The dataset of GPS radiosonde observations was obtained at an interval of 2 seconds in the cold region hydrology experimental area in March, 2008 and the arid region hydrology experimental area from May to July, 2008. The items were the air temperature, relative humidity, air pressure, the dew temperature, the water vapor mixing ratio, latitudinal and longitudinal wind speeds, the wind speed and direction. Simultaneous with the satellite/airplane overpass, GPS radiosonde observations were carried out: Binggou watershed on Mar. 14, A'rou on Mar. 15, Binggou watershed on Mar. 15, Biandukou on Mar. 17, Binggou watershed on Mar. 22, Binggou watershed on Mar. 29, and A'rou on Apr. 1 for the upper stream experiments; Linze grassland station on May 30, Yingke oasis on Jun.1, Huazhaizi desert station on Jun. 4, Linze grassland station on Jun. 5, Linze grassland station on Jun. 6, Huazhaizi desert station on Jun. 16, Yingke oasis on Jun. 29, Binggou watershed on Jul. 5, Yingke oasis on Jul. 7, Linze grassland station on Jul. 11, and Yingke oasis at 0, 4:10, 8:09, and 12:09 on Jul. 14 for middle stream experiments.
GU Lianglei, HU Zeyong, LI Maoshan, MA Weiqiang, SUN Fanglei
Terra (EOS am-1), the flagship of the EOS earth observation series, was the first satellite to be launched on December 18, 1999.ASTER is primarily used for high-resolution observations of surface radiation balance. Compared with Landsat series satellites, ASTER has improved spectral and spatial resolution, and significantly increased short-wave infrared and thermal infrared bands.ASTER has a total of 14 wavebands, including 3 visible and near-infrared wavebands, 5 short-wave infrared wavebands and 5 thermal infrared wavebands. The resolution is 15m, 30m and 90m respectively, and the scanning width is 60km, 30m and 90m respectively.Heihe river basin ASTER remote sensing image data set through the international cooperation data from NASA's web site (https://wist.echo.nasa.gov/). Data naming rules as follows: assuming that the name of the ASTER image for "ASTL1B0103190215190103290064", then ASTL1B said ASTER L1B products, 003 on behalf of the version number namely VersionID, (010319) represents the next 6 digits observation date will be March 19, 2001, followed by six digits (021519) represents the observation time (02:15:19), followed by the last six digits (010329) representing the processing date is March 29, 2001, the last four digits (0064) representing the four-digit sequence code. At present, there are 258 scents of ASTER data in heihe river basin.The acquisition time is:2000-04-25, 2000-04-27 (2 scenes), 2000-05-04, 2000-05-15 (4 scenes), 2000-05-20 (9 scenes), 2000-05-29 (3 scenes), 2000-05-31 (2 scenes), 2000-06-12, 2000-06-14 (5 scenes), 2000-06-21 (3 scenes), 2000-06-30 (8 scenes), 2000-07-18, 2000-07-23 (3 scenes), 2000-08-03 (4 scenes),2000-08-08 (9 scenes), 2000-08-17 (7 scenes), 2000-08-19 (4 scenes), 2000-08-26 (3 scenes), 2000-09-02 (4 scenes), 2000-10-02 (7 scenes), 2000-10-04 (6 scenes), 2000-10-29 (3 scenes), 2000-11-21, 2001-02-18 (2 scenes), 2001-02-25, 2001-03-11 (5 scenes), 2001-03-22 (4 scenes),2001-03-27 (4 scenes), 2001-03-29 (9 scenes), 2001-04-07 (2 scenes), 2001-04-12 (2 scenes), 2001-04-14 (6 scenes), 2001-07-10, 2001-07-12 (8 scenes), 2001-07-21 (8 scenes), 2001-08-13 (8 scenes), 2001-08-20 (7 scenes), 2001-08-22, 2001-08-27 (2 scenes), 2001-08-29,2001-09-03 (2 scenes), 2001-11-15 (7 scenes), 2002-02-01, 2002-03-30 (2 scenes), 2002-04-17 (2 scenes), 2002-05-24, 2002-06-04 (6 scenes), 2002-06-09, 2002-06-13, 2002-06-25, 2002-08-14 (3 scenes), 2002-09-29, 2002-10-19 (2 scenes), 2002-11-11 (2 scenes),2002-12-29 (4 scenes), 2003-04-18, 2003-05-24 (2 scenes), 2003-07-25, 2003-07-30, 2003-8-10 (5 scenes), 2003-08-12, 2003-08-17, 2003-09-09 (11 scenes), 2003-09-13 (4 scenes), 2003-10-15, 2003-10-18, 2003-10-29 (9 scenes), 2003-11-30, 2004-03-14, 2005-03-20,2005-06-05, 2005-08-11, 2007-10-22, 2007-11-14, 2007-11-23, 2007-12-04, 2008-01-28, 2008-02-13, 2008-05-03 (4 scenes), 2008-05-05, 2008-05-17, 2008-06-04 (2 scenes), 2008-06-13.
