Brief Introduction:The high-cold regions in China include the Qinghai Tibetan Plateau, and the alpine regions of Gansu, Inner Mongolia and Xinjiang, with a total area of about 2.9 million square kilometers. Due to the complexity of topography and geomorphology, the worldwide researches more and more focus on the surface processes of the Qinghai Tibetan Plateau and its adjacent areas. The High-cold Region Observation and Research Network for Land Surface Processes & Environment of China (HORN) has gradually formed. It integrates 17 stations of Chinese Academy of Sciences, for long term observations and researches of land surface processes, including glaciers, permafrost, lades, alpine ecosystem in the high-cold regions of China. It provides a platform support for integrated researches of earth system, through condensation of scientific problems, integration of monitoring resources, improvement of observation capability and level, long-term continuous monitoring of surface processes and environmental changes in cold regions. It also provides data support for revealing the law of climate change and water resources formation and transformation in the headwaters of big rivers, exploring the changes of ecosystem structure and service function, grasping the mechanism of natural disasters such as ice and snow freezing and thawing, and promoting the sustainable development of regional economy and society, etc. A network integrated center is set up to organize research and carry out the specific implementation of network construction. It consists of an office, an observation technology service group and a data integration management group. The participating units of HORN should sign construction/research contracts in order to implement contract-based management, perform all tasks in the contracts and accept the examination and acceptance of the network organization. The network construction should give priority to scientific research, coordinated development, relatively balanced allocation of infrastructure and observation instruments, and free sharing of data within the network. For the principle of sharing and opening, the observatories of the network are open to the whole country. The network cooperates with relevant units through consultation, agreement or contract according to specific tasks and costs; the original observation data are gradually shared based on the principle of first the network, then the department and then the society. The network carries out planned and coordinated cooperation with foreign scientific research institutions and universities, which can improve the level of network observation and expand the content of observation through the cooperation. The HORN is managed by the Chinese Academy of Sciences in the allocation of funds and resources.

Number of Datasets:465

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    The dataset of airborne imaging spectrometer (OMIS-II) mission was obtained in the Linze station-Linze grassland flight zone on Jun. 6, 2008. Data after radiometric correction and calibration and geometric approximate correction were released. The flying time of each route was as follows: {| ! id ! flight ! file ! starttime ! lat ! long ! alt ! image linage ! endtime ! lat ! long ! alt |- | 1 || 1-13 || 2008-06-06_09-32-22_DATA.BSQ || 09:56:32 || 39.167 || 100.044 || 2945.9 || 5718 || 10:02:53 || 39.362 || 100.191 || 2936.7 |- | 2 || 1-12 || 2008-06-06_10-02-38_DATA.BSQ || 10:08:42 || 39.373 || 100.193 || 2956.1 || 5565 || 10:14:53 || 39.182 || 100.049 || 2953.1 |- | 3 || 1-11 || 2008-06-06_10-14-39_DATA.BSQ || 10:19:51 || 39.177 || 100.039 || 2931.2 || 5432 || 10:25:54 || 39.363 || 100.179 || 2958.3 |- | 4 || 1-10 || 2008-06-06_10-25-39_DATA.BSQ || 10:31:50 || 39.376 || 100.182 || 2959.7 || 5396 || 10:37:50 || 39.190 || 100.041 || 2952.7 |- | 5 || 1-9 || 2008-06-06_10-37-35_DATA.BSQ || 10:43:06 || 39.179 || 100.026 || 2956.4 || 5399 || 10:49:06 || 39.368 || 100.169 || 2939.0 |- | 6 || 1-8 || 2008-06-06_10-48-51_DATA.BSQ || 10:55:20 || 39.383 || 100.174 || 2943.2 || 5643 || 11:01:36 || 39.1922 || 100.029 || 2944.8 |- | 7 || 1-7 || 2008-06-06_11-01-22_DATA.BSQ || 11:07:04 || 39.185 || 100.0175 || 2947.2 || 5306 || 11:12:58 || 39.373 || 100.159 || 2943.9 |- | 8 || 1-6 || 2008-06-06_11-12-43_DATA.BSQ || 11:18:57 || 39.386 || 100.162 || 2948.1 || 5604 || 11:25:10 || 39.196 || 100.018 || 2950.5 |- | 9 || 1-5 || 2008-06-06_11-24-56_DATA.BSQ || 11:30:22 || 39.188 || 100.006 || 2934.0 || 5469 || 11:36:26 || 39.378 || 100.149 || 2935.4 |- | 10 || 1-4 || 2008-06-06_11-36-12_DATA.BSQ || 11:42:30 || 39.389 || 100.151 || 2935.4 || 5570 || 11:48:41 || 39.198 || 100.007 || 2949.0 |- | 11 || 1-3 || 2008-06-06_11-48-27_DATA.BSQ || 11:54:21 || 39.205 || 100.005 || 2915.2 || 5028 || 11:59:57 || 39.380 || 100.138 || 2908.8 |- | 12 || 1-2 || 2008-06-06_11-59-42_DATA.BSQ || 12:06:00 || 39.395 || 100.142 || 2931.0 || 5523 || 12:12:08 || 39.205 || 99.999 || 2950.0 |- | 13 || 1-1 || 2008-06-06_12-11-53_DATA.BSQ || 12:18:17 || 39.197 || 99.985 || 2916.5 || 5451 || 12:24:20 || 39.389 || 100.131 || 2907.9 |}

