GIMMS (glaobal inventory modelling and mapping studies) NDVI data is the latest global vegetation index change data released by NASA C-J-Tucker and others in November 2003. This dataset is a long-term GIMMS vegetation index dataset of the Qinghai Lake Basin, which includes changes in the vegetation index from 1981 to 2006. The time resolution is 15 days and the spatial resolution is 8 km. GIMMS NDVI data recorded the changes of vegetation in 22a area in the format of satellite data.
National Aeronautics and Space Administration
Data for 100000 desert map qaidam river basin, cutting since China 1:100000 desert sand data set, the data of TM images in 2000 data sources, to interpret, extraction, revision, using remote sensing and geographic information system technology combining 1:100000 scale mapping, the desert, sand and gravel gobi for thematic mapping.The desert codes are as follows: mobile sandy land 2341010, semi-mobile sandy land 2341020, semi-fixed sandy land 2341030, gobi desert 2342000, saline alkaline land 2343000.
WANG Jianhua
The data is river data set of the qaidam river basin, revised according to topographic map and TM remote sensing image, scale 250000, projected longitude and latitude, data including spatial data and attribute data, attribute data fields: HYD_CODE (river code), Name (river Name), SHAPE_leng (river length).
National Basic Geographic Information Center
Sponsored by the European commission VEGETATION sensors in March 1998 by SPOT - 4 was deployed, from April 1998 to receive SPOTVGT for global VEGETATION observation data, the data by the Swedish Kiruna ground station is responsible for receiving, the image quality monitoring center in Toulouse, France is responsible for the image quality and provide the related parameters (e.g., scaling),Eventually, Belgium's Flemish Institute for Technological Research (Vito) 's VEGETATION processing Centre (CTIV) was responsible for pre-processing the data into 1km of daily global data.Preprocessing includes atmospheric correction, radiometric correction, and geometric correction to produce the maximum synthesis of NDVI data in 10 days, and set the value from -1 to -0.1 to -0.1, and then convert to the DN value of 0-250 through the formula DN= (NDVI+0.1)/0.004. This data set is mainly for normalized vegetation index (NDVI) of the qaidam river basin in the long time series, including spectral reflectance of four bands synthesized every 10 days from 1998 to 2008 and maximum NDVI in 10 days. The spatial resolution is 1km and the temporal resolution is 10 days.File formats :.hfr and.img.The file naming rule is CHN_NDV_YYYYMMDD, where YYYYMMDD is the date of the day that the file represents and is the main identifier that distinguishes it from other files.Remote sensing image files with suffixes.img and.hdf, which are used by users to analyze vegetation index, can be opened in ENVI and ERDAS software
Greet Janssens, Flemish Institute for Technological Research (VITO)
This data is from the central station of environmental monitoring in gansu province. The data includes three observation elements that are disclosed on the network, namely PH, permanganate index and ammonia nitrogen. The data format is a text file. The first column is the city name, the second column is PH, the third column is permanganate index, the fourth column is ammonia nitrogen, and the fifth column is the observation date. The data include 6 sections of gushuizi, niubei village, wufo temple, shichuan bridge, xincheng bridge and bikou. Gansu section of the Yellow River: xincheng bridge (lanzhou upstream section), shichuan bridge (lanzhou - baiyin junction section), wufo temple (gansu-ningxia junction section), niubei village (gansu-shaanxi junction section).Bailong river wudu section :(section of gushuizi village). Lanzhou city bridge automatic water quality monitoring station is located in xigu district, lanzhou city, gansu province.Point coordinates 103 degrees 35 minutes 02 seconds east longitude, 36 degrees 07 minutes 20 seconds north latitude.Yellow River system (Yellow River main stream), state - controlled provincial boundary section.By lanzhou city environmental monitoring station custody.It's 35 kilometers away.Built in March 2001. PH: the index that characterizes the acidity and alkalinity of water. When the pH value is 7, it is neutral, less than 7 is acidic, and greater than 7 is alkaline.The pH value of natural surface water is generally between 6 and 9. When algae grow in the water, they absorb carbon dioxide due to photosynthesis, resulting in an increase in surface pH value. Permanganate index (CODMn) : the amount consumed when treating surface water samples with potassium permanganate as the oxidant, expressed as mg/L of oxygen.Under these conditions, reductive inorganic substances (ferrous salts, sulphides, etc.) and organic pollutants in water can consume potassium permanganate, which is often used as a comprehensive indicator of the degree of surface water pollution by organic pollutants.Also known as the chemical oxygen demand potassium permanganate method, as distinct from the chemical oxygen demand (COD) of the potassium dichromate method, which is often used to monitor wastewater discharge. Ammonia nitrogen (nh3-n) : ammonia nitrogen exists in water in the form of dissolved ammonia (also known as free ammonia, NH3) and ammonium salt (NH4+). The ratio of the two depends on the pH value and water temperature of the water, and the content of ammonia nitrogen is expressed by the amount of N element.The main sources of ammonia nitrogen in the water are domestic sewage and some industrial wastewater (such as coking and ammonia synthesis industry) and surface runoff (mainly refers to the fertilizer used in farmland entering rivers, lakes, etc.). This data will be updated automatically and continuously according to the data source.
