The dataset of automatic meteorological observations was obtained at the Dayekou Maliantan grassland station (E100°18′/N38°33′, 2817m) from Nov. 2, 2007 to Dec. 31, 2009. The experimental area with a flat and open terrain was slightly sloping from southeast to northwest. The landscape was mainly grassland, with vegetation 0.2-0.5m high. Observation items were multilayer gradient (2m and 10m) of the wind speed, the air temperature and air humidity, the air pressure, precipitation, four components of radiation, the multilayer soil temperature (5cm, 10cm, 20cm, 40cm, 80cm, and 120cm), soil moisture (5cm, 10cm, 20cm, 40cm, 80cm, and 120cm), and soil heat flux (5cm & 15cm). The raw data were level0 and the data after basic processes were level1, in which ambiguous ones were marked; the data after strict quality control were defined as Level2. The data files were named as follows: station+datalevel+AMS+datadate. Level2 or above were strongly recommended to domestic users. As for detailed information, please refer to Meteorological and Hydrological Flux Data Guide.
MA Mingguo, Wang Weizhen, TAN Junlei, HUANG Guanghui, Zhang Zhihui
On 25 July 2012, a Leica ALS70 airborne laser scanner boarded on the Y-12 aircraft was used to obtain the point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 5500 m with the point cloud density 1 points per square meter. Aerial LiDAR-DEM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
The aim of the simultaneous observation of land surface temperature is obtaining the land surface temperature for different kinds of underlying surface, including the lager areas of homogeneous vegetation with high coverage, water, and concrete floor, while the thermal imager go into the experimental areas of the low reaches. All the land surface temperature data will be used for validation of the retrieved land surface temperature from thermal imager 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 On 1 August, 2014 2. Observation samples Three field samples were chosen in the fly zone, which were large areas of homogeneous vegetation (with high coverage), water, and concrete floor. 3. Observation method Surface temperature values were observed continuously for each sample using handheld infrared thermometers during the imager went into the flying area. 4. Instrument parameters and calibration The field of view of the handheld infrared thermometer is one degree and the emissivity was assumed to be 0.95. All instruments were calibrated on 31 July, 2014 using a black body. 5. Data storage All the observation data were stored in an excel.
Li Yimeng, REN Zhiguo, Zhou Shengnan, MA Mingguo
On 19 July 2012 (UTC+8), Leica ALS70 airborne laser scanner carried by the Harbin Y-12 aircraft was used in a LiDAR airborne optical remote sensing experiment. The relative flight altitude is 1500 m (the elevation of 2700 m). Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm with the point cloud density 4 points per square meter. Based on the original Airborne LiDAR-DEM data production were obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
This dataset contains the flux measurements from the Shenshawo desert station eddy covariance system (EC) in the middle reaches of the Heihe hydrometeorological observation network from 15 September, 2012, to 31 December, 2013. The site (100.493° E, 38.789° N) was located in the desert surface, near Zhangye city in Gansu Province. The elevation is 1594 m. The EC was installed at a height of 4.6 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&Li7500) 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 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. The 10 Hz data were missing during 8 December to 22 December, 2012, and data in this period were replaced with 30 min flux output by data logger. Data during 25 May to 29 May, 2013 were missing due to calibration of CO2/H2O gas analyzer. 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.
LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
This dataset contains the automatic weather station (AWS) measurements from Shenshawo sandy desert station in the flux observation matrix from 1 June to 21 September, 2012. The site (100.49330° E, 38.78917° N) was located in a desert surface, which is near Zhangye city, Gansu Province. The elevation is 1594 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (HMP45AC; 5 m and 10 m, towards north), air pressure (PTB110; 2 m), rain gauge (52203; 10 m), wind speed (03001; 5 m and 10 m, towards north), wind direction (03001; 10 m, towards north), a four-component radiometer (CNR1; 4 m, towards south), two infrared temperature sensors (IRTC3; 4 m, vertically downward), soil temperature profile (109; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), soil moisture profile (CS616; -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), and soil heat flux (HFP01; 3 duplicates, 0.06 m). 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), air pressure (press, hpa), precipitation (rain, mm), wind speed (Ws_5 m and 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, 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
