The dataset of ground truth measurements synchronizing with airborne WiDAS mission was obtained in the Linze grassland foci experimental area on May 30, 2008. WiDAS, composed of four CCD cameras, one mid-infrared thermal imager (AGEMA 550), and one infrared thermal imager (S60), can acquire CCD, MIR and TIR band data. The simultaneous ground data included the land surface temperature measured by the hand-held infrared thermometer in the reed plot A, the saline plots B and C, the alfalfa plot D and the barley plot E, the maximum of which were 120m×120m and the minimum were 30m×30m, and soil gravimetric moisture, volumetric moisture, and soil bulk density after drying measured by the cutting ring and the mean soil temperature from 0-5cm measured by the probe thermometer in plot A, B and C; the soil temperature, soil moisture, the loss tangent, soil conductivity, the real part and the imaginary part of soil complex permittivity measured by the POGO soil sensor, and the mean soil temperature from 0-5cm measured by the probe thermometer in plot D and E. See WATER: Dataset of setting of the sampling plots and stripes in the foci experimental area of Linze station for more information.
CAO Yongpan, CHAO Zhenhua, GE Chunmei, HAN Xujun, HU Xiaoli, HUANG Chunlin, LIANG Ji, WANG Shuguo, WU Yueru, FENG Lei, YU Fan, WANG Jing
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at A’rou Superstation in the hydrometeorological observation network of Heihe River Basin between 14 October, 2012, and 31 December, 2013. There were two types of LASs at A’rou Superstation: German BLS450 and China zzlas. The north tower was set up with the zzlas receiver and the BLS450 transmitter, and the south tower was equipped with the zzlas transmitter and the BLS450 receiver. Zzlas has been in use since 14 October, 2012, and the observation period of BLS450 was from 9 August to 10 December, 2013. The site (north: 100.467° E, 38.050° N; south: 100.450° E, 38.033° N) was located in Caodaban village of A’rou town in Qilian county, Qinghai Province. The underlying surface between the two towers was alpine meadow. The elevation is 3033 m. The effective height of the LASs was 9.5 m, and the path length was 2390 m. The data were sampled at 5 Hz and 1 Hz intervals for BLS450 and zzlas, respectively, and then averaged over 1 min. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (BLS450: Cn2>7.25E-14, zzlas: Cn2>7.84E-14). (2) The data were rejected when the demodulation signal was small (BLS450: Average X Intensity<1000; zzlas: Demod>-20 mv). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl, 1992 and Andreas, 1988 were selected for BLS450 and zzlas, respectively. Several instructions were included with the released data. (1) The data were primarily obtained from BLS450 measurements, and missing flux measurements from the BLS450 instrument were substituted with measurements from the zzlas instrument. The missing data were denoted by -6999. Due to the drift of the zzlas signal, data from 10 November to 23 November, 2012, and 14 March to 10 April, 2013, were excluded. Due to the LAS tower’s lean, the data from 10 April to 31 May, 2013, were not collected. (2) The dataset contained the following variables: data/time (yyyy-m-d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xls format. 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.
LI Xin, CHE Tao, XU Ziwei, ZHANG Yang, TAN Junlei
The dataset of ground truth measurement synchronizing with Envisat ASAR was obtained in No. 1, 2 and 3 quadrates of the A'rou foci experimental area on Jun. 19, 2008. GPR observations were also carried out in one sampling strip. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:17 BJT. Simultaneous with the satellite overpass, numerous ground data were collected, the soil temperature, soil volumetric moisture, the loss tangent, soil conductivity, and the real part and the imaginary part of soil complex permittivity were acquired by the POGO soil sensor, and the mean soil temperature from 0-5cm by the probe thermometer. Those provide reliable ground data for retrieval and validation of the surface temperature and evapotranspiration from remote sensing approaches. Four files were included, ASAR data, No. 1, 2 and 3 quadrates data.