    2019-08-19 0 0 View Details

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Phenology camera observation data set of Sidaoqiao superstation, 2018)

    The dataset contains phenological camera observation data collected at the Arou Superstation in the midstream of the Heihe integrated observatory network from June 13 to November 16, 2018. The instrument was developed with data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures high-quality data with a resolution of 1280×720 by looking-downward. The calculation of the greenness index and phenology are following 3 steps: (1) calculate the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) according to the region of interest, (2) perform gap-filling for the invalid values, filtering and smoothing, and (3) determine the key phenological parameters according to the growth curve fitting (such as the growth season start date, Peak, growth season end, etc.) There are also 3 steps for coverage data processing: (1) select images with less intense illumination, (2) divide the image into vegetation and soil, and (3) calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.

    2019-07-31 0 0 View Details

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Phenology camera observation data set of Daman superstation, 2018)

    The dataset contains phenological camera observation data collected at the Arou Superstation in the midstream of the Heihe integrated observatory network from June 13 to November 16, 2018. The instrument was developed with data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures high-quality data with a resolution of 1280×720 by looking-downward. The calculation of the greenness index and phenology are following 3 steps: (1) calculate the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) according to the region of interest, (2) perform gap-filling for the invalid values, filtering and smoothing, and (3) determine the key phenological parameters according to the growth curve fitting (such as the growth season start date, Peak, growth season end, etc.) There are also 3 steps for coverage data processing: (1) select images with less intense illumination, (2) divide the image into vegetation and soil, and (3) calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (GCC), phenological phase and fractional cover (FC). Please refer to Liu et al. (2018) for sites information in the Citation section.

    2019-07-31 0 1 View Details

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Phenology camera observation data set of Arou superstation, 2018)

    The dataset contains phenological camera observation data collected at the Arou Superstation in the midstream of the Heihe integrated observatory network from June 13 to November 16, 2018. The instrument was developed with data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures high-quality data with a resolution of 1280×720 by looking-downward. The calculation of the greenness index and phenology are following 3 steps: (1) calculate the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) according to the region of interest, (2) perform gap-filling for the invalid values, filtering and smoothing, and (3) determine the key phenological parameters according to the growth curve fitting (such as the growth season start date, Peak, growth season end, etc.) There are also 3 steps for coverage data processing: (1) select images with less intense illumination, (2) divide the image into vegetation and soil, and (3) calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.

