This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Dunhuang Station from January 1 to December 31, 2018. The site (93.708° E, 40.348° N) was located on a wetland in the Dunhuang west lake, Gansu Province. The elevation is 990 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4m and 8 m, towards north), wind speed and direction profile (windsonic; 4m and 8 m, towards north), air pressure (1 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), soil heat flux (-0.05 and -0.1m ), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation in the south of tower, -0.05 and -0.2 m), photosynthetically active radiation (4 m, towards south), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_2 m, RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 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) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_0.05m, Ts_0.2m) (℃), soil moisture (Ms_0.05m, Ms_0.2m) (%, volumetric water content), soil conductivity (Ec_0.05m, Ec_0.2m)(μs/cm), sun time(h). 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. The data were missing during Jan. 23 to Jan. 24 because of collector failure; the data during Mar. 17 and May 24 were wrong because of the tower body tilt; The air humidity data were rejected due to program error. (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: 2018-6-10 10:30.
0 2019-09-15
The data set includes estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on the MODIS 16-day synthetic NDVI product (MOD13A2 collection 6). Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 2001 to 2014, and the spatial resolution is 1 km.
0 2019-09-14
The dataset of airborne LiDAR mission in the Dayekou watershed flight zone on Jun. 23, 2008 included peak pulse data (*.LAS), full waveform data (.lgc), CCD photos, DEM, DSM and DOM. The flight routes were as follows: {| ! flight route ! startpoint lat ! startpoint lon ! endpoint lat ! endpoint lon ! altitude (m) ! length (km) ! photos |- | 8 || 38°32′52.25″ || 100°12′35.26″ || 38°30′25.65″ || 100°18′31.76″ || 3650 || 9.7 || 34 |- | 9 || 38°32′57.99″ || 100°12′39.09″ || 38°30′31.59″ || 100°18′35.14″ || 3650 || 9.7 || 34 |- | 10 || 38°33′03.74″ || 100°12′42.91″ || 38°30′40.25″ || 100°18′31.88″ || 3650 || 9.5 || 34 |- | 11 || 38°33′12.80″ || 100°12′38.68″ || 38°30′46.10″ || 100°18′35.47″ || 3650 || 9.8 || 35 |- | 12 || 38°33′18.55″ || 100°12′42.51″ || 38°30′54.86″ || 100°18′31.99″ || 3650 || 9.6 || 35 |- | 13 || 38°33′24.30″ || 100°12′46.34″ || 38°31′00.95″ || 100°18′34.98″ || 3650 || 9.5 || 36 |- | 14 || 38°33′30.05″ || 100°12′50.16″ || 38°31′09.54″ || 100°18′31.92″ || 3650 || 9.3 || 35 |- | 15 || 38°33′35.80″ || 100°12′53.99″ || 38°31′15.47″ || 100°18′35.29″ || 3750 || 9.3 || 35 |- | 16 || 38°33′41.55″ || 100°12′57.82″ || 38°31′21.66″ || 100°18′38.05″ || 3750 || 9.3 || 35 |- | 17 || 38°33′47.30″ || 100°13′01.65″ || 38°31′27.25″ || 100°18′42.27″ || 3750 || 9.3 || 35 |- | 19 || 38°34′02.11″ || 100°13′01.25″ || 38°31′45.61″ || 100°18′33.27″ || 3750 || 9.1 || 45 |- | 20 || 38°34′07.86″ || 100°13′05.07″ || 38°31′51.54″ || 100°18′36.64″ || 3750 || 9.1 || 45 |- | 21 || 38°34′13.61″ || 100°13′08.90″ || 38°32′00.12″ || 100°18′33.60″ || 3750 || 8.9 || 45 |- | 22 || 38°34′19.36″ || 100°13′12.73″ || 38°32′05.45″ || 100°18′38.44″ || 3750 || 8.9 || 45 |- | 23 || 38°34′25.10″ || 100°13′16.56″ || 38°32′14.72″ || 100°18′33.72″ || 3750 || 8.7 || 45 |- | 24 || 38°34′30.85″ || 100°13′20.39″ || 38°32′20.48″ || 100°18′37.52″ || 3750 || 8.7 || 45 |- | 25 || 38°34′36.60″ || 100°13′24.22″ || 38°32′26.24″ || 100°18′41.32″ || 3750 || 8.7 || 45 |- | 26 || 38°34′45.66″ || 100°13′19.98″ || 38°32′31.98″ || 100°18′45.15″ || 3750 || 8.9 || 45 |}
0 2019-09-11
The purpose of differential GPS positioning survey is to unify multiple survey areas into the same coordinate system and realize accurate absolute positioning through joint survey with national high-level control point coordinates. Under the national geodetic coordinate system of 2000, the accurate positioning of flux observation matrix, hulugou small watershed, tianmuchi small watershed and dayokou watershed and target is completed. In order to realize the geometric correction and absolute positioning of optical image, SAR image and airborne lidar data, the layout of ground control points and high-precision measurement are completed. In the middle reaches of the area, one national high-level control point is jointly surveyed in the five directions of East, South, West, North and middle. Measuring instrument: There are 3 sets of triple R8 GNSS system. Measurement principle: For the control network encryption point, it is connected with the high-level known points in four quadrants around the survey area and distributed evenly in the survey area. For the ground control point (GCP), the obvious characteristic points (such as house corner, road intersection, inflection point, etc.) of the ground layout target and the independent ground objects