The Qinghai Tibet Plateau belongs to the plateau mountain climate. The precipitation, its seasonal distribution and the change of precipitation forms have been one of the hot spots in the global climate change research. The data includes precipitation data of Qinghai Tibet Plateau, with spatial resolution of 1km * 1km, temporal resolution of month and year, and time coverage of 2000, 2005, 2010 and 2015. The data are obtained by Kring interpolation of meteorological data of National Meteorological Science Information Center. The data can be used to analyze the temporal and spatial distribution of precipitation over the Qinghai Tibet Plateau. In addition, the data can also be used to analyze the temporal and spatial variation of precipitation over the Qinghai Tibet Plateau, which is of great significance to the study of the ecological environment of the Qinghai Tibet Plateau.
Photosynthetic effective radiation absorption coefficient photosynthetically active radiation component is an important biophysical parameter. It is an important land characteristic parameter of ecosystem function model, crop growth model, net primary productivity model, atmosphere model, biogeochemical model and ecological model, and is an ideal parameter for estimating vegetation biomass. The data set contains the data of photosynthetically active radiation absorption coefficient in Qinghai Tibet Plateau, with spatial resolution of 500m, temporal resolution of 8D, and time coverage of 2000, 2005, 2010 and 2015. The data source is MODIS Lai / FPAR product data mod15a2h (C6) on NASA website. The data are of great significance to the analysis of vegetation ecological environment in the Qinghai Tibet Plateau.
This dataset is a pixel-based maximum fractional vegetation cover map within the Yellow River source region on the Qinghai-Tibet Plateau, with an area of about 44,000 square kilometers. Based on the time series images acquired from MODIS with a resolution of 250 m and Landsat-8 with a resolution of 30 m in 2015 during the vegetation growing season, the data are derived using dimidiate pixel model and time interpolation. The spatial resolution of the image is 30 m, using the WGS 1984 UTM projected coordinate system, and the data is in the format of grid.
The data set contains the observation data of the 10m tower automatic meteorological station on December 31, 2015 on January 1, 2015 at solstice.The station is located in east garden town, huailai county, hebei province.The latitude and longitude of the observation point is 115.7880E, 40.3491N, and the altitude is 480m. The automatic weather station is installed on a 10m tower, the acquisition frequency is 30s, and the output time is 10min.The observation factors include air temperature and relative humidity (5m), and the direction is due north.The wind speed (10m), the wind direction (10m), the direction is due to the north;Air pressure (installed in waterproof box);Rainfall (10m);The four-component radiation (5m), the direction is due to the south;The infrared surface temperature (5m), the arm is facing south, and the probe is facing vertically downward.The soil temperature and humidity probe was buried at 1.5m to the south of the meteorological tower. The buried depth of the soil temperature probe was 0cm, 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm. The buried depth of the soil moisture sensor was 2cm, 4cm, 10cm, 10cm, 10cm, 10cm, 20cm, 80cm, 120cm and 160cm.The average soil temperature was buried 2,4 cm underground.Soil hot flow plates (3) are buried in the ground 6cm. Processing and quality control of observation data :(1) ensure 144 data per day (every 10min). If data is missing, it will be marked by -6999;(2) eliminate the moments with duplicate records;(3) data that is obviously beyond the physical meaning or the range of the instrument is deleted;(4) the format of date and time is unified, and the date and time are in the same column.For example, the time is: 10:30 on June 10, 2015.Data missing due to damage of charging controller from May 30 to June 5 and October 1 to October 9.Soil heat flux G1 due to the heat flux plate problem, the data of April 19 solstice on May 20 was missing. Data released by the automatic weather station include:Date/Time, air temperature and humidity observation (Ta_5m, RH_5m) (℃, %), wind speed (Ws_10m) (m/s), wind direction (WD) (°), pressure (hpa), precipitation (Rain) (mm), four-component radiation (DR, UR, DLR, ULR, Rn) (W/m2), surface radiation temperature (IRT_1, IRT_2) (℃),Soil heat flux (Gs_1, Gs_2, Gs_3) (W/m2), multi-layer soil moisture (Ms_2cm, Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm, Ms_120cm, Ms_160cm) (%), multi-layer soil temperature (Ts_2cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (℃), average soil temperature TCAV (℃). Please refer to Guo et al. (2020) for information of observation test or site, and Liu et al. (2013) for data processing.
The data set contains the observation data of the vorticity correlator of 10m tower on December 31, 2015 from January 1, 2015 to solstice.Station is located in huailai county, hebei province, east garden town, under the surface of irrigated corn.The latitude and longitude of the observation point is 115.7880E, 40.3491N, and the altitude is 480m.The acquisition frequency of vortex correlativity instrument is 10Hz, the frame height is 5m, the ultrasonic direction is due to the north, and the distance between the ultrasonic anometer (CSAT3) and the CO2/H2O analyzer (Li7500A) is 15cm. The released data is the 30-minute data obtained from the post-processing of the original collected 10Hz data with Eddypro software. The main steps of the processing include: outfield value elimination, delay time correction, coordinate rotation (secondary coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output after processing was also screened :(1) the data when the instrument was wrong was removed;(2) data of 1h before and after precipitation were excluded;(3) the missing rate of 10Hz original data is more than 10% every 30min;(4) the observed data of weak turbulence at night were excluded (u* less than 0.1m/s).The average period of observation data was 30 minutes, 48 data a day, and the missing data was marked as -6999.May 14 solstice May 20 and May 24 solstice June 6 due to power converter damage, data missing. The observation data released by vortex correlator include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (K), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), the length of cloth hoff, sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest are 2).The meaning of data time, such as 0:30 represents the average between 0:00 and 0:30;The data is stored in *.xls format. Please refer to Guo et al, 2020 for information of observation test or site, and Liu et al. (2013) for data processing.
