This dataset contains the automatic weather station (AWS) measurements from site No.13 in the flux observation matrix from 6 May to 20 September, 2012. The site (100.37852° E, 38.86074° N) was located in a cropland (maize surface) in Daman irrigation district, which is near Zhangye, Gansu Province. The elevation is 1550.73 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (HMP45D; 5 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 10 m), wind speed and direction (034B; 10 m, towards north), a four-component radiometer (CNR4; 6 m, towards south), two infrared temperature sensors (IRTC3; 6 m, vertically downward), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), soil moisture profile (EC20-5; -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), and soil heat flux (HFT3; 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_5 m and RH_5 m) (℃ and %, respectively), air pressure (press, hpa), precipitation (rain, mm), wind speed (Ws_10 m, m/s), wind direction (WD_10 m, °), 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, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2 and Gs_3, W/m^2), soil temperature profile (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_60 cm, and Ts_100 cm, ℃), and soil moisture profile (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, and Ms_100 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
The No. 2 hydrological section is located at 312 Heihe River Bridge (100.411° E, 38.998° N, 1485 m) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. The dataset contains observations recorded by the No.2 hydrological section from 19 June, 2012, to 31 December, 2013. This section consists of two river sections, i.e., the east section, which is denoted as No. 1 and the west section, which is denoted as No. 2. The width of this section is 90 meters and consists of a gravel bed; the cross-sectional area is unstable because of human factors. The water level was measured using an SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The dataset of ground truth measurements synchronizing with EO-1 Hyperion was obtained in the Yingke oasis foci experimental area from Sep. 5 to Sep. 10, 2007 during the pre-observation period. It was carried out by the 3rd and 2nd sub-projects of CAS’s West Action Plan along Zhangye city-Yingke oasis-Huazhaizi, and on the very day of 10, one scene of Hyperion was captured. sampling plot time north latitude east longitude elevation notes 1 9:58 38°53′53.2″ 100°26′09.7″ 1500 cauliflower land east to the road 2 10:51 38°52′39.8″ 100°25′33.1″ 1510 cabbage land east to the road 3 11:35 38°52′39.0″ 100°25′34.6″ 1510 east to No. 2 sampling plot, maize and intercropping wheat reaped 4 12:24 38°51′53.0″ 100°25′08.0″ 1510 maize seed 5 13:08 38°51′54.2″ 100°25′09.5″ 1520 north to No. 4 sampling plot, maize and intercropping wheat reaped 6 14:40 38°51′23.5″ 100°24′45.0″ 1510 west to the road, maize seed, serious blights (red spider) 7 15:40 38°49′26.6″ 100°23′23.7″ 1540 intercrop land of sea buckthorn and beet 8 16:18 38°49′06.9″ 100°23′30.5″ 1540 tomato land, rich of amaranth weeds 9 16:18 38°49′06.4″ 100°23′30.8″ 1540 beet land 10 16:18 38°49′06.9″ 100°23′30.5″ 1540 tomato land with less weeds 11 10:30 38°48′28.3″ 100°24′11.4″ 1540 sea buckthorn seedling land west to the road 12 11:24 38°48′09.3″ 100°24′10.1″ 1550 sun flower land east to the road, intercropping wheat reaped 13 12:38 38°46′16.3″ 100°23′14.2″ 1600 dry rice land 14 12:45 38°46′16.2″ 100°23′14.0″ 1600 rape land 15 12:54 38°46′15.6″ 100°23′13.8″ 1600 buckwheat land 16 14:52 38°45′55.5″ 100°23′00.1″ 1610 maize (without intercrop) 17 15:28 38°45′57.5″ 100°22′28.3″ 1630 maize (without intercrop) 18 16:20 38°43′17.3″ 100°22′53.4″ 1730 gobi (Bassia dasyphylla and margarite dominate) 19 17:40 38°42′31.8″ 100°22′56.8″ 1780 gobi (Bassia dasyphylla and Sympegma regelii dominate) 20 10:27 38°36′25.1″ 100°20′33.2″ 2260 wheatgrass dominates 21 11:10 38°36′24.4″ 100°20′38.1″ 2260 abandoned composite land 22 11:30 2260 near site 22, wheatgrass and composite cenosis 23 bare land 24 13:09 38°38′46.3″ 100°23′08.5″ 2030 alfalfa land 25 14:39 38°44′30.8″ 100°22′41.0″ 1660 poplar 26 9:47 38°58′11.4″ 100°26′18.3″ 1460 rice land Observation items included: (1) quadrat surveys (2) LAI by LAI-2000 (3) ground object reflectance spectra by ASD FieldSpec Pro (350-2500nm)from Gansu Meteorological Administration (4) the land surface temperature and the canopy radiative temperature by the hand-held thermal infrared sensor (5) the photosynthesis rate by LI-6400 (6) the radiative temperature by ThermaCAM SC2000 (7) Atmospheric parameters by CE318 to retrieve the total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, and various parameters at 550nm to obtain horizontal visibility with the help of MODTRAN or 6S codes (8) chlorophyll consistency by portable SPAD Those provide reliable ground data for developing and validating retrieval meathods of biophysical parameters from EO-1 Hyperion images.
