Terra (EOS am-1), the flagship of the EOS earth observation series, was the first satellite to be launched on December 18, 1999.ASTER is primarily used for high-resolution observations of surface radiation balance. Compared with Landsat series satellites, ASTER has improved spectral and spatial resolution, and significantly increased short-wave infrared and thermal infrared bands.ASTER has a total of 14 wavebands, including 3 visible and near-infrared wavebands, 5 short-wave infrared wavebands and 5 thermal infrared wavebands. The resolution is 15m, 30m and 90m respectively, and the scanning width is 60km, 30m and 90m respectively.Heihe river basin ASTER remote sensing image data set through the international cooperation data from NASA's web site (https://wist.echo.nasa.gov/). Data naming rules as follows: assuming that the name of the ASTER image for "ASTL1B0103190215190103290064", then ASTL1B said ASTER L1B products, 003 on behalf of the version number namely VersionID, (010319) represents the next 6 digits observation date will be March 19, 2001, followed by six digits (021519) represents the observation time (02:15:19), followed by the last six digits (010329) representing the processing date is March 29, 2001, the last four digits (0064) representing the four-digit sequence code. At present, there are 258 scents of ASTER data in heihe river basin.The acquisition time is:2000-04-25, 2000-04-27 (2 scenes), 2000-05-04, 2000-05-15 (4 scenes), 2000-05-20 (9 scenes), 2000-05-29 (3 scenes), 2000-05-31 (2 scenes), 2000-06-12, 2000-06-14 (5 scenes), 2000-06-21 (3 scenes), 2000-06-30 (8 scenes), 2000-07-18, 2000-07-23 (3 scenes), 2000-08-03 (4 scenes),2000-08-08 (9 scenes), 2000-08-17 (7 scenes), 2000-08-19 (4 scenes), 2000-08-26 (3 scenes), 2000-09-02 (4 scenes), 2000-10-02 (7 scenes), 2000-10-04 (6 scenes), 2000-10-29 (3 scenes), 2000-11-21, 2001-02-18 (2 scenes), 2001-02-25, 2001-03-11 (5 scenes), 2001-03-22 (4 scenes),2001-03-27 (4 scenes), 2001-03-29 (9 scenes), 2001-04-07 (2 scenes), 2001-04-12 (2 scenes), 2001-04-14 (6 scenes), 2001-07-10, 2001-07-12 (8 scenes), 2001-07-21 (8 scenes), 2001-08-13 (8 scenes), 2001-08-20 (7 scenes), 2001-08-22, 2001-08-27 (2 scenes), 2001-08-29,2001-09-03 (2 scenes), 2001-11-15 (7 scenes), 2002-02-01, 2002-03-30 (2 scenes), 2002-04-17 (2 scenes), 2002-05-24, 2002-06-04 (6 scenes), 2002-06-09, 2002-06-13, 2002-06-25, 2002-08-14 (3 scenes), 2002-09-29, 2002-10-19 (2 scenes), 2002-11-11 (2 scenes),2002-12-29 (4 scenes), 2003-04-18, 2003-05-24 (2 scenes), 2003-07-25, 2003-07-30, 2003-8-10 (5 scenes), 2003-08-12, 2003-08-17, 2003-09-09 (11 scenes), 2003-09-13 (4 scenes), 2003-10-15, 2003-10-18, 2003-10-29 (9 scenes), 2003-11-30, 2004-03-14, 2005-03-20,2005-06-05, 2005-08-11, 2007-10-22, 2007-11-14, 2007-11-23, 2007-12-04, 2008-01-28, 2008-02-13, 2008-05-03 (4 scenes), 2008-05-05, 2008-05-17, 2008-06-04 (2 scenes), 2008-06-13.
0 2020-06-08
This dataset contains the flux measurements from the Alpine meadow and grassland ecosystem Superstation superstation eddy covariance system (EC) belonging to the Qinghai Lake basin integrated observatory network from September 2 to December 18 in 2018. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The EC was installed at a height of 4.5 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 (CSAT3A &EC150) was about 0.17 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, 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): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). 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 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. Data during December 18 to December 24, 2018 were missing due to the data collector failure. 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/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), 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. Detailed information can be found in the suggested references.
