The data set includes soil pH data of representative soil samples collected from July 2012 to August 2013 in the Heihe River Basin. The first soil survey was conducted in 2012. After the representativeness evaluation of collected samples, we conducted an additional sampling in 2013. These samples are representative enough to represent the soil variation in the Heihe River Basin, of which the soil variation in each landscape could be accounted for. The sampling depths in field refer to the sampling specification of Chinese Soil Taxonomy, in which soil samples were taken from genetic soil horizons.
ZHANG Ganlin
The data set includes soil bulk density data of representative soil samples collected from July 2012 to August 2013 in the Heihe River Basin. The first soil survey was conducted in 2012. After the representativeness evaluation of collected samples, we conducted an additional sampling in 2013. These samples are representative enough to represent the soil variation in the Heihe River Basin, of which the soil variation in each landscape could be accounted for. The sampling depths in field refer to the sampling specification of Chinese Soil Taxonomy, in which soil samples were taken from genetic soil horizons.
ZHANG Ganlin
The data set includes soil organic carbon concentrations data of representative soil samples collected from July 2012 to August 2013 in the Heihe River Basin. The first soil survey was conducted in 2012. After the representativeness evaluation of collected samples, we conducted an additional sampling in 2013. These samples are representative enough to represent the soil variation in the Heihe River Basin, of which the soil variation in each landscape could be accounted for. The sampling depths in field refer to the sampling specification of Chinese Soil Taxonomy, in which soil samples were taken from genetic soil horizons.
ZHANG Ganlin
This dataset includes data recorded by the Heihe integrated observatory network obtained from an observation system of Meteorological elements gradient of Daman Superstation from January 1 to December 31, 2018. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation, which is near Zhangye city, Gansu Province. The elevation is 1556 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (AV-14TH;3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 2.5 m, 8 m in west of tower), four-component radiometer (PIR&PSP; 12 m, towards south), two infrared temperature sensors (IRTC3; 12 m, towards south, vertically downward), photosynthetically active radiation (LI190SB; 12 m, towards south, vertically upward; another four photosynthetically active radiation, PQS-1; two above the plants (12 m) and two below the plants (0.3 m), towards south, each with one vertically downward and one vertically upward), soil heat flux (HFP01SC; 3 duplicates with G1 below the vegetation; G2 and G3 between plants, -0.06 m), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (CS616; -0.02, -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_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 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) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2, and Gs_3, between plants) (W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, 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), above the plants photosynthetically active radiation of upward and downward (PAR_U_up and PAR_U_down) (μmol/ (s m-2)), and below the plants photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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 meterological data during September 17 and November 7 and TCAV data after November 7 were wrong because the malfunction of datalogger. 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: 2018-6-10 10:30. 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.
LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
Soil bulk density, porosity, water content, water characteristic curve, saturated hydraulic conductivity, particle analysis, infiltration rate, and sampling point location information in the upper reaches of the Heihe River Basin. 1. The data is for 2014 supplementary sampling for 2012, using the ring knife to take the original soil; 2. The soil bulk density is the dry bulk density of the soil and is measured by the drying method. The original ring-shaped soil sample collected in the field was thermostated at 105 ° C for 24 hours in an oven, and the soil dry weight was divided by the soil volume (100 cubic centimeters) , unit: g/cm 3 . 3. Soil porosity is obtained according to the relationship between soil bulk density and soil porosity; 4. Soil infiltration analysis data set, the data is the field experimental measurement data from 2013 to 2014. 5. The infiltration data is measured by “MINI DISK PORTABLE TENSION INFILTROMETER”, and the approximate saturated hydraulic conductivity under a certain negative pressure is obtained. 6. Soil particle size data was measured at the Grain Granulation Laboratory of the Key Laboratory of the Ministry of Education of Lanzhou University. The measuring instrument is a Malvern laser particle size analyzer MS2000. 7. The saturated hydraulic conductivity is measured according to the enamel hair self-made instrument of Yi Yanli (2009). The Marioot bottle was used to maintain the head during the experiment; at the same time, the Ks measured at the time was converted to the Ks value at 10 °C for analysis and calculation. 8. Soil water content data is measured using ECH2O, including 5 layers of soil water content and soil temperature. 9. The water characteristic curve is measured by the centrifuge method: the undisturbed soil of the ring cutter collected in the field is placed in a centrifuge, and each of the speeds is measured at 0, 310, 980, 1700, 2190, 2770, 3100, 5370, 6930, 8200, 11600. The secondary rotor weight is obtained.
HE Chansheng
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.
Qu Yonghua, XU Ziwei
This dataset includes data recorded by the Heihe integrated observatory network obtained from a Cosmic-ray Soil Moisture Observing System for soil moisture observation at the Daman Superstation from January 1 to December 31, 2018. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation area, which is near Zhangye city, Gansu Province. The elevation is 1556 m. 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 variables: battery (Batt, V), temperature (T, C), 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) 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) neutron count differed from the previous value by more than 20%; 2) An air pressure correction was applied to the quality-controlled raw data according to the equation contained in the equipment manual; 3) After the quality control and corrections were applied, soil moisture was calculated using the equation in Zreda et al. (2012), 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 was calibrated using the in situ observed soil moisture by SoilNET within the footprint; 4) Based on the calibrated N0 and corrected N1C, the hourly soil moisture was computed using the equation from the equipment manual. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Zhu et al. (2015) for data processing) in the Citation section.
ZHU Zhongli, XU Ziwei, LI Xin, CHE Tao, TAN Junlei, REN Zhiguo, ZHANG Yang
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.
Qu Yonghua, XU Ziwei
This dataset includes data recorded by the Heihe integrated observatory network obtained from an observation system of Meteorological elements gradient of A’rou Superstation from January 1 to December 31, 2018. The site (100.464° E, 38.047° N) was located on a cold grassland surface in the Caodaban village, A’rou Town, Qilian County, Qinghai Province. The elevation is 3033 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP45C; 1, 2, 5, 10, 15 and 25 m, towards north), wind speed profile (010C; 1, 2, 5, 10, 15 and 25 m, towards north), wind direction profile (020C; 2 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 5 m, towards south), four-component radiometer (CNR4; 5 m, towards south), two infrared temperature sensors (SI-111; 5 m, towards south, vertically downward), photosynthetically active radiation (PAR-LITE; 5 m, towards south, vertically upward), soil heat flux (HFP01SC; 3 duplicates, -0.06 m, 2 m in the south of tower), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m, 2 m in the south of tower), soil temperature profile (109; 0, -0.02, -0.04, -0.06, -0.1, -0.15, -0.2, -0.3, -0.4, -0.6, -0.8, -1.2, -1.6, -2, -2.4, -2.8 and -3.2 m, 3 duplicates in -0.04 m and -0.1 m), and soil moisture profile (CS616; -0.02, -0.04, -0.06, -0.1, -0.15, -0.2, -0.3, -0.4, -0.6, -0.8, -1.2, -1.6, -2, -2.4, -2.8 and -3.2 m, 3 duplicates in -0.04 m and -0.1 m). The observations included the following: air temperature and humidity (Ta_1 m, Ta_2 m, Ta_5 m, Ta_10 m, Ta_15 m and Ta_25 m; RH_1 m, RH_2 m, RH_5 m, RH_10 m, RH_15 m and RH_25 m) (℃ and %, respectively), wind speed (Ws_1 m, Ws_2 m, Ws_5 m, Ws_10 m, Ws_15 m and Ws_25 m) (m/s), wind direction (WD_2 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/m2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/(s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm_1, Ts_4 cm_2, Ts_4 cm_3, Ts_6 cm, Ts_10 cm_1, Ts_10 cm_2, Ts_10 cm_3, Ts_15 cm, Ts_20 cm, Ts_30 cm, Ts_40 cm, Ts_60 cm, Ts_80 cm, Ts_120 cm, Ts_160 cm, Ts_200 cm, Ts_240 cm, Ts_280 cm and Ts_320 cm) (℃), and soil moisture (Ms_2 cm, Ms_4 cm_1, Ms_4 cm_2, Ms_4 cm_3, Ms_6 cm, Ms_10 cm_1, Ms_10 cm_2, Ms_10 cm_3, Ms_15 cm, Ms_20 cm, Ms_30 cm, Ms_40 cm, Ms_60 cm, Ms_80 cm, Ms_120 cm, Ms_160 cm, Ms_200 cm, Ms_240 cm, Ms_280 cm and Ms_320 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 average soil temperature was rejected during February 16 to March 31 and April 15 to May 20 because of broken of the sensor line; Soil heat flux were wrong occasionally during November to December. 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: 2018-9-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, ZHANG Yang, TAN Junlei
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.
Qu Yonghua, XU Ziwei
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.