are adopted and evenly distributed in the survey area. For the ground points with high accuracy requirements, the principle of average value of multiple (at least three) measurements is adopted. Measurement method: In the test area, the control network is encrypted, and GPS static measurement and national high-level control network are used for joint measurement and calculation. During measurement, multiple GPS receivers conduct static synchronous observation at different stations, and the observation time is strictly in accordance with the control network measurement specifications. The ground points in the test area are accurately located. GPS-RTK positioning technology is used and the national high-level control points are used to calibrate to the local coordinate system. When the mobile station obtains the fixed solution during the coordinate acquisition, the measurement is carried out again and the single measurement lasts for 5S. Measuring position: (1) Flux observation matrix 17 stations, Las tower, waternet, soilnet and bnunet nodes in the core area of flux observation matrix; ground control points in CASI flight area; ground corner reflector positions in radar coverage area; ground target positions in lidar flight area. (2) Hulugou small watershed Ground target location of lidar flight area. (3) Tianmuchi small watershed Ground target location of lidar flight area. (4) Dayokou Basin Satellite image geometric correction ground control point. Data format: GPS static survey, the original data format is ". Dat" and ". T01" (or ". T02") files (or converted renix data) and "field record". GPS-RTK survey, the original project is ". Job" file (or converted ". DC" file). The test results are submitted in the format of exported ". CSV" data, which can be viewed and edited by Excel software. Measurement time: June 19, 2012 to July 30, 2012
0 2020-03-13
Microwave emissivity of the surface characterization of the object to launch the ability of microwave radiation, spaceborne passive microwave emissivity can on macro, large scale integral expression of epicontinental microwave radiation is a passive microwave surface parameters in quantitative inversion experience for one of the important basic data, is also on the large scale understand epicontinental microwave radiation in a way.This data set is considered to carry on the Aqua satellite advanced microwave scanning radiometer (amsr-e) and moderate resolution imaging spectroradiometer (MODIS) synchronous observation characteristics, using the MODIS land surface temperature and atmospheric water vapor data as input, by considering the effects of atmospheric emissivity estimation model, produced a global sky conditions during the running of amsr-e sensor (June 2002 ~ October 2011) of the epicontinental multichannel bipolar microwave instantaneous emission rate.Through product low-frequency radio signal, data alignment, statistic analysis, the different emissivity characteristics of surface coverage condition, frequency dependence and correlation studies conducted confirmatory analysis, the results show that the instantaneous dynamic details of emissivity is rich, standard deviation within 0.02 month daily variation, the change of time and space, frequency dependent on and related to the understanding of the natural physical process. This data set includes amsr-e global land surface daily, daily, daily, monthly and monthly products in the whole life cycle, which can be used to carry out satellite based passive microwave remote sensing simulation, land surface model, and inversion research of land surface temperature, snow cover, atmospheric precipitation/moisture/precipitation.The projection coordinates of the data adopt the standard EASE-GRID projection, and the data storage method is binary floating point lattice (the size of the matrix is 1383*586). After the data is obtained, ENVI/IDL and other software or the corresponding program code can be read in the form of binary files. All land surface emissivity data produced are named according to the following rules: RADI_AMSRE_EM # # # # _yyymmdd_EG_V. Bin For example, file name: RADI_AMSRE_EM01_20060101_EG_V# EM##: 01 means daily, 05 means 5 days, 10 means ten days, HM means half a month, MO means a month Yyyymmdd: yyyy means year, mm means month, and dd means date V##: version number, such as 0.1, 1.0, etc., the units digit is the official version RADI: institute of remote sensing and digital earth, Chinese academy of sciences AMSRE: advanced microwave scanning radiometer
0 2020-03-28
This dataset contains the flux measurements from the Bajitan Gobi station eddy covariance system (EC) in the flux observation matrix from 31 May to 15 September, 2012. The site (100.30420° E, 38.91496° N) was located in Gobi surface, which is near Zhangye, Gansu Province. The elevation is 1562.00 m. The EC was installed at a height of 4.6 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.
