center
Eo-1 (Earth Observing Mission) is a new Earth Observing satellite developed by NASA to replace Landsat7 in the 21st century. It was launched on November 21, 2000.The orbit of eo-1 satellite is basically the same as that of Landsat7, which is a solar synchronous orbit with an orbital altitude of 705km and an inclination Angle of 98.7°, which is 1min less than that of Landsat7 and crosses the equator.On board of EO 1 3 kinds of sensors, namely, the Advanced Land Imager (ALI (the Advanced Land Imager), atmospheric correction instrument AC (Atmosp heric Corrector) and compose a specular as spectrometer (Hyperion), Hyperion sensor is first spaceborne hyperspectral mapping measurement instrument, the hyperspectral data a total of 242 bands, spectral range is 400 ~ 2500 nm, spectral resolution up to 10 nm, ground resolution of 30 m. Currently, there are 6 scenes of eo-1 Hyperion data in heihe river basin.The coverage and acquisition time were: 4 scenes in the encrypted observation area of zhangye urban area + yingke oasis encrypted observation area (2007-09-10, 2008-05-12, 2008-05-20, 2008-07-15).Two scenes of the iceditch watershed observation area were encrypted, the time was 2008-03-17, 2008-03-22, respectively. Product grade is L1 without geometric correction. The eo-1 Hyperion remote sensing data set of heihe integrated remote sensing joint experiment was acquired by researcher wang jian and Beijing normal university through purchase. (note: "+" represents simultaneous coverage)
Institute of Remote Sensing and Digital earth, Chinese Academy of Sciences
The spot satellite series in France consists of five stars, of which spot 5 is the best. It was launched in May 2002, with a height of 830km, an orbit inclination of 98.7 degrees, and a sun synchronous quasi regression orbit, with a regression period of 26 days. Linear array sensor (CCD) and push scan scanning technology were used for imaging. SPOT5 satellite carries two high-resolution geometric imagers (HRG), one high-resolution Stereo Imager (HRS) and one wide field vegetation detector (VGT). It has five working bands, multi spectral band spatial resolution is 10m (short wave infrared spatial resolution is 20m), panchromatic band spatial resolution is 2.5m. At present, there are three spots of SPOT5 data in Heihe River Basin. The coverage and acquisition time are respectively: 1 scene in Linze area, including multispectral image with resolution of 10m and panchromatic image with resolution of 2.5m, with time of 2008-07-04; 1 scene in Zhangye City, with resolution of 2.5m, with time of 2008-03-29; 1 scene of multispectral data with resolution of 10m, with time of 2008-08-10. The product level is L1, and the product has undergone rough geometric correction. SPOT5 image is mainly used as the base map of geometric precision correction in Heihe experiment. The spot 5 remote sensing data set of Heihe comprehensive remote sensing joint experiment was purchased by Beijing Normal University.
Institute of Remote Sensing and Digital earth, Chinese Academy of Sciences
The dataset is the HWSD Soil texture data set of the qaidam basin. The data is from the Harmonized World Soil Database (HWSD) constructed by the United Nations food and agriculture organization (FAO) and Vienna institute for international applied systems (IIASA), which was released in version 1.1 on March 26, 2009.The data resolution is 1km.The main soil classification system adopted is fao-90.The main fields in the soil property list include SU_SYM90 (soil name in the FAO90 soil classification system) SU_SYM85(FAO85 classification) T_TEXTURE(top layer soil texture) (19.5);ROOTS: String(deep classification of obstacles to the bottom of the soil);SWR: String (soil moisture content characteristics);ADD_PROP: Real (specific type of soil in a soil unit related to an agricultural use);T_GRAVEL: Real (percent by volume);T_SAND: Real;T_SILT: Real (silt content);T_CLAY: Real;T_USDA_TEX: Real (USDA soil texture classification);T_REF_BULK: Real (soil bulk density);T_OC: Real (organic carbon content);T_PH_H2O: Real T_CEC_CLAY: Real;T_CEC_SOIL: Real (cation exchange capacity of soil) T_BS: Real (basic saturation);T_TEB: Real (commutative base);T_CACO3: Real (carbonate or lime content) T_CASO4: Real (sulfate content);T_ESP: Real (exchangeable sodium);T_ECE: Real.The attribute field beginning with T_ represents the upper soil attribute (0-30cm), and the attribute field beginning with S_ represents the lower soil attribute (30-100cm) (FAO 2009).This data can provide model input parameters for earth system modelers, and agricultural perspectives can be used to study eco-agricultural zoning, food security and climate change.
