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.
0 2019-05-23
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
0 2020-08-19
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
0 2019-09-14
The dataset is the ground verification point dataset of land cover and vegetation type in the Source Region of Yellow River (in the north of Zaling Lake, Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.
0 2020-10-13
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.
0 2019-09-15
This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
0 2020-06-04
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.
0 2020-07-25
The source of the data is a 1:2500000-scale map series, "Geocryological Map of Russia and Neighboring Republics", published by Russia from 1991 to 1996, which is labelled in Russian and includes a total of 16 images. In 1998, Zaitsev and others translated it into English. In this study, seven of the images were digitized: 1) Distribution of frozen and unfrozen ground, 2) Mean annual temperature of unfrozen ground at the depth of zero annual amplitude (note that there is some uncertainty because the depth of zero amplitude is not provided, and data on this parameter is generally lacking), 3) Thickness of permafrost, 4) Depth from the surface and thickness of relict permafrost, 5) Distribution of permafrost containing cryopegs, 6) Thickness of permafrost containing cryopegs, 7) Distribution of permafrost with depth. 1. The data include multiple vector layers: (1) permafrost distribution, (2) permafrost temperature, (3) permafrost thickness, (4) permafrost formation conditions, and (5) the correction image. 2. The permafrost distribution map includes the following fields: AREA, PERIMETER, FROZEN_, FROZEN_ID: POLY_, POLY_, RINGS_OK, RINGS_NOK, A, FROZEN_SOI (frozen soil layer), and temperature. FROZEN_SOI are the Chinese and English representations of the type of frozen soil, respectively. 4. Frozen soil properties: Frozen soil Continuous predominantly unfrozen 1-5 Continuous permafrost -3- -5 Continuous unfrozen ground 4-6 Discontinuous permafrost 0.5- -2 Predominantly continuous permafrost -1- -3 Predominantly unfrozen ground 1-3 5. Projection information: PROJCS["Asia_North_Equidistant_Conic", GEOGCS["GCS_North_American_1927", DATUM["North_American_Datum_1927", SPHEROID["Clarke_1866",6378206.4,294.9786982]], PRIMEM["Greenwich",0.0], UNIT["Degree",0.0174532925199433]], PROJECTION["Equidistant_Conic"], PARAMETER["False_Easting",0.0], PARAMETER["False_Northing",0.0], PARAMETER["longitude_of_center",100.0], PARAMETER["Standard_Parallel_1",15.0], PARAMETER["Standard_Parallel_2",58.3], PARAMETER["latitude_of_center",60.0], UNIT["Meter",1.0]]
0 2020-09-15
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.
0 2019-09-12
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.
0 2019-09-15
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