This data set contains data of meteorological elements observation system of farmland station downstream of heihe hydrometeorological observation network from January 1, 2014 to December 31, 2014.The station is located at sidao bridge, dalai hubu town, ejin banner, Inner Mongolia.The latitude and longitude of the observation point are 101.1338e, 42.0048n, and 875m above sea level.The four-component radiometer is installed at 6m, facing due south;The two infrared thermometer sensors are installed at the position of 6m, facing south, and the probe is facing vertically downward.The two photosynthetic radiometers are installed at the position of 6m, facing due south, and the probes are vertically up and down in one direction.The soil temperature probe is buried at 0cm on the surface, 2cm and 4cm underground, and 2m to the south of the meteorological tower.The soil moisture sensor (installed on March 15, 2014) was buried 2cm and 4cm underground, in the south due to 2m from the meteorological tower.The soil hot flow plates (3) are successively buried in the ground 6cm away from the weather tower 2m due south. Radiation observation projects are: four components (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit: c), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watts per square meter), soil temperature (Ts_0cm Ts_2cm Ts_4cm) (unit: c), soil moisture (Ms_2cm, Ms_4cm) (unit:Volume water content, percentage), up and down photosynthetic effective radiation (PAR_up, PAR_down) (unit: micromole/m s). Processing and quality control of observation data :(1) ensure 144 data per day (every 10min). If data is missing, it will be marked by -6999;Due to site reconstruction, data was missing between April 15, 2014 and July, 2014;(2) eliminate the moments with duplicate records;(3) data that is obviously beyond the physical meaning or the range of the instrument is deleted;(4) the part marked by red letter in the data is the data in question;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: 2014-9-10-10:30;(6) the naming rule is: AWS+ site name. Please refer to Li et al.(2013) for hydrometeorological network or site information, and Liu et al.(2011) for observation data processing.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
The dataset of water content of forest canopy components (the twig and the leaf) measurements was obtained at the super site (100m×100m) around the Dayekou Guantan forest station on Jun. 5, 2008. The sample tree was selected according to different diameters at breast height. 5 diameter classes were divided and in each class, 10 trees were selected and altogether 30 trees were selected as sampling trees. Branches in different parts were picked by the tree pruner and the twig and the leaf were separated manually, whose green weight was measured by the scales on the scene and dry weight by oven drying in the lab. Those provide reliable data for the reconstruction of the 3D structure of the forest scene, and for modelling active and passive remote sensing mechanisms and the simulation of remote sensing images.
BAI Lina, TIAN Xin, WANG Bengyu, CHEN Erxue
The sampling and distribution of plant materials in the arid regions of the middle and lower reaches of Heihe River Basin. The plants are mainly shrubs and a few herbs. The numbering of plant materials is consistent with the morphological structural characteristics analysis table and is used in correspondence with each other.
LIU Yubing
The dataset of ground truth measurements synchronizing with MODIS, ALOS PALSAR and AMSR-E was obtained in the Biandukou foci experimental area on May 24, 2008. Observation items included: (1) the surface temperature in No. 1 (grassland), No. 2 (the rape land), No. 3 (the rape land), No. 4 (the wheat land) and No. 5 quadrate (wheat and rape); (2) the soil moisture by WET in No. 2 quadrate; (3) GPR and WET; (4) The spectrum by ASD Fieldspec FRTM (Boulder, Co, USA), 350nm-2500nm, 3nm for the visible near-infrared band and 10nm for the shortwave infrared band). The spectrum data were archived in the ASCII format, with the first five rows as the file header and the following two columns as wavelength (nm) and reflectance (percentage) respectively, and can be opened by .txt or wordpad. The .txt file was not reflectance but intermediate file for further calculation. Raw data were binary files direct from ASD (by ViewSpecPro). The surface radiative temperature and the physical temperature were measured by the handheld infrared thermometer. Besides, the cover type was also recorded. The data can be opened by Microsoft Office. Soil moisture was acquired by WET and the cutting ring. The data can be opened by Microsoft Office. Six data files were included, soil moisture, the surface temperature, GPR, coverage photos and preprocessed data, ground objects spectrum and satellite images.