On July 10, 2012, the airborne flight and ground observation was synchronously carried out in the PLMR quadrat of Yingke Oasis and the Huazhaizi Desert. PLMR (Polarimetric L-band Multibeam Radiometer) is a dual-polarized (H/V) L-band microwave radiometer with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, and a resolution of 1 km (relative flight height of 3 km).The radiometer has 6 beams to observe synchronously, and the incident angles are ±7º，±21.5º，±38.5º, and the sensitivity is less than 1K. The flight observation mainly covers the artificial oasis eco-hydrological test area in the middle reaches. This ground-synchronized data set provides a basic ground dataset for developing and validating passive microwave remote sensing inversion soil moisture algorithms. Quadrat and sampling strategy: The observation area is located in the transition zone between the southern margin of Zhangye Oasis and Anyang beach desert, the west side of Zhang (Zhangye)-Da (Daman) highway. It is divided into two parts by the main canal of the Dragon Canal from North to South. The Southwest area is a desert quadrat with the size of 1 km×1 km. The desert is relatively homogeneous, so soil moisture of 5 points in the 1 km quadrat are collected (1 point of each corner and the center point, in the actual measurement process, several extra points can be measured along the road). The four corner points are 600 meters away from each other,except the diagonal direction. And the southwest corner point is Huazhaizi Desert Station, for the convenience of comparison with weather station data. On the northeast side, a large size quadrat of 6 km×1.6 km is selected for simultaneous observation of the oasis underlying surface.In order to obtain the brightness temperature comparison with the PLMR observation, the quadrat was chose based on the following factors :surface coverage representative, avoiding the residential and greenhouses, crossing the oasis farmland and part of the Southern desert, accessibility, and observation time(road consumption). Taking the resolution of PLMR observations into consideration, in the synchronous observation, 11 sampling lines (East-West distribution) were collected with an interval of 160 meters from the East to the West. Each line from the North to the South was separated by 21 points with an interval of 80 meters. And 4 Hydraprobe Data Acquisition System (HDAS, Reference 2) were used to measure simultaneously. Measurement contents: About 230 points of the quadrat were collected, 2 observations were performed on each point, that is, 2 observations were performed on each sampling point of the film mulched corn field, 1 inside the film (marked as a in the data record), 1 outside the film (marked as b in the data record). Since the HDAS system useed the POGO portable soil sensor, the soil temperature, soil moisture (volumetric water content), loss tangent, soil electrical conductivity, soil complex dielectric real part and imaginary part were obtained by observation. No special simultaneous sampling of vegetation was carried out on the same day. Data: The data set includes two parts: soil moisture observation and vegetation observation. The former saves the data as a vector file, the spatial position is the position of each sampling point (WGS84+UTM 47N), and the measurement information of soil moisture is recorded in the attribute file.
WANG Shuguo, LI Xin
The dataset of spectral reflectance observations was obtained in the Linze grassland foci experimental area. Spectral reflectance of the cement floor, reed, the saline field, alfalfa, barley and the pool in the reed plot A, the saline plot B, the alfalfa plot D and the barley plot E were measured by ASD and the reference board (20% and 40%). Observation date Plot 2008-05-30 A、B、D、E 2008-06-03 B 2008-06-06 E 2008-06-15 The cement floor (the station) 2008-06-15 The alfalfa field 2008-06-15 The pool in Majuntan 2008-06-15 The cement floor in Majuntan 2008-06-16 A、B、 D、 E Five files on original binary data, one software file and six record forms were archived. See WATER: Dataset of setting of the sampling plots and stripes in the foci experimental area of Linze station for more information.
Wang Jing, HU Xiaoli, JIANG Xi, MA Mingguo, WANG Xufeng, YU Yingjie, HAO Xiaohua
Leaf area index (LAI), as a structural parameter of vegetation canopy, is an important input parameter for many inversion models such as energy and biomass inversion model. Firstly, vegetation points and ground points are separated in Terrasolid software. Then the transmittance of laser points is calculated, and the transmittance is the proportion of ground points to all points. After laser pulse hits the canopy, some energy passes through the voids between branches and leaves and continues to move forward until the energy is blocked, so some laser points will finally reach the ground. In this study, the ratio of the energy passing through the avoids to the energy of the canopy is used as the Laser Penetration Index (LPI). The LPI of each sample point at each scale in the study area was calculated.