    2019-07-31 0 1 View Details

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Phenology camera observation data set of Mixed forest superstation, 2018)

    The dataset contains phenological camera observation data collected at the Arou Superstation in the midstream of the Heihe integrated observatory network from June 13 to November 16, 2018. The instrument was developed with data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures high-quality data with a resolution of 1280×720 by looking-downward. The calculation of the greenness index and phenology are following 3 steps: (1) calculate the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) according to the region of interest, (2) perform gap-filling for the invalid values, filtering and smoothing, and (3) determine the key phenological parameters according to the growth curve fitting (such as the growth season start date, Peak, growth season end, etc.) There are also 3 steps for coverage data processing: (1) select images with less intense illumination, (2) divide the image into vegetation and soil, and (3) calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.

    2019-07-31 0 3 View Details

  • Weather data at 2800m above sea level in Qinhai spruce stand of Pailougou watershed

    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.

    2019-07-24 0 0 View Details

  • HiWATER:Dataset of Hydrometeorological observation network (eddy covariance system of Populus forest station, 2014)

    This dataset contains eddy correlation instrument observation data from the Huyanglin station downstream of the Heihe Hydrological and Meteorological Observation Network from January 1, 2014 to December 31, 2014. The site is located in Sidaoqiao, Ejin Banner, Inner Mongolia, and the underlying surface is Populus euphratica. The latitude and longitude of the observation point is 101.1236E, 41.9928N, and the altitude is 876m. The vortex correlator has a height of 22 m and a sampling frequency of 10 Hz. The ultrasonic orientation is in the north direction, and the distance between the ultrasonic wind speed temperature meter (CSAT3) and the CO2/H2O analyzer (Li7500) is 17 cm. The original observation data of the eddy correlation meter is 10 Hz, and the released data is 30 minutes of data processed by Eddypro software. The main steps of the processing include: outlier removal, time-lag correction, coordinate rotation (secondary coordinate 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. From February 21 to March 13, the data is missing due to problems in memory card and wireless transmission module. 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), stability Z/L (dimensionless), 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).

    2019-07-24 0 2 View Details

  • HiWATER:Dataset of Hydrometeorological observation network (an observation system of Meteorological elements gradient of Sidaoqiao Superstation, 2014)

    This dataset contains the data of the meteorological element gradient observation system of the Sidaoqiao superstation downstream of the Heihe Hydrometeorological Observation Network from January 1, 2014 to December 31, 2014. The site is located in Sidaoqiao, Dalaihu Town, Ejin Banner, Inner Mongolia. The underlying surface is Tamarix. The latitude and longitude of the observation point is 101.1374E, 42.0012N, and the altitude is 873m. The air temperature, relative humidity and wind speed sensors are respectively set at 5m, 7m, 10m, 15m, 20m and 28m, with 6 layers facing the north; the wind direction sensor is set at 15m, facing the north; the barometer is installed in the waterproof box. The tipping bucket rain gauge is installed at 28m; the four-component radiometer is installed at 10m, facing south; two infrared thermometers are installed at 10m, facing south, the probe orientation is vertically downward; two photosynthetically active radiometers are installed At 10m, facing south, and the probe is vertically upward and downward respectively; the soil moisture sensor is installed 2m on the south side of the tower body, and the soil heat flow plates (self-correcting type) (3 pieces) are buried in turn in the ground 6cm deep; The average soil temperature sensor TCAV is buried in the ground 2cm, 4cm; the soil temperature probe is buried in the ground surface 0cm and underground 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm; soil moisture sensors are buried in the underground 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm. Observed items include: wind speed (WS_5m, WS_7m, WS_10m, WS_15m, WS_20m, WS_28m) (unit: m/s), wind direction (WD_15m) (unit: degree), air temperature and humidity (Ta_5m, Ta_7m, Ta_10m, Ta_15m, Ta_20m, Ta_28m and RH_5m, RH_7m, RH_10m, RH_15m, RH_20m, RH_28m) (unit: centigrade, percentage), pressure (unit: hectopascal), precipitation (Rain) (unit: mm), four-component radiation (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts/square meter), surface radiation temperature (IRT_1, IRT_2) (unit: centigrade), up and down photosynthetically active radiation (PAR_U_up, PAR_U_down) (unit: micromol/square Msec), average soil temperature (TCAV) (unit: centigrade), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watts/square meter), soil moisture (Ms_2cm, Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm) , Ms_120cm, Ms_160cm) (unit: volumetric water content, percentage), soil temperature (Ts_0cm, Ts_2cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: centigrade). Processing and quality control of the observation data: (1) ensure 144 data per day (every 10 minutes), when there is missing data, it is marked by -6999; From September 8, 2014 to November 8, due to the sensor problems, the data is missing; on May 9, 2014, the soil moisture probe was re-buried, and the data before and after is inconsistent; (2) eliminate the moment with duplicate records; (3) delete the data that is obviously beyond the physical meaning or the range of the instrument; (5) the format of date and time is uniform, and the date and time are in the same column. For example, the time is: 2014-9-10 10:30; (6) the naming rules are: AWS+ site name. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).