CAO Yongpan, GE Chunmei, HAN Xujun,
The dataset of ground truth measurements synchronizing with the airborne microwave radiometers (L&K bands) mission was obtained along the sample lines 1, 2, 3, 4, 5 and 6 of the Linze grassland foci experimental area on May 25, 2008. Complementary measurements were carried out along Line 7 on Jun. 2. 25 points at intervals of 100m were selected at each line. Simultaneous with the satellite overpass, numerous ground data were collected, the soil temperature, soil moisture, the loss tangent, soil conductivity, the real part and the imaginary part of soil complex permittivity measured by the POGO soil sensor, the mean soil temperature from 0-5cm measured by the probe thermometer, and the surface radiative temperature measured three times by the hand-held infrared thermometer in L1, L2, L3 and L4; soil volumetric moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity were measured by WET, the mean soil temperature from 0-5cm measured by the probe thermometer, and the surface radiative temperature measured three times by the hand-held infrared thermometer in L5 and L6; the soil temperature, soil moisture, the loss tangent, soil conductivity, the real part and the imaginary part of soil complex permittivity by the POGO soil sensor, the mean soil temperature from 0-5cm measured by the probe thermometer, and the surface radiative temperature measured by the hand-held infrared thermometer, and soil gravimetric moisture, volumetric moisture, and soil bulk density measured by the cutting ring in L7. See WATER: Dataset of setting of the sampling plots and stripes in the foci experimental area of Linze station for more information.
CHAO Zhenhua, GE Chunmei, HAN Xujun, HUANG Chunlin, RAN Youhua, SONG Yi
Evapotranspiration monitoring is very important for agricultural water resource management, regional water resource utilization planning and sustainable development of social economy. The limitation of traditional monitoring et method is that it can't be observed in large area at the same time, so it can only be limited to the observation point. Therefore, the cost of personnel and equipment is relatively high. It can't provide the ET data of different land use types and crop types. Remote sensing can be used for quantitative monitoring of ET. the feature of remote sensing information is that it can reflect not only the macro structural characteristics of the earth's surface, but also the micro local differences. This data uses MODIS data and m-sebal model from June to September 2012 and time scale expansion scheme based on reference evaporation ratio to estimate the spatial and temporal distribution of evapotranspiration in the whole growth season of the middle reaches of Heihe River, and uses ground observation data to evaluate m-sebal model and time scale expansion scheme in detail. Its time resolution is day by day, spatial resolution is 250m, and data coverage is in the middle reaches of Heihe River, unit: mm. The projection information of the data is as follows: UTM projection, 47N.
ZHOU Yanzhao, ZHOU Jian
Groundwater is the main water source of desert riparian plants, and also the most important environmental factor affecting the normal physiological status of plants. In this project, an observation field was set up in Populus euphratica forest near the Alxa Desert eco hydrological experimental research station from 2011 to 2013 By manually measuring the groundwater depth every month in the year, it can provide basic data support for the study on the transpiration water consumption mechanism of Populus euphratica, and also can be used for the estimation of ecological water demand in the study area.
SI Jianhua
The dataset of soil frozen penetration measured by the soil frozen tube was obtained at the super site (100m×100m, pure Qinghai spruce) around the Dayekou Guantan forest station. Observation time was 8:00 each morning from Jun. 1 to Dec. 31, 2008. The soil frozen tube was laid beneath the spruce for diurnal soil frozen depth changes and the maximum depth (cm) was recorded.
TAN Junlei
Some economic data of Zhangye City from 2001 to 2012 include: per capita GDP, GDP, the proportion of fiscal revenue to GDP, per capita fiscal revenue, industrial contribution rate, the proportion of town population to total population, the proportion of added value of tertiary industry to GDP, the proportion of added value of secondary industry to GDP, industrial comprehensive benefit index, contribution rate of total assets, contribution rate of fixed assets, social labor productivity, G DP growth rate
ZHANG Dawei
The dataset includes the chlorophyll content of vegetation in different site which has different types of vegetation, acquired on 8 July, 2012, in order to validate the Chlorophyll products. Observation instruments: Sampling, Acetone extraction method Measurement methods: To analyze the influence height on chlorophyll , we select 12 different corn samples based on the height of corn. To compare the chlorophyll content of different types of vegetation, we also select 3 types of vegetation sample on the first EC tower, 1 beans sample near the seventeenth EC tower and 3 reed samples on wetland. A total of selected 19 different samples are analyzed in the laboratory in the College of Life Science, Hexi. We extract chlorophyll a, chlorophyll b, the content of total chlorophyll of selected samples. Dataset contents: Chlorophyll a, chlorophyll b, the content of total chlorophyll Measurement time: 8 July, 2012
Jia Shuzhen
This dataset contains the flux measurements from the Huazhaizi desert steppe station eddy covariance system (EC) in the flux observation matrix from 6 June to 15 September, 2012. The site (100.31860° E, 38.76519° N) was located in a desert surface, which is near Zhangye, Gansu Province. The elevation is 1731.00 m. The EC was installed at a height of 2.85 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
This dataset contains the LAI measurements from the Daman superstation in the middle reaches of the Heihe integrated observatory network from June 11 to September 18 in 2018. The site (100.372° E, 38.856°N) was located in the maize surface, near Zhangye city in Gansu Province. The elevation is 1556 m. There are 3 observation samples, each of which is about 30m×30m in size, and the latitude and longitude ranges are (100.373297°E~100.374205°E, 38.857871°N~38.858390°N), (100.373918°E~100.373897°E, 38.854025°). N~38.854941°N), (100.368007°E~100.369044°E, 38.850678°N~38.851580°N). Five sub-canopy nodes and one above-canopy node are arranged in each sample. The LAI data is obtained from LAINet measurements following four steps: (1) the raw data is light quantum (level 0); (2) the daily LAI can be obtained using the software LAInet (level 1); (3) the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); (4) for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.