MA Mingguo, LI Xin, SU Peixi, DING Songchuang, GAO Song, YAN Qiaodi, ZHANG Lingmei, WANG Xufeng, Qian Jinbo, BAI Yunjie, HAO Xiaohua, Liu Qiang, Wen Jianguang, XIN Xiaozhou, WANG Xiaoping, HAN Hui
BJ-1 dataset includes 11 scenes, covering the upper and middle reaches of the Heihe river basin, which were acquired on 10-21-2007, 11-19-2007, 01-09-2008, 03-03-2008, 04-04-2008, 04-16-2008, 05-01-2008, 05-16-2008, 07-01-2008, 07-06-2008 and 07-08-2008. The sensor was MSI, substar resolution was 32m, fov was 22.06°, the orbit was 686km high and the dip angle was 98.1725°, the focal distance was 150mm, CCD pixel was 7μm, the near infrared band was 760nm-900nm, red wave band was 630nm-690nm and green wave band was 520nm-620nm. The data version is Level 2, which was released after geometric correction. BJ-1 dataset was acquired from "Dragon Programme" (grant number: 5322).
LI Xin
The dataset of LST (Land Surface Temperature) observations was obtained by three handheld infrared thermometers (5# and 6# from Cold and Arid Regions Environmental and Engineering Research Institute, and also one from Institute of Geographic Sciences and Natural Resources, which were all calibrated) in the Linze station foci experimental area. The temperature was measured 14-30 times in 40 subplots of the west-east desert strip on May 24, 25 and 28, 2008, 12-30 times in 9 subplots of north-south strip on May 24, 25 and 28, 17 times from P1 to P6 strips on Jul. 4 and 8, three times of 147 points along LY06 strip on Jun. 6, 15, 29 and Jul. 11, LY07 strip on May 30, Jun. 6, 15, 29 and Jul. 11 and LY08 strip on May 30, Jun. 6 and 10, and three times in Wulidun farmland quadrates on May 25, Jun. 29 and Jul. 11. Calibration was carried out in the desert on May 25, and in Pingchuan reservoir on May 30. Data were archived as Excel files. See the metadata record “WATER: Dataset of setting of the sampling plots and stripes in the Linze station foci experimental area” for more information of the quadrate locations.
BAI Yanfen, SONG Yi, DING Songchuang, GAO Song, HAO Xiaohua, PAN Xiaoduo, Qian Jinbo, SHU Lele, SONG Yi, WANG Yang, XU Zhen, YAN Qiaodi, ZHU Shijie, YU Yingjie, DONG Jian, JIANG Hao, LI Shihua
The No. 7 hydrological section is located at Pingchuan Heihe River Bridge (100.097° E, 39.334° N, 1375 m) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. The dataset contains observations recorded by the No.7 hydrological section from 17 June, 2012, to 31 December, 2013. The width of this section is 130 meters. The water level was measured using an SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
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.
LI Xin, XU Ziwei
Er’ba Reservoir surface temperature of water body can offer in situ calibration data for TASI, WiDAS and L band sensor used in aerospace experiment. Observation Site: This site is 14 KM away from East of ZhangYe city. It’s located in Er’ba village, JianTan town, ZhangYe city. The coordinates of this site: 38°54′57.14" N, 100°36′57.39" E. Observation Instrument: The observation system consists of two SI-111 infrared radiometers (Campbell, USA) and two 109SS temperature probes (Campbell, USA). Two SI-111 sensors, one installed vertically downward to water surface, another face to south of zenith angle 35°. Temperature probes float under water surface at 0 cm. SI-111 sensor installed at 3.0 m height, 3.4 m away from water edge. Observation Time: This site operates from 27 May, 2012 to 27 September, 2012. Observation data laagered by every 5 seconds uninterrupted. Output data contained sample data of every 5 seconds and mean data of 1 minute. Accessory data： Water surface infrared temperature (by SI-111), sky infrared temperature (by SI-111), water surface temperature (by 109ss) 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: TarT_Atm, Sky infrared temperature (℃) @ facing south of zenith angle 35°; SBT_Atm, body temperature of SI-111 sensor (℃) measured sky; TarT_Sur, water surface infrared temperature @ 3.0 m height; SBT_Sur, body temperature of SI-111 sensor (℃) measured water surface; WaterT_1, WaterT_2, water surface temperature (℃) measured by 109SS temperature probes. 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.
