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A China soil characteristics dataset(2010)

A multi-layer soil particle-size distribution dataset (sand, silt and clay content), based on USDA (United States Department of Agriculture) standard for regional land and climate modelling in China. was developed The 1:1,000,000 scale soil map of China and 8595 soil profiles from the Second National Soil Survey served as the starting point for this work. We reclassified the inconsistent soil profiles into the proper soil type of the map as much as possible because the soil classification names of the map units and profiles were not quite the same. The sand, silt and clay maps were derived using the polygon linkage method, which linked soil profiles and map polygons considering the distance between them, the sample sizes of the profiles, and soil classification information. For comparison, a soil type linkage was also generated by linking the map units and soil profiles with the same soil type. The quality of the derived soil fractions was reliable. Overall, the map polygon linkage offered better results than the soil type linkage or the Harmonized World Soil Database. The dataset, with a 1-km resolution, can be applied to land and climate modelling at a regional scale. Data characteristics: projection:projection Coverage: China Resolution: 0.00833 (about 1 km) Data format: FLT, TIFF Value range: 0%-100% Document describing: Floating point raster files include: Sand1. FLT, clay1. FLT -- surface (0-30cm) sand, clay content. Sand2. FLT, clay2. FLT -- content of sand and clay in the bottom layer (30-100cm). PSD. HDR -- header file: Ncols - the number of columns Nrows- rows Xllcorner - latitude in the lower left corner Yllcorner - longitude of the lower left corner Cellsize - cellsize NODATA_value - a null value byteorder - LSBFIRST, Least Significant Bit First TIFF raster files include: Sand1. Tif, clay1. Tif - surface (0-30cm) sand, clay content. Sand2. Tif, clay2. Tif - bottom layer (30-100cm) sand, clay content.

2020-05-28

SRTM DEM dataset in China (2000)

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.

2020-04-08

1: 1 million wetland data of Jilin Province (2000)

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.

2020-04-07

The map of desert distribution in 1:2,000,000 in China (1974)

Desertification is a kind of land degradation with aeolian sands as the main symbol caused by the uncoordinated human-land relationship in arid, semi-arid and some semi-humid regions of northern China. Data source: edited by the China Institute of Glacial and Frozen Desert and coordinated by the Institute of Geography of the Chinese Academy of Sciences. Based on aerial photographs from the 1970s and field research, a 1: 2 million desert map was drawn. Mapping of the 14 million "Map of the People's Republic of China" published in 1971. First, the data set content 1.Desert_Ch_2009 (desert distribution) 2.Dune_hight_Ch_200 (dune height) 3.Gobi_Ch_200 (Gobi) 4.Wind_eroded_land_Ch_200 (wind erosion data) The fields of the desertification attribute table are as follows: (1) Semifixed (semi-fixed dunes): undulating sandy land (2-1), thicket dunes (2-2), parabolic dunes (2-3), beam nest dunes (2-4), sand ridges And dendritic sand ridge (2-5), honeycomb sand dune (2-6), honeycomb sand ridge (2-7), composite sand ridge (2-8) (2) Fixation (fixed dune): flat sandy land (3-1), grassland bush (3-2), sand ridge (3-3), honeycomb sand dune (3-4) (3) Migratory: Crescent sand dunes and dune chains (1-1), Crescent sand ridges and dunes (1-2), Lattice dunes and Lattice dune chains (1-3), Fish scales Sand dunes (1-4), feathery dunes (1-5), pyramid dunes (1-6), composite dunes and dune chains (1-7), composite dunes (1-8), composite Dome-shaped dunes (1-9), chain-shaped sand hills (sand dunes) (1-10), stacked chain-shaped sand hills (1-11), compound ridge-shaped sand hills (1-12), composite chain-shaped Sand Mountain (1-13), Pyramid Sand Mountain (1-14) (4) class_id: encoding of desertification attributes Projection information PROJCS ["Albers", GEOGCS ["GCS_Beijing_1954", DATUM ["Beijing_1954", SPHEROID ["Krasovsky_1940", 6378245.0,298.3]], PRIMEM ["Greenwich", 0.0], UNIT ["Degree", 0.0174532925199433]], PROJECTION ["Albers_Conic_Equal_Area"], PARAMETER ["False_Easting", 0.0], PARAMETER ["False_Northing", 0.0], PARAMETER ["longitude_of_center", 105.0], PARAMETER ["Standard_Parallel_1", 25.0], PARAMETER ["Standard_Parallel_2", 47.0], PARAMETER ["latitude_of_center", 0.0], UNIT ["Meter", 1.0]]