0 2020-07-25
Land surface hydrological modeling is sensitive to near-surface air temperature, which is especially true for the cryosphere. The lapse rate of near-surface air temperature is a critical parameter when interpolating air temperature from station data to gridded cells. To obtain spatially distributed, fine-resolution near-surface (2 m) air temperature in the mainland China, monthly air temperature from 553 Chinese national meteorological stations (with continuous data from 1962 to 2011) are divided into 24 regional groups to analyze spatiotemporal variations of lapse rate in relation to surface air temperature and relative humidity. The results are as follows: (1) Evaluation of estimated lapse rate shows that the estimates are reasonable and useful for temperature-related analyses and modeling studies. (2) Lapse rates generally have a banded spatial distribution from southeast to northwest, with relatively large values on the Tibetan Plateau and in northeast China. The greatest spatial variability is in winter with a range of 0.3°C–0.9°C / 100m, accompanied by an inversion phenomenon in the northern Xinjiang Province. In addition, the lapse rates show a clear seasonal cycle. (3) The lapse rates maintain a consistently positive correlation with temperature in all seasons, and these correlations are more prevalent in the north and east. The lapse rates exhibit a negative relationship with relative humidity in all seasons, especially in the east. (4) Substantial regional differences in temporal lapse rate trends over the study period are identified. Increasing lapse rates are more pronounced in northern China, and decreasing trends are found in southwest China, which are more notable in winter. An overall increase of air temperature and regional variation of relative humidity together influenced the change of lapse rate. The dataset is represented in an Execel document, the annual and seasonal air temperate lapse rates are included.
0 2020-04-06
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Arou Superstation in the Heihe integrated observatory network from January 1 to December 31 in 2018. There were two types of LASs at Arou Superstation: BLS450 and zzlas, produced by Germany and China, respectively. The north tower was set up with the zzlas receiver and the BLS450 transmitter, and the south tower was equipped with the zzlas transmitter and the BLS450 receiver. The site (north: 100.471° E, 38.057° N; south: 100.457° E, 38.038° N) was located in Caodaban village of A’rou town in Qilian county, Qinghai Province. The underlying surface between the two towers was alpine meadow. The elevation is 3033 m. The effective height of the LASs was 9.5 m, and the path length was 2390 m. The data were sampled 1 minute at both BLS450 and zzlas. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (BLS450: Cn2>7.25E-14, zzlas: Cn2>7.84E-14). (2) The data were rejected when the demodulation signal was small (BLS450: Mininum X Intensity<50; zzlas: Demod>-20mv). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl, 1992 and Andreas, 1988 were selected for BLS450 and zzlas, respectively. Detailed can refer to Liu et al. (2011, 2013). Several instructions were included with the released data. (1) The data were primarily obtained from BLS450 measurements, and missing flux measurements from the BLS450 instrument were substituted with measurements from the zzlas instrument. The missing data were denoted by -6999. Due to the problems of storing and wireless transmission, data from 5 July to 24 August, were not collected. (2) The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) (for data processing) in the Citation section.
0 2020-07-25
Due to the short snow duration and thin snow layer on the Tibetan Plateau, dynamic monitoring data for daily fractional snow cover are urgently needed in order to better understand water cycling and other processes. This data set is based on MODIS Snow Cover Daily L3 Global 500 m Grid data and includes the Normalized Difference Snow Index (NDSI) data product generated from MODIS/Terra data (MOD10A1) and MODIS/Aqua data (MYD10A1). The data are in the .hdf format. The projection method is sinusoidal map projection. Combining the advantages of 90 m SRTM terrain data and fractional snow cover estimation algorithms under multiple cloud coverage types, the fractional snow cover under different cloud coverage conditions can be re-estimated to meet the production requirements of the daily less cloud (< 10%) data products in High Asia. On the basis of this method, the MODIS daily fractional snow cover data set over High Asia (2002-2016) was constructed. By taking the binary snow product under cloudless conditions as a reference, the spatial and temporal comparisons between snow distribution and snow coverage show that the spatio-temporal characteristics of the product and the binary products are highly consistent. Taking the winter of 2013 as an example, when the fractional snow cover is greater than 50%, the correlation can reach 0.8628. This data set provides daily fractional snow cover data for use in studying snow dynamics, the climate and environment, hydrology, energy balance, and disaster assessment in High Asia.