LIU Shaomin, Qu Yonghua, XU Ziwei
This dataset contains the LAI measurements from the Sidaoqiao in the downstream of the Heihe integrated observatory network from June 16 to October 18 in 2018. The site was located in Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 870 m. There are 2 observation samples, around Sidaoqiao superstation (101.1374E, 42.0012N) and Mixed forest station (101.1335E, 41.9903N), each of which is about 30m×30m in size. 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.
Qu Yonghua, XU Ziwei
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.
Qu Yonghua, XU Ziwei
This is the LAINet dataset measured in the corn field at the Xiaoman irrigation district (from 25 June, to 24 August, 2012). The time used in this dataset is in UTC+8 Time. Instrument: LAINet- A wireless sensor network for leaf area index measurement, Beijing Normal University Measurement Mode: LAINet observation system is formed by 3 kinds of sensor nodes, they are respectively (1) node below the canopy, sensors up-looking are used for measure the transmitted radiation through the canopy, which are deployed horizontally; (2) node above canopy: sensors up-looking are used for measure the total sun incident radiation, which are deployed horizontally; (3) sink or router node, which is designed for receiving and transmitting data measured by the above node and below node. Data Processing: the original data obtained from sensors is received by sink nodes, and forms the original dataset in days after pre-processed. The observation for transmittance of the canopy is acquired by calculating the ratio of the radiation through the canopy and the total incident radiation above the canopy at different sun elevation angles during a day. The retrieval of LAI is based on the multi-angle transmittance data. LAINet dataset is composed of original LAI data, LAI data after calculating the mean value in 5 days interval and the longitude and latitude of the measurement nodes. All the data are stored in the format of Excel. As for the data after calculating the mean value in 5 days, we take the number of aggregation nodes as the name of the sheet. Data saved in a sheet is from an sink node which receives the measurement data from the child nodes. The original data records the LAI of every node in the observation day. In the sheet of two kinds of data above, the meaning of the column is as follows: DOY, node one, node two, …, and node N.
MA Mingguo
The data set contains observation data of cosmic-ray instrument (crs) from January 1, 2017 to December 31, 2017. The site is located in the farmland of Daman Irrigation District, Zhangye, Gansu Province, and the underlying surface is cornfield. The latitude and longitude of the observation site is 100.3722E, 38.8555N, the altitude is 1556 meters. The bottom of the instrument probe is 0.5 meter from the ground, and the sampling frequency is 1 hour. The original observation items of the cosmic-ray instrument include: voltage Batt (V), temperature T (°C), relative humidity RH (%), air pressure P (hPa), fast neutron number N1C (number / hour), thermal neutron number N2C (number / hour), fast neutron sampling time N1ET (s) and thermal neutron sampling time N2ET (s). The data was released after being processed and calculated. The data includes: Date Time, P (pressure hPa), N1C (fast neutrons one/hour), N1C_cor (pressure-corrected fast neutrons one/hour) and VWC ( soil water content %), it was processed mainly by the following steps: 1) Data Screening There are four criteria for data screening: (1) Eliminating data with a voltage less than or equal to 11.8 volts ; (2) Eliminating data with a relative humidity greater than or equal to 80%; (3) Eliminating data with a sampling time interval not within 60 ± 1 minute; (4) Eliminating data with fast neutrons that vary by more than 200 in one hour. In addition, missing data is supplemented with -6999. 2) Air Pressure Correction The original data is corrected by air pressure according to the fast neutron pressure correction formula mentioned in the instrument manual, and the corrected fast neutron number N1C_cor is obtained. 3) Instrument Calibration In the process of calculating soil moisture, it is necessary to calibrate the N0 in the calculation formula. N0 is the number of fast neutrons under the situation with low antecedent soil moisture . Usually, soil samples in the source area are used to obtain measured soil moisture (or obtained by relatively dense soil moisture wireless sensors) θm (Zreda et al. 2012) and the fast neutron correction data N in corresponding time periods, then NO can be obtained by reversing the formula. Here, the instrument is calibrated according to the Soilnet soil moisture data in the source region of the instrument, and the relationship between the soil volumetric water content θv and the fast neutron is established. The data of June 26-27, and July 16-17, respectively, which have obvious differences in dry and wet conditions, were selected. The data from June 26 to 27 showed low soil moisture content, so the average of the three values of 4 cm, 10 cm and 20 cm was used as the calibration data, and the variation range was 22% to 30%; meanwhile , the data from July 16 to 17 showed high soil moisture content, so the average of the two values of 4cm and 10 cm was used as the calibration data, and the variation range was 28% - 39%, and the final average N0 was 3597. 4) Soil Moisture Calculation According to the formula, the hourly soil water content data is calculated. Please refer to Liu et al. (2018) for information of hydrometeorological network or site, and Zhu et al. (2015) for observation data processing.
LIU Shaomin, ZHU Zhongli, XU Ziwei, LI Xin, CHE Tao, TAN Junlei, REN Zhiguo
This dataset is the Fractional Vegetation Cover observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observations lasted for a vegetation growth cycle from May 2012 to September 2012 (UTC+8). Instruments and measurement method: Digital photography measurement is implemented to measure the FVC. Plot positions, photographic method and data processing method are dedicatedly designed. Details are described in the following: 0. In field measurements, a long stick with the camera mounted on one end is beneficial to conveniently measure various species of vegetation, enabling a larger area to be photographed with a smaller field of view. The stick can be used to change the camera height; a fixed-focus camera can be placed at the end of the instrument platform at the front end of the support bar, and the camera can be operated by remote control. 1. For row crop like corn, the plot is set to be 10×10 m2, and for the orchard, plot scale is 30×30 m2. Shoot 9 times along two perpendicularly crossed rectangular-belt transects. The picture generated of each time is used to calculate a FVC value. “True FVC” of the plot is then acquired as the average of these 9 FVC values. 2. The photographic method used depends on the species of vegetation and planting pattern: Low crops (<2 m) in rows in a situation with a small field of view (<30 ), rows of more than two cycles should be included in the field of view, and the side length of the image should be parallel to the row. If there are no more than two complete cycles, then information regarding row spacing and plant spacing are required. The FVC of the entire cycle, that is, the FVC of the quadrat, can be obtained from the number of rows included in the field of view. 3. High vegetation in rows (>2 m) Through the top-down photography of the low vegetation underneath the crown and the bottom-up photography beneath the tree crown, the FVC within the crown projection area can be obtained by weighting the FVC obtained from the two images. Next, the low vegetation between the trees is photographed, and the FVC that does not lie within the crown projection area is calculated. Finally, the average area of the tree crown is obtained using the tree crown projection method. The ratio of the crown projection area to the area outside the projection is calculated based on row spacing, and the FVC of the quadrat is obtained by weighting. 4. FVC extraction from the classification of digital images. Many methods are available to extract the FVC from digital images, and the degree of automation and the precision of identification are important factors that affect the efficiency of field measurements. This method, which is proposed by the authors, has the advantages of a simple algorithm, a high degree of automation and high precision, as well as ease of operation.