Gansu environmental monitoring center station
The data set is the lake distribution map of the qaidam river basin, with a scale of 250,000, projection: longitude and latitude, data including spatial data and attribute data, lake attribute fields: NAME (NAME of the lake), CODE (CODE of the lake).
National Basic Geographic Information Center
The distribution map of permafrost and ground-ice around the Arctic is the only data map of permafrost compiled by the international permafrost association in collaboration with permafrost research institutes of several countries in 1997. The map describes the distribution and properties of permafrost and subsurface ice conditions in the northern hemisphere (20°N to 90°N). Permafrost was divided into continuous (90-100%), discontinuous (50-90%), sporadic (10-50%), island (<10%) and non-permafrost by continuous division of permafrost scope. The subsurface ice abundance at the top 20 m is divided by the percentage of ice volume (>20%, 10-20%, <10% and 0%). Published ESRI-shape files are based on 1:10 million paper maps (Brown et al. 1997). The map can be used in related research such as global climate change, polar resource development and environmental protection. The China section is shown in thumbnail. See the reference for more information (Heginbottom et al. 1993). The format of the data is the ESRI shapefile, you can download it on the snow and ice data center (http://nsidc.org/data/ggd318.html).
O. Ferrians, J. A. Heginbottom, E. Melnikov
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.
WANG Jianhua, LIU Jiyuan, ZHUANG Dafang, ZHOU Wancun, WU Shixin
This data is from "China 1:100,000 land use data".China 1:100,000 land use data was constructed in three years based on Landsat MSS, TM and ETM remote sensing data by using satellite remote sensing as a means to organize remote sensing science and technology teams from 19 institutes affiliated to the Chinese academy of sciences (cas) in the "eighth five-year plan" major application project "national macro survey and dynamic research on remote sensing of resources and environment". In 1995, guizhou province adopted a hierarchical land cover classification system, which divided the country into 6 primary categories (arable land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 secondary categories.It is the most accurate land use data product in China and has played an important role in national land resource survey, hydrological and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
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.
WANG Jianhua, LIU Jiyuan, ZHUANG Dafang, ZHOU Wancun, WU Shixin
This data is from "China 1:100,000 land use data".China 1:100,000 land use data was constructed in three years based on Landsat MSS, TM and ETM remote sensing data by using satellite remote sensing as a means to organize remote sensing science and technology teams from 19 institutes affiliated to the Chinese academy of sciences (cas) in the "eighth five-year plan" major application project "national macro survey and dynamic research on remote sensing of resources and environment". The landuse data of guizhou province in the late 1980s adopted a hierarchical land cover classification system, which divided the country into 6 primary categories (farmland, woodland, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 secondary categories.It is the most accurate land use data product in China and has played an important role in national land resource survey, hydrological and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
This data is from "China 1:100,000 land use data".China 1:100,000 land use data was constructed in three years based on Landsat MSS, TM and ETM remote sensing data by using satellite remote sensing as a means to organize remote sensing science and technology teams from 19 institutes affiliated to the Chinese academy of sciences (cas) in the "eighth five-year plan" major application project "national macro survey and dynamic research on remote sensing of resources and environment".The land use data of guizhou province adopts a hierarchical land cover classification system, which divides the country into 6 primary categories (arable land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 secondary categories.It is the most accurate land use data product in China and has played an important role in national land resource survey, hydrological and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, WU Shixin, ZHOU Wancun
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.