ZHAO Chuanyan, MA Wenying
This data set includes the eddy correlation data of Shenshawo Desert Station in the middle reaches of Heihe Hydrometeorological Observation Network from January 1, 2015 to April 12, 2015. The site is located in Zhangye City, Gansu Province, and the underlying surface is desert. The latitude and longitude of the observation point is 100.49330E, 38.78917N, and the altitude is 1594.00m. The height of eddy correlator is 4.6 m, the sampling frequency is 10 Hz, the ultrasonic orientation is positive north, and the distance between the ultrasonic wind speed thermometer (CSAT3) and the CO2/H2O analyzer (Li7500) is 15 cm. The original observation data of the eddy correlation meter is 10 Hz, and the released data is 30-minute data processed by Eddypro software. The main steps of the processing include: outlier removal, time-lag correction, coordinate rotation (double rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction, etc. At the same time, the quality evaluation of each flux value is conducted, it mainly contains atmosphere state stability test(Δst) and integrated turbulence characteristic test(ITC). The 30-min flux value output by Eddypro software was also screened: (1) data from the instrument error was eliminated; (2) data 1 h before and after precipitation was removed; (3) data from the deletion rate greater than 10% within every 30 min of the 10 Hz raw data. (4) eliminating observation data of weak turbulence at night (u* less than 0.1 m/s). The average time period of observation data is 30 minutes, 48 data per day, and the missing data is labeled -6999. Abnormal data caused by instrument drift and other reasons are marked in red. Published observations include: date/time Date/Time, wind direction Wdir(°), horizontal wind speed Wnd(m/s), lateral wind speed standard deviation Std_Uy(m/s), ultrasonic virtual temperature Tv(°C), water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar (m/s), Obukhov length L (m), sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), sensible heat flux quality identification QA_Hs, latent heat flux quality identification QA_LE, carbon dioxide flux quality identification QA_Fc. The quality identification of sensible heat, latent heat, and carbon dioxide flux is divided into three levels (quality mark 0: (Δst <30, ITC<30); 1: (Δst <100, ITC<100); the rest is 2). The meaning of the data time, such as 0:30 represents an average of 0:00-0:30; the data is stored in *.xls format. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
This dataset contains the automatic weather station (AWS) measurements from Huazhaizi desert steppe station in the flux observation matrix from 2 June to 21 September, 2012. The site (100.31860° E, 38.76519° N) was located in a desert steppe surface, which is near Zhangye city, Gansu Province. The elevation is 1731 m. There are two equipment in the site, and installed by Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences (CAREERI) and Beijing Normal University (BNU), respectively. The installation heights and orientations of BNU were as follows: two infrared temperature sensors (SI-111; 2.65 m, south, vertically downward), soil heat flux (HFP01; 3 duplicates, -0.06 m), soil temperature profile (AV-10T; 0, -0.02, -0.04 m), and soil moisture profile (CS616; -0.02, -0.04 m). For the CAREERI installation: air temperature and humidity profile (HMP45A; 1, 1.99 and 2.99 m, north), wind speed profile (03102; 0.48, 0.98, 1.99 and 2.99 m, north), wind direction (03302; 4 m, north), air pressure (PTB210; in waterproof box), rain gauge (CTK-15PC; 0.7 m), four-component radiometer (CNR1; 2.5 m, south), soil temperature profile (107; -0.04, -0.1, -0.18, -0.26, -0.34, -0.42 and -0.5 m), soil moisture profile (ML2X; -0.02, -0.1, -0.18, -0.26, -0.34, -0.42, -0.5, and -0.58 m, 3 duplicates in -0.02 m). The observations included the following: (1) 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) (℃), and soil moisture (Ms_2 cm, Ms_4 cm) (%). (2) air temperature and humidity (Ta_1 m, Ta_1.99 m and Ta_2.99 m; RH_1 m, RH_1.99 m and RH_2.99 m) (℃ and %, respectively), wind speed (Ws_0.48 m, Ws_0.98 m, Ws_1.99 m and Ws_2.99 m) (m/s), wind direction (WD_4 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), soil temperature (Ts_4 cm, Ts_10 cm, Ts_18 cm, Ts_26 cm, Ts_34 cm, Ts_42 cm and Ts_50 cm) (℃), soil moisture (Ms_2 cm_1, Ms_2 cm_2, Ms_2 cm_3, Ms_10 cm, Ms_18 cm, Ms_26 cm, Ms_34 cm, Ms_42 cm, Ms_50 cm and Ms_58 cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The BNU data were averaged over intervals of 10 min, The CAREERI data were averaged over intervals of 30 min. A total of 144 runs per day were recorded in BNU data and 48 records per day in CAREERI data. (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: 2012-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to 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 flux measurements from the Huazhaizi desert station eddy covariance system (EC) in the middle reaches of the Heihe hydrometeorological observation network from 24 September, 2012, to 31 December, 2013. The site (100.319° E, 38.765° N) was located in the desert steppe surface, near Zhangye city in Gansu Province. The elevation is 1731 m. The EC was installed at a height of 2.85 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&Li7500) 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 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. The 10 Hz data were missing during 8 December to 22 December, 2012, and data in this period were replaced with 30 min flux output by data logger. Due to the malfunction of data logger in July, the 10 Hz data were missing, and data during this period were replaced by the 30 min data logger output data. 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.
LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
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
On 25 August 2012, Leica ALS70 airborne laser scanner boarded on the Y-12 aircraft was utilized to obtain point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 5200 m with the point cloud density 1 point per square meter. Aerial LiDAR-DEM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
250m/1km month compositing Fraction Vegetation Cover (FVC) data set of Heihe River Basin provides the results of monthly FVC synthesis in 2011-2014. The data is produced by using MODIS vegetation index products MOD13A2 and MOD13Q1 based on dimidiate pixel model.