MU Xihan, HUANG Shuai, MA Mingguo
This dataset is the FPAR observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observation period is from 24 May to 19 July, 2012 (UTC+8). Measurement instruments: AccuPAR (Beijing Normal University) Measurement positions: Core Experimental Area of Flux Observation Matrix 18 corn samples, 1 orchard sample, 1 artificial white poplar sample Measurement methods: For corn, to measure the incoming PAR on the canopy, transmission PAR under the canopy, reflected PAR on the canopy, reflected PAR under the canopy. For orchard and white poplar forest, to measure the incoming PAR outside of the canopy, transmission PAR under the canopy. Corresponding data: Land cover, plant height, crop rows identification
MA Mingguo
The dataset includes the fractional vegetation cover data generated from the stations of crop land, wetland, Gebi desert and desert steppe in Yingke Oasis and biomass data generated from the stations of crop land (corn) and wetland. The observations lasted for a vegetation growth cycle from 19 May, 2012 to 15 September, 2012. 1. Fractional vegetation cover observation 1.1 Observation time 1.1.1 Station of the crop land: The observations lasted from 20 May, 2012 to 15 September, 2012, and in five-day periods for each observation before 31 July and in ten-day periods for each observation after 31 July. The observation time for the station of crop land (corn) are 2013-5-20, 2013-5-25, 2013-5-30, 2013-6-5, 2013-6-10, 2013-6-16, 2013-6-22, 2013-6-27, 2013-7-2, 2013-7-7, 2013-7-12, 2013-7-17, 2013-7-27, 2013-8-3, 2013-8-13, 2013-8-25, 2013-9-5 and 2013-9-15. 1.1.2 The other four stations: The observations lasted from 20 May, 2012 to 15 September, 2012 and in ten-day periods for each observation. The observation time for the crop land are 2013-5-20, 2013-6-5, 2013-6-16, 2013-6-27, 2013-7-7, 2013-7-17, 2013-7-27, 2013-8-3, 2013-8-13, 2013-8-25, 2013-9-5 and 2013-9-15. 1.2 method 1.2.1 Instruments and measurement method Digital photography measurement is implemented to measure the FVC. Plot positions, photographic method and data processing method are dedicatedly designed. In field measurements, a long stick with the camera mounted on one end is beneficial to conveniently measure various species of vegetation, enabling a larger area to be photographed with a smaller field of view. The stick can be used to change the camera height; a fixed-focus camera can be placed at the end of the instrument platform at the front end of the support bar, and the camera can be operated by remote control. 1.2.2 Design of the samples Three and two plots with the area of 10×10 m^2 were measured for the station of the crop land and wetland, respectively. One plot with the area of 10×10 m^2 was measured for the other three stations. Shoot 9 times along two perpendicularly crossed rectangular-belt transects. The picture generated of each time is used to calculate a FVC value. “True FVC” of the plot is then acquired as the average of these 9 FVC values. 1.2.3 Photographic method The photographic method used depends on the species of vegetation and planting pattern. A long stick with the camera mounted on one end is used for the stations of crop land and wetland. For the station of the crop land, rows of more than two cycles should be included in the field of view (<30), and the side length of the image should be parallel to the row. If there are no more than two complete cycles, then information regarding row spacing and plant spacing are required. The FVC of the entire cycle, that is, the FVC of the quadrat, can be obtained from the number of rows included in the field of view. For other three stations, the photos of FVC were obtained by directly photographing for the lower heights of the vegetation. 1.2.4 Method for calculating the FVC The FVC calculation was implemented by the Beijing Normal University. The detail method can be found in the reference below. Many methods are available to extract the FVC from digital images, and the degree of automation and the precision of identification are important factors that affect the efficiency of field measurements. This method, which is proposed by the authors, has the advantages of a simple algorithm, a high degree of automation and high precision, as well as ease of operation (see the reference). 2. Biomass observation 2.1. Observation time 2.1.1 Station of the crop land: The observations lasted from 20 May 2012 to 15 September 2012, and in five-day periods for each observation before 31 July and in ten-day periods for each observation after 31 July. The observation time for the crop land are 2013-5-25, 2013-5-30, 2013-6-5, 2013-6-10, 2013-6-16, 2013-6-22, 2013-6-27, 2013-7-2, 2013-7-7, 2013-7-12, 2013-7-17, 2013-7-27, 2013-8-3, 2013-8-13, 2013-8-25, 2013-9-5 and 2013-9-15. 2.1.2 The station of wetland: The observations lasted from 20 May 2012 to 15 September 2012, and in ten-day periods for each observation. The observation time for the crop land are 2013-6-5, 2013-6-16, 2013-6-27, 2013-7-7, 2013-7-17, 2013-7-27, 2013-8-3, 2013-8-13, 2013-8-25, 2013-9-5 and 2013-9-15. 2.2. Method Station of the crop land: Three plots were selected and three strains of corn for each observation were random selected for each plot to measure the fresh weight (the aboveground biomass and underground biomass) and dry weight. Per unit biomass can be obtained according to the planting structure. Station of the wetland: Two plots of reed with the area of 0.5 m × 0.5 m were random selected for each observation. The reed of the two plots was cut to measure the fresh weight (the aboveground biomass) and dry weight. 2.3. Instruments Balance (accuracy 0.01 g); drying oven 3. Data storage All observation data were stored in excel. Other data including plant spacing, row spacing, seeding time, irrigation time, the time of cutting male parent and the harvest time of the corn for the station of cropland were also stored in the excel.
GENG Liying, Jia Shuzhen, Li Yimeng, MA Mingguo
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by the vehicle borne microwave radiometer on November 21-22, 2013 in Wuxing village farmland, Ganzhou District, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 21-22, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, which can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 2.5m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by vehicle borne microwave radiometer from November 19 to 20, 2013 in Wuxing village farmland, Ganzhou District, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 19-20, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 2.5m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by vehicle borne microwave radiometer from November 18 to 19, 2013 in Wuxing village farmland, Ganzhou District, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 18-19, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 3.5m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
The data set contains cosmic ray instrument (CRS) observations from January 1, 2016 to December 31, 2016.The station is located in gansu province zhangye city da man irrigated area farmland, under the surface is corn field.The longitude and latitude of the observation point are 100.3722e, 38.8555n, and 1556m above sea level. The bottom of the instrument probe is 0.5m from the ground, and the sampling frequency is 1 hour. Original observations of cosmic ray instruments include: voltage Batt (V), temperature T (c), relative humidity RH (%), pressure P (hPa), fast neutron number N1C (hr), thermal neutron number N2C (hr), fast neutron sampling time N1ET (s) and thermal neutron sampling time N2ET (s).The data published are processed and calculated. The data headers include Date Time, P (pressure hPa), N1C (fast neutron number/hour), N1C_cor (fast neutron number/hour with revised pressure) and VWC (soil volume moisture content %). The main processing steps include: 1) data filtering There are four criteria for data screening :(1) data with voltage less than and equal to 11.8 volts are excluded;(2) remove the data of air relative humidity greater than and equal to 80%;(3) data whose sampling interval is not within 60±1 minute are excluded;(4) the number of fast neutrons removed changed by more than 200 in one hour compared with that before and after.In addition, the missing data was supplemented by -6999. 2) air pressure correction According to the fast neutron pressure correction formula mentioned in the instrument instruction manual, the original data were revised to obtain the revised fast neutron number N1C_cor. 3) instrument calibration In the process of calculating soil moisture, N0 in the calculation formula should be calibrated.N0 is the number of fast neutrons under the condition of soil drying. The measured soil moisture (or through relatively dense soil moisture wireless sensor) m (Zreda et al. Here, according to Soilnet soil water data in the source area of the instrument, the instrument was calibrated to establish the relationship between soil volumetric water content v and fast neutrons.Selection of dry and wet conditions are the obvious difference of June 26, 2012-27 and July 16-17, four days of data, including June 26-27 rate data showed that soil moisture is small, so the selection of 4 cm, 10 and 20 cm as the rate of the three values of average data, its range is 22% 30%, and July 16-17 rate data showed that soil moisture is bigger, so select 4 cm and 10 cm as two value average rate data, the range of 28% - 39%, final N0 an average of 3597. 4) soil moisture calculation According to the formula, the hourly soil water content data were calculated. Please refer to Liu et al. (2018) for information of hydrometeorological network or site, and Zhu et al. (2015) for observation data processing.
LIU Shaomin, ZHU Zhongli, XU Ziwei, LI Xin, CHE Tao, TAN Junlei, REN Zhiguo
The data set includes the river level observation data of No. 4 point in the dense runoff observation of the middle reaches of Heihe River from May 20, 2015 to March 11, 2016. The instrument maintenance was completed again on May 20, 2015. The observation point is located in Heihe bridge, Shangbao village, Jing'an Township, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n39.065 °, e100.433056 °, 1431m above sea level, and 58m wide river channel. In 2012, hobo pressure type water level gauge was used for water level observation with acquisition frequency of 30 minutes; since 2013, sr50 ultrasonic distance meter was used with acquisition frequency of 30 minutes. On June 25, 2014, the instrument was damaged and re purchased. The record was restarted on May 20, 2015. The data includes the following parts: Water level observation, observation frequency 30 minutes, unit (cm); For information of hydrometeorological network or station, please refer to Li et al.(2013), and for observation data processing, please refer to He et al.(2016).
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The data set includes the observation data of river water level and velocity at No.7 point in the dense observation of runoff in the middle reaches of Heihe River from January 1, 2015 to March 11, 2016. The sensor was abnormal at the end of 2014, and the commissioning was normal on March 25 after maintenance. The observation point is located in Heihe bridge, Pingchuan Township, Linze County, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n39.331667 °, e100.099722 °, altitude 1375 meters, and channel width 130 meters. In 2015, sr50 ultrasonic distance meter was used for water level observation, with acquisition frequency of 30 minutes. Data description includes: Water level observation, observation frequency 30 minutes, unit (cm); The missing data are uniformly represented by the string -6999. For information of hydrometeorological network or station, please refer to Li et al.(2013), and for observation data processing, please refer to He et al.(2016).
LIU Shaomin, LI Xin, XU Ziwei
The 1 km / 5-day Lai data set of Heihe River basin provides the 5-day Lai synthesis results of 2010-2014. The data uses Terra / MODIS, Aqua / MODIS, as well as domestic satellites fy3a / MERSI and fy3b / MERSI sensor data to build a multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. Multi-source remote sensing data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm first classifies the quality of multi-source data sets, which can be divided into first level data, second level data and third level data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. The purpose of quality evaluation and classification is to provide the basis for the selection of the optimal data set and the design of inversion algorithm flow. Leaf area index product inversion algorithm is designed to distinguish mountain land and vegetation type, using different neural network inversion model. Based on global DEM map and surface classification map, PROSAIL model is used for continuous vegetation such as grassland and crops, and gost model is used for forest and mountain vegetation. Using the reference map generated by the measured ground data of the forests in the upper reaches of Heihe River and the oasis in the middle reaches, and scaling up the corresponding high-resolution reference map to 1km resolution, compared with the Lai product, the product has a good correlation between the farmland and the forest area and the reference value, and the overall accuracy basically meets the accuracy threshold of 0.5%, 20% specified by GCOS. By cross comparing this product with Lais products such as MODIS, geov1 and glass, the accuracy of this Lai product is better than that of similar products compared with reference value. In a word, the synthetic Lai data set of 1km / 5 days in Heihe River Basin comprehensively uses multi-source remote sensing data to improve the estimation accuracy and time resolution of Lai parameter products, so as to better serve the application of remote sensing data products.