WANG Jianhua, LIU Jiyuan, ZHUANG Dafang, ZHOU Wancun, WU Shixin
The data used in this research was provided by the Pathfinder database of the EROS (Earth Resource Observation System) data center. The vegetation index NDVI was prepared by using the NOAA-AVHRR data source after radiation correction and geometric rough correction. Every day, each track image is processed with geometric fine correction, removal of bad lines, and removal of clouds, etc., and then NDVI calculation and synthesis. The daily NDVI calculation formula is: 1000 × (b2-b1) / (b2 + b1), where b1 and b2 are the first and second channels of AVHRR. Parameter table of Pathfinder AVHRR Parameter / Variable Definition Unit Range NDVI Normalized Vegetation Index None (-1,1) CLAVR identification Cloudiness index from CLAVR algorithm None (0,30) QC identification Data quality identification None (0,16) Scanning angle Sensor angle Radian (-1.05, 1.05) Solar zenith angle Solar zenith angle per pixel Radian (0, 1.04) Relative zenith angle Relative zenith angle of the sensor Radian (-1.05, 1.05) Ch1 reflectance Reflectance of the first channel (0.58-0.68um) Percent (0,100) Ch2 reflectance Reflectivity of the second channel (0.72--1.10um) Percentage (0, 100) Ch3 brightness temperature Bright temperature value of the third channel (3.55-3.95um) Kelvin temperature scale (160, 340) Ch4 brightness temperature Brightness value of the fourth channel (10.3-11.3um) Kelvin temperature scale (160, 340) Ch5 brightness temperature Bright temperature value of the fifth channel (11.5-12.5um) Kelvin temperature scale (160, 340) The data set includes data on NDVI in China's sub-regions from 1981 to June-September 2001, and data on tens of months in each of the years 1982, 1986, 1991, and 1996 (a total of 343 in 84 months, of which 1981 in June 1981). Data are missing in January and July 1st, and September 3rd 1994) Dataset attributes and format: This data set is stored in a year folder, which contains .HDR header files, .IMG files, and .JPG image files under the same file name. The data in the IMG is stored as integers. The naming rules are as follows: avhrrpf. *. Intfgl.yymmdd_geo where * represents ch1 or ch2 or ch4 or ch5 or ndvi, please refer to Table 1 for its specific meaning and range; yy represents the last two digits of the year; mm represents the month; dd represents the specific date. Data projection: Size is 963, 688 Coordinate System is: 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"]] Origin = (70.035426000000001, 54.945585999999999) Pixel Size = (0.072727000000000, -0.072727000000000) Corner Coordinates: Upper Left (70.0354260, 54.9455860) (70d 2'7.53 "E, 54d56'44.11" N) Lower Left (70.0354260, 4.9094100) (70d 2'7.53 "E, 4d54'33.88" N) Upper Right (140.0715270, 54.9455860) (140d 4'17.50 "E, 54d56'44.11" N) Lower Right (140.0715270, 4.9094100) (140d 4'17.50 "E, 4d54'33.88" N) Center (105.0534765, 29.9274980) (105d 3'12.52 "E, 29d55'38.99" N) Band 1 Block = 963x1 Type = UInt16, ColorInterp = Undefined Computed Min / Max = 1.000,55480.000
Tucker, C.J., J.E.Pinzon, M.E.Brown
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). 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 data set is a subset extraction from China, including spectral reflectance of four bands synthesized every 10 days and 10 days' maximum NDVI. It is data from 1998 to 2007 with a spatial resolution of 1km and a temporal resolution of 10 days. File format: Hfr and img files. The file naming rule is: CHN _ NDV _ YYYMMDD, where YYYYMMDD is the date of the day represented by the file and is also the main identifier different from other files. The remote sensing image files with suffix. IMG and. HDF used by users to analyze vegetation index can be opened in ENVI and ERDAS software. Coordinate system and projection Plate_Carree (Lon/Lat) PROJ_CENTER_LON 0.000000 PROJ_CENTER_LAT 0.000000 PIXEL_SIZE_UNITS DEGREES/PIXEL PIXEL_SIZE_X 0.0089285714 PIXEL_SIZE_Y 0.0089285714 SEMI_AXIS_MAJ 6378137.000000 SEMI_AXIS_MIN 6356752.314000 UL_LON (DEG) 73.000000 UL_LAT (DEG) 54.000000 LR_LON (DEG) 135.500000 LR_LAT (DEG) 5.000000 Corner coordinates are: Corner Coordinates: Upper Left ( 69.9955357, 55.0044643) Lower Left ( 69.9955357, 14.9955358) Upper Right ( 137.0044641, 55.0044643) Lower Right ( 137.0044641, 14.9955358) Where 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.
Greet Janssens, Food and Agriculture Organization of the United Nations（FAO）
The dataset of ground truth measurement synchronizing with Envisat ASAR was obtained in No. 1, 2 and 3 quadrates of the A'rou foci experimental area on Jun. 19, 2008. GPR observations were also carried out in one sampling strip. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:17 BJT. Simultaneous with the satellite overpass, numerous ground data were collected, the soil temperature, soil volumetric moisture, the loss tangent, soil conductivity, and the real part and the imaginary part of soil complex permittivity were acquired by the POGO soil sensor, and the mean soil temperature from 0-5cm by the probe thermometer. Those provide reliable ground data for retrieval and validation of the surface temperature and evapotranspiration from remote sensing approaches. Four files were included, ASAR data, No. 1, 2 and 3 quadrates data.