LIU Shaomin, Qu Yonghua, XU Ziwei, LI Xin
The dataset of airborne Polarimetric L-band Multibeam Radiometers (PLMR) was acquired on 2 August, 2012, located in the middle reaches of the Heihe River Basin. The aircraft took off at 9:00 am (UTC+8) from Zhangye airport and landed at 14:00 pm, with the flight time of 5 hours. The flight was performed in the altitude of about 2300 m and at the speed of about 220-250 km during the observation, corresponding to an expected ground resolution of about 700 m. The PLMR instrument flown on a small aircraft operates at 1.413 GHz (L-band), with both H- and V-polarizations at incidence angles of ±7.5°, ±21.5° and ±38.5°. PLMR ‘warm’ and ‘cold’ calibrations were performed before and after each flight. The processed PLMR data include 2 DAT files (v-pol and h-pol separately) and 1 KMZ file for each flying day. The DAT file contains all the TB values together with their corresponding beam ID, incidence angle, location, time stamp (in UTC) and other flight attitude information as per headings. The KMZ file shows the gridded 1-km TB values corrected to 38.5 degrees together with flight lines. Cautions should be taken when using these data, as the RFI contaminations are often higher than expected at v-polarization.
CHE Tao, Gao Ying, LI Xin
This dataset contains the flux measurements from the Sidaoqiao superstation eddy covariance system (EC) in the lower reaches of the Heihe hydrometeorological observation network from 6 July to 31 December, 2013. The site (101.137° E, 42.001° N) was located in the Tamarix surface, Ejin Banner in Inner Mongolia. The elevation is 873 m. The EC was installed at a height of 8 m, and 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&Li7500A) was 0.15 m. The 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 the 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. 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), as 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), which represent high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened using a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected 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 0.12 m/s. There were 48 records per day, and the missing data were replaced with -6999. 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 *.xls format. 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
This dataset includes 5 sub-datasets obtained from measurements in the flux observing matrix at observing site No.15 (the Daman superstation) and 13. Specifically, the sub-datasets include the following: (1) a dataset that contains atmospheric water vapor D/H and 18O/16O isotopic and flux ratio measurements from site No.15 from 27 May to 21 September in 2012, (2) a dataset that contains D/H and 18O/16O isotopic ratios of water in soil and in corn xylem at site No.15 from 27 May to 21 September 2012, (3) a dataset that contains atmospheric water vapor D/H and 18O/16O isotopic ratios at site No.13 when airborne surveys occurred, and (4) a dataset that contains D/H and 18O/16O isotopic ratios of water in soil and in corn xylem at sites No.13 and 15 when airborne surveys occurred, (5) a dataset that contains the ratios of evaporation and transpiration to evapotranpiration at site No.15. The experiment area was located in a corn cropland in the Daman irrigation district of Zhangye, Gansu Province, China. The positions of observing sites No.15 and 13 were 100.3722° E, 38.8555° N and 100.3785° E, 38.8607° N, respectively, with an elevation of 1552.75 m above sea level. The atmospheric water vapor D/H and 18O/16O isotopic and flux ratios at site No.15 were continuously measured using an in situ observation system. The system consisted of an H218O, HDO and H2O analyzer (Model L1102-i, Picarro Inc.), a CTC HTC-Pal liquid auto sampler (LEAP Technologies) and a multichannel solenoid valve (Model EMT2SD8 MWE, Valco Instruments CO. Inc.). The heights of the two intakes were 0.5 and 1.5 m above the