ZHONG Bo, WU Junjun
The No. 5 hydrological section is located at Gaoya Hydrological Station (39°08′06.35″ N，100°25′58.23″ E, 1420 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. This hydrological section is for intercomparison of flow measurement between ADCP and manual method. The dataset contains observations from the No.5 hydrological section from 10 August, 2012, to 24 November, 2012. The width of this section is 58 meters. The water level was measured using HOBO pressure range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following sections: Water level (recorded every 30 minutes) and Discharge. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.
ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
On 25 July 2012, Leica ALS70 airborne laser scanner carried by the Harbin Y-12 aircraft was used in a LiDAR airborne optical remote sensing experiment. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second ,third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 5500 m with the point cloud density 1 points per square meter. Airborne LiDAR-DEM and DSM data production were obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
Meteorological elements are indicators of atmospheric variables or phenomena indicating weather conditions at a given place and at a given time. We used automatic forest weather station to monitor the meteorological elements data of Pailugou Watershed at 2800m above sea level. The main meteorological elements monitored include total radiation, net radiation, temperature, relative humidity, wind speed, and wind direction, which basically reflect the changes in meteorological elements in the Qinghai spruce forest.
CHANG Xuexiang
ET（Evapotranspiration）monitoring is essential for agricultural water management, regional water resources utilization planning, and socio-economic sustainable development.The limitations of the traditional monitoring ET method are mainly that large-area simultaneous observations cannot be made and can only be limited to observation points. Therefore, the cost of personnel and equipment is relatively high, and it is unable to provide ET data on the surface, nor to provide the ET data of different land use types and crop types. Quantitative monitoring of ET can be achieved by remote sensing. The characteristics of remote sensing information are that it can reflect both the macroscopic structural characteristics of the Earth's surface and the microscopic local differences. Monthly evapotranspiration datasets (2000-2013) with 30m spatial resolution over oasis in the Middle Reaches of Heihe River Basin Version 1.0 are based on multi-source remote sensing data. The latest ET Watch model is used to estimate the raster image data. Its temporal resolution is monthly and spatial resolution is 30 meters. The data cover the middle reaches of Zhangye oasis area in millimeters. The data types include month, quarter, and year data. The projection information of the data is as follows: Albers equivalent conical projection, Central meridian: 110 degrees, First secant: 25 degrees, Second secant: 47 degrees, Coordinate west deviation: 4000000 meters. The file naming rules are as follows: Monthly cumulative ET value file name: heihe-midoasis-30m_2013m01_eta.tif Among them, heihe indicates the Heihe River Basin, midoasis indicates the middle oasis area, 30m indicates the resolution is 30 meters, 2013 indicates 2013, m01 indicates January, eta indicates actual evapotranspiration data, and tif indicates that the data is in tif format; The ET value file for each season is named: heihe-midoasis-30m_2013s01_eta.tif Among them, heihe indicates the Heihe River Basin, midoasis indicates the middle oasis area, 30m indicates the resolution is 30 meters, 2013 indicates 2013, s01 indicates 1-3 months, for the first quarter, eta indicates actual evapotranspiration data, and tif indicates that the data is in tif format; The annual cumulative value file name: heihe-midoasis-30m_2013y_eta.tif Among them, heihe indicates the Heihe River Basin, midoasis indicates the middle oasis area, 30m indicates the resolution is 30 meters, 2013 indicates 2013, y indicates the year, eta indicates the actual evapotranspiration data, and tif indicates that the data is in tif format.
WU Bingfang
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 Huangcaogou station between 7 June, 2013, and 31 December, 2013. The site (100.731° E, 38.003° N) was located on a cold grassland surface in the Huangcaogou village, E’bao town, Qilian County, Qinghai Province. The elevation is 3137 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP45D; 5 m, north), wind speed and direction profile (03001; 10 m, north), air pressure (CS100; in the tamper box on the ground), rain gauge (TE525M; 10 m), four-component radiometer (CNR1; 6 m, south), two infrared temperature sensors (IRTC3; 6 m, south, vertically downward), soil heat flux (HFT3; 3 duplicates, -0.06 m), soil temperature profile (AV-10T; 0, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and soil moisture profile (ECh2o-5; -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_5 m; RH_5 m) (℃ and %, respectively), wind speed (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/m2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), and soil moisture (Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content). 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 of wind direction were missing during 12 June, 2013 and 24 September, 2013. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2013-9-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), Liu et al. (2011) (for data processing) in the Citation section.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, ZHANG Yang, TAN Junlei
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