MA Mingguo
The data set provided the cloudless Fractional Snow Cover area (FSC) time-series product basing on the MODIS data and covered the Heihe River Basin from January 2010 to December 2013. They also provide the high spatial (500 m) and temporal (1 day) resolution. Firstly, the end-member were automatically extracted by the fast autonomous spectral end-member determination (N-FINDR) maximizing volume iteration algorithm. Combining N-FINDR with the orthogonal subspace projection (OSP) approach, we propose an improved end-member extraction algorithm using a maximizing, volume-based iterative method. All the 6 end-members were extracted including snow, soil, water, bare land, vegetation, and cloud, respectively. Then, the 10-day spectral library time series based on prior knowledge of Heihe basin are built for 2009. The primary data were produced using the fully constrained least squares (FCLS) linear spectral mixture analysis method by the spectral library. Finally，the cubic spline interpolation algorithm were used to the eliminate the cloud pixels completely and obtain the data set. The data are validated by the fractional snow cover derived from Landsat imagery and the results indicate that the improved algorithm can obtain the end-member information accurately, and the retrieved fractional snow cover has better accuracy than the MODIS fractional snow-cover product (MOD10A1). So the data set can provide more accurate input for the hydrology and climate model.
HUANG Xiaodong, ZHANG Ying, TANG Zhiguang, LI Xin
The dataset of ground truth measurement synchronizing with the airborne LiDAR mission and Envisat ASAR was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on Jun. 19, 2008. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:17 (Beijing Time). The observation item was soil moisture by TDR ( the probe with a length of 5cm) in the maize plot of Yingke oasis station, the wheat plot and some temporary sample points (details in GPS.txt).
GE Yingchun, SHU Lele, XIA Chuanfu, ZHOU Mengwei
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
The fractional vegetation cover observation was carried out for the typical underlying surface in the lower reaches of the Heihe River Basin during the aviation flight experiment in 2014. The observation started on 24 July, 2014 and finished on 1 August, 2014. 1. Observation time On days of 24 July, 27 July, 30 July, 31 July and 1 August, 2014 2. Samples method Large areas with homogeneous vegetation (greater than 100 m * 100 m) were chosen as the observation samples. And forty field samples were selected according to the characteristics of vegetation distribution in the low reaches. The land-use types including the cantaloupe, the Tamarix chinensis, the reeds, the weeds, the Karelinia caspica, the Sophora alopecuroides and so on. 3. Observation methods 3.1 Instruments and measurement method Digital photography measurement is implemented to measure the FVC. Plot positions, photographic method and data processing method are dedicatedly designed. 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. 3.2 Photographic method The photographic method used depends on the species of vegetation and planting pattern. A long stick with the camera mounted on one end is used for the Tamarix chinensisi and reeds. For the Tamarix chinensisi and reeds, rows of more than two cycles should be included in the field of view (<30), 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. For other vegetation , the photos of FVC were obtained by directly photographing for the lower heights of the vegetation. 3.3 Method for calculating the FVC The detail method of the FVC calculation can be found in the reference below. 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 (see the reference). 4 Data storage The observation recorded data were stored in excel and the original FVC data were stored in photos.