National Aeronautics and Space Administration
This dataset is the Fractional Vegetation Cover observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observations lasted for a vegetation growth cycle from May 2012 to September 2012 (UTC+8). Instruments and measurement method: Digital photography measurement is implemented to measure the FVC. Plot positions, photographic method and data processing method are dedicatedly designed. Details are described in the following: 0. In field measurements, a long stick with the camera mounted on one end is beneficial to conveniently measure various species of vegetation, enabling a larger area to be photographed with a smaller field of view. The stick can be used to change the camera height; a fixed-focus camera can be placed at the end of the instrument platform at the front end of the support bar, and the camera can be operated by remote control. 1. For row crop like corn, the plot is set to be 10×10 m2, and for the orchard, plot scale is 30×30 m2. Shoot 9 times along two perpendicularly crossed rectangular-belt transects. The picture generated of each time is used to calculate a FVC value. “True FVC” of the plot is then acquired as the average of these 9 FVC values. 2. The photographic method used depends on the species of vegetation and planting pattern: Low crops (<2 m) in rows in a situation with a small field of view (<30 ), rows of more than two cycles should be included in the field of view, and the side length of the image should be parallel to the row. If there are no more than two complete cycles, then information regarding row spacing and plant spacing are required. The FVC of the entire cycle, that is, the FVC of the quadrat, can be obtained from the number of rows included in the field of view. 3. High vegetation in rows (>2 m) Through the top-down photography of the low vegetation underneath the crown and the bottom-up photography beneath the tree crown, the FVC within the crown projection area can be obtained by weighting the FVC obtained from the two images. Next, the low vegetation between the trees is photographed, and the FVC that does not lie within the crown projection area is calculated. Finally, the average area of the tree crown is obtained using the tree crown projection method. The ratio of the crown projection area to the area outside the projection is calculated based on row spacing, and the FVC of the quadrat is obtained by weighting. 4. FVC extraction from the classification of digital images. Many methods are available to extract the FVC from digital images, and the degree of automation and the precision of identification are important factors that affect the efficiency of field measurements. This method, which is proposed by the authors, has the advantages of a simple algorithm, a high degree of automation and high precision, as well as ease of operation.
MU Xihan, HUANG Shuai, MA Mingguo
This biophysical permafrost zonation map was produced using a rule-based GIS model that integrated a new permafrost extent, climate conditions, vegetation structure, soil and topographic conditions, as well as a yedoma map. Different from the previous maps, permafrost in this map is classified into five types: climate-driven, climate-driven/ecosystem-modified, climate-driven/ecosystem protected, ecosystem-driven, and ecosystem-protected. Excluding glaciers and lakes, the areas of these five types in the Northern Hemisphere are 3.66×106 km2, 8.06×106 km2, 0.62×106 km2, 5.79×106 km2, and 1.63×106 km2, respectively. 81% of the permafrost regions in the Northern Hemisphere are modified, driven, or protected by ecosystems, indicating the dominant role of ecosystems in permafrost stability in the Northern Hemisphere. Permafrost driven solely by climate occupies 19% of permafrost regions, mainly in High Arctic and high mountains areas, such as the Qinghai-Tibet Plateau.