LI Jing, Yin Gaofei, YIN Gaofei, ZHONG Bo, WU Junjun, WU Shanlong
This set of data is the simulation result of the newly developed land eco-hydrological model CLM_LTF.This model is on top of the land-surface process model CLM4.5 developed by NCAR, coupling the groundwater lateral flow module and considering the role of human irrigation. The model runs from 1981 to 2013, with a spatial resolution of 30 arc seconds (0.0083 degrees), a time step of 1,800 seconds, and a simulation range of the heihe river basin.Air force in 1981-2012 is used by the Chinese academy of sciences institute of the qinghai-tibet plateau of qinghai-tibet plateau more layers of data assimilation and simulation center development areas of China high space-time resolution ground meteorological elements drive data set, air is forced to use 2013 national meteorological information center of wind pressure high resolution made by the wet precipitation temperature radiation data set.The land cover data is a 1km land cover grid data set for the MICLCover heihe river basin, and the irrigation data is shown in "monthly 30-arcsecond resolution surface water and groundwater irrigation data set for the heihe river basin 1981-2013" of the scientific data center for cold and dry regions.The mode output is the monthly average. The document is described as follows: Groundwater depth data: heihe_zwt.nc 2cm soil moisture data: heihe_h2osoi_2cm. nc 100cm soil moisture data: heihe_h2osoi_100cm.nc Evaporation data: Heihe_evaptanspiration. Nc The data is in netcdf format.There are three dimensions, which are month, lat, and lon. Where, month is a month, and the value is 0-395, representing each month from 1981 to 2013. Lat is grid latitude information, and lon is grid longitude information. The data is stored in the data variable. The underground water depth data is in m, the soil moisture data is in m^3/m^3, and the evapotranspiration data is in mm/month
XIE Zhenghui
Hydrological data of Heihe River: investigation data of water diversion process of Heihe River. Methods: field investigation, interview, data collection and electronization; Content overview: this data includes the documents, documents and research reports obtained from the investigation of the water diversion process of Heihe River by Tsinghua University, mainly including the interview records of Mr. Zhou Kan, the party who made the water diversion plan. Time and space: 1950-2010; Heihe River Basin
WANG Zhongjing, ZHENG Hang
On June 15, 2012, the satellite transit ground synchronous observation was carried out in the TerraSAR-X sample near the super station in the dense observation area of Daman. TerraSAR-X satellite carries X-band synthetic aperture radar (SAR). The daily transit image is HH / VV polarized, with a nominal resolution of 3 m, an incidence angle of 22-24 ° and a transit time of 19:03 (Beijing time), which mainly covers the ecological and hydrological experimental area of the middle reaches artificial oasis. The local synchronous data set can provide the basic ground data set for the development and verification of active microwave remote sensing soil moisture retrieval algorithm. Quadrat and sampling strategy: Six natural blocks are selected in the southeast of the super station, with an area of about 100 m × 100 m. One plot in the northwest corner of the sample plot is watermelon field, others are corn. The basis of sample selection is: (1) considering different vegetation types, i.e. watermelon and corn; (2) considering the visible light pixel, the sample size of 100m square can guarantee at least 4 30 M-pixel is located in the sample; (3) the location of the sample is near the super station, with convenient transportation. The observation of the super station is in the north, and there is a water net node on both sides of the East and the west, which makes it possible to integrate these observations in the future; (4) in addition, there are some obvious points around the sample, which can ensure that the geometric correction of the SAR image is more accurate in the future. Considering the resolution of the image, 21 splines (distributed from east to West) are collected at 5 m intervals. Each line has 23 points (north-south direction) at 5 m intervals. Four hydroprobe data acquisition systems (HDAS, reference 2) are used to measure at the same time. The sampling interval is controlled by the scale and moving splines on the measuring line to make up for the lack of using hand-held GPS. Measurement content: About 500 points on the quadrat were obtained, and each point was observed twice, i.e. in each sampling point, once in the film (marked a in the data record) and once out of the film (marked b in the data record); although the watermelon land was also covered with film, considering that it was not laid horizontally, only the soil moisture at the non covered position was measured (marked b in both data records). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and imaginary part of soil complex dielectric are observed. The vegetation team completed the measurement of biomass, Lai, vegetation water content, plant height, row ridge distance, chlorophyll, etc. Data: This data set includes two parts: soil moisture observation and vegetation observation. The former saves the data format as a vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file; the vegetation sampling information is recorded in the excel table.
WANG Shuguo, MA Mingguo, LI Xin
The aim of the simultaneous observation of river surface temperature is obtaining the river surface temperature of different places, while the sensor of thermal infrared go into the experimental areas of artificial oases eco-hydrology on the middle stream. All the river surface temperature data will be used for validation of the retrieved river surface temperature from thermal infrared sensor and the analysis of the scale effect of the river surface temperature, and finally serve for the validation of the plausibility checks of the surface temperature product from remote sensing. 1. Observation sites and other details Ten river sections were chosen to observe surface temperature simultaneously in the midstream of Heihe River Basin on 3 July and 4 July, 2012, including Sunan Bridge, Binhe new area, Heihe Bridge, Railway Bridge, Wujiang Bridge, Gaoya Hydrologic Station, Banqiao, Pingchuan Bridge, Yi’s Village, Liu’s Bridge. Self-recording point thermometers (observed once every 6 seconds) were used in Railway Bridge and Gaoya Hydrologic Station while handheld infrared thermometers (observed once of the river section temperature for every 15 minutes) were used in other eight places. 2. Instrument parameters and calibration The field of view of the self-recording point thermometer and the handheld infrared thermometer are 10 and 1 degree, respectively. The emissivity of the latter was assumed to be 0.95. All instruments were calibrated on 6 July, 2012 using black body during observation. 3. Data storage All the observation data were stored in excel.
HE Xiaobo, Jia Shuzhen
This dataset contains the spectra of white cloth and black cloth obtained in the simultaneous time during the airborn remote sensing which supports the airboren data preprocessing as CASI, SASI and TASI , and the spetra of the typical targets in the middle reaches of the Heihe River Basin. Instruments: SVC-HR1024 from IRSA, ASD Field Spec 3 from CEODE, Reference board Measurement method: the spectra radiance of the targets are vertically measured by the SVC or ASD; before and after the target, the spectra radiance of the reference board is measured as the reference. This dataset contains the spectra recorded by the SVC-HR1024 ( in the format of .sig which can be opened by the SVC-HR1024 software or by the notepad ) and the ASD (in the format of .asd), the observation log (in the format of word or excel), and the photos of the measured targets. Observation time: 15-6-2012, the spectra of typical targets in the EC matrix using SVC 16-6-2012, the spectra of typical targets in the wetland by SVC 29-6-2012, the spectra of typical vegetation and soil in Daman site and Gobi site by ASD 29-6-2012, the spectra of white cloth and black cloth by ASD which is simultaneous with the airborne CASI data 30-6-2012, the spectra of vegetation and soil in the desert by ASD 5-7-2012, the spectra of white cloth and black cloth by ASD which is simultaneous with the airborne CASI data 7-7-2012, the spectra of corn in the Daman site for the research of daily speral variation. 8-7-2012, the spectra of white cloth and black cloth by ASD which is simultaneous with the airborne CASI data 8-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation 9-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation 10-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation 11-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation. The time used in this dataset is in UTC+8 Time.
XIAO Qing, MA Mingguo
The data set include crop leaf stomatal conductance observed at four sample regions, that is the soil moisture control experimental field at Daman county, and the super station, and Shiqiao sample plots at Wuxing village in Zhangye city. 1) Objective Crop leaf stomatal conductance, a key biophysical parameter, was observed as model parameter or a priori knowledge for crop growth model, or evapotranspiration estimation. 2) Measuring instruments Leaf porometer. 3) Measuring site a. the soil moisture control experimental field at Daman county, Twelve soil water treatments are set. The crop leaf stomatal conductance for each treatment is measured on 17, 23 and 29 May, and 3, 9, 14 and 24 June, and 5 and 12 July. b. the Super Station The crop leaf stomatal conductance at the super station is measured on 22 and 28 May, 5, 11, 18, and 25 June, and 1, 8, 15, 22 and 31 July, 9, 15 and 22 August, and 3 and 11 September. c. the Shiqiao sample site The crop leaf stomatal conductance at the Shiqiao village is measured on 17, 22 and 28 May, 4, 11, 17 and 25 June, 1, 8, 15, 22, and 30 July, 8, 16 and 27 August, and 9 September. 4) Data processing The observational data was recorded in the sheets and reorganized in the EXCEL sheets. The time used in this dataset is in UTC+8 Time.
