It includes the social and economic data of Gansu, Qinghai and Inner Mongolia from 2000 to 2012. The specific indicators include GDP, income, population, employment, medical care, education, land area, finance and a series of social and economic indicators;
0 2020-07-28
This dataset includes daily water vapor and precipitation isotopes (δ18O and δD) and daily meteorological parameters including temperature, relative humidity, vapor concentration, air pressure, and precipitation amount at Nanjing, eastern China. Water vapor isotopes (δ18Ov and δDv) were online measured during November 2012 to December 2018 by a Wavelength Scanned Cavity Ring-Down Spectrometer (WS-CRDS, model: Picarro L2120-i) at the Station for Observing Regional Processes of the Earth System of Nanjing University (SORPES-NJU, 32.12°N, 118.95°E, 55 m above sea level) on the Xianlin Campus of the Nanjing University, about 20 km east of downtown Nanjing in the Eastern China. The uncertainties were determined to be less than 0.2‰ for δ18Ov and 1.0‰ for δDv. Precipitation isotopes were also measured by Picarro L2120-i during September 2011 to December 2018, with the analytical uncertainty of less than 0.1‰ for δ18O and 0.5‰ for δD.
5050 2020-09-21
The Antarctic Peninsula is also called "Palmer peninsula" or "Graham land". Located in the southwest polar continent, it is the largest peninsula in the Antarctic continent and the farthest peninsula extending northward into the ocean (63 ° south latitude), bordering the Weddell Sea and berengske sea in the East and West. The Antarctic Peninsula is known as the "tropics" of Antarctica. This is a typical sub polar marine climate. Compared with the Antarctic continent, it is one of the warmest and wettest regions in Antarctica. There are a small number of pioneer plants distributed on the islands in the marginal area, mainly bryophytes and lichens. The plant abundance data products of Antarctic Peninsula and its surrounding areas are matched with remote sensing images through measured spectra, and the end element spectra of moss, lichen, rock, sea and snow are extracted with pure pixel PPI. The linear mixture model (LMM) is applied to calculate.
0 2019-10-26
The dataset of ground truth measurements for snow synchronizing with the airborne PHI mission was obtained in the Binggou watershed foci experimental area on Mar. 24, 2008. Observation items included: (1) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork in BG-A. (2) Snow parameters as the snow surface temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, and snow density by the aluminum case in BG-A1, BG-A2, BG-B, BG-D, BG-E and BG-F5 (three sampling units each) from 11:11-12:35 (BJT) with the airplane overpass. 64 points were selected by four groups. (3) Snow albedo by the total radiometer in BG-A. (4) The snow spectrum by ASD (Xinjiang Meteorological Administration) in BG-A11 Two files including raw data and preprocessed data were archived.
0 2019-09-14
On 25 July 2012, a Leica ALS70 airborne laser scanner boarded on the Y-12 aircraft was used to obtain the point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 5500 m with the point cloud density 1 points per square meter. Aerial LiDAR-DEM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
0 2019-09-15
QuickBird satellite was launched by Digital Globe corporation on October 18, 2001. It has 4 multi-spectral bands and 1 panchromatic band, with a spatial resolution of 0.61m for panchromatic bands and a spatial resolution of 2.5m for multi-spectral bands and a width of 16.5 * 16.5 km. There are two QuickBird remote sensing images in heihe river basin.The acquisition time and coverage were: 2004-03-23, covering zhangye area;2004-08-08, covering danokou and drainage ditch drainage basin. The product level is level L2 and has been geometrically corrected by the system.
0 2020-03-15
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
0 2020-06-10
The micro-meteorological field is located in the grassland of Pailugou watershed of Qilian Mountain with an altitude of 2700m. The data were recorded from January 2011 to July 2012, and the time interval was half an hour, including 1.5m humidity, 3m temperature, 2.8m air pressure, 1.3m rainfall, 2.2m wind speed, 3.1m total radiation, the units are %, °C, Pa, m, m/s, W•M-2.
0 2020-03-31
The dataset is the HWSD soil texture dataset in the north slope of the Tianshan River Basin. The data comes from the Harmonized World Soil Database (HWSD) constructed by the Food and Agriculture Organization of the United Nations (FAO) and the Vienna International Institute for Applied Systems (IIASA). Version 1.1 was released on March 26, 2009. The data resolution is 1km. The soil classification system used is mainly FAO-90. The main fields of the soil attribute table include: SU_SYM90 (soil name in FAO90 soil classification system) SU_SYM85 (FAO85 classification) T_TEXTURE (top soil texture) DRAINAGE (19.5); ROOTS: String (depth classification of obstacles to the bottom of the soil); SWR: String (soil moisture characteristics); ADD_PROP: Real (a specific soil type related to agricultural use in the soil unit); T_GRAVEL: Real (gravel volume percentage); T_SAND: Real (sand content); T_SILT: Real (silt content); T_CLAY: Real (clay content); 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 (pH) T_CEC_CLAY: Real (cation exchange capacity of cohesive layer soil); T_CEC_SOIL: Real (cation exchange capacity of soil) T_BS: Real (basic saturation); T_TEB: Real (exchangeable base); T_CACO3: Real (carbonate or lime content) T_CASO4: Real (sulfate content); T_ESP: Real (exchangeable sodium salt); T_ECE: Real (conductivity). The attribute field beginning with T_ indicates the upper soil attribute (0-30cm), and the attribute field beginning with S_ indicates the lower soil attribute (30-100cm) (FAO 2009). The data can provide model input parameters for modelers of the Earth system, and the agricultural perspective can be used to study eco-agricultural zoning, food security, and climate change.
0 2020-06-01
Taking 2000 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but also is widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of real GDP per capita), the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of industrial structure changing in China and the research area lagged behind the growth of GDP, so it was adjusted according to the need of the future industrial structure scenarios of the research area.
0 2020-04-28
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