The data set contains the observation data of large aperture scintillator from January 1, 2015 to December 31, 2015. Two large aperture scintillation meters, bls450 and zzlas, are installed respectively. The site is located in donghuayuan Town, Huailai County, Hebei Province. The longitude and latitude of the observation point are 115.7880e, 40.3491n and 480m above sea level. The effective height of the large aperture scintillator is 14m, the optical path length is 1870m, the longitude and latitude of the transmitter are 115.8023e, 40.3596n, and the longitude and latitude of the receiver are 115.7825e and 40.3522n. The acquisition frequency of bls450 and zzlas is 5Hz and 1Hz respectively, with an average output of 1min. The original data of large aperture scintillator is 1 min, and the released data is 30 min average data after processing and quality control. The sensible heat flux is mainly obtained by iterative calculation based on Monin obkhov similarity theory and combined with automatic weather station data. In the process of iterative calculation, for bls450, the stability function of thiermann and Grassl, 1992 is selected; for zzlas, the stability function of Andreas, 1988 is selected. The main quality control steps include: (1) eliminating the data of cn2 saturation; (2) eliminating the data with weak demodulation signal intensity; (3) eliminating the data of precipitation time and one hour before and after; (4) eliminating the data of weak turbulence under stable conditions (U * less than 0.1m/s). Several explanations about the published data are as follows: (1) the Las data is mainly bls450, and the missing time is supplemented by zzlas observation, and the missing time is marked with - 6999. (2) Data header: date / time: date / time, cn2: structure parameter of air refraction index (m-2 / 3), H_ Las: sensible heat flux (w / m2). The meaning of data time, for example, 0:30 represents the average of 0:00-0:30; the data is stored in *. XLS format. Guo et al, 2020 is used for site introduction and Liu et al, 2013 for data processing
Net Primary Productivity (NPP) reflects the efficiency of plant fixation and conversion of light energy as a compound. It refers to the amount of organic matter accumulated per unit time and unit area of green plants. It is the organic matter produced by plant photosynthesis. The remainder of the Gross Primary Productivity (GPP) minus Autotrophic Respiration (RA), also known as net primary productivity. As an important part of the surface carbon cycle, NPP not only directly reflects the production capacity of vegetation communities under natural environmental conditions, but also is an important component to measure regional land use/cover change. The net primary productivity data product uses the light energy utilization (GLOPEM) model algorithm to invert multiple scale raster data products obtained from various satellite remote sensing data (Landsat, MODIS, etc.), which is also the main factor for determining and regulating ecological processes.
The data summarizes the agricultural and socio-economic status of the five Central Asian countries ( Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan ) in 2015.The data comes from the statistical yearbooks of the five Central Asian countries ( Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan ), including the Total Population, Cultivated Land Area, Grain production area, GDP, The proportion of agricultural GDP to total GDP and The proportion of industrial GDP to total GDP and Forest Area. Detailed statistics of the six socio-economic factors of the five Central Asian countries are given. Statistics show that each of the six elements of the five Central Asian countries has its own focus. The data provides basic data for the project, facilitates analysis of the ecological and social situation in Central Asia, and provides data support for the future data analysis of the project.
The remote sensing image interpretation mark is also called the interpretation factor, which can directly reflect the image features of the ground object information. The interpreter uses these marks to identify the nature, type or condition of the feature or phenomenon on the image, so it is for the remote sensing image data. Human-computer interactive interpretation is of great significance. The image used in the data to establish the interpretation mark avoids the summer with high vegetation coverage, and avoids the data with more snow cover, cloud cover or smog influence.According to the basic geographic information data extraction requirements, the combination of the remote sensing image band combination order and the full color band are selected.Avoid data loss when enhancing data. The requirement for selecting a typical marker-building area on an image is that the range is moderate to reflect the typical features of the type of landform, including as many basic geographic information elements as possible in the type of landform and the image quality is good. After the selection of the marking area is completed, look for all the basic geographic information element categories contained in the marking area, and then select various typical maps as the collection marks, then go to the field for field verification,including 3429 sampling reference points and 1,870 photos, and the translation of the library was established, and the unreasonable parts were modified until they were consistent with the field. At the same time, the ground photo of the map is taken to make the image and the actual ground elements relate to each other, expressing the authenticity and intuitiveness of the remote sensing image interpretation mark, and to deepen the user's understanding of the interpretation mark.
Meteorological data are a set of weather data, which can be divided into climatological data and weather data. This data set mainly includes rainfall data and temperature data in meteorological data (In the data set, ‘pre’ represents rainfall and ‘T2’ represents temperature).The data set is from CRU（Climate Research Unit）global grid data provided by the university of east Anglia in the UK（http://www.cgiar-csi.org/）. The CRU data set is interpolated from observations at 365 sites across central Asia, Many researchers have found that the data is relatively accurate in central Asia. This data set uses CRU to obtain rainfall and temperature data of five central Asian countries through Arcgis batch cutting.Meteorological data is widely used and can be integrated with resources in different fields. It plays an important role in the development and construction of transportation, new energy, agriculture, mobile Internet software development and service, public management and smart city, smart transportation, smart food and other fields based on big data technology.