    2019-07-21 0 2 View Details

  • HiWATER:Dataset of Hydrometeorological observation network (automatic weather station of shenshawo sandy desert station, 2015)

    This data set includes observation data of meteorological elements in the Shenshawo Desert Station in the middle of the Heihe Hydrometeorological Observation Network from January 1, 2015 to April 12, 2015. The site is located in Shenshawo, Zhangye City, Gansu Province, and the underlying surface is desert. The latitude and longitude of the observation point is 100.4933E, 38.7892N, and the altitude is 1594m. The air temperature and relative humidity sensors are installed at 5m and 10m, facing the north; the barometer is installed at 2m; the tipping bucket rain gauge is installed at 10m; the wind speed sensor is set at 5m, 10m, and the wind direction sensor is set at 10m, facing the north; the four-component radiometer is installed at 6m, facing south; two infrared thermometers are installed at 6m, facing south, the probe orientation is vertically downward; the soil temperature probe is buried in the ground surface 0cm and underground 2cm, 4cm, 10cm, 20cm 40cm, 60cm and 100cm, in the south of the 2m from the meteorological tower; soil moisture sensors are buried in the underground 2cm, 4cm, 10cm, 20cm, 40cm, 60cm and 100cm, in the south of the 2m from the meteorological tower, and among them a repetitive soil moisture sensor (Ms_40cm_2) was embedded in the underground 40cm on May 6, 2014.soil heat flux plates (3 pieces) are buried in the ground 6 cm in order. Observation items include: air temperature and humidity (Ta_5m, RH_5m, Ta_10m, RH_10m) (unit: centigrade, percentage), air pressure (Press) (unit: hectopascal), precipitation (Rain) (unit: mm), wind speed (WS_5m, WS_10m) (unit: m / s), wind direction (WD_10m) (unit: degree), four-component radiation (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts / square meter), surface radiation temperature (IRT_1, IRT_2 ) (unit: centigrade), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watts/square meter), soil moisture (Ms_2cm, Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_60cm, Ms_100cm) (unit: volumetric water content, percentage) and soil temperature (Ts_0cm, Ts_2cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_60cm, Ts_100cm) (unit: centigrade). Processing and quality control of the observation data: (1) ensure 144 data per day (every 10 minutes), when there is missing data, it is marked by -6999; From March 19, 2015 to March 26, due to the collector problem, the data is missing; (2) eliminate the moment with duplicate records; (3) delete the data that is obviously beyond the physical meaning or the range of the instrument; (5) the format of date and time is uniform, and the date and time are in the same column. For example, the time is: 2015-6-10 10:30; (6) the naming rules are: AWS+ site name. The station was dismantled after April 12. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).

    2019-07-20 0 0 View Details

  • HiWATER:Dataset of Hydrometeorological observation network (No.3 runoff observation system of Railway bridge on the Heihe River, 2014)

    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).

    2019-07-19 0 0 View Details