RAN Youhua, M. Torre Jorgenson, LI Xin, JIN Huijun, Wu Tonghua, Li Ren, CHENG Guodong
The dataset of ground truth measurements synchronizing with ASTER was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on May 28, 2008. Observation items included: (1) Atmospheric parameters in Huazhaizi desert No. 2 plot by CE318 (produced by CIMEL in France). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. Those data include the raw data in .k7 format and can be opened by ASTPWin. ReadMe.txt is attached for detail. Processed data (after retrieval of the raw data) in Excel format are on optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number. (2) Photosynthesis by LI-6400. Raw data were archived in the user-defined format (by notepat.exe) and processed data were in Excel format. (3) Reflectance spectra in Yingke oasis maize field by ASD FieldSpec (350-2500nm, the vertical canopy observation and the transect observation) from Institute of Remote Sensing Applications (CAS), and in Huazhaizi desert No. 2 plot by ASD FieldSpec (350-1603nm, the vertical observation and the transect observation for reaumuria soongorica and the bare land) from Beijing Academy of Agriculture and Forestry Sciences. The grey board and the black and white cloth were also used for calibration spectrum. Raw data were binary files direct from ASD (by ViewSpecPro), and pre-processed data on reflectance were in Excel format. (4) Coverage fraction of maize and wheat by the self-made instrument and the camera (2.5m-3.5m above the ground) in Yingke oasis maize field. Based on the length of the measuring tape and the bamboo pole, the size of the photo can be decided. GPS date were also collected and the technology LAB was applied to retrieve the coverage of the green vegetation. Besides, such related information as the surrounding environment was also recorded. Data included the primarily measured image and final fraction of vegetation coverage. (5) the radiative temperature of maize, wheat and the bare land in Yingke oasis maize field by ThermaCAM SC2000 using ThermaCAM SC2000 (1.2m above the ground, FOV = 24°×18°),. The data included raw data (read by ThermaCAM Researcher 2001), recorded data and the blackbody calibrated data (archived in Excel format). (6) the radiative temperature by the automatic thermometer (FOV: 10°; emissivity: 0.95), 3 for maize canopy, the bare land and wheat canopy in Yingke oasis maize field, one for maize canopy in Huazhaizi desert maize field, and 2 for vegetation and the desert bare land in Huazhaizi desert No. 2 plot,at nadir at a time interval of one second. Raw data, blackbody calibrated data and processed data were all archived in Excel format. (7) Maize albedo by the shortwave radiometer in Yingke oasis maize field. R =10H (R for FOV radius; H for the probe height). Data were archived in Excel format. (8) LAI in Yingke oasis maize field. The maximum leaf length and width of each maize and wheat were measured. Data were archived in Excel format. (9) FPAR (Fraction of Photosynthetically Active Radiation) of maize and wheat by SUNSACN and the digital camera in Yingke oasis maize field. FPAR= (canopyPAR-surface transmissionPAR-canopy reflection PAR+surface reflectionPAR) /canopy PAR; APAR=FPAR* canopy PAR. Data were archived in the table format of Word. (10) The radiative temperature in Yingke oasis maize field (the transect observation), Yingke oasis wheat field (the transect observation), Huazhaizi desert maize field (the transect observation) and Huazhaizi desert No. 2 plot (the diagonal observation) by the handheld infrared thermometer (BNU and Institute of Remote Sensing Applications). Raw data (in Word format), blackbody calibrated data and processed data (in Excel format) were all archived.
CHAI Yuan, CHEN Ling, KANG Guoting, QIAN Yonggang, REN Huazhong, WANG Haoxing, WANG Jianhua, SHU Lele, LI Li, LIU Sihan, XIN Xiaozhou, ZHANG Yang, ZHOU Chunyan, ZHOU Mengwei, TAO Xin, WANG Dacheng, LI Xiaoyu, CHENG Zhanhui, YANG Tianfu, HUANG Bo, LI Shihua, LUO Zhen
The dataset of runoff plot observations was obtained in the Binggou watershed foci experimental area from Jun. 19 to Oct. 17, 2008. The runoff plot (38°03′, 100°13′, 3472m, with a slope of 20.16°) was 10m long, 5m wide and 80cm deep, with soil depth about 50cm and sandy clay and gravels beneath (50-80cm). The main vegetation type is scrub (about 20cm high) and grass (about 3cm high). Observation items included the surface flow, interflow (80cm down the land surface), and precipitation at a fixed point at the right of the runoff plot. One subfolder and two data files (directions on data observations and raw data) were archived.