0 2019-09-15
The GCMs dataset used in this dataset is CMIP3 comparison plan data (A1B (Medium Carbon Emissions, Global Common Development Scenarios that Focus on Economic Growth), A2 (High Carbon Emissions, Focus on Regional development scenarios for economic growth) and B1 (low carbon emissions, global common development scenarios that emphasize environmentally sustainable development) from the 24 GCM outputs in IPCC AR4 provided by PCMDI. This dataset uses the Delta method for downscaling, uses the 20C3M dataset from 1961 to 1990 as a reference, and uses the SRES dataset from 2010 to 2099 as the future scenario.
0 2020-03-28
The VEGETATION sensor sponsored by the European Commission was launched by SPOT-4 in March 1998. Since April 1998, SPOTVGT data for global vegetation coverage observation has been received by Kiruna ground station in Sweden. The image quality monitoring center in Toulouse, France is responsible for image quality and provides relevant parameters (such as calibration coefficient). Finally, the Belgian flemish institute for technological research (Vito)VEGETATION processing Centre (CTIV) is responsible for preprocessing into global data of 1km per day. Pretreatment includes atmospheric correction, radiation correction, geometric correction, production of 10 days to maximize the synthesized NDVI data, setting the value of -1 to -0.1 to -0.1, and then converting to the DN value of 0-250 through the formula DN=(NDVI+0.1)/0.004. The data set is a subset extraction from China, including spectral reflectance of four bands synthesized every 10 days and 10 days' maximum NDVI. It is data from 1998 to 2007 with a spatial resolution of 1km and a temporal resolution of 10 days. File format: Hfr and img files. The file naming rule is: CHN _ NDV _ YYYMMDD, where YYYYMMDD is the date of the day represented by the file and is also the main identifier different from other files. The remote sensing image files with suffix. IMG and. HDF used by users to analyze vegetation index can be opened in ENVI and ERDAS software. Coordinate system and projection Plate_Carree (Lon/Lat) PROJ_CENTER_LON 0.000000 PROJ_CENTER_LAT 0.000000 PIXEL_SIZE_UNITS DEGREES/PIXEL PIXEL_SIZE_X 0.0089285714 PIXEL_SIZE_Y 0.0089285714 SEMI_AXIS_MAJ 6378137.000000 SEMI_AXIS_MIN 6356752.314000 UL_LON (DEG) 73.000000 UL_LAT (DEG) 54.000000 LR_LON (DEG) 135.500000 LR_LAT (DEG) 5.000000 Corner coordinates are: Corner Coordinates: Upper Left ( 69.9955357, 55.0044643) Lower Left ( 69.9955357, 14.9955358) Upper Right ( 137.0044641, 55.0044643) Lower Right ( 137.0044641, 14.9955358) Where Upper Left is the upper left corner, Lower Left is the lower left corner, Upper Right is the upper right corner, and Lower Right is the lower right corner.
