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"]]

0 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)

0 2020-04-01

Exchange data of research project on glacier change trend and its impact on water resources change in Tarim River Basin (2003-2005)

The glacial change trend in the Tarim River Basin and its impact on water resources change belong to the National Natural Science Foundation of China's Western Environment and Ecological Science major research project. The time is 2003.1-2005.12. The project submitted data: Kochikarbachi Glacier Observation Data (excel): Including precipitation, wind direction, wind speed and temperature data 1.3300a_climate (2003.6.29-2004.6.22): 4 hours data during the day, including field date, time, wind speed, wind up, temperature. 2.4200b_climate (2004.1.29-2004.5.12): 6:00, 8:00, 9:00, 10:00, 12:00, 14:00, 16:00, 18:00, 20:00, 22: 00, 23:00 observation data, including field date, time, wind speed, wind up, temperature. 3.3700_Precipitation: 13 days daily precipitation from 2003.7 to 2005.9 4.4200_Precipitation: 18-day daily precipitation between 2003.7 and 2006. 6

0 2020-04-01

1:150,000 desertification type and land division map of Naiman Banner

This data is digitized from the "Naiman Banner Desertification Types and Land Consolidation Zoning Map" of the drawing. The specific information of this map is as follows: * Editors: Zhu Zhenda and Qiu Xingmin * Editor: Feng Yushun * Re-photography and Mapping: Feng Yushun, Liu Yangxuan, Wen Zi Xiang, Yang Taiyun, Zhao Aifen, Wang Yimou, Li Weimin, Zhao Yanhua, Wang Jianhua * Field trips: Qiu Xingmin and Zhang Jixian * Cartographic unit: compiled by Desert Research Office of Chinese Academy of Sciences * Publishing House: Shanghai China Printing House * Scale: 1: 150000 * Published: May 1984 * Legend: Severe Desertification Land, Intensely Developed Desertification Land, Developing Desertification Land, Potential Desertification Land, Non-desertification Land, Fluctuating Sandy Loess Plain, Forest and Shrub, Saline-alkali Land, Mountain Land, Cultivated Land and Midian Land 2. File Format and Naming Data is stored in ESRI Shapefile format, including the following layers: Naiman banner desertification type map, rivers, roads, reservoirs, railways, zoning 3. Data Attributes Desertification Class Vegetation Background Class Desertified land and cultivated sand dunes under development. Midland in Saline-alkali Land Severely desertified land Reservoir Trees and shrubbery Mountain Strongly developing desertified land Potential desertified land Lakes Non-desertification land Undulating sand-loess plain 2. Projection information: Angular Unit: Degree (0.017453292519943295) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Beijing_1954 Spheroid: Krasovsky_1940 Semimajor Axis: 6378245.000000000000000000 Semiminor Axis: 6356863.018773047300000000 Inverse Flattening: 298.300000000000010000

0 2020-04-01

Long term vegetation index dataset of the Yellow River upstream – Spot vegetation (1998-2011)