LI Hongyi, LI Zhe, BAI Yunjie, XIN Bingjie
The data set was produced based on the SRTM DEM data collected by Space Shuttle Radar terrain mission in 2016, the reference data such as river, lake and other water system auxiliary data , using the arcgis hydrological model to analyze and extract the river network. There are 12 sub-basins over the Tibet Plateau, including AmuDayra、Brahmaputra、Ganges、Hexi、Indus、Inner、Mekong、Qaidam、Salween、Tarim、Yangtze、Yellow. The outer boundary is based on the 2500-metre contour line and national boundaries.
ZHANG Guoqing
"Heihe River Basin Ecological hydrological comprehensive atlas" is supported by the key project of Heihe River Basin Ecological hydrological process integration research. It aims at data arrangement and service of Heihe River Basin Ecological hydrological process integration research. The atlas will provide researchers with a comprehensive and detailed background introduction and basic data set of Heihe River Basin. Heihe River Basin water system map is one of the hydrological and water resources part of the atlas, with a scale of 1:2500000, positive axis isometric conic projection and standard latitude of 25 47 n. Data sources: river data of Heihe River Basin, reservoir distribution data of Heihe River Basin, residential area data of Heihe River Basin in 2009, administrative boundary data of one million Heihe River Basin in 2008, Lake data of Heihe River Basin and other basic geographic data. The upper reaches of Heihe River Basin are located in Qilian County, Haibei Tibetan Autonomous Prefecture, Qinghai Province, and the northern foot of Qilian Mountain in Zhangye, Jiuquan City, Sunan and Subei counties of Gansu Province. The middle reaches are located in Shandan, Minle, Ganzhou, Linze, Gaotai, Sunan, Suzhou, Jiayuguan and Yumen counties of Gansu Province. The lower reaches are located in Jinta, Gansu Province, Ejina Banner and Alxa Right Banner of Inner Mongolia, involving three provinces (autonomous regions), 16 cities and counties (District, banner), 56 towns, 45 townships and 4 Sumu. Table 1 shows the information about the administrative divisions of Heihe River Basin.
WANG Jianhua, ZHAO Jun, WANG Xiaomin, FENG Bin
This dataset contains the flux observation matrix measurements obtained from the automatic weather station (AWS) at the Daman superstation between 10 May and 26 September, 2012. The site (100.37223° E, 38.85551° N) was located in a cropland (maize surface) in the Daman irrigation, which is near Zhangye, Gansu Province. The elevation is 1556.06 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (AV-14TH; 3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 2.5 m), four-component radiometer (PSP&PIR; 12 m, towards south), two infrared temperature sensors (IRTC3; 12 m, vertically downward), photosynthetically active radiation (LI-190SB; 12 m, towards south), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (CS616; -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and soil heat flux (HFP01SC; 3 duplicates with one below the vegetation; and the other between plants, -0.06 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m, m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30 m, and WD_40 m, °), air pressure (press, hpa), precipitation (rain, mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation; W/m^2), infrared temperature (IRT_1 and IR_2, ℃), photosynthetically active radiation (PAR, μmol/ (s m^-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2, and Gs_3, W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm, ℃), and soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm, %). The data processing and quality control steps were as follows. (1) The AWS data were averaged over intervals of 10 min; therefore, there were 144 records per day. The missing data were filled with -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) In this dataset, the time of 0:10 corresponds to the average data for the period between 0:00 and 0:10; the data were stored in *.xlsx format. (5) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, LI Xin, XU Ziwei
This dataset includes data recorded by the Hydrometeorological observation network obtained from the automatic weather station (AWS) at the observation system of Meteorological elements gradient of Sidaoqiao barren-land station between 9 July, 2013, and 31 December, 2013. The site (101.133° E, 41.999° N) was located on a barren-land surface in the Sidaoqiao, Dalaihubu Town, Ejin Banner, Inner Mongolia Autonomous Region. The elevation is 878 m. The installation heights and orientations of different sensors and measured quantities were as follows: four-component radiometer (CNR4; 24 m, south), two infrared temperature sensors (SI-111; 24 m, south, vertically downward), soil heat flux (HFP01; 3 duplicates, -0.06 m), and soil temperature profile (AV-10T; 0, -0.02 and -0.04 m). The observations included the following: four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m^2), and soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm) (℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. Data were missing during 24 September, 2013 and 26 September, 2013 because of the malfunction of datalogger. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2013-9-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), Liu et al. (2011) (for data processing) in the Citation section.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
ALOS PRISM dataset includes 13 scenes; one covers the A'rou foci experimental area on Mar. 19, 2008, one covers the Haichaoba on Mar. 19, 2008, one covers the Biandukou foci experimental area on Apr. 17, 2008, and one covers the Linze grassland and Linze station foci experimental areas on Apr. 22, 2008. The data version is LB2, which was released after radiometric correction and geometric correction.