BAI Yunjie, CAO Yongpan, CHE Tao, DU Ziqiang, HAO Xiaohua, WANG Zhixia, WU Yueru, CHAI Yuan, CHANG Sheng, QIAN Yonggang, SUN Xiaoqing, WANG Jindi, YAO Dongping, ZHAO Shaojie, ZHENG Yue, ZHAO Yingshi, LI Xiaoyu, PATRICK Klenk, HUANG Bo, LI Shihua, LUO Zhen
1 km land cover map of heihe river basin is ran youhua et al. (2009;2011) develop a subset of China's 1 km land cover map (MICLCover) incorporating multi-source local information.The MICLCover land cover map adopts the IGBP land cover classification system, based on the evidence theory, which integrates the 1:100,000 land use data of China in 2000, the vegetation pattern of China vegetation atlas (1:100,000), the 1:100,000 glacier distribution map of China, the 1:100,000 swamp wetland map of China and the land cover product of MODIS in 2001 (MOD12Q1).The verification results of MICLCover showed that the overall consistency of MICLCover and China's land use map reached 88.84% on the level of 7 categories. Among them, the consistency of cultivated land, city, wetland and water type reached more than 95%.Through visual comparison with the land cover data product of MODIS2001 and IGBPDISCover land cover map in three typical areas, MICLCover keeps the overall accuracy of China's land use map and increases the leaf attribute and leaf shape information of China's vegetation map, while reflecting more detailed local land cover details.Using the national forest resources survey data, the verification results in gansu, yunnan, zhejiang, heilongjiang and jilin provinces showed that the accuracy of forest types of MICLCover was significantly improved compared with that of MODIS land cover products.The forest type of MICLCover was verified with the forest resource survey data of qilian mountain national nature reserve administration of gansu province. The results showed that the accuracy of MICLCover forest type in this area was 82.94%. Anyhow, MICLCover land cover map while maintaining the overall precision of the Chinese land use data condition, supplement the vegetation map of China on vegetation types and vegetation season phase information, update the Chinese wetland figure, Chinese ice figure the latest information, the accuracy of China's land cover data is greatly improved, more general classification system, the data can provide higher precision for land surface process model of land cover information.
RAN Youhua, LI Xin
The dataset of ground truth measurements for snow synchronizing with the airborne microwave radiometers (K&Ka bands) mission was obtained in the Binggou watershed foci experimental area on Mar. 30, 2008. Those provide reliable data for retrieval of snow parameters and properties, especially for dry and wet snow identification. Observation items included: (1) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the snowfork in BG-A; (2) Snow parameters including snow depth, the snow surface temperature synchronizing with the airborne microwave radiometers (K&Ka bands), the snow layer temperature, the snow grain size and snow density in BG-A (10 points), BG-B (6 points), BG-F (12 points), BG-H (21 points) and BG-I (20 points); For each snow pit, the snowpack was divided into several layers with 10-cm intervals of snow depth. The layer depth (by the ruler), the snow grain size (by the handheld microscope), snow density (by the cutting ring) and the snow temperature (by the probe thermometer) were obtained at each snow pit. Two files including raw data and the preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, GU Juan, HAO Xiaohua, LI Hongyi, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Jianhua, WANG Xufeng, WU Yueru, XU Zhen, ZHU Shijie, LI Hua, CHANG Cun, MA Zhongguo, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu, CHE Tao
From May to October 2012, the monthly Lai vegetation index product data of 30 meters in Heihe River Basin was retrieved by using the environmental satellite CCD image, and the inversion method was based on the look-up table method and go + Hapke model. In the inversion process, Nelson parameters are determined according to vegetation types.
