This dataset includes the observational data that were collected by two sets of Cosmic-ray Soil Moisture Observation System (COSMOS), named crs_a and crs_b, which were installed near the Daman Superstation in the flux observation matrix from 1 June through 20 September 2012. The land cover in the footprint was maize crop, and the site was located with the cropland of the Daman Irrigation District, Zhangye, Gansu Province. Crs_a was located at 100.36975° E, 38.85385° N and 1557.16 m above sea level; Crs_b was located at 100.37225° E, 38.85557° N and 1557.16 m above sea level. The bottom of the probe was 0.5 m above the ground; the sampling interval was 1 hour. The raw COSMOS data include the following: battery (Batt, V), temperature (T, ℃), relative humidity (RH, %), air pressure (P, hPa), fast neutron counts (N1C, counts per hour), thermal neutron counts (N2C, counts per hour), sample time of fast neutrons (N1ET, s), and sample time of thermal neutrons (N2ET, s). The distributed data include the following variables: Date, Time, P, N1C, N1C_cor (corrected fast neutron counts) and VWC (volume soil moisture, %), which were processed as follows: 1) Quality control Data were removed and replaced by -6999 when (a) the battery voltage was less than 11.8 V, (b) the relative humidity was greater than 80% inside the probe box, (c) the counting data were not of one-hour duration and (d) then neutron count differed from the previous value by more than 20%. 2) Air pressure correction An air pressure correction was applied to the quality-controlled raw data according to the equation contained in the equipment manual. The procedure was previously described by Jiao et al. (2013) and Zreda et al. (2012). 3) Calibration After the quality control and corrections were applied, soil moisture was calculated using the equation in Desilets et al. (2010), where N0 is the neutron counts above dry soil and the other variables are fitted constants that define the shape of the calibration function. Here, the parameter N0 must be calibrated using the in situ observed soil moisture within the footprint. This procedure was previously described by Jiao et al. (2013) and Zreda et al. (2012) 4) Computing the soil moisture Based on the calibrated N0 and corrected N1C, the hourly soil moisture was computed using the equation from the equipment manual. This procedure was previously described by Jiao et al, (2013) and Zreda et al. (2012) For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Zhu et al. (2015) (for data processing) in the Citation section.
0 2019-09-14
The accurate estimation of sapwood area and heartwood area is the main means to convert the transpiration water consumption scale. In October 2011, this project investigated the sapwood and heartwood of 98 Populus euphratica in Ejin Oasis and measured the width of sapwood and heartwood. The relation curve of sapwood area with DBH and height was established. Please refer to LI Wei, SI Jianhua,FENG Qi, YU Teng fei. Response of Transpiration to Water Vapour Pressure Defferential of Populus euphratica. Journal of Desert Research, 2013, 33(5): 1377-1384. for details.
0 2019-09-15
The 1:1,000,000 Antarctic settlements data set includes vector spatial data of Antarctic settlements and its related attributes：City name (ENG_NAME), city population (CNTEY_NAME), (CNTRY_CODE), etc. The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, WGS84 datum surface，Antarctic specific projection parameters（South_Pole_Stereographic）.
0 2019-09-15
This data is from "China 1:100,000 land use data".China 1:100,000 land use data was constructed in three years based on Landsat MSS, TM and ETM remote sensing data by using satellite remote sensing as a means to organize remote sensing science and technology teams from 19 institutes affiliated to the Chinese academy of sciences (cas) in the "eighth five-year plan" major application project "national macro survey and dynamic research on remote sensing of resources and environment". The landuse data of guizhou province in the late 1980s adopted a hierarchical land cover classification system, which divided the country into 6 primary categories (farmland, woodland, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 secondary categories.It is the most accurate land use data product in China and has played an important role in national land resource survey, hydrological and ecological research.