Japan Aerospace Exploration Agency
The data set includes ASTER GDEM data and its Mosaic. ASTER Global DEM (ASTER GDEM) is a Global digital elevation data product jointly released by NASA and Japan's ministry of economy, trade and industry (METI) on June 29, 2009. The DEM data is based on the observation results of NASA's new earth observation satellite TERRA.It is produced by the ASTER(Advanced Space borne Thermal Emission and Reflection Radio meter) sensor, which collects 1.3 million stereo image data, covering more than 99% of the earth's land surface.The data has a horizontal accuracy of 30 m (95% confidence) and an elevation accuracy of 7-14 m (95% confidence).This data is the third global elevation data, which is significantly higher than previous SRTM3 DEM and GTOPO30 data. We from NASA's web site (http://wist.echo.nasa.gov/api) to download the data of heihe river basin, and through the data center to distribute.The data distributed by the center completely retains the original appearance of the data without any modification to the data.If users need details about ASTER GDEM preparation process, please refer to the data documents of metadata connections, or visit http://www.ersdac.or.jp/GDEM/E/3.html or directly from https://lpdaac.usgs.gov/ reading and ASTER Global DEM related documents. ASTER GDEM is divided into several data blocks of 1×1 degree in distribution, and the distribution format is zip compression format. Each compressed file includes three files. The file naming format is as follows: ASTGTM_NxxEyyy_dem.tif ASTGTM_NxxEyyy_num.tif reademe.pdf Where xx is the starting latitude and yyy is the starting longitude._dem. Tif is the dem data file, _num. Tif is the data quality file, and reademe is the data description file. In order to facilitate users to use the data, on the basis of the fractional ASTER GDEM data, we splice fractional SRTM data to prepare the ASTER GDEM Mosaic map of the black river basin, which retains all the original features of ASTER GDEM without any resamulation. This data includes two files: heihe_aster_gdem_mosaic_dem.img Heihe_Aster_GDEM_Mosaic_num. Img The data is stored in the format of Erdas image, where the file _dem.img is the dem data file and the file _num. Img is the data quality file.
National Aeronautics and Space Administration
This dataset is the LAI observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observation period is from 24 May to 20 September 2012 (UTC+8). Measurement instruments: LAI-2000 (Beijing Normal University) Measurement positions: Core Experimental Area of Flux Observation Matrix 18 corn samples, 1 orchard sample, 1 artificial white poplar sample Measurement methods: To measure the incoming sky radiation on the canopy firstly. Then the transmission sky radiation are mearued under the canopy for serveral times. The canopy LAI is retrieved by using the gap probability model.
Li Yun, Wang Yan, MA Mingguo
This data was measured in middle stream of the Heihe River Basin in year 2012. Soil texture, porosity, bulk density, saturated water conductivity, soil organic matter were measured for each layer of the soil profile which is very close to the AMS sites. This data can be used in land surface model and ecological model. Soil profile position: The coordinate of the profile is listed as follow. No.1 to No.17 is corresponding to the AMS number in the Matrix. No. x y 1 100.3582 38.89322 2 100.3541 38.88697 3 100.3763 38.89057 5 100.3506 38.87577 6 100.3597 38.8712 7 100.3652 38.87677 8 100.3765 38.87255 9 100.3855 38.87241 10 100.3957 38.87569 11 100.342 38.86994 12 100.3663 38.86516 13 100.3785 38.86077 14 100.3531 38.85869 16 100.3641 38.8493 17 100.3697 38.84512 15 (superstation) 100.3721 38.85547 Gebi 100.3058 38.91801 Huazhaizi 100.3189 38.7652 Shenshawo 100.4926 38.78794 Instruments: Soil texture: Microtrac laser particle analyzer Porosity: Ring sampler law Bulk density: Ring sampler law Saturated Water Conductivity: hydrostatic head method Soil organic matter: Total organic carbon analyzer (TOC-VCPH) Measuring time: 2012-5-20 to 2012-7-10 (UTC+8). Measuring content: Soil texture, porosity, bulk density, saturated water conductivity, soil organic matter.