FAN Wenjie
The data set contains the vortex correlator observation data of the farmland station downstream of heihe hydrometeorological observation network from January 1, 2015 to November 5, 2015.The station is located in the four Bridges of ejin banner in Inner Mongolia.The latitude and longitude of the observation point are 101.1338e, 42.0048n, and 875m above sea level.The height of the vortex correlation instrument is 3.5m, the sampling frequency is 10Hz, the ultrasonic direction is due to the north, and the distance between the ultrasonic wind speed and temperature instrument (CSAT3) and the CO2/H2O analyzer (Li7500A) is 15cm. The original observation data of vorticity correlativity is 10Hz, and the released data is the data of 30 minutes processed by Eddypro software. The main steps of its processing include: outfield value elimination, delay time correction, coordinate rotation (secondary coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output by Eddypro software was also screened.(2) data of 1h before and after precipitation were excluded;(3) the missing rate of 10Hz original data is more than 10% every 30min;(4) the observed data of weak turbulence at night were excluded (u* less than 0.1m/s).The average period of observation data was 30 minutes, 48 data a day, and the missing data was marked as -6999.Suspicious data caused by instrument drift shall be identified in red.On April 21, solstice, June 22, the instrument was being replaced, during which the data was missing, and the station was dismantled on November 5. Observations published include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE,Carbon dioxide flux mass identification QA_Fc.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest are 2).The meaning of data time, such as 0:30 represents the average between 0:00 and 0:30;The data is stored in *.xls format. Please refer to Li et al. (2013) for hydrometeorological network or site information, and Liu et al. (2011) for observation data processing.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
The dataset of airborne imaging spectrometer (OMIS-II) mission was obtained in the Linze station-Linze grassland flight zone on Jun. 15, 2008. Data after radiometric correction and calibration and geometric approximate correction were released. The flying time of each route was as follows: {| ! id ! flight ! file ! starttime ! lat ! long ! alt ! image liange ! endtime ! lat ! long ! lat |- | 1 || reservoir 1 || 2008-06-15_11-55-28_DATA.BSQ || 12:12:48 || 39.013 || 100.236 || -1.0 || 2540 || 12:15:37 || 39.085 || 100.150 || -1.0 |- | 2 || 1-13 || 2008-06-15_12-15-51_DATA.BSQ || 12:20:47 || 39.172 || 100.048 || 2867.7 || 5572 || 12:26:58 || 39.359 || 100.190 || 2867.8 |- | 3 || 1-12 || 2008-06-15_12-27-13_DATA.BSQ || 12:31:59 || 39.366 || 100.188 || 2846.6 || 5067 || 12:37:37 || 39.185 || 100.051 || 2867.8 |- | 4 || 1-11 || 2008-06-15_12-37-51_DATA.BSQ || 12:42:52 || 39.179 || 100.039 || 2878.8 || 5542 || 12:49:02 || 39.363 || 100.179 || 2884.8 |- | 5 || 1-10 || 2008-06-15_12-49-16_DATA.BSQ || 12:54:29 || 39.373 || 100.179 || 2909.9 || 5116 || 13:00:10 || 39.187 || 100.039 || 2897.3 |- | 6 || 1-9 || 2008-06-15_13-00-24_DATA.BSQ || 13:05:30 || 39.182 || 100.028 || 2864.2 || 5498 || 13:11:37 || 39.366 || 100.167 || 2859.7 |- | 7 || 1-8 || 2008-06-15_13-11-51_DATA.BSQ || 13:17:22 || 39.377 || 100.169 || 2846.8 || 5114 || 13:23:02 || 39.191 || 100.029 || 2862.3 |- | 8 || 1-7 || 2008-06-15_13-23-17_DATA.BSQ || 13:28:06 || 39.187 || 100.0187 || 2857.1 || 5497 || 13:34:13 || 39.372 || 100.158 || 2842.5 |- | 9 || 1-6 || 2008-06-15_13-34-27_DATA.BSQ || 13:39:10 || 39.380 || 100.158 || 2909.7 || 5184 || 13:44:55 || 39.197 || 100.019 || 2861.8 |- | 10 || 1-5 || 2008-06-15_13-45-10_DATA.BSQ || 13:50:09 || -1.000 || -1.000 || -1.0 || 5488 || 13:56:09 || -1.000 || -1.000 || -1.0 |- | 11 || 1-4 || 2008-06-15_13-56-23_DATA.BSQ || 14:01:20 || -1.000 || -1.000 || -1.0 || 5353 || 14:07:18 || -1.000 || -1.000 || -1.0 |- | 12 || 1-3 || 2008-06-15_14-07-32_DATA.BSQ || 14:12:36 || -1.000 || -1.000 || -1.0 || 5350 || 14:18:30 || -1.000 || -1.000 || -1.0 |- | 13 || 1-2 || 2008-06-15_14-18-46_DATA.BSQ || 14:22:48 || -1.000 || -1.000 || -1.0 || 5236 || 14:28:31 || -1.000 || -1.000 || -1.0 |- | 14 || 1-1 || 2008-06-15_14-28-49_DATA.BSQ || 14:34:02 || -1.000 || -1.000 || -1.0 || 5964 || 14:40:11 || -1.000 || -1.000 || -1.0 |- | 15 || reservoir 2 || 2008-06-15_14-40-51_DATA.BSQ || 14:51:05 || -1.000 || -1.000 || -1.0 || 6846 || 14:58:35 || -1.000 || -1.000 || -1.0 |}
Liu Liangyun, LI Xin, MA Mingguo
On 7 July 2012 (UTC+8), a CASI/SASI sensor boarded on the Y-12 aircraft was used to obtain the visible/near Infrared hyperspectral image, which is located in the observation experimental area. The relative flight altitude is 2000 meters, The wavelength of CASI and SASI is 380-1050 nm and 950-2450 nm, respectively. The spatial resolution of CASI and SASI is 1 m and 2.4 m, respectively. Through the ground sample points and atmospheric data, the data product are recorded in reflectance processed by geometric correction and atmospheric correction based on 6S model.