Xu Fengying, Wang Jing, Huang Yongsheng, LI Xin, MA Mingguo
Biological productivity refers to the material production capacity of organisms and their groups or even larger scale (including ecosystem and biosphere). It changes with the environment. Therefore, it becomes an indicator of environmental change and the health of the earth system. Net primary productivity (NPP) of vegetation refers to the remaining part of total organic matter (GPP) produced by photosynthesis of green plants in unit time unit area after deducting autotrophic respiration (RA). The NPP products in Heihe River Basin mainly focus on the important parameters par and FPAR of the model of light energy utilization, and improve the algorithm and product production. The FPAR inversion model that distinguishes the direct radiation from the scattered radiation and the par inversion method based on the combination of static and polar orbit satellites are proposed. Finally, the net primary productivity data set of Heihe River Basin is produced by using the light utilization model. The algorithm improves the temporal and spatial resolution of data products, and the accuracy of products is also significantly improved.
LI Li, ZHONG Bo, WU Junjun, WU Shanlong, XIN Xiaozhou
The dataset combined with crop phrenology data and field management data which were investigated near the 13 eddy covariance (EC) stations. 1.1 Objective of investigation Objectives of investigation is to supply assistant information for experiment on EC, meteorology, and biophysics parameter. 1.2 Investigation spots and items Investigation spots include Jiu She of Shiqiao village (EC3), Xiaoman southern road (EC16), Wu She of Five stars village (EC13), Wu She of Xiaoman village (EC14), Er She of Shiqiao village (EC5), Liu She of Zhonghua village (EC11), Liu She of Shiqiao village (EC2), Wu She of JinCheng village (EC7), EC6, Liu She of Jincheng village (EC8), Yi She of Kangning village (EC9), Er She of Kangning village (EC10), and Si She of Jingcheng village (EC12). Investigation items comprise crop type, crop name, seed time, seed type, plant span, row span, field area, germination time, three leaves period, seven leaves period, farming way, farming time, irrigation time, irrigation water volume, fertilization time, fertilization type, and fertilization rate. The time used in this dataset is in UTC+8 Time. 1.3 Data collection Data was collected by using ask-reply approach according to investigation tables.
GE Yingchun, Ma Chunfeng, LI Xin
The dataset includes channel flow measured at the second irrigation stage in spring (22 May, 2012), the third irrigation stage in spring (18 June, 2012) and the first irrigation stage in autumn (16 July, 2012). The time used in this dataset is in UTC+8 Time. 1.1 Objective of measurement Objective of measuring channel flow are to provide the conference data for irrigation water optimal allocation model according to obtain reality water volume measured at Dou channel and Mao channel. Data set also is used to reference data for other observations such as eddy, biophysical parameters. 1.2 Observation measures and principle Measures: flow meter named Flowatch, which is made in Switzerland, observation precision: 0.1m/s; and rule, observation of which is 1cm. Principle: Flowatch, which is mechanical-based, is used to compute the velocity of the fluid according to vanes speed. The flow of channels is computed by using observed flow velocity and channel sectional area calculated on the basis of channel engineer sectional parameters and water level. 1.3 Observation location and items Observation spots include Yingyi branch San dou (Liu She, Shang’er She, and Xia’er She of Shiqiao village), Si Dou (Qi She, Ba She, and Jiu She of Shiqiao village), and Wu Dou (Yi She of Shiqiao village) at Yingke irrigation district, and seven Mao channels branched from five star branch channel Si Dou San Nong. Observation time is described as followed: Second stage irrigation in summer: 2012-5-22: Si Dou, Yingyi branch channel: Jiu She (Shiqiao village) 2012-5-23: Si Dou, Yingyi branch channel: Ba She (Shiqiao village) 2012-5-24 to 2012-5-25: Si Dou, Yingyi branch channel: Qi She (Shiqiao village) 2012-5-26 to 2012-5-28: Wu Dou, Yingyi branch channel: Yi She (Shiqiao village) 2012-5-28 to 2012-5-29: San Dou, Yingyi branch channel: Xia’er She (Shiqiao village) 2012-5-29 to 2012-5-30: San Dou, Yingyi branch channel: Shang’er She (Shiqiao village) 2012-5-30 to 2012-6-2: San Dou, Yingyi branch channel: Liu She (Shiqiao village) 2012-6-6: Yi Mao, Er Mao, San Mao, Si Mao, and Wu Mao branched from Five star branch channel Si Dou San Nong: Five star village 2012-6-7: Liu Mao, and Qi Mao branched from Five star branch channel Si Dou San Nong: Five stars village Third stage irrigation in summer: 2012-6-18 to 2012-6-19: Si Dou, Yingyi branch channel: Jiu She (Shiqiao village) 2012-6-19 to 2012-6-20: Si Dou, Yingyi branch channel: Ba She (Shiqiao village) 2012-6-20 to 2012-6-21: Si Dou, Yingyi branch channel: Qi She (Shiqiao village) 2012-6-22 to 2012-6-24: Wu Dou, Yingyi branch channel: Yi She (Shiqiao village) 2012-6-24 to 2012-6-26: San Dou, Yingyi branch channel: Xia’er She (Shiqiao village) 2012-6-26 to 2012-6-27: San Dou, Yingyi branch channel: Shang’er She (Shiqiao village) 2012-6-27 to 2012-6-30: San Dou, Yingyi branch channel: Liu She (Shiqiao village) 2012-7-1 to 2012-7-2: Yi Mao, Er Mao, San Mao, Si Mao, Wu Mao, Liu Mao, and Qi Mao branched from Five star branch channel Si Dou San Nong: Five stars village First stage irrigation in Autumn: 2012-7-16 to 2012-7-18: Si Dou, Yingyi branch channel: Jiu She (Shiqiao village) 2012-7-18 to 2012-7-19: Si Dou, Yingyi branch channel: Ba She (Shiqiao village) 2012-7-19 to 2012-7-21: Si Dou, Yingyi branch channel: Qi She (Shiqiao village) 2012-7-21 to 2012-7-24: Wu Dou, Yingyi branch channel: Yi She (Shiqiao village) 2012-7-24 to 2012-7-25: San Dou, Yingyi branch channel: Xia’er She (Shiqiao village) 2012-7-25 to 2012-7-27: San Dou, Yingyi branch channel: Shang’er She (Shiqiao village) 2012-7-27 to 2012-7-31: San Dou, Yingyi branch channel: Liu She (Shiqiao village) 2012-7-27 to 2012-7-28: Yi Mao, Er Mao, San Mao, Si Mao, Wu Mao, Liu Mao, and Qi Mao branched from Five star branch channel Si Dou San Nong: Five stars village Second stage irrigation in Autumn: 2012-8-8 to 2012-8-9: Si Dou, Yingyi branch channel: Jiu She (Shiqiao village) 2012-8-9 to 2012-8-10: Si Dou, Yingyi branch channel: Ba She (Shiqiao village) 2012-8-10 to 2012-8-12: Si Dou, Yingyi branch channel: Qi She (Shiqiao village) 2012-8-13 to 2012-8-15: Wu Dou, Yingyi branch channel: Yi She (Shiqiao village) 2012-8-15 to 2012-8-17: San Dou, Yingyi branch channel: Xia’er She (Shiqiao village) 2012-8-17 to 2012-8-19: San Dou, Yingyi branch channel: Shang’er She (Shiqiao village) 2012-8-19 to 2012-8-22: San Dou, Yingyi branch channel: Liu She (Shiqiao village) 2012-8-24 to 2012-8-25: Yi Mao, Er Mao, San Mao, Si Mao, Wu Mao, Liu Mao, and Qi Mao branched from Five star branch channel Si Dou San Nong: Five stars village Observed items: average flow velocity of channel (m/s), water level of channel (m), water temperature (℃), engineer sectional parameters of channel (investigation). Average flow velocity and water level of channel are measured one time per hour when channel flow is stable. However, the two items are measured two times or more times when channel flow is unstable. 1.4 Data process Observed data is saved in excel sheet, types of which include channel flow velocity, channel sectional area, water level, and water temperature. Channel flow and irrigation water volume are calculated by using observed data according to data per-process approach.