MA Mingguo, WANG Xufeng, WANG Haibo, YU Wenping
The surface air temperature dataset of the Tibetan Plateau is obtained by downscaling the China regional surface meteorological feature dataset (CRSMFD). It contains the daily mean surface air temperature and 3-hourly instantaneous surface air temperature. This dataset has a spatial resolution of 0.01°. Its time range for surface air temperature dataset is from 1979 to 2018. Spatial dimension of data: 73°E-106°E, 23°N-40°N. The surface air temperature with a 0.01° can serve as an important input for the modeling of land surface processes, such as surface evapotranspiration estimation, agricultural monitoring, and climate change analysis.
DING Lirong, ZHOU Ji, WANG Wei , MA Jin
The dataset of ground truth measurements synchronizing with Landsat TM was obtained in the Linze grassland and Linze station foci experimental area on Sep. 23, 2007 during the pre-observation periods, and one scene was captured well. These data can provide reliable ground data for retrieval and validation of land surface temperatures with EO-1 Hyperion remote sensing approaches. Observation items included: (1) the land surface radiative temperature by the hand-held infrared thermometer, which was calibrated; (2) GPS by GARMIN GPS 76; (3) atmospheric parameters at Daman Water Management office measured by CE318 (produced by CIMEL in France). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. These data include the raw data in .k7 format and can be opened by ASTPWin software. ReadMe.txt is attached for detail. Processed data (after retrieval of the raw data) in Excel contain optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number. (4) ground-based land surface temperature measurements by the thermal imager in the Heihe gobi, west of Zhangye city.
CHE Tao, BAI Yunjie, DING Songchuang, GAO Song, HAN Xujun, HAO Xiaohua, LI Hongyi, LI Xin, LI Zhe, LIANG Ji, PAN Xiaoduo, QIN Chun, RAN Youhua, WANG Xufeng, WU Yueru, YAN Qiaodi, ZHANG Lingmei, FANG Li, LI Hua, Liu Qiang, Wen Jianguang, MA Hongwei, YAN Yeqing, YUAN Xiaolong
The dataset of ground truth measurement synchronizing with EO-1 Hyperion was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on May 25, 2008. Observation items included: (1) Atmospheric parameters on the ICBC resort office roof by CE318 (produced by CIMEL in France). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. Those data include the raw data in k7 format and can be opened by ASTPWin. ReadMe.txt is attached for detail. Processed data (after retrieval of the raw data) in Excel format are on optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number. (2) Ground object reflectance spectra f new-born rape and the bare land in Biandukou foci experimental area by ASD FieldSpec (350~2500 nm) from BNU. Raw data were binary files direct from ASD (by ViewSpecPro), and pre-processed data on reflectance were in Excel format. (3) Soil moisture (0-40cm) by the cutting ring and the soil temperature (0-40cm) by the thermocouple in Huazhaizi desert No. 1 plot and the windbreak forest; and soil moisture and the soil temperature (0-100cm) in Yingke oasis maize field. Data were archived in Excel format. (4) LAI. The maximum leaf length and width of each alfalfa and barley were measured. Data were archived in Excel format. (5) Coverage of maize and wheat in Yingke oasis maize field, of vegetation (Reaumuria soongorica) in Huazhaizi desert No. 1 and 2 plots by the self-made coverage instrument and the camera (2.5m-3.5m above the ground). Based on the length of the measuring tape and the bamboo pole, the size of the photo can be decided GPS date were also collected and the technology LAB was applied to retrieve the coverage of the green vegetation. Besides, such related information as surroundings environment was also recorded. Data included the primarily measured image and final fraction of vegetation coverage.