Food and Agriculture Organization of the United Nations（FAO）
The VEGETATION sensor sponsored by the European Commission was launched by SPOT-4 in March 1998. Since April 1998, SPOTVGT data for global vegetation coverage observation has been received by Kiruna ground station in Sweden. The image quality monitoring center in Toulouse, France is responsible for image quality and provides relevant parameters (such as calibration coefficient number). Finally, the Belgian flemish institute for technological research (Vito)VEGETATION processing Centre (CTIV) is responsible for preprocessing into global data of 1km per day. Pretreatment includes atmospheric correction, radiation correction, geometric correction, production of 10 days to maximize the synthesized NDVI data, setting the value of -1 to -0.1 to -0.1, and then converting to the DN value of 0-250 through the formula DN= (NDVI+0.1)/0.004. The dataset is a long-time series vegetation index dataset of Qinghai Lake Basin, which is mainly aimed at normalized difference vegetation index (NDVI). It includes spectral reflectance of four bands synthesized every 10 days from 1998 to 2008 and maximum NDVI for 10 days, with a spatial resolution of 1km and a temporal resolution of 10 days.
Flemish Institute for Technological Research (VITO)
Ⅰ. Overview This dataset is derived from the global 30m-resolution digital elevation product dataset, which is processed using the data of the first version (v1) of ASTER GDEM. Its spatial resolution is 30m. Due to the influence of clouds, lines, pits, bulges, dams or other anomalies generated by the boundary stacking, there are local anomalies in the first version of the original data of ASTER GDEM, so the digital elevation processed by ASTER GDEM v1 Data products have data anomalies in individual areas, and users need to pay attention to them during use. In addition, this data set can complement the SRTM global 90m resolution elevation dataset. Ⅱ. Data processing description ASTER GDEM is a fully automated method to process and generate ASTER archived data of 1.5 million scenes, including 1,264,118 ASTER DEM data based on independent scenes generated through stereo correlation. After de-cloud processing, residual outliers are removed, and the average value is taken as the final pixel value of ASTER GDEM object area. After correcting the remaining abnormal data, the global ASTER GDEM data was generated by 1°× 1° sharding. Ⅲ. Data content description The dataset covers the entire upper reaches of the Yellow River, and each data file name is generated based on the latitude and longitude of the lower left (southwest) Angle of the fractal geometry center. For example, the lower-left coordinate of the ASTGTM_N40E116 file is 40 degrees north latitude and 116 degrees east longitude. ASTGTM_N40E116_dem and ASTGTM_N40E116_num correspond to digital elevation model (DEM) and quality control (QA) data, respectively. Ⅳ. Data usage description ASTER GDEM data can be calculated and visualized. It has a broad application prospect in various fields, especially in mapping, surface deformation and military fields.Specifically, it mainly includes the following aspects: In scientific research, ASTER GDEM data plays an important role in geology, geophysics, seismic research, horizontal modeling, volcano monitoring and remote sensing image registration.The three-dimensional model of the ground is built by using high-precision digital terrain elevation data, which can be embedded and superimposed with the image of the ground to observe subtle changes of the earth surface. In civil and industrial applications, ASTER GDEM data can be used for civil engineering calculation, dam site selection, land use planning, etc. In communications, digital topographic data can help businesses build better broadcast towers and determine the best location of mobile phone booths.In terms of aviation safety, ASTER GDEM digital elevation data can be used to establish the enhanced aircraft landing alarm system, which greatly improves the aircraft landing safety coefficient. In the military, ASTER GDEM data is the basic information platform of C4ISR (army automatic command system), which is indispensable in the study of battlefield regional structure, combat direction, battlefield preset, combat deployment, troop concentration in projection, protection conditions, logistics support and other aspects.
XUE Xian DU Heqiang
The data is clipped from "1: 1 million wetland data of China". "1: 1 million wetland data of China" mainly reflects the national marsh wetland information in the 2000s. It is expressed in geographic coordinates using the decimal degree. The main contents include: marsh wetland types, wetland water supply types, soil types, main vegetation types, geographical area, etc. Implemented the "Standard for Information Classification and Coding of Sustainable Development Information Sharing System of China". Data source of this database: 1:20 swamp map (internal version), Tibetan Plateau 1: 500,000 swamp map (internal version), swamp survey data 1: 1 million and national 1: 4 million swamp map; processing steps are: data source selection, preprocessing, digitization and encoding of marsh wetland elements, data editing processing, establishing topological relationships, edge processing, projection conversion, linking with attribute databases such as place names and obtaining attribute data.