GE Yingchun, MA Chunfeng, Xu Fengying, LI Xin
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by the vehicle borne microwave radiometer and synchronous measurement from November 24-25, 2013 in the desert of Minle County, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 24-25, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage, 18.7ghz h polarization damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 2.3m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
The purpose of differential GPS positioning survey is to unify multiple survey areas into the same coordinate system and realize accurate absolute positioning through joint survey with national high-level control point coordinates. Under the national geodetic coordinate system of 2000, the accurate positioning of flux observation matrix, hulugou small watershed, tianmuchi small watershed and dayokou watershed and target is completed. In order to realize the geometric correction and absolute positioning of optical image, SAR image and airborne lidar data, the layout of ground control points and high-precision measurement are completed. In the middle reaches of the area, one national high-level control point is jointly surveyed in the five directions of East, South, West, North and middle. Measuring instrument: There are 3 sets of triple R8 GNSS system. Measurement principle: For the control network encryption point, it is connected with the high-level known points in four quadrants around the survey area and distributed evenly in the survey area. For the ground control point (GCP), the obvious characteristic points (such as house corner, road intersection, inflection point, etc.) of the ground layout target and the independent ground objects are adopted and evenly distributed in the survey area. For the ground points with high accuracy requirements, the principle of average value of multiple (at least three) measurements is adopted. Measurement method: In the test area, the control network is encrypted, and GPS static measurement and national high-level control network are used for joint measurement and calculation. During measurement, multiple GPS receivers conduct static synchronous observation at different stations, and the observation time is strictly in accordance with the control network measurement specifications. The ground points in the test area are accurately located. GPS-RTK positioning technology is used and the national high-level control points are used to calibrate to the local coordinate system. When the mobile station obtains the fixed solution during the coordinate acquisition, the measurement is carried out again and the single measurement lasts for 5S. Measuring position: (1) Flux observation matrix 17 stations, Las tower, waternet, soilnet and bnunet nodes in the core area of flux observation matrix; ground control points in CASI flight area; ground corner reflector positions in radar coverage area; ground target positions in lidar flight area. (2) Hulugou small watershed Ground target location of lidar flight area. (3) Tianmuchi small watershed Ground target location of lidar flight area. (4) Dayokou Basin Satellite image geometric correction ground control point. Data format: GPS static survey, the original data format is ". Dat" and ". T01" (or ". T02") files (or converted renix data) and "field record". GPS-RTK survey, the original project is ". Job" file (or converted ". DC" file). The test results are submitted in the format of exported ". CSV" data, which can be viewed and edited by Excel software. Measurement time: June 19, 2012 to July 30, 2012
LIU Xiangfeng, MA Mingguo
During the period of middle stream experiment in 2012, closed chamber and gas chromatography method was used to measure soil respiration of different land surface, including farmland, orchard, wetland, sparse grassland (Huazhaizi), Gobi, desert. Instrument: Assimilation Chamber Measuring method: Assimilation chamber consists of two parts: the base and the box. Base made of PVC material, the bottom buried in the soil. The box is made of stainless steel cubes, with one open side. When measuring the box cover on the base, air in the box was sampled using injector. The extracted air was injected into the gas sampling bag, and shipped back to the laboratory analysis of the concentration of CO2 by gas chromatography in Institute of Botany, The Chinese Academy of Sciences. Using the difference of concentration of CO2 at two times to calculate soil respiration. Each measurement points are located three repeat. After five minutes sealed box cover start mining the 1st sample, and then taken once every sample interval of 10 minutes, four times in total mining. Date content: Data content includes header information and once every 10 days three times repeated observations and the average of the three times. Measuring location: Gobi (Bajitan Gobi station), Wetland (Zhangye wetland Station), Sparse grassland (Huazhaizi desert steppe Station), Desert (Shenshawo sandy desert Station), Orchard (site No.17 eddy covariance system), Maize Farmland (Daman Superstation) Measuring time: 16-6-2012, 28-6-2012, 9-7-2012, 18-7-2012, 30-7-2012, 11-8-2012, 21-8-2012, 2-9-2012, 13-9-2012, 22-9-2012 (UTC+8).
MA Mingguo, LI Xianglan
The dataset includes two parts that are: 1) channel flow, crop pattern, field management, and socio-economy data measured at super-station in 2008, 2010, 2011, 2012 (UTC+8), respectively. 2) irrigation data, crop pattern, and socio-economy data investigated at Daman irrigation district and Yingke irrigation district, respectively. 1.1 Objective of investigation Objectives of investigation for two parts data are to obtain crop pattern and irrigation water volume change with time, and to supply parameter for irrigation water optimal allocation model. 1.2 Investigation spots and items Investigation spots include six water management stations that are Dangzhai, Hua’er, Daman, Xiaoman, Jiantan, and Ershilidun, respectively, at Daman irrigation district. Investigation items comprise water allocation time, branch channel inflow, Dou channel inflow, irrigation area, channel water use efficiency, water price, and water fee. Investigation time is described as followed: 2012.03.16 to 2012.04.04, Spring irrigation; 2012.04.04 to 2012.05.14, Summer irrigation; 2012.05.20 to 2012.06.24, Summer irrigation; 2012.05.16 to 2012.07.06, Summer irrigation; 2012.07.15 to 2012.08.02, Autumn irrigation; 2012.08.10 to 2012.08.26, Autumn irrigation. Investigation spots include eight water management station that are Chang’an, Shangqin, Dangzhai, Liangjiadun, Shimiao, Xiaoman, Xindun, and Yangou, respectively, at Yingke irrigation district. Investigation time and items is described as followed: Year Data items Spots 2008, 2010, 2011 Irrigation data: Irrigation time, water level of Dou channel, channel flow, irrigation area Xiaoman county, Shangtouzha village 2012 Irrigation data: Irrigation time, water level of Dou channel, channel flow, irrigation area Chang’an, Shangqin, Dangzhai, Liangjiadun, Shimiao, Xiaoman, Xindun, Yangou 2012 Well data: Well deep, groundwater abstraction, irrigation area Chang’an, Liangjiadun, Shangqin 2012 Socio-economy data: population, agricultural income, un-agricultural income, water use for living, average residential area, education Chang’an, Xiaoman, Liangjiadun, Shangqin 2012 Field management: fertilizer name, fertilization time, fertilization rate, pesticide name, pesticide rate, time Chang’an, Xiaoman, Liangjiadun, Shangqin 2008, 2010, 2011, 2012 Crop pattern: crop name, seed time, harvest time, crop area, irrigation quota, field water use efficiency, crop yield, crop production value Xiaoman, Chang’an, Liangjiadun, Shangqin 1.3 Data collection Data was collected by cooperating with water management department of Yingke and Daman.
GE Yingchun, Xu Fengying, LI Xin
On July 7, 2012, airborne ground synchronous observation was carried out in plmr quadrats of Yingke oasis and huazhaizi desert. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The flight mainly covers the middle reaches of the artificial oasis eco hydrological experimental area. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: The observation area is located in the transition zone between the southern edge of Zhangye Oasis and anyangtan desert, on the west side of Zhangye Daman highway, and across the trunk canal of Longqu in the north and the south, which is divided into two parts. In the southwest, there is a 1 km × 1 km desert quadrat. Because the desert is relatively homogeneous, here 1 The soil moisture of 5 points (1 point and center point around each side, and several more points can be measured during walking along the road in the actual measurement process) is collected in KM quadrat. The four corner points are 600 m apart from each other except the diagonal direction. The southwest corner point is huazhaizi desert station, which is convenient to compare with the data of meteorological station. On the northeast side, a large sample with an area of 1.6km × 1.6km was selected to carry out synchronous observation on the underlying surface of oasis. The selection of quadrat is mainly based on the consideration of the representativeness of surface coverage, avoiding residential buildings and greenhouses as much as possible, crossing oasis farmland and some deserts in the south, accessibility, and observation (road consumption) time, so as to obtain the comparison of brightness and temperature with plmr observation. Considering the resolution of plmr observation, 11 splines (east-west distribution) were collected at the interval of 160 m in the east-west direction. Each line has 21 points (north-south direction) at the interval of 80 M. four hydraprobe data acquisition systems (HDAS, reference 2) were used for simultaneous measurement. Measurement content: About 230 points on the quadrat were obtained, each point was observed twice, that is to say, two times were observed at each sampling point, one time was inside the film (marked as a in the data record) and one time was outside the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and virtual part of soil complex dielectric are observed. Vegetation parameter observation was carried out in some representative soil water sampling points, and the measurement of plant height and biomass (vegetation water content) was completed. Data: This data set includes two parts: soil moisture observation and vegetation observation. The former saves the data format as a vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file; the vegetation sampling information is recorded in the excel table.