corn canopy. The water vapor D/H and 18O/16O isotopic ratio analyzer recorded signals at 0.2 Hz; data were recorded for 2 minutes per intake. The data were block-averaged to hourly intervals. The sampling frequency of soil and xylem at site No. 15 was 1-3 days. The atmospheric water vapor D/H and 18O/16O isotopic and flux ratios at site No.13 were measured using a cold traps/mass spectrometer. The sampling frequency of atmospheric water vapor, soil water and xylem water at site No.13 was the same as that of the airborne surveys. Briefly, the Picarro analyzer measurements were calibrated during every 3 h switching cycle using a two-point concentration interpolation procedure in which the water vapor mixing ratio was dynamically controlled to track the ambient water vapor mixing ratio. Possible delta stretching effects were not considered. A schematic diagram of the Picarro analyzer and its operation principles and calibration procedure are described elsewhere in the literature (Huang et al., 2014; Wen et al. 2008, 2012). The dataset of atmospheric water vapor D/H and 18O/16O isotopic and flux ratios at site No.15 includes the following variables: Timestamp (time, timestamp without time zone), Number (available record number), δD for r1 (δD for the lower intake, ‰), δD for r2 (δD for the higher intake, ‰), δ18O for r1 (δ18O for the lower intake, ‰), δ18O for r2 (δ18O for the higher intake, ‰), vapor mixing ratio for r1 (vapor mixing ratio for the lower intake, mmol/mol), vapor mixing ratio for r2 (vapor mixing ratio for the higher intake, mmol/mol), δET_D (δD of evapotranspiration, ‰), and δET_18O (δ18O of evapotranspiration, ‰). The dataset of D/H and 18O/16O isotopic ratios of water in soil and in corn xylem at site No.15 includes the following variables: Timestamp (time, timestamp without time zone), Remark (treatment: soil without mulch (Ld)=1; soil with mulch (Fm)=2; soil with male corns (F)=3; Xylem=4), δD (‰), and δ18O (‰). The dataset for the ratio of soil evaporation and transpiration to the evapotranspiration at site 15 includes the following variables: Timestamp (time, timestamp without time zone), E/ET (ratio of soil evaporation to the evapotranspiration, %), and T/ET (ratio of transpiration to the evapotranspiration, %). The mean (±one standard deviation) ratio of transpiration to evapotranspiration was 86.7±5.2% (the range was 71.3 to 96.0%). The mean (±one standard deviation) ratio of soil evaporation to the evapotranspiration was 13.3 ±5.2% (the range was 4.0 to 28.7%). The dataset of atmospheric water vapor D/H and 18O/16O isotopic ratio at site No. 13 when airborne surveys occurred includes the following variables: Timestamp1 (start time, timestamp without time zone), Timetamp2 (end time, timestamp without time zone), Height (observation height, cm), δD (‰), and δ18O (‰). The dataset of D/H and 18O/16O isotopic ratios of water in soil and in corn xylem at sites No. 13 and 15 when airborne surveys occurred include the following variables, Timestamp (time, timestamp without time zone), Remark (treatment: soil without mulch (Ld)=1; soil with mulch (Fm)=2; Xylem=4), δD (‰), δ18O (‰), and Location (observing site 13 or 15) . The missing measurements were replaced with -6999. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Wen et al. (2016) (for data processing) in the Citation section.
WEN Xuefa, LIU Shaomin, LI Xin
The data set includes soil pH data of representative soil samples collected from July 2012 to August 2013 in the Heihe River Basin. The first soil survey was conducted in 2012. After the representativeness evaluation of collected samples, we conducted an additional sampling in 2013. These samples are representative enough to represent the soil variation in the Heihe River Basin, of which the soil variation in each landscape could be accounted for. The sampling depths in field refer to the sampling specification of Chinese Soil Taxonomy, in which soil samples were taken from genetic soil horizons.