ZHANG Shuqing
The data was compiled from "China's 1:100 million wetlands data" to get a figure of 1 million wetlands in gansu province. "China 1:100,000 wetland data" mainly reflects the information of marshes and wetlands throughout the country in the 2000s, and is represented by geographical coordinates in decimal scale. The main contents include: types of marshes and wetlands, types of water supply, types of soil, types of main vegetation, and geographical regions.The information classification and coding standard of China sustainable development information sharing system was implemented.Data source of this database: 1:20 swamp map (internal version), 1:500 000 swamp map (internal version) of qinghai-tibet plateau, 1:100 000 swamp survey data and 1:400 000 swamp map of China;The processing steps are as follows: data source selection, preprocessing, marshland element digitization and coding, data editing and processing, establishment of topological relationship, edge-to-edge processing, projection transformation, connection with attribute database such as geographical name and acquisition of attribute data.
ZHANG Shuqing
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".
LIU Shaomin LI Xin XU Ziwei
Original information on the long-term dry-wet index (1500-2000) in western China is obtained by integrating data on dry-wet/drought-flood conditions and precipitation amounts in the western region published over more than a decade. The integrated data sets include tree rings, ice cores, lake sediments, historical materials, etc., and there are more than 50 such data sets. In addition to widely collecting representative data sets on dry-wet changes in the western region, this study also clarifies the main characteristics of the dry-wet changes and climate zones in the western region, and the long-term dry-wet index sequence was generated by extracting representative data from different zones. The data-based dry-wet index sequence has a 10-year temporal resolution for five major characteristic climate zones in the western region over nearly four hundred years and a high resolution (annual resolution) for three regions over the past five hundred years. The five major characteristic climate zones in the western region with a 10-year dry-wet index resolution over the last four hundred years are the arid regions, plateau bodies, northern Xinjiang, Hetao region, and northeastern plateau, and the three regions with a annual resolution over the last five hundred years are the northeastern plateau, Hetao region, and northern Xinjiang. For a detailed description of the data, please refer to the data file named Introduction of Dry-Wet Index Sequence Data for West China.doc.
QIAN Weihong LIN Xiang
These data are digitized for the Geocryological Regionalization and Classification Map of the Frozen Soil in China (1:10 million) (Guoqing Qiu et al., 2000; Youwu Zhou et al., 2000), adopting a geocryological regionalization and classification dual series system. The geocryological regionalization system and classification system are used on the same map to reflect the commonality and individuality of the formation and distribution of frozen soil at each level. The geocryological regionalization system consists of three regions of frozen soil: (1) the frozen soil region of eastern China; (2) the frozen soil region of northwestern China; and (3) the frozen soil region of southwestern China (Tibetan Plateau). Based on the three large regions, 16 regions and several subregions are further divided. In the division of the geocryological boundary in the frozen soil area, the boundary between major regions I and III mainly consults the results of Bingyuan Li (1987). The boundary between major regions II and III is the northern boundary of the Tibetan Plateau, which is the Kunlun Mountains-Altun Mountains-Northern Qilian Mountains and the piedmont line. The boundary between major regions I and II is in the area of Helan Mountain-Langshan Mountain. The boundary of the secondary region is divided by the geomorphological conditions in regions II and III. However, in region I, it is mainly divided by the ratio of the annual temperature range A to the annual mean temperature T, and the frozen depths of various regions are taken into consideration. The classification system is divided into 8 types based on the continuity of frozen soil, the time of existence of frozen soil and the seasonal frozen depth. The various classifications of boundaries are mainly taken from the "Map of Snow, Ice and Frozen Ground in China" (1:4 million) (Yafeng Shi et al., 1988) and consult some new materials, whereas the seasonal frozen soil boundary is mainly based on the weather station data. The definitions of each classification are as follows: (1) Large permafrost: the continuous coefficient is 90%-70%; (2) Large-island permafrost: the continuous coefficient is 70%-30%; (3) Sparse island-shaped permafrost: the continuous coefficient is <30%; (4) Permafrost in the mountains; (5) Medium-season seasonal frozen soil: the maximum seasonal frozen depth that can be reached is >1 m; (6) Shallow seasonal frozen soil: the maximum seasonal frozen depth that can be reached is <1 m; (7) Short-term frozen soil: less than one month of storage time; and (8) Nonfrozen soil. According to the data, China's permafrost areas sum to approximately 2.19 × 106 km², accounting for 22.83% of China's territory. Among those areas, the mountain permafrost is found over 0.42×106 km2, which is 4.39% of the territory of China. The seasonal frozen soil area is approximately 4.76×106 km², accounting for 49.6% of China's territory, and the instantaneous frozen soil area is approximately 1.86×106 km², i.e., 19.33% of China's territory. For more information, please see the references (Youwu Zhou et al., 2000).