WANG Shuguo, MA Mingguo, LI Xin
This data set includes the 2013 observation data of 10 water net nodes in the 5.5km × 5.5km observation matrix (red box in the thumbnail) of Yingke / Daman irrigation area in the middle reaches of Heihe River. The 10 water net nodes contain 4cm and 10cm two-layer hydro probe II probes to observe the main variables such as soil moisture, soil temperature, conductivity and complex permittivity; the si-111 infrared temperature probe is set up at 4m height to observe the surface infrared radiation temperature of the underlying surface. The time and frequency of conventional observation is 10 minutes. In order to ensure the accurate synchronization of si-111 and remote sensing, one minute intensive observation is conducted at 00:00-04:30, 08:00-18:00 and 21:00-24:00 every day. This data set can provide spatiotemporal continuous observation data set for remote sensing estimation of key water and heat variables of heterogeneous surface, remote sensing authenticity test, ecological hydrology research, irrigation optimization management and other research. For details, please refer to "2013 middle reaches of Heihe River waternet data document 20141231. Docx"
KANG Jian, LI Xin, MA Mingguo
Soil respiration rate was measured at the super station of Daman irrigation district in Zhangye city using the open circuit soil carbon flux measurement system LI-8100 (LI-COR, Lincoln, NE, USA) 1) Objective: The aim of soil respiration rate measurement is to explore the diurnal variation characteristics of soil respiration rate and to provide a scientific basis for the assessment of farmland ecosystem carbon cycle and carbon balance. 2) Measurement instruments and ways Measurement instruments: the open type of cold dry soil carbon flux measurement system LI-8100 (LI-COR, Lincoln, NE, USA). Measurement means: soil respiration chamber was placed in PVC ring (10 cm of diameter, 5 cm of height), which was inserted into the soil about 1 to 2 cm 1 d before measurement. The observation is automatic with a power supply of solar panels. 3) Measurement time Soil respiration rate was continuously measured mainly in the corn growing season. The time used in this dataset is in UTC+8 Time. 4) Data processing The data was periodically collected from the data collection instrument and saved as *.81x file, then was converted to text format file using LI-8100 (M) PC Client v2.0.0 software.
Wang Jing, Huang Yongsheng, LI Yuan, LI Xin, MA Mingguo
The dataset of photosynthesis was observed by LI-6400XT Portable Photosynthesis System in the artificial oasis eco-hydrology experimental area of the Heihe River Basin. Observation items included two main crops in the middle reaches of Heihe river: wheat and maize, which located in the town of Pingchuan in Linze and the Super Station of Wuxing, respectively. Observation periods lasted from mid-May to September. This dataset included the raw observation data and the pretreatment data of wheat and maize observed by LI-6400 during the observation periods. Objectives of observation: The photosynthetic datasets can be used in the study of plant physiological ecology characteristic and the simulation and validation for the eco-hydrological models. Instrument and theory of the observation: (1) Measuring instrument: LI-6400XT Portable Photosynthesis System; (2) Measuring theory: Using the infrared gas analyzer to measure the change of CO2 concentration, and then measuring the differences of CO2 concentration between the sample chamber and the referenced chamber so as to acquire the net productivity of the leaf. Time and site of observation: (1) Observation site of the wheat: in the town of Pingchuan in Linze; Observation time: 2012-05-17，2012-06-08 to 2012-6-13; (2) Observation site of the maize: in the Super Station of Wuxing; Observation time: from 2012-05-19 to 2012-08-15. The time used in this dataset is in UTC+8 Time. Data processing: The raw data of LI-6400 were archived in text format and can be opened by text editor or excel, the preprocessed data were in Excel format. Every time period of observation was archived in a single document, named as “date + type + time”, every leaf was recorded 3 times, and then added a remark.
WANG Haibo
This dataset is the LAI observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observation period is from 24 May to 20 September 2012 (UTC+8). Measurement instruments: LAI-2000 (Beijing Normal University) Measurement positions: Core Experimental Area of Flux Observation Matrix 18 corn samples, 1 orchard sample, 1 artificial white poplar sample Measurement methods: To measure the incoming sky radiation on the canopy firstly. Then the transmission sky radiation are mearued under the canopy for serveral times. The canopy LAI is retrieved by using the gap probability model.
Li Yun, Wang Yan, MA Mingguo
Zhanye Airport desert observation system can offer in situ calibration data for TASI, WiDAS and L band sensor used in aerospace experiment. Observation Site: This point is located in a large, homogeneous and flatten desert near by Zhangye Airport. The main vegetation type is Sparse and low shrub. The coordinates of this site: 38°4′41.30" N, 100°41′48.10" E. Observation Instrument: The observation system consists of two SI-111 infrared radiometers (Campbell, USA), one installed vertically downward to land surface, another face to south of zenith angle 35°. SI-111 sensor installed at 4.0 m height. Observation Time: This site operates from 10 June, 2012 to today. Observation data laagered by every 5 seconds uninterrupted. Output data contained sample data of every 5 seconds and mean data of 1 minute. Accessory data: Land surface infrared temperature (by SI-111), sky infrared temperature (by SI-111) can be obtained. Dataset is stored in *.dat file, which can be read by Microsoft excel or other text processing software (UltraEdit, et. al). Table heads meaning: TarT_Atm, Sky infrared temperature @ facing south of zenith angle 35° (℃); SBT_Atm, body temperature of SI-111 sensor (℃) measured sky; TarT_Sur, land surface infrared temperature @ 4.0 m height; SBT_Sur, body temperature of SI-111 sensor (℃) measured land surface. Dataset is stored day by day, named as: data format + site name + interval time + date + time. The detailed information about data item showed in data header introduction in dataset.
MA Mingguo
On 29 June 2012, CASI sensor carried by the Harbin Y-12 aircraft was used in a visible near Infrared hyperspectral airborne remote sensing experiment, which is located in the observation experimental area (30×30 km). The land cover pattern product in the middle reaches of the Heihe River Basin were obtained at a spatial resolution of 1 m, using CASI aerial data with high spatial and spectral resolution．A hierarchical classification structure integrated by pixel-based classification and object-based classification is used to obtain production．According to surveyed reference data about land cover and visual interpretation from high resolution imagery，the accuracy of the classification result of land cover was evaluated，and the result showed that overall accuracy was 84.61 %，Kappa coefficient was 0.8262．
XIAO Qing, Liu Liangyun
This data was measured in middle stream of the Heihe River Basin in year 2012. Soil texture, porosity, bulk density, saturated water conductivity, soil organic matter were measured for each layer of the soil profile which is very close to the AMS sites. This data can be used in land surface model and ecological model. Soil profile position: The coordinate of the profile is listed as follow. No.1 to No.17 is corresponding to the AMS number in the Matrix. No. x y 1 100.3582 38.89322 2 100.3541 38.88697 3 100.3763 38.89057 5 100.3506 38.87577 6 100.3597 38.8712 7 100.3652 38.87677 8 100.3765 38.87255 9 100.3855 38.87241 10 100.3957 38.87569 11 100.342 38.86994 12 100.3663 38.86516 13 100.3785 38.86077 14 100.3531 38.85869 16 100.3641 38.8493 17 100.3697 38.84512 15 (superstation) 100.3721 38.85547 Gebi 100.3058 38.91801 Huazhaizi 100.3189 38.7652 Shenshawo 100.4926 38.78794 Instruments: Soil texture: Microtrac laser particle analyzer Porosity: Ring sampler law Bulk density: Ring sampler law Saturated Water Conductivity: hydrostatic head method Soil organic matter: Total organic carbon analyzer (TOC-VCPH) Measuring time: 2012-5-20 to 2012-7-10 (UTC+8). Measuring content: Soil texture, porosity, bulk density, saturated water conductivity, soil organic matter.
MA Mingguo, WANG Xufeng, WANG Haibo, YU Wenping
During lidar and widas flight in summer 2012, the ground synchronously carried out the continuous observation of differential GPS of ground base station, and obtained the synchronous GPS static observation data, which is used to support the synchronous solution of aviation flight data. Measuring instrument: Two sets of triple R8 GNSS system. Zgp8001 sets Time and place of measurement: On July 19, 2012, EC matrix lidar flew and observed at mjwxb (northwest of Maojiawan) and sbmz (shibamin) two base stations at the same time On July 25, 2012, lidar of hulugou small watershed and tianmuchi small watershed in the upper reaches flew, observed in XT Xiatang, lidar of Zhangye City calibration field in the middle reaches, and observed in mjwxb (northwest of Maojiawan) On July 26, 2012, lidar flight of hulugou small watershed and tianmuchi small watershed in the upper reaches was observed in XT Xiatang, lidar flight of Zhangye City calibration field in the middle reaches was observed in HCZ (railway station) On August 1, 2012, the upper east and West branches of widas flew and observed in yng (yeniugou) On August 2, 2012, the midstream EC matrix test area widas flew and observed in HCZ (railway station) On August 3, 2012, the midstream EC matrix test area widas flew and observed in mjwxb (northwest Maojiawan) Data format: Original data format before differential preprocessing.
LIU Xiangfeng, MA Mingguo
250m/1km month compositing Fraction Vegetation Cover (FVC) data set of Heihe River Basin provides the results of monthly FVC synthesis in 2011-2014. The data is produced by using MODIS vegetation index products MOD13A2 and MOD13Q1 based on dimidiate pixel model.