CHEN Ling, QIAN Yonggang, REN Huazhong, WANG Haoxing, YAN Guangkuo, GE Yingchun, SHU Lele, WANG Jianhua, XU Zhen, GUANG Jie, LI Li, XIN Xiaozhou, ZHANG Yang, ZHOU Chunyan, TAO Xin, YAN Binyan, YAO Yanjuan
Plant functional types (PFT) is a combination of large plant species according to the ecosystem function and resource utilization mode of plant species. Each planting functional type shares similar plant attributes, which simplifies the diversity of plant species into the diversity of plant function and structure.The concept of plant-functional has been advocated by ecologists especially ecosystem modelers.The basic assumption is that globally important ecosystem dynamics can be expressed and simulated through limited plant functional types.At present, vegetation-functional model has been widely used in biogeographic model, biogeochemical model, land surface process model and global dynamic vegetation model. For example, the land surface process model of the national center for atmospheric research (NCAR) in the United States has changed the original land cover information into the applied plant-functional map (Bonan et al., 2002).Functional plant has been used in the dynamic global vegetation model (DGVM) to predict the changes of ecosystem structure and function under the global change scenario. 1. Functional classification system of Plant 1 Needleleaf evergreen tree, temperate 2 Needleleaf evergreen tree, boreal 3 Needleleaf deciduous tree 4 Broadleaf evergreen tree, tropical 5 Broadleaf evergreen tree, temperate 6 Broadleaf deciduous tree, tropical 7 Broadleaf deciduous tree, temperate 8 Broadleaf deciduous tree, boreal 9 Broadleaf evergreen shrub, temperate 10 Broadleaf deciduous shrub, temperate 11 Broadleaf deciduous shrub, boreal 12 C3 grass, arctic 13 C3 grass 14 C4 grass 15 Crop 16 Permanent wetlands 17 Urban and built-up lands 18 Snow and ice 19 Barren or sparsely vegetated lands 20 Bodies of water 2. Drawing method China's 1km plant function map is based on the climate rules of land cover and plant function conversion proposed by Bonan et al. (Bonan et al., 2002).Ran et al., 2012).MICLCover land cover map is a blend of 1:100000 data of land use in China in 2000, the Chinese atlas (1:10 00000) the type of vegetation, China 1:100000 glacier map, China 1:10 00000 marshes and MODIS land cover 2001 products (MOD12Q1) released the latest land cover data, using IGBP land cover classification system.The evaluation shows that it may be the most accurate land cover map on the scale of 1km in China.Climate data is China's atmospheric driven data with spatial resolution of 0.1 and temporal resolution of 3 hours from 1981 to 2008 developed by he jie et al. (2010).The data incorporates Princeton land-surface model driven data (Sheffield et al., 2006), gewex-srb radiation data (Pinker et al., 2003), TRMM 3B42 and APHRODITE precipitation data, and observations from 740 meteorological stations and stations under the China meteorological administration.According to the evaluation results of RanYouhua et al. (2010), GLC2000 has a relatively high accuracy in the current global land cover data set, and there is no mixed forest in its classification system. Therefore, the mixed forest in the MICLCover land cover diagram USES GLC2000 (Bartholome and Belward, 2005).The information in xu wenting et al., 2005) was replaced.The data can be used in land surface process model and other related researches.