0 2020-03-30
This dataset contains data on river water level and flow velocity at No.3 in the intensive runoff observation in the middle reaches of Heihe River runoff from July 28, 2014 to December 31, 2014. The observation point is located at Heihe Bridge, Lan-Xin Railway, Zhangye City, Gansu Province. The riverbed is gravel and the section is stable. The latitude and longitude of the observation point is N39°2'33.08", E100°25'49.42", the altitude is 1443 meters, and the river channel width is 50 meters. The water level observation is measured by SR50 ultrasonic range finder with a frequency of 60 minutes. The flow profile observation is conducted by StreamPro micro ADCP. The data declaration includes the following two parts: Water level observation, the observation frequency is 60 minutes, unit (cm); data covering time period from July 28, 2014 to December 31, 2014; Flow observation, unit (m3); monitoring flow and obtaining water level flow curve according to different water levels. The process of the runoff changing is obtained by observing the water level process. The missing data is uniformly represented by the string -6999. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to He et al. (2016).
0 2019-09-13
The dataset of airborne WiDAS mission was obtained in the Zhangye-Yingke-Huazhaizi flight zone on Jul. 11, 2008. Intra-band data available for general users include Level-2C data (after geometric, radiometric and atmospheric corrections), Level-1B browse image (after intra-band matching) and Level-2B browse image (after registration). The raw data, Level-1A, and data processing parameters were filed; applications would be evaluated prior to access. Data processing started in Aug. 2008 and ended in Apr. 2009, and in Nov. 2009, CCD data were reprocessed to adjust radiometric calibration. The flying time of each route was as follows: {| ! id ! flight ! relative height ! starttime ! endtime ! data size ! data state ! data quality ! ground targets |- | 1 || 3#6 || 3196.6m || 13:23:54 || 13:31:18 || 112 || processed;complete || good || Huazhaizi desert plot 1 |- | 2 || 3#10_1 || 3167.6m || 13:36:06 || 13:44:34 || 128 || processed;complete || good || Zhangye city, the wetland park, Yingke weather station maize field, Yingke wheat field, and Huazhaizi desert maize plot |- | 3 || 3#10_2 || 1607.2m || 13:52:14 || 13:59:34 || 111 || processed;complete || good || Zhangye city, the wetland park, Yingke weather station maize field, Yingke wheat field, and Huazhaizi desert maize plot |- | 4 || 3#10_3 || 823.3m || 14:13:46 || 14:14:34 || 133 || processed;complete || good || Zhangye city, the wetland park, Yingke weather station maize field, Yingke wheat field, and Huazhaizi desert maize plot |}
0 2019-05-23
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.
0 2019-09-12
The aerosol optical thickness data of Qomolangma station and Namuco station in the Qinghai Tibet Plateau is based on the observation data products of Qomolangma station and Namuco station from the atmospheric radiation view of the Institute of Qinghai Tibet Plateau of the Chinese Academy of Sciences. The data coverage time is from 2017 to 2019, the time resolution is hour by hour, the coverage sites are Qomolangma station and Namuco station, the longitude and latitude coordinates are (Qomolangma station: 28.365n, 86.948e, Namuco station Mucuo station: 30.7725n, 90.9626e). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is TXT.
0 2020-10-14
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 Jingyangling station between 15 August, 2013, and 31 December, 2013. The site (101.116° E, 37.838° N) was located on a cold meadow surface in the Jingyangling, Qilian County, Qinghai Province. The elevation is 3750 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP45AC; 5 m, north), wind speed and direction profile (034B; 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 (SI-111; 6 m, south, vertically downward), soil heat flux (HFP01; 3 duplicates, -0.06 m), soil temperature profile (109-L; 0, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and soil moisture profile (CS616; -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/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_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 radiation data were missing because of wiring problem. 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.
0 2019-09-14
I. overview The data set includes wind and sand activity data of Ulanbuh Desert and Kubuqi Desert along the upper Yellow River from April to May 2011 and April 2012, mainly including wind speed profile, surface roughness, wind-sand flow structure, sand transport rate data under different vegetation coverage and different parts of sand dunes. II. Data Processing Instructions The wind speed and direction are observed by 014A wind speed sensor 024A wind direction sensor and CR200 data acquisition instrument produced by MetOne company, and the sediment transport amount is observed by stepped sediment collection instrument. III. Description of Data Content The data are stored in EXCEL table, mainly including wind speed profile, surface roughness, wind-sand flow structure and sand transport rate data under different vegetation coverage. IV. Data Usage Instructions This paper evaluates the sandstorm hazards along the Yellow River, estimates the amount of sandstorm entering the Yellow River in the upper reaches of the Yellow River, and provides data support for the establishment of an early warning system for sandstorm hazards in the region.
0 2020-03-29
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