CAO Yongpan, GE Chunmei, HAN Xujun,
The dataset of fresh snow properties observations was obtained at the temporary sampling plot in the Qilian county on Mar. 20, 2008. Those provide reliable data for retrieval of snow parameters from remote sensing approaches. Observation items included: (1) Snow parameters such as snow depth, snow grain size by the handheld microscope, and snow density by the snow shovel (2) Fresh snow albedo by the total radiometer (3) Fresh snow spectrum by ASD Two files including raw data and preprocessed data were archived.
GE Chunmei, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, LIU Yan, ZHANG Pu
The dataset of ground truth measurements for snow synchronizing with Envisat ASAR was obtained in the Binggou watershed foci experimental area on Mar. 15, 2008. The Envisat ASAR data were acquired in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:34 BJT. Observation items included: (1) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the snowfork in BG-B, BG-D, BG-E and BG-F; (2) Snow parameters including the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, snow density by the aluminum case, snow depth by the ruler, and the snow surface temperature synchronizing with ASAR in BG-H, BG-D, BG-E and BG-F; (3) The snow spectrum by the portable ASD (Xinjiang Meteorological Administration) synchronizing with ASAR in BG-H15; the major and minor axis and shape of the snow layer grain through the self-made snow sieve. Two files including raw data and the preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, HAO Xiaohua, LI Hongyi, LIANG Ji, SHU Lele, WANG Xufeng, XU Zhen, MA Mingguo, QU Wei, REN Jie, CHANG Cun, DOU Yan, MA Zhongguo, LIU Yan, ZHANG Pu
The dataset of spectral reflectance observations was obtained by ASD (Analytical Sepctral Devices) in the Yingke oasis and Huazhaizi desert steppe foci experimental areas. Reflectance was calculated based on the equation R = (DN1/DN0)×R0, DN1 indicating DN of the targets, R0 and DN0 the reflectance and DN of the grey board. The reflectance spectra of maize and wheat canopy, the component leaf of the maize and BRDF in Yingke oasis maize field, Yingke oasis wheat field, Huazhaizi desert maize field, the transect spectrum in Huazhaizi desert No. 1 and 2 plots and Linze and Biandukou foci experimental area were measured on May 20, 24, 25, 28 and 30, Jun. 1, 4, 9, 14, 16, 18, 20, 22, 23, 24, 26, 29 and 30, Jul. 1, 4, 5, 6, 7, 9 and 11, 2008. Four ASD devices were used, from Peking University (350-2500nm), Institute of Remote Sensing Applications (350-2500nm), Beijing Academy of Agriculture and Forestry Sciences (350-1065nm) and BNU respectively. The reference boards were 40%, 50% and 99%. The above spectral reflectance dataset was synchronizing with WiDAS (Wide-angle Infrared Dual-mode line/area Array Scanner), OMIS-II and various spaceborne sensors. Raw data were binary files direct from ASD (by ViewSpecPro), and pre-processed data on reflectance were in Excel format.
CHEN Ling, REN Huazhong, WANG Haoxing, XIAO Yueting, YAN Guangkuo, ZHOU Hongmin, GE Yingchun, LI Xin, SHU Lele, GUANG Jie, LIU Sihan, SU Gaoli, XIA Chuanfu, Wen Jianguang, ZHANG Yang, ZHOU Chunyan, FAN Wenjie, TAO Xin, YAN Binyan, YAO Yanjuan, YANG Guijun, CHENG Zhanhui, Liu Liangyun, YANG Tianfu
Natural changes and human impacts of typical karst environments in historical periods: stalagmite recording project is a major research program of "Environmental and Ecological Science in Western China" sponsored by the National Natural Science Foundation of China. The person in charge is Tan Ming, a researcher at the Institute of Geology and Geophysics, Chinese Academy of Sciences. The project runs from January 2002 to December 2009. The temperature data of Beijing hot months (May, June, July and August) in 2650 (665 B.C.-A.D. 1985) are the results of the project. The data are reconstructed according to the correlation between the annual thickness of stalagmites in Shihua Cave in Beijing and meteorological observation data. The temperature signals reflected by soil carbon dioxide and cave dripping are amplified by the soil-organic matter-carbon dioxide system and recorded by the annual sequence of stalagmites. Although the general trend of temperature has decreased in recent thousands of years, the reconstructed temperature reveals that the climate has experienced repeated rapid warming on a century scale. This result is related to other records in the northern hemisphere, indicating that there is a hemispheric influence on the periodic changes of temperature in the sub-millennium scale. The data contains a txt file with attribute fields such as yr.AD, layer number, original thickness (um), maximum error in um (+-), sedimentary trend, detrended thickness (um), reconstructed temperature, maximum error in degree C (+ -), temperature anomaly, temperature anomaly + error, temperature anomaly-error, maximum error in age (yr. +-).
TAN Ming, ZHANG Hucai, LI Tieying
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