0 2019-09-12
1 High resolution gridded West Antarctic surface mass balance dataset, its project is Polar Stereographic Projection 2. The kriging like interpolation method is used to reconstruct the high‐spatial resolution surface mass balance (SMB) over the West Antarctic Ice Sheet (WAIS) from 1800 to 2010, based on ice core records, the outputs of the European Centre for Medium‐Range Weather Forecasts “Interim” reanalysis (ERA‐Interim) as well as the latest polar version of the Regional Atmospheric Climate Model (RACMO2.3p2). 3. Its accuracy is higher than reanalysis data. 4. Temporal resolution: 1800-2010; Temporal resolution: 1 year; Spatial coverage : the whole West Antarctic Ice Sheet, Spatial resolution: 25km х 25km
0 2020-01-19
1、 The basin boundary of Heihe River Basin is based on the high-precision digital elevation model (DEM), which is obtained by using GIS hydrological analysis function analysis, and refers to remote sensing image, topographic map, ground investigation and previous research results. The surface catchment area of Heihe River basin covers an area of about 255000 km2, starting from the middle section of Qilian Mountains in the south, the Gobi Altai Mountains in Mongolia in the north, the Mazong mountains in the West and the Yabulai mountains in the East. Compared with the traditional Heihe River Basin, the new basin has increased Badain Jilin desert, Guizi lake, the northern part of Mazong mountain and the southern foot of Altai Mountain in Outer Mongolia Gobi. Explanation: the nanshihe River and beishihe River are the rivers formed by the leakage of the alluvial fan of Shule River. They form an independent hydrological unit (Huahai basin water systems) with Ganhaizi as the end lake, together with youYou River, Baiyang River and duanshankou river. The relationship between the hydrological unit and the Heihe River Basin is greater than that between the hydrological unit and the Shule River, which should be regarded as a part of the Heihe River Basin. Considering the current situation of modern water resources utilization, Beishi river has been directly connected with the main stream of Shule River through artificial transformation, and it is an important channel for water transmission from Shule River to Ganhaizi, and has become an important tributary of Shule River in fact. Under the influence of a series of water conservancy projects, the surface hydraulic connection between youyou River, Baiyang River and Shule River is far greater than that between youyou River and TaoLai river. 2、 Revised boundary of Yellow River Commission in Heihe River Basin On the basis of the Heihe River basin boundary revised by the Yellow River Water Conservancy Commission of the Ministry of water resources in 2005, the revised boundary of Heihe River Basin is obtained by using high-precision digital elevation model (DEM), reference remote sensing image, 1:100000 topographic map, ground investigation and other data. The basin boundary is about 76000 km2, among which the upper Qilian mountain middle section boundary is extracted strictly according to the ridge line by using DEM according to the GIS hydrological analysis function, and the lower north boundary is divided according to the boundary line according to the international convention. 3、 Study area boundary of Heihe River Basin According to the extended study area generated by the basin boundary of Heihe River Basin, it is mainly for the demand of model data input. The above three boundaries are to provide a unified study area boundary for the planned project of Heihe River Basin. It is suggested to use the revised boundary of Heihe River Basin yellow Committee as the core study area boundary.
0 2020-03-08
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). Please refer to Liu et al. (2018) for sites information in the Citation section.
0 2020-07-25
A land surface temperature observation system was set up in apple orchard near by the No.17 eddy covariance system of the MUlti-Scale Observation experiment on Evapotranspiration over heterogeneous land surfaces 2012 (MUSOEXE-12). This observation site can offer in situ calibration data of apple trees for TASI, WiDAS and L band sensor used in aerospace experiment. Observation Site: This point is located in a large and homogeneous apple orchard in Zhangye Experiment Field, Gansu Academy of Agricultural Sciences. It’s 4 meters away from southwest of No.17 eddy covariance system, and observation height is 4.55 m. Crown size of observed apple tree is 4 m × 4 m. Underlying surface of observation site is mainly apple trees. The coordinates of this site: 38°50′41.70" N，100°22′11.40" E. Observation Instrument: The observation system consists of one SI-111 infrared radiometers (Campbell, USA) installed vertically downward to apple tree. Observation Time: This site operates from 3 August, 2012 to 27 September, 2012. Observation data laagered by every 1 minute uninterrupted. Output data contained sample data of every 1 minute. Accessory data: Land surface (apple tree) infrared temperature (by SI-111) can be obtained. Dataset is stored in *.dat file, which can be read by Microsoft excel or other text processing software (UltraEdit, et. al). Table heads meaning: Target_C_Avg, apple tree temperature @ 4.55 m (℃); SBT_C_Avg, body temperature of SI-111 sensor (℃). Dataset is stored day by day, named as: data format + site name + interval time + date + time. The detailed information about data item showed in data header introduction in dataset.
0 2019-09-15
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