XIAO Qing, Wen Jianguang
This dataset contains the flux measurements from site No.8 eddy covariance system (EC) in the flux observation matrix from 28 May to 21 September, 2012. The site (100.37649° E, 38.87254° N) was located in a cropland (maize surface) in Yingke irrigation district, which is near Zhangye, Gansu Province. The elevation is 1550.06 m. The EC was installed at a height of 3.2 m; the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500) was 0.15 m. Raw data acquired at 10 Hz were processed using the Edire post-processing software (University of Edinburgh, http://www.geos.ed.ac.uk/abs/research/micromet/EdiRe/), including spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. Moreover, the observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC), which was proposed by Foken and Wichura [1996]: class 1 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), representing high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 3% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day; the missing data were replaced with -6999. Moreover, suspicious data were marked in red. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m^3), CO2 mass density (CO2, mg/m^3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m^2), latent heat flux (LE, W/m^2), carbon dioxide flux (Fc, mg/ (m^2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xlsx format. 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
The data set contains the vortex correlator observation data of sidaqiao superstation in the downstream of heihe hydrometeorological observation network from January 1, 2016 to December 31, 2016.The station is located in the fourth bridge of ejin banner in Inner Mongolia, tamarisk is the underlying surface.The latitude and longitude of the observation point is 101.1374e, 42.0012n, and the altitude is 873 m.The height of the vortex correlativity instrument is 8m, the sampling frequency is 10Hz, the ultrasonic direction is due to the north, and the distance between the ultrasonic wind speed and temperature instrument (CSAT3) and the CO2/H2O analyzer (Li7500) is 15cm. The original observation data of vorticity correlativity is 10Hz, and the released data is the data of 30 minutes processed by Eddypro software. The main steps of its processing include: outfield value elimination, delay time correction, coordinate rotation (secondary coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output by Eddypro software was also screened.(2) data of 1h before and after precipitation were excluded;(3) the missing rate of 10Hz original data is more than 10% every 30min;(4) the observed data of weak turbulence at night were excluded (u* less than 0.1m/s).The average period of observation data was 30 minutes, 48 data a day, and the missing data was marked as -6999.From April 14 to 23, data was missing due to errors and calibration of the vortex system Li7500.During the period from May 1 to August 23, the intermittent error of Li7500 resulted in the loss of latent heat flux and carbon dioxide flux.Suspicious data caused by instrument drift shall be identified in red. Observations published include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE,Carbon dioxide flux mass identification QA_Fc.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest are 2).The meaning of data time, such as 0:30 represents the average between 0:00 and 0:30;The data is stored in *.xls format. Please refer to Li et al. (2013) for hydrometeorological network or site information, and Liu et al. (2011) for observation data processing.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No. 1 and 2 quadrates of the A'rou foci experimental area on Oct. 18, 2007 during the pre-observation period. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:17 BJT. Both the quadrates were divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. 25 sampling points were chosen, including centers and corners of each subsites. Simultaneous with the satellite overpass, numerous ground data were collected, soil volumetric moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity by the WET soil moisture sensor; the surface radiative temperature by the hand-held infrared thermometer; soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). Meanwhile, vegetation parameters as height, coverage and water content were also observed. Surface roughness was detailed in the "WATER: Surface roughness dataset in the A'rou foci experimental area". Those provide reliable ground data for retrieval and validation of soil moisture and freeze/thaw status from active remote sensing approaches.