2020-04-07

Glacier inventory of the Pengqu Basin, Himalayas, China (2004)

This glacier inventory is jointly supported by the International Centre for Integrated Mountain Development (ICIMOD) and United Nationenvironment Programme / Regional Resourc Centre, Asia and The Pacific (UNEP / RRC-AP), Cold and Arid Region Environmental and Engineering Research Institute(CAREERI). 1. The glacier inventory uses landsat (TM, ETM) and Aster remote sensing data to reflect the current state of glaciers in the Himalayas in 2004. 2. Glacier inventory coverage: Pumqu (Arun), Rongxer (Tama Koshi), Poiqu (Bhote-Sun Koshi), Jilongcangbu (Trishuli), Zangbuqin (Budhigandaki), Majiacangbu (Humla Karnali), etc. watersheds in Himalayan. 3. The glacier inventory includes: glacier location, glacier code, glacier name, glacier area, glacier length, glacier thickness, ice reserves, glacier type, glacier orientation, etc. 4. Data projection information: Projection: Transverse_Mercator False_Easting: 500000.000000 False_Northing: 0.000000 Central_Meridian: 87.000000 Scale_Factor: 0.999600 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) Geographic Coordinate System: GCS_WGS_1984 Angular Unit: Degree (0.017453292519943299) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_WGS_1984 Spheroid: WGS_1984 Semimajor Axis: 6378137.000000000000000000 Semiminor Axis: 6356752.314245179300000000 Inverse Flattening: 298.257223563000030000 For detailed data description, please refer to the document and report.

2020-04-07

MODIS Snow cover product dataset in China (2000-2004)

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)

2020-04-07

Dataset of near-surface air temperature lapse rates in the mainland China (1962-2011)

Land surface hydrological modeling is sensitive to near-surface air temperature, which is especially true for the cryosphere. The lapse rate of near-surface air temperature is a critical parameter when interpolating air temperature from station data to gridded cells. To obtain spatially distributed, fine-resolution near-surface (2 m) air temperature in the mainland China, monthly air temperature from 553 Chinese national meteorological stations (with continuous data from 1962 to 2011) are divided into 24 regional groups to analyze spatiotemporal variations of lapse rate in relation to surface air temperature and relative humidity. The results are as follows: (1) Evaluation of estimated lapse rate shows that the estimates are reasonable and useful for temperature-related analyses and modeling studies. (2) Lapse rates generally have a banded spatial distribution from southeast to northwest, with relatively large values on the Tibetan Plateau and in northeast China. The greatest spatial variability is in winter with a range of 0.3°C–0.9°C / 100m, accompanied by an inversion phenomenon in the northern Xinjiang Province. In addition, the lapse rates show a clear seasonal cycle. (3) The lapse rates maintain a consistently positive correlation with temperature in all seasons, and these correlations are more prevalent in the north and east. The lapse rates exhibit a negative relationship with relative humidity in all seasons, especially in the east. (4) Substantial regional differences in temporal lapse rate trends over the study period are identified. Increasing lapse rates are more pronounced in northern China, and decreasing trends are found in southwest China, which are more notable in winter. An overall increase of air temperature and regional variation of relative humidity together influenced the change of lapse rate. The dataset is represented in an Execel document, the annual and seasonal air temperate lapse rates are included.

2020-04-06

Map of snow, ice, and frozen ground in China (1988)

The map is "1:4 Million Ice, Snow and Frozen Soil Map of China" compiled by Mr. Shi Yafeng and Mr. Meadson. The working map compiled by the map is "Chinese Pinyin Edition of the People's Republic of China", which retains the water system and mountain annotation of the map and adds some mountain annotation. The compilation of frozen soil map is based on the actual data of frozen soil survey and exploration, interpretation of remote sensing data, temperature conditions and topographic characteristics that affect the formation and distribution of frozen soil. The height of glacier snow line is expressed by isolines. Seasonal snow accumulation and seasonal icing are based on the data of 1600 meteorological observation stations and the results of many years of investigation in China. They are expressed by isoline notation and symbols. The selection of cold (periglacial) phenomena is a representative and schematic representation observed on the spot. The boundary line between permafrost and non-permafrost is mapped by calculation based on the field data, and its comprehensive degree is relatively high (Tö pfer, 1982) "China Ice and Snow Frozen Soil Map" reflects the scale, types and characteristics of distribution of glaciers, snow cover, frozen soil and periglacial, as well as its value in scientific research and the prospect of utilization and prevention in production practice. It shows our achievements in glacier and frozen soil research in the past 30 years.