I. Overview The long-term sequence China Vegetation Index dataset is mainly for the normalized vegetation index (NDVI), based on four bands synthesized every 10 days from 1 April 1998 to 31 December 2011 with a spatial resolution of 1 km. Spectral reflectance and 10-day maximized NDVI dataset. Ⅱ. Data processing description The VEGETATION sensor was launched by SPOT-4 in March 1998, and has received SP0T VGT data for global vegetation coverage observation since April 1998. It has a very complete and efficient image ground processing mechanism system. The VEGETATION data is mainly received by the Kiruna ground station in Sweden. The image quality monitoring center in Toulouse, France is responsible for image quality and provides related parameters (such as calibration coefficients). Finally, the image processing and archiving center of VITO Institute in Belgium Global VEGETATION data archiving and user orders. Among them, VGT-P (prototype) data products mainly provide scientific researchers with high-quality physical quantity prototype data in order to facilitate their research and development of algorithms and application models. The data undergoes strict systematic error correction and resampling into a longitude and latitude network projection, the pixel resolution is lkm, and the pixel brightness value is the reflectivity of the ground features on the top layer of the atmosphere. In addition to providing four bands of raw data, relevant auxiliary parameters such as atmospheric conditions, system information (solar zenith angle, azimuth, field of view, and reception time) and terrain data are also provided according to user needs. VGT-S (synthesis) products provide atmospheric-corrected surface reflectance data, and use multi-band synthesis techniques to obtain a normalized vegetation index (w) data set with lkm resolution. VGI-S products include the spectral reflectance and NDVI data set (s1) of four bands synthesized daily, the spectral reflectance of four bands synthesized every 10 days, and the maximum NDVI data set (S10) every 10 days to reduce cloud and The impact of BRDF, while S10 was also resampled into 4km resolution (S10.4) and 8km resolution (S10.8) datasets. VGT-S products are widely used for their high time resolution. This data set contains the spectral reflectance of four bands synthesized every 10 days and the 10-day maximized NDVI data set (S10). The pre-processing of SPOT source data includes atmospheric correction, radiation correction, and geometric correction. NDVI data with a maximum of 10 days of synthesis is generated, and the values ​​of -1 to -0.1 are set to -0.1, and then formula YDN = (JNDVI +0.1) /0.004 Convert to a YDN value from 0 to 250. Ⅲ. Data content description The long-term sequence China Vegetation Index dataset is mainly for the normalized vegetation index (NDVI), based on four bands synthesized every 10 days from 1 April 1998 to 31 December 2011 with a spatial resolution of 1 km. Spectral reflectance and 10-day maximized NDVI dataset. The SPOT-VEGETATION-NDVI data set contains .zip compressed files with time resolution from April 1, 1998 to December 31, 2011. After decompression, it is an ESRI-GRID file with a scene every 10 days. The SPO-VEGETATION-NDVI data set naming rules are: v-yymmdd, where v is the abbreviation of vegetation, yymmdd represents the date of the file, and is the main identifier that distinguishes other files. Ⅳ. Data usage description An important feature of the Vegetation Index product is that it can be converted into leaf crown biophysical parameters. Vegetation index (VI) also plays an "intermediate variable" in the acquisition of vegetation biophysical parameters (such as foliar index LAI, green shade, fAPAR, etc.). The relationship between vegetation indices and vegetation biophysical parameters is currently being studied using globally representative ground, aircraft and satellite observation datasets. These data can be used to evaluate the performance of the VI algorithm before satellite launch, and also provide the conversion coefficient between the vegetation index product and the biophysical characteristics of the leaf crown. The use of biophysical data is part of the Vegetation Index Verification Program. Vegetation index products will play a major role in several Earth Observation System (EOS) studies and are also part of global and regional biosphere model products in recent years.

0 2020-03-31

1: 1 million wetland data of Jiangsu Province

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.

0 2020-03-31

1:1 million wetland data of Zhejiang Province

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.

0 2020-03-31

Shanghai 1:1 million wetland data

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.

0 2020-03-31

MODIS daily cloudless snow products in the Tibetan Plateau (2002-2010)

This data is 2002.07.04-2010.12.31 MODIS daily cloudless snow products in the Tibetan Plateau. Due to the snow and cloud reflection characteristics, the use of optical remote sensing to monitor snow is severely disturbed by the weather. This product is based on the most commonly used cloud removal algorithm, using the MODIS daily snow product and passive microwave data AMSR-E snow water equivalent product, and the daily cloudless snow product in the Tibetan Plateau is developed. The accuracy is relatively high. This product has important value for real-time monitoring of snow cover dynamic changes on the Tibetan Plateau. Projection method: Albers Conical Equal Area Datum: D_Krasovsky_1940 Spatial resolution: 500 m Data format: tif Naming rules: maYYMMDD.tif, where ma represents the data name; YY represents the year (01 represents 2001, 02 represents 2002 ...); MM represents the month (01 represents January, 02 represents February ...); DD represents the day (01 Means 1st, 02 means 2nd ...).

0 2020-03-31

Ages of geomorphic surfaces along the Heihe River Basin

According to the formation age of the river terraces in the upper reaches of the Heihe River, the Optically Stimulated Luminescence dating analysis was performed on the sediments of Heihe terrace. The samples were mainly fluvial sand reservoirs, and the measuring object was quartz grains in sand. The formation age of the 6th-order terrace near Qilian in the upper reaches of the Heihe River was measured by Optically Stimulated Luminescence dating. The terrace age results showed that the terraces were mainly formed since the late Pleistocene (60ka-7ka). The sample sites of the chronological data are distributed near the mainstream of the Heihe River in the northwest of Qilian County in the upper reaches of the Heihe River.

0 2020-03-31