0 2020-03-31
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Sidaoqiao Superstation (two sites) in the hydrometeorological observation network of Heihe River Basin. There were two types of LASs at site 1: German BLS900 and Netherlands Kipp&zonen. The north tower was set up with the BLS900/Kipp&zonen receiver, and the south tower was equipped with the BLS900/Kipp&zonen transmitter. The observation period of BLS900_1 and Kipp&zonen were from 11 July to 13 November, 2013, and 11 July to 12 September, 2013, respectively. There was one type of LAS at site 2: German BLS900. The north tower was set up with the BLS900 receiver, and the south tower was equipped with the BLS900 transmitter. BLS900_2 has been in use since 16 September, 2013. The Sidaoqiao Superstation (site1, north: 101.147° E, 42.005° N, south: 101.131° E, 41.987° N; site 2, north: 101.137° E, 42.008° N, south: 101.121° E, 41.990° N) was located in Ejinaqi, Inner Mongolia. The underlying surfaces between the two towers were tamarisk, populus, bare land and farmland. The elevation is 873 m. The effective height of the LASs was 25.5 m, and the path length of site 1 and site 2 were 2390 m and 2380 m, respectively. The data were sampled at 5 Hz and 1 Hz intervals for BLS900 and zzlas, respectively, and then averaged over 1 min. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (BLS900_1: Cn2>7.25E-14, Kipp&zonen: Cn2>7.84E-14, BLS900_2: Cn2>7.33E-14). (2) The data were rejected when the demodulation signal was small (BLS900: Average X Intensity<1000; Kipp&zonen: Demod>-20mv). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl, 1992 and Andreas, 1988 were selected for BLS900 and Kipp&zonen, respectively. Several instructions were included with the released data. (1) The data of site 1 were primarily obtained from BLS900_1 measurements, and missing flux measurements from the BLS900_1 instrument were substituted with measurements from the Kipp&zonen instrument. The missing data were denoted by -6999. The data of site 2 were obtained from BLS900_2 measurements, missing data were denoted by -6999. Due to the problems of BLS900_1 transmitter, the data after 13 November, 2013, were not collected. (2) The dataset contained the following variables: data/time (yyyy-m-d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xls format. 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
This data set contains the observation data of vorticity correlation-meter at the upper reaches of heihe hydrometeorological observation network from January 1, 2015 to December 25, 2015.The station is located in qilian county, qinghai province.The longitude and latitude of the observation point are 98.9406e, 38.8399N and 3739 m above sea level.The rack height of the vortex correlativity meter is 4.5m, the sampling frequency is 10Hz, the ultrasonic orientation is due north, and the distance between the ultrasonic wind speed and temperature meter (CSAT3) and CO2/H2O analyzer (Li7500) is 15cm. The original observation data of the vortex correlativity instrument is 10Hz, and the published data is the 30-minute data processed by Eddypro software. The main processing steps include: outliers, delay time correction, coordinate rotation (quadratic 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 by Eddypro software was also screened :(1) data when instrument error was eliminated;(2) data of 1h before and after precipitation are excluded;(3) remove the data with a missing rate of more than 10% in the original 10Hz data within every 30 minutes;(4) the observation data of weak turbulence at night (u* less than 0.1m/s) were excluded.The average observation period was 30 minutes, 48 data per day, and the missing data was marked as -6999.Suspicious data caused by instrument drift, etc., shall be marked in red font.Calibration of the eddy current system Li7500 from April 16 to 18, with data missing;Abnormal CO2 concentration occurred after September 23, resulting in an error in CO2 flux.When 10Hz data is missing due to a problem with the memory card storage data (1.8-3.8,7.23-9.13), the data will be replaced by the 30-min flux data output by the collector. The published observational data 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 (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), 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,Quality indicator for co2 flux QA_Fc.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 is 2).The meaning of data time, such as 0:30 represents the average of 0:00-0:30;The data is stored in *.xls format. For information of hydrometeorological network or station, please refer to Liu et al. (2018), and for observation data processing, please refer to Liu et al. (2011).
0 2020-04-10
1、 Data Description: the data includes the river flow data at the outlet of No.2 catchment of hulugou small watershed from May 4, 2016 to September 3, 2016. 2、 Sampling location: the coordinates of river flow monitoring section are located at the outlet of No. 2 catchment near the red wall, with the coordinates of 99 ° 52 ′ 58.40 ″ E and 38 ° 14 ′ 36.85 ″ n.
0 2020-06-03
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