2020-04-02

AMSR-E/aqua daily gridded brightness temperatures of China

This dataset includes passive microwave remote sensing brightness temperatures data for longitude and latitude projections and 0.25 degree resolution from 2002 to 2008 in China. 1. Data processing process: NSIDC produces AMSR-E gridded brightness temperature data by interpolating AMSR-E data (6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz) to the output grids from swath space using an Inverse Distance Squared (ID2) method. 2. Data format: Brightness temperature files: two-byte unsigned integers, little-endian byte order Time files: two-byte signed integers, little-endian byte order 3. Data naming: ID2rx-AMSRE-aayyyydddp.vnn.ccc (China-ID2r1-AMSRE-D.252002170A.v03.06V) ID2 Inverse Distance Squared r1 Resolution 1 swath input data AMSRE Identifies this an AMSR-E file D.25 Identifies this as a quarter degree file yyyy Four-digit year ddd Three-digit day of year p Pass direction (A = ascending, D = descending) vnn Gridded data version number (for example, v01, v02, v03) ccc AMSR-E channel indicator: numeric frequency (06, 10, 18, 23, 36, or 89) followed by polarization (H or V) 4. Cutting range: Corner Coordinates: Upper Left (60.0000000, 55.0000000) (60d 0'0.00 "E, 55d 0'0.00" N) Lower Left (60.0000000, 15.0000000) (60d 0'0.00 "E, 15d 0'0.00" N) Upper Right (140.0000000, 55.0000000) (140d 0'0.00 "E, 55d 0'0.00" N) Lower Right (140.0000000, 15.0000000) (140d 0'0.00 "E, 15d 0'0.00" N) Center (100.0000000, 35.0000000) (100d 0'0.00 "E, 35d 0'0.00" N) Origin = (60.000000000000000, 55.000000000000000) 5. Data projection: GEOGCS ["WGS 84", DATUM ["WGS_1984", SPHEROID ["WGS 84", 6378137,298.257223563, AUTHORITY ["EPSG", "7030"]], TOWGS84 [0,0,0,0,0,0,0], AUTHORITY ["EPSG", "6326"]], PRIMEM ["Greenwich", 0, AUTHORITY ["EPSG", "8901"]], UNIT ["degree", 0.0174532925199433, AUTHORITY ["EPSG", "9108"]], AUTHORITY ["EPSG", "4326"]]

2020-04-01

1:4 million map of the Glaciers, Frozen Ground and Deserts in China (2006)

The compilation basis of frozen soil map includes: (1) frozen soil field survey, exploration and measurement data; (2) aerial photo and satellite image interpretation; (3) topo300 1km resolution ground elevation data; (4) temperature and ground temperature data. Among them, the distribution of permafrost in the Qinghai Tibet Plateau adopts the research results of nanzhuo Tong et al. (2002). Using the measured annual average ground temperature data of 76 boreholes along the Qinghai Tibet highway, regression statistical analysis is carried out to obtain the relationship between the annual average ground temperature and latitude, elevation, and based on this relationship, combined with the gtopo30 elevation data (developed under the leadership of the center for earth resources observation and science and technology, USGS) Global 1 km DEM data) to simulate the annual mean ground temperature distribution over the whole Tibetan Plateau. Taking the annual average ground temperature of 0.5 ℃ as the boundary between permafrost and seasonal permafrost, the boundary between discontinuous Permafrost on the plateau and island Permafrost on the plateau is delimited by referring to the map of ice and snow permafrost in China (1:4 million) (Shi Yafeng et al., 1988); in addition, the division map of Permafrost on the big and small Xing'an Mountains in the Northeast (Guo Dongxin et al., 1981), the distribution map of permafrost and underground ice around the Arctic (b According to rown et al. 1997) and the latest field survey data, the Permafrost Boundary in Northeast China has been revised; the Permafrost Boundary in Northwest mountains mostly uses the boundary defined in the map of ice and snow permafrost in China (1:4 million) (Shi Yafeng et al., 1988). According to the data, the area of permafrost in China is about 1.75 × 106km2, accounting for about 18.25% of China's territory. Among them, alpine permafrost is 0.29 × 106km2, accounting for about 3.03% of China's territory. For more information, please refer to the specification of "1:4 million map of glacial and frozen deserts in China" (Institute of environment and Engineering in cold and dry areas, Chinese Academy of Sciences, 2006)

2020-04-01