BAI Yunjie, HAO Xiaohua, LI Hongyi, LI Xin, LI Zhe
The data set contains the flux observation data of scintillator with large aperture from sidaoqiao station downstream of heihe hydrometeorological observation network.There are two groups of large aperture scintillators at the downstream sidaoqiao station.On the east side (point 1), there is a large aperture scintillator of model BLS900. The north tower is the receiving end and the south tower is the transmitting end. The observation period of BLS900_1 is from March 13, 2014 to December 31, 2014.On the west side (no. 2 point), there is a large aperture scintillator of BLS900 model. The north tower is the receiving end and the south tower is the transmitting end, and the observation time of BLS900_2 is from January 1, 2014 to November 8, 2014.The station is located in ejin banner of Inner Mongolia, the underlying surface involves tamarisk, populus populus, bare land and cultivated land.The latitude and longitude of the north tower of point 1 is 101.147e, 42.005n, and that of the south tower is 101.131e, 41.987n.The latitude and longitude of the north tower at point 2 is 101.137e, 42.008n, and the latitude and longitude of the south tower is 101.121e, 41.990 N, with an altitude of about 873m.The effective height of the large aperture scintillation instrument is 25.5m, the diameter length of LAS at point 1 is 2390m, and that of LAS at point 2 is 2380m, and the sampling frequency is 1min. Large aperture flicker meter raw observation data for 1 min, data released for 30 min after processing and quality control of data, including sensible heat flux is mainly combined with the automatic meteorological station observation data, based on similarity theory alonzo mourning - Mr. Hoff is obtained by iterative calculation, the quality control of the main steps include: (1) excluding Cn2 reach saturation data (BLS900_1: Cn2 > 7.25 e-14, BLS900_2: Cn2 > 7.33 E - 14).(2) data with weak demodulation signal strength (Average X Intensity<1000) were eliminated;(3) data at the time of precipitation were excluded;(4) data of weak turbulence under stable conditions were excluded (u* < 0.1m/s).In the iterative calculation process, for BLS900, the stability universal function of Thiermann and Grassl, 1992 was selected.Please refer to Liu et al.(2011, 2013) for detailed introduction. Some notes on the released data :(1) the data of LAS point 1 in the downstream is mainly BLS900_1, and the missing moment is marked by -6999;LAS data of downstream point 2 is mainly BLS900_2, and the missing moment is marked by -6999.(2) data table head: Date/Time: Date/Time (format: yyyy-m-d h:mm), Cn2: structural parameters of air refraction index (unit: m-2/3), H_LAS: sensible heat flux (unit: W/m2).The meaning of data time, such as 0:30 represents the average between 0:00 and 0:30;The data is stored in *.xls format. Please refer to Li et al.(2013) for hydrometeorological network or site information, and Liu et al.(2011) for observation data processing.
LIU Shaomin, LI Xin, XU Ziwei, CHE Tao, REN Zhiguo, TAN Junlei
"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. The administrative division map of the Heihe river basin is one of the basic geographic sections of the atlas, with the scale of 1:2500000, the normal axis equal product conic projection, and the standard latitude line: north latitude: 25 47. Data source: 1 million administrative boundary data of Heihe River Basin in 2008, road data of Heihe River Basin in 2010, residential area data of Heihe River Basin in 2009, and 100000 river data of 2009.
WANG Jianhua, ZHAO Jun, WANG Xiaomin
The aerosol optical thickness data of the Arctic Alaska station is based on the observation data products of the atmospheric radiation observation plan of the U.S. Department of energy at the Arctic Alaska station. The data coverage time is updated from 2017 to 2019, with the time resolution of hour by hour. The coverage site is the northern Alaska station, with the longitude and latitude coordinates of (71 ° 19 ′ 22.8 ″ n, 156 ° 36 ′ 32.4 ″ w). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is NC format. The aerosol optical thickness data of Qomolangma station and Namuco station in the Qinghai Tibet Plateau is based on the observation data products of Qomolangma station and Namuco station from the atmospheric radiation view of the Institute of Qinghai Tibet Plateau of the Chinese Academy of Sciences. The data coverage time is from 2017 to 2019, the time resolution is hour by hour, the coverage sites are Qomolangma station and Namuco station, the longitude and latitude coordinates are (Qomolangma station: 28.365n, 86.948e, Namuco station Mucuo station: 30.7725n, 90.9626e). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is TXT.
WANG Xufeng, KANG Jian, Li Dazhi, Wang Zuocheng, Dong Cunhui, LI Xin, MA Mingguo
Based on the field survey results of this project, the previous hydrogeological survey results and the prediction and judgment of desert depressions, we obtained more than 600 known water level points in badain jaran desert and its surrounding areas, and drew a first-order approximate contour map of the groundwater level in badan jaran desert by using the measured or predicted groundwater level data.This isometric chart fills a gap in the study of groundwater in badain jaran desert. The so-called first-order approximation is the distribution of the macroscopic groundwater level, which reaches a resolution of 1 km on the spatial scale, and it is assumed that the groundwater level in the shallow and deep layers is the same, and the groundwater in the quaternary and bedrock distribution areas remains continuous.The error level of the first-order approximate contour is ± 10 m, which mainly comes from the uncertainty of ground elevation data. This data set contains a vector diagram of the groundwater level contour line and a raster data file.