ZHANG Ganlin
The dataset of surface roughness measurements by phototaking was obtained in the Huazhaizi desert steppe foci experimental area. Observation items included: (1) Surface roughness synchronizing with ASAR and MODIS in Huazhaizi desert No. 2 plot on May 24, 2008. (2) Surface roughness synchronizing with WiDAS in Huazhaizi desert No. 1 plot on May 30, 2008. The self-made roughness reference board (Cold and Arid Regions Environmental and Engineering Research Institute, CAS), the digital camera and the compass were used. Sample points were selected at equal intervals along the diagonals and marked in the photos.
XU Zhen, SHU Lele, WANG Jianhua
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 Daman Superstation between 22 September, 2012, and 31 December, 2013. The site (100.4464° E, 38.9751° N) was located on a wetland (reed surface) in Zhangye National Wetland Park, Gansu Province. The elevation is 1460 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP45AC; 5 and 10 m, north), wind speed profile (03002; 5 and 10 m, north), wind direction profile (03002; 10 m, north), air pressure (CS100; 2 m), rain gauge (TE525M; 10 m), four-component radiometer (CNR1; 6 m, south), two infrared temperature sensors (SI-111; 6 m, south, vertically downward), soil heat flux (HFP01; 3 duplicates, -0.06 m), soil temperature profile (109ss-L; 0, -0.02, -0.04, -0.1, -0.2 and -0.4 m), and four photosynthetically active radiation (PQS-1; install on 28 July, 2013, two above the plants, 6 m, south, one vertically downward and one vertically upward; two below the plants, 0.25 m, south, one vertically downward and one vertically upward). The observations included the following: air temperature and humidity (Ta_5 m and Ta_10 m; RH_5 m and RH_10 m) (℃ and %, respectively), wind speed (Ws_5 m and Ws_10 m) (m/s), wind direction (WD_10 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 IRT_2) (℃), soil heat flux (Gs_1, 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 and Ts_40 cm) (℃), on the plants photosynthetically active radiation of upward and downward (PAR_U_up and PAR_U_down) (μmol/ (s m^-2)), and below the plants photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m^-2)). 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 10 May, 2013 and 30 May, 2013 because of datalogger malfunction; the wind speed data were missing during 1 September, 2013 and 5 September, 2013 because of sensor malfunction. 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: 2013-6-10 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
A total of 137 soil samples of different vegetation types, different altitudes and different terrains were collected from June 2012 to August 2012. The soil layer of each sample point was divided into three layers of 0-10cm, 10-20cm and 20-30cm, with an altitude of 2700-3500m m. The vegetation types were divided into five types: Picea crassifolia forest, Sabina przewalskii, subalpine scrub meadow, grassland and dry grassland. At the same time of sampling, hand-held GPS is used to record the location information and environmental information of each sampling point, including longitude, latitude, altitude, slope, aspect, terrain curvature, vegetation type, soil thickness, maximum root depth, etc. Soil bulk density: The measurement method of soil bulk density is to put the sample into an envelope and dry it in an oven at 105℃ for 24 hours, then take it out and place it for 30 minutes to weigh. The ratio of the weighing result to the volume of the ring cutter is the soil bulk density, and the unit is g/cm3. Soil mechanical composition: hydrometer method is used to measure the soil mechanical composition, which includes the content of soil sand, silt and clay.
ZHAO Chuanyan, MA Wenying
This data includes the accessibility of 15 kinds of public facilities and services, such as roads and schools, in the communities of 1280 households at domestic and abroad, as well as the farmers' satisfaction with these public facilities and public services by comparing that with 3 years ago and current status with neighboring village. This data is used to support the analysis of the material capital part of sustainable livelihood. The data was collected by the research group through field survey in 2019. Before collecting the data, the research group and invited experts conducted a pretest and improved the survey questionnaire; Before the formal investigation, the members participating in the data collection were strictly trained; In the formal survey, each questionnaire is checked three times before it is filed. This data is of great value for understanding the physical capital accessibility and satisfaction of rural households in environment-economic fragile areas, and is an important supplement to national and macro data.
XIE Yaowen
This data is the water level data of 2011-2012, which is observed by water level recorder. From July 14 to September 9, 2011, the observation was recordered every five minutes; from June 4 to July 10, 2012, the observation was recordered every ten minutes. The data content is the temperature and atmospheric pressure inside the hole, and the data is the daily scale data. The data shall be opened with HOBO software.
ZHAO Chuanyan, MA Wenying
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