Guo Dong, WANG Haibo, Zhou Shengnan
The dataset of airborne LiDAR mission in the Dayekou flight zone on Jun. 20, 2008 included peak pulse data (*.LAS), full waveform data (.lgc), CCD photos, DEM, DSM and DOM. The DEM, DSM and DOM data are stored along with the Dataset of airborne LiDAR mission in the Dayekou flight zone on Jun. 23, 2008. The flight routes were as follows: {| ! flight route ! startpoint lat ! startpoint lon ! endpoint lat ! endpoint lon ! altitude (m) ! length (km) ! photos |- | 1 || 38°32′05.38″ || 100°12′24.59″ || 38°29′32.76″ || 100°18′35.69″ || 3650 || 10.1 || 49 |- | 2 || 38°32′11.13″ || 100°12′28.42″ || 38°29′42.06″ || 100°18′30.89″ || 3650 || 9.9 || 46 |- | 3 || 38°32′16.88″ || 100°12′32.24″ || 38°29′47.81″ || 100°18′34.72″ || 3650 || 9.9 || 47 |- | 4 || 38°32′22.63″ || 100°12′36.07″ || 38°29′56.20″ || 100°18′32.15″ || 3650 || 9.7 || 45 |- | 5 || 38°32′28.38″ || 100°12′39.90″ || 38°30′02.62″ || 100°18′34.33″ || 3650 || 9.7 || 47 |- | 6 || 38°32′37.44″ || 100°12′35.66″ || 38°30′10.63″ || 100°18′32.68″ || 3650 || 9.8 || 44 |- | 7 || 38°32′46.50″ || 100°12′31.43″ || 38°30′19.72″ || 100°18′28.37″ || 3650 || 9.8 || 47 |}
NI Wenjian, BAO Yunfei, ZHOU Mengwei, WANG Tao, CHI Hong, FAN Fengyun, LIU Qingwang, PANG Yong, LI Shiming, HE Qisheng, Liu Qiang, LI Xin, MA Mingguo
The object of this dataset is to support the atmospheric correction data for the satellite and airborne remote-sensing. It provides the atmospheric aerosol and the column content of water vapor. The dataset is sectioned into two parts: the conventional observations data and the observations data synchronized with the airborne experiments. The instrument was on the roof of the 7# in the Wuxing Jiayuan community from 1 to 24 in June. After 25 June, it was moved to the ditch in the south of the Supperstaiton 15. The dataset provide the raw observations data and the retrieval data which contains the atmosphere aerosol optical depth (AOD) of the wavebands at the center of 1640 nm, 1020 nm, 936 nm, 870 nm, 670 nm, 500 nm, 440 nm, 380 nm and 340 nm, respectively, and the water vapor content is retrieved from the band data with a centroid wavelength of 936 nm. The continuous data was obtained from the 1 June to 20 September in 2012 with a one minute temporal resolution. The time used in this dataset is in UTC+8 Time. Instrument: The sun photometer is employed to measure the character of atmosphere. In HiWATER, the CE318-NE was used.
YU Wenping, WANG Zengyan, MA Mingguo
The data set include crop leaf stomatal conductance observed at four sample regions, that is the soil moisture control experimental field at Daman county, and the super station, and Shiqiao sample plots at Wuxing village in Zhangye city. 1) Objective Crop leaf stomatal conductance, a key biophysical parameter, was observed as model parameter or a priori knowledge for crop growth model, or evapotranspiration estimation. 2) Measuring instruments Leaf porometer. 3) Measuring site a. the soil moisture control experimental field at Daman county, Twelve soil water treatments are set. The crop leaf stomatal conductance for each treatment is measured on 17, 23 and 29 May, and 3, 9, 14 and 24 June, and 5 and 12 July. b. the Super Station The crop leaf stomatal conductance at the super station is measured on 22 and 28 May, 5, 11, 18, and 25 June, and 1, 8, 15, 22 and 31 July, 9, 15 and 22 August, and 3 and 11 September. c. the Shiqiao sample site The crop leaf stomatal conductance at the Shiqiao village is measured on 17, 22 and 28 May, 4, 11, 17 and 25 June, 1, 8, 15, 22, and 30 July, 8, 16 and 27 August, and 9 September. 4) Data processing The observational data was recorded in the sheets and reorganized in the EXCEL sheets. The time used in this dataset is in UTC+8 Time.
Xu Fengying, Wang Jing, Huang Yongsheng, LI Xin, MA Mingguo
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No. 1 and 2 quadrates of the Biandukou foci experimental area on Oct. 18, 2007, during the pre-observation period. The 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. Simultaneous with the satellite overpass, numerous ground data were collected: the soil temperature , volumetric soil moisture (cm^3/cm^3), soil salinity (s/m), soil conductivity (s/m) by the Hydra probe, the surface radiative temperature by the handheld infrared thermometer, gravimetric soil moisture, volumetric soil moisture, and soil bulk density by drying soil samples from the cutting ring (100cm^3). Meanwhile, vegetation parameters as height, coverage and water content were also observed. Those provide reliable ground data for the development and validation of soil moisture, soil freeze/thaw algorithms and the forward model from active remote sensing approaches.