WANG Xusheng, HU Xiaonong
The data set contains meteorological observation data of shenshawo desert station in the middle reaches of the hehe river meteorological observation network from January 1, 2014 to December 31, 2014.The station is located in shensha wo, zhangye city, gansu province.The latitude and longitude of the observation point are 100.4933e, 38.7892N, and 1594m above sea level.Air temperature and relative humidity sensors are set up at 5m and 10m, facing due north.The barometer is installed at 2m;The inverted bucket rain gauge is installed at 10m;The wind speed sensor is set up at 5m, 10m, and the wind direction sensor is set up at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;The two infrared thermometers are installed at the position of 6m, facing south, and the probe is facing vertically downward.The soil temperature probe is buried at 0cm on the surface and 2cm, 4cm, 10cm, 20cm, 40cm, 60cm and 100cm underground, in the south due to 2m from the meteorological tower.Soil moisture sensors were buried in the ground at 2cm, 4cm, 10cm, 20cm, 40cm, 60cm and 100cm, respectively, in the south due to 2m from the meteorological tower.The soil hot flow plates (3) are successively buried in the ground at 6cm. Observation items are: air temperature and humidity (Ta_5m RH_5m Ta_10m, RH_10m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_5m, WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:C), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: w/m), soil moisture (Ms_2cm, Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm_1, Ms_40cm_2, Ms_60cm, Ms_100cm) (unit: volume water content, percentage), and soil temperature (Ts_0cm, Ts_2cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_60cm, Ts_100cm) (unit: Celsius). Processing and quality control of observation data :(1) ensure 144 data per day (every 10min). If data is missing, it will be marked by -6999;Due to the adjustment of observation factors, some data were missing between 5.5-5.6, 2014.(2) eliminate the moments with duplicate records;(3) data that is obviously beyond the physical meaning or the range of the instrument is deleted;(4) the part marked by red letter in the data is the data in question;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: 2014-6-10-10:30;(6) the naming rule is: AWS+ site name. Please refer to Li et al.(2013) for hydrometeorological network or site information, and Liu et al.(2011) for observation data processing.
LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
The distributed eco hydrological model needs high-precision precipitation spatial distribution information as input. Due to the scarcity of stations, the station interpolation precipitation can not reflect the spatial distribution of precipitation in Heihe mountain area. The regional climate model (RCM) simulation results provide the information of precipitation elevation relationship at different locations. The relationship is corrected according to the observed precipitation elevation gradient of hulugou watershed, and the precipitation elevation gradient at different locations of the watershed is obtained. Based on the gradient and the multi-year average value of precipitation observed at the station, the precipitation climate background field is established to represent the multi-year average spatial distribution of precipitation in the basin. Then, based on the daily precipitation observation data of 16 meteorological stations and 25 hydrological stations, and the precipitation spatial distribution information provided by the precipitation climate background field, the daily grid precipitation data is obtained by interpolation. The interpolation year of this data is 1960-2014, the spatial interpolation precision is 3-km, and the time precision is day by day data (the daily period is from 8:00 a.m. to 8:00 a.m. the next day). The results show that the interpolation precipitation is reliable. The data is stored in ASCII file. The file name of each file is in the form of precyyyymmdd.asc. Yyyy is the year, mm is the month and DD is the day. Each ASCII file represents the grid precipitation data of the day, in mm.
YANG Dawen
The dataset contains phenological camera observation data collected at the Arou Superstation in the midstream of the Heihe integrated observatory network from June 13 to November 16, 2018. The instrument was developed with data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures high-quality data with a resolution of 1280×720 by looking-downward. The calculation of the greenness index and phenology are following 3 steps: (1) calculate the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) according to the region of interest, (2) perform gap-filling for the invalid values, filtering and smoothing, and (3) determine the key phenological parameters according to the growth curve fitting (such as the growth season start date, Peak, growth season end, etc.) There are also 3 steps for coverage data processing: (1) select images with less intense illumination, (2) divide the image into vegetation and soil, and (3) calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (GCC), phenological phase and fractional cover (FC). Please refer to Liu et al. (2018) for sites information in the Citation section.
Qu Yonghua, XU Ziwei, LI Xin
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