RAN Youhua, LI Xin
The integration of geomorphological information in western China was completed by a team led by Dr. Xie Chuanjie, Institute of Geography, Resources and Environment, Chinese Academy of Sciences. These include the national geomorphological database of 1: 4 million and the western geomorphological database of 1: 1 million. The geomorphological data of 1: 4 million are tracked, collected and collated by the Geography Department of the National Planning Commission of the Chinese Academy of Sciences, "China Geomorphological Map (1: 4 million)" edited by Li Bingyuan and "Geomorphological Map of China and Its Adjacent Areas (1: 4 million)" edited by Chen Zhiming. Scan and register the data, vectorize all registered maps by ArcMap software, and establish their own classification and code systems. Geomorphological types are divided into basic geomorphological types and morphological structure types (point, line and surface representation) according to map spots (common staining) and symbols. Data are divided into structural geomorphology and morphological geomorphology. Projection information: Projection: Albers False_Easting: 0.000000 False_Northing: 0.000000 Central_Meridian: 105.000000 Standard_Parallel_1: 25.000000 Standard_Parallel_2: 47.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) Geographic Coordinate System: datumg Angular Unit: Degree (0.017453292519943299) Prime Meridian: <custom> (0.000000000000000000) Datum: D_Krasovsky_1940 Spheroid: Krasovsky_1940 Semimajor Axis: 6378245.000000000000000000 Semiminor Axis: 6356863.018773047300000000 Inverse Flattening: 298.300000000000010000
CHENG Weiming, ZHOU Chenghu
The dataset of ground truth measurement synchronizing with the airborne imaging spectrometer (OMIS-II) mission was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on Jun. 16, 2008. Observation items included: (1) The radiative temperature by the handheld radiometer in Yingke oasis maize field (from BNU, the vertical canopy observation, the transect observation and the diagonal observation), Yingke oasis wheat field (only for the transect temperature), and Huazhaizi desert No. 2 plot (the NE-SW diagonal observation). Besides, the maize radiative temperature and the physical temperature were also measured both by the handheld radiometer and the probe thermometer in the maize plot of 30m near the resort. The data included raw data (in Word format), recorded data and the blackbody calibrated data (in Excel format). (2) Atmospheric parameters in Huazhaizi desert No. 2 plot by CE318 (produced by CIMEL in France). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. Those data include the raw data in k7 format and can be opened by ASTPWin. ReadMe.txt is attached for detail. Processed data (after retrieval of the raw data) in Excel format are on optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number. (3) The radiative temperature of maize, wheat and the bare land in Yingke oasis maize field and Huazhaizi desert maize field by ThermaCAM SC2000 (1.2m above the ground, FOV = 24°×18°), The data included raw data (read by ThermaCAM Researcher 2001), recorded data and the blackbody calibrated data (archived in Excel format). (4) The reflectance spectra by ASD through the vertical canopy observation and the transect observation in Yingke oasis maize field (350-2500nm , from BNU), and Huazhaizi desert maize field and Huazhaizi desert No. 1 plot (350-2500nm , from Cold and Arid Regions Environmental and Engineering Research Institute, CAS). The data included raw data (in .doc format), recorded data and the blackbody calibrated data (in Excel format). (5) The radiative temperature by the automatic thermometer (FOV: 10°; emissivity: 1.0), observing straight downwards at intervals of 1s in Yingke oasis maize field (one from BNU and the other from Institute of Remote Sensing Applications), Huazhaizi desert maize field (only one from BNU for continuous radiative temperature of the maize canopy) and Huazhaizi desert No. 2 plot (two for reaumuria soongorica canopy and the bare land). Raw data, blackbody calibrated data and processed data were all archived in Excel format. (6) Photosynthesis of maize and wheat of Yingke oasis maize field by LI6400, carried out according to WATER specifications. Raw data were archived in the user-defined format (by notepat.exe) and processed data were in Excel format. (7) Soil moisture in Yingke oasis maize field. The sample was fetched by the soil auger and weighed by the scales before and after drying. Data were archived in Excel format. (8) FPAR (Fraction of Photosynthetically Active Radiation) of maize and wheat by SUNSACN and the digital camera in Yingke oasis maize field. FPAR= (canopyPAR-surface transmissionPAR-canopy reflection PAR+surface reflectionPAR) /canopy PAR; APAR=FPAR* canopy PAR. Data were archived in the table format of Word. (9) Maize albedo by the shortwave radiometer in Yingke oasis maize field. R =10H (R for FOV radius; H for the probe height). Data were archived in Excel format.
CHEN Ling, REN Huazhong, ZHOU Hongmin, CAO Yongpan, SHU Lele, WU Yueru, XU Zhen, LI Li, LIU Sihan, XIA Chuanfu, XIN Xiaozhou, ZHOU Chunyan, ZHOU Mengwei, FAN Wenjie, TAO Xin, FENG Lei, LIANG Wenguang, YU Fan, WANG Dacheng, YANG Guijun, LI Xiaoyu, Liu Liangyun
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