ZHOU Youwu GUO Dongxin QIU Guoqing
This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
International Centre for Integrated Mountain Development (ICIMOD) United Nationenvironment Programme/Regional Resourc Centre, Asia and The Pacific (UNEP/RRC-AP)
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.
YUE Tianxiang ZHAO Na
This data set contains the oxygen isotope, dust, anion and accumulation data obtained from the deep ice core drilled in 1992 in the Guliya ice cap, which is located in the west Kunlun Mountains on the Tibetan Plateau. The length of the ice core was 308.6 m. The ice core was cut into samples, 12628 of which were used to measure the oxygen isotope values, 12480 of which were used to measure the dust concentrations, and 9681 of which were used to measure the anion concentrations. Data Resource: National Centers for Environmental Information（http://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/ice-core）. Processing Method: Average. The data set contains 4 tables, namely: oxygen isotope, dust and anion data from different depths in the Guliya ice core, 10-year mean data of oxygen isotopes, dust, anions and net accumulation in the Guliya ice core, 400-year mean data of oxygen isotopes, dust and anions in the Guliya ice core, and chlorine-36 data from different depths. Table 1: Data on oxygen isotopes, dust and anion concentrations at different depths in the Guliya ice core. a. Name explanation Field 1: Depth Field 2: Oxygen isotope value Field 3: Dust concentration (diameter 0.63 to 20 µm) Field 4: Cl- Field 5: SO42- Field 6: NO3- b. Dimensions (unit of measure) Field 1: m Field 2: ‰ Field 3: particles/mL Field 4: ppb Field 5: ppb Field 6: ppb Table 2: 10-year mean oxygen isotope, dust, anion and net accumulation data for the Guliya ice core (0-1989) a. Name explanation Field 1: Start time Field 2: End time Field 3: Oxygen isotope value Field 4: Dust concentration (diameter 0.63 -20 µm) Field 5: Cl- Field 6: SO42- Field 7: NO3- Field 8: Net accumulation b. Dimensions (unit of measure) Field 1: Dimensionless Field 2: Dimensionless Field 3: ‰ Field 4: particles/mL Field 5: ppb Field 6: ppb Field 7: ppb Field 8: cm/year Table 3: 400-year mean oxygen isotope, dust and anion data for the Guliya ice core. a. Name explanation Field 1: Time Field 2: Oxygen isotope Field 3: Dust concentration (diameter 0.63-20 µm) Field 4: Cl- Field 5: SO42- Field 6: NO3- b. Dimensions (unit of measure) Field 1: Millennium Field 2: ‰ Field 3: particles/mL Field 4: ppb Field 5: ppb Field 6: ppb Table 4: Chlorine-36 data at different depths a. Name explanation Field 1: Depth Field 2: 36Cl Field 3: 36Cl error Field 4: Year b. Dimensions (unit of measure) Field 1: m Field 2: 104 atoms g-1 Field 3: % Field 4: Millennium
National Centers for Environmental Information (NCEI)
The data set includes precipitation data of main areas in Qinghai Province from 1988 to 2016 such as Xining, Haidong, Menyuan, Huangnan, Hainan, Guoluo, Yushu and Haixi. The data were derived from the Qinghai Society and Economics Statistical Yearbook and the Qinghai Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook. The data table records the monthly and annual precipitation in 8 regions of Qinghai with units of millimeters. The data set is mainly applied in geography and socioeconomic research.
Qinghai Provincial Bureau of Statistics
The data set includes the average wind speed data of main areas in Qinghai Province from 1988 to 2016 such as Xining, Haidong, Menyuan, Huangnan, Hainan, Guoluo, Yushu and Haixi. The data were derived from the Qinghai Society and Economics Statistical Yearbook and the Qinghai Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook. The data table records the monthly and annual average wind speed in eight regions of Qinghai. Unit: m / s The data set is mainly applied in geography and socioeconomic research.
Qinghai Provincial Bureau of Statistics
The data set contains data on the annual sunshine hours and total solar radiation in Tibet from 1988 to 1994. The data were derived from the Tibet Society and Economics Statistical Yearbook and the Tibet Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook. The table contains 5 fields. Field 1: Year Interpretation: Year of the data Field 2: Location Field 3: Annual sunshine hours Unit：hour Field 4: Annual sunshine percentage Unit: % Field 5: Total solar radiation Unit: Kcal/cm2 • Year
National Bureau of Statistics
The data set includes data on lakes with an altitude over 5,000 meters in Tibet from 1988 to 2016. The data were derived from the Tibet Society and Economics Statistical Yearbook and the Tibet Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook. The table contains 5 fields. Field 1: Year Field 2: Lake Name Field 3: Lake altitude Unit: meter Field 4: Lake area Unit: square kilometers Field 5: Lake Type
National Bureau of Statistics