BAI Yunjie, CAO Yongpan, WANG Jian, Wang Weizhen, WANG Xufeng, JIN Rui, Qu Yonghua, ZHOU Hongmin
The aim of the simultaneous observation of land surface temperature is obtaining the land surface temperature of different kinds of underlying surface, including greenhouse film, the roof, road, ditch, concrete floor and so on, while the sensor of thermal infrared go into the experimental areas of artificial oases eco-hydrology on the middle stream. All the land surface temperature data will be used for validation of the retrieved land surface temperature from thermal infrared sensor and the analysis of the scale effect of the land surface temperature, and finally serve for the validation of the plausibility checks of the surface temperature product from remote sensing. 1. Observation time and other details On 25 June, 2012, ditch and asphalt road surface temperatures were observed once every five minutes using handheld infrared thermometers recorded. On 26 June, 2012, ditch and asphalt road surface temperatures were observed once every five minutes using handheld infrared thermometers while greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 29 June, 2012, concrete floor surface temperatures were observed continuously using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 30 June, 2012, asphalt road, ditch, bare soil, melonry and ridge of field surface temperatures were observed continuously using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 10 July, 2012, asphalt road, ditch, bare soil, melonry and ridge of field surface temperatures were observed once every one minute using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, concrete floor surface temperatures were observed once every six second using self-recording point thermometer. On 26 July, 2012, asphalt road, concrete floor, bare soil and melonry surface temperatures were observed once every one minute using handheld infrared thermometers during the sensor of WiDAS go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. On 2 August, 2012, corn field and concrete floor surface temperatures were observed using handheld infrared thermometers. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. For corn field, twelve sites were selected according to the flight strip of the WiDAS sensor, and for each site one plot surface temperatures were recorded continuously during the sensor of WiDAS go into the region. On 3 August, 2012, corn field and concrete floor surface temperatures were observed using handheld infrared thermometers. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. For corn field, fourteen sites were selected according to the flight strip of the WiDAS sensor, and for each site three plots surface temperatures were recorded continuously during the sensor of WiDAS go into the region. 2. Instrument parameters and calibration The field of view of the self-recording point thermometer and the handheld infrared thermometer are 10 and 1 degree, respectively. The emissivity of the latter was assumed to be 0.95. The observation heights of the self-recording point thermometer for the greenhouse film and the concrete floor were 0.5 m and 1 m, respectively. All instruments were calibrated three times (on 6 July, 5 August and 20 September, 2012) using black body during observation. 3. Data storage All the observation data were stored in excel.
GENG Liying, Jia Shuzhen, WANG Haibo, PENG Li, Dong Cunhui
The dataset of photosynthesis was observed by LI-6400XT Portable Photosynthesis System in the artificial oasis eco-hydrology experimental area of the Heihe River Basin. Observation items included two main crops in the middle reaches of Heihe river: wheat and maize, which located in the town of Pingchuan in Linze and the Super Station of Wuxing, respectively. Observation periods lasted from mid-May to September. This dataset included the raw observation data and the pretreatment data of wheat and maize observed by LI-6400 during the observation periods. Objectives of observation: The photosynthetic datasets can be used in the study of plant physiological ecology characteristic and the simulation and validation for the eco-hydrological models. Instrument and theory of the observation: (1) Measuring instrument: LI-6400XT Portable Photosynthesis System; (2) Measuring theory: Using the infrared gas analyzer to measure the change of CO2 concentration, and then measuring the differences of CO2 concentration between the sample chamber and the referenced chamber so as to acquire the net productivity of the leaf. Time and site of observation: (1) Observation site of the wheat: in the town of Pingchuan in Linze; Observation time: 2012-05-17，2012-06-08 to 2012-6-13; (2) Observation site of the maize: in the Super Station of Wuxing; Observation time: from 2012-05-19 to 2012-08-15. The time used in this dataset is in UTC+8 Time. Data processing: The raw data of LI-6400 were archived in text format and can be opened by text editor or excel, the preprocessed data were in Excel format. Every time period of observation was archived in a single document, named as “date + type + time”, every leaf was recorded 3 times, and then added a remark.
WANG Haibo
The dataset of spectral reflectance measurements was obtained in the A'rou foci experimental area on Mar. 12, 2008. The land was covered with snow 10cm deep. Observed objects included snow, sandstone, grass and ice. The 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. Raw data were binary files direct from ASD (by ViewSpecPro).
PENG Danqing, ZHENG Yue
This dataset contains the flux measurements from site No.1 eddy covariance system (EC) in the flux observation matrix from 4 June to 17 September 2012. The site (100.35813° E, 38.89322° N) was located in a cropland (vegetable surface) in the Yingke irrigation district, which is near Zhangye, Gansu Province. The elevation is 1552.75 m. The EC was installed at a height of 3.8 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 (Gill&Li7500A) was 0.2 m. Raw data acquired at 10 Hz were processed using the Eddypro post-processing software (Li-Cor Company, http://www.licor.com/env/products/ eddy_covariance/software.html), including spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, angle of attack 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
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