ZHONG Bo, WU Junjun
On 10 July 2012 (UTC+8), TASI sensor carried by the Harbin Y-12 aircraft was used in a visible near Infrared hyperspectral airborne remote sensing experiment, which is located in the observation experimental area (30×30 km), Linze region and Heihe riverway. The relative flight altitude is 2500 meters. The wavelength of TASI is 8-11.5 μm with a spatial resolution of 3 meters. Through the ground sample points and atmospheric data, the data are recorded in surface radiance processed by geometric correction and atmospheric correction.
XIAO Qing, Wen Jianguang
The albedo product was obtained based on the visible and near-infrared hyperspectral radiometer (29 June, 2012) which covered the artificial oasis eco-hydrology experimental area (5.5 km*5.5 km)with a 5 m spatial resolution.
XIAO Qing, Wen Jianguang
On 19 August 2012, a Leica ALS70 airborne laser scanner boarded by the Y-12 aircraft was used to obtain the point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 2900 m with the point cloud density 1 point per square meter. Aerial LiDAR-DSM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
On 19 August 2012, a Leica ALS70 airborne laser scanner boarded on the Y-12 aircraft was used to obtain the Lidar point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 2900 m with the point cloud density 1 point per square meter. Aerial LiDAR-DEM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
The first dataset of ground truth measurements synchronizing with airborne Polarimetric L-band Multibeam Radiometer (PLMR) mission was obtained in the Yingke oasis and Huazhaizi desert steppe on 28-29 June, 7, 10, 26 July, 2 August, 2012 (UTC+8). The dataset of ground truth measurements synchronizing with airborne Polarimetric L-band Multibeam Radiometer (PLMR) mission was obtained in the Linze Inland River Basin Comprehensive Research Station on 3 July, 2012. PLMR is a dual-polarization (H/V) airborne microwave radiometer with a frequency of 1.413 GHz, which can provide multi-angular observations with 6 beams at ±7º, ±21.5º and ±38.5º. The PLMR spatial resolution (beam spot size) is approximately 0.3 times the altitude, and the swath width is about twice the altitude. The measurements were conducted in the southwest part of the Zhangye Oasis, which included two sampling plots. One was located in Gobi desert with an area of 1 km × 1 km. Due to its homogeneous landscape, around 10 points were sampled to acquire the situation of soil water content. The other sampling plot was designed in farmlands with a dominant plant type of maize. Ground measurements took place along 16 transects, which were arranged parallelly with an interval of 160 m between each other in the east-west direction. In each 2.4 km long transect, soil moisture was sampled at every 80 m in the north-south direction. Steven Hydro probes were used to collect soil moisture and other measurements. For each sampling point in farmland, two measurements were acquired within an area of 1 m2, with one for the soil covered by plastic film (point name was tagged as LXPXXA) and the other for exposed soil (point name was tagged as LXPXXB). The field campaign started from 11:00 AM, but stopped at 4:00 PM on 28 June because of rain. The rest of measurements were completed from 10:30 AM to 5:30 PM on 29 June. Concurrently with soil moisture sampling, vegetation properties were measured at around 10 locations within the farmland sampling plot. Observation items included: Soil parameters: volumetric soil moisture (inherently converted from measured soil dielectric constant), soil temperature, soil dielectric constant, soil electric conductivity. Vegetation parameters: biomass, vegetation water content, canopy height. Data and data format: This dataset includes two parts of measurements, i.e. soil and vegetation parameters. The former is as shapefile, with measured items stored in its attribute table. The measured vegetation parameters are recorded in an Excel file.
WANG Shuguo, LI Xin
This dataset includes the retrieved soil moisture products from the airborne PLMR microwave radiometer on 30 June, 7 July, 10 July, 26 July and 2 August, 2012 (UTC+8), in the HiWATER artificial oasis eco-hydrology experimental area of Heihe river basin. The soil moisture (SM), vegetation water content (VWC) and surface roughness (Hr) are simultaneously retrieved based on six brightness temperatures at three incidence angles (7°, 21.5°, 38.5°) and with dual polarization (H and V), by using the Levenberg-Marquardt optimization algorithm. The spatial resolution of the soil moisture products is 700 m, which represent the ~5 cm surface soil moisture according to the L-band observation wavelength. This dataset is in the format of asc, and with UTM projection (47°N). The validation against the eco-hydrological wireless sensor network observations and artificial synchronized observation shows that the total accuracy of this dataset can achieve 0.05 cm^3/cm^3, and that of the products on 7 July and 10 July even less than 0.04 cm^3/cm^3. This dataset can be helpful for the land surface process/hydrological process simulation and data assimilation, surface flux estimation, artificial irrigation management and spatial scaling research.
Li Dazhi, KANG Jian, LI Xin
The data set include crop height observed at four sample regions, that is the soil moisture control experimental field at Daman county, and the EC plots, the super station, and Shiqiao sample plots at Wuxing village in Zhangye city. 1) Objective Crop height, a key biophysical parameter, was observed for evapotranspiration estimation in regional scale and the retrieval of other biophysical parameters as well as the application in eco-hydrological models. 2) Measurement instrument: Steel tape. 3) Measurement site a. the soil moisture control experimental field at Daman county, Twelve soil water treatments are set. The wheat height are measured on 17, 23 and 29 May, and 3, 9, 14 and 24 June, and 5 and 12 July. b. the EC site Maize height at 14 EC site (EC-2,EC-3,EC-5,EC-6,EC-7,EC-8,EC-9, EC-10, EC-11, EC-12, EC-13, EC-14, EC-15, EC-16) are measured on 14, 21, 25 and 31 May, 7, 13, 23 and 28 June, 3, 13, 18 and 23 July, 3, 12 and 28 August. c. the super station Maize height at the super station is measured on 22 and 28 May, 5, 11, 18, and 25 June, and 1, 8, 15, 22 and 31 July, 9, 15 and 22 August, and 3 and 11 September. d. the Shiqiao sample site Maize height at the Shiqiao village is measured on 17, 22 and 28 May, 4, 11, 17 and 25 June, 1, 8, 15, 22, and 30 July, 8, 16 and 27 August, and 9 September. 4) Data processing The observational data was recorded in the sheets and reorganized in the EXCEL sheets. The time used in this dataset is in UTC+8 Time.
Wang Jing, Xu Fengying, Huang Yongsheng, LI Xin, MA Mingguo
The dataset contains vegetation type and plant structure in the middle reaches of the Heihe River Basin, which was used to validate products from remote sensing. It was generated from investigating the land cover strips of CASI and SASI the middle reaches of the Heihe River Basin between 25 June and 6 August in 2012. Instruments: High-precision handheld GPS (2-3 m) and digital camera were used as main tools in the survey. Measurement method: Vegetation range in the middle reaches of the Heihe River Basin and survey route could be decided with the help of Google Earth. Wuxing village in Xiaoman town was selected to survey detailed and other places were investigated as far to reach as possible. Main methods were to write down the longitude and latitude, phenology of the plant structure, take photos for the vegetation. Dataset contains: longitude and latitude, vegetation type, area and phenology. Observation Place: CASI flight area in artificial oasis in the middle reaches, CASI stripe flight area in the middle reaches and Zhangye district. Date: From 25 June and 6 August in 2012.
Zhang Miao
The data set include crop biomass observed at four sample regions, that is the soil moisture control experimental field at Daman county, and the EC plots, the super station, and Shiqiao sample plots at Wuxing village in Zhangye city. 1) Objective Crop biomass, a key biophysical parameter, was observed for calibration and validation of crop growth model and the retrieval of other biophysical parameters as well as the application in eco-hydrological models. 2) Measurement instrument: Electronic balance (±0.1g) and oven. 3) Measurement site a. the soil moisture control experimental field at Daman county, Twelve soil water treatments are set. The wheat biomass for each treatment is measured on 17, 23 and 29 May, and 3, 9, 14 and 24 June, and 5 and 12 July. b. the EC site Maize biomass at 14 EC site (EC-2,EC-3,EC-5,EC-6,EC-7,EC-8,EC-9, EC-10, EC-11, EC-12, EC-13, EC-14, EC-15, EC-16) are measured on 14, 21, 25 and 31 May, 7, 13, 23 and 28 June, 3, 13, 18 and 23 July, 3, 12 and 28 August. c. the super station Maize biomass at the super station is measured on 22 and 28 May, 5, 11, 18, and 25 June, and 1, 8, 15, 22 and 31 July, 9, 15 and 22 August, and 3 and 11 September. d. the Shiqiao sample site Maize biomass at the Shiqiao village is measured on 17, 22 and 28 May, 4, 11, 17 and 25 June, 1, 8, 15, 22, and 30 July, 8, 16 and 27 August, and 9 September. 4) Data processing The observational data was recorded in the sheets and reorganized in the EXCEL sheets.
Xu Fengying, Wang Jing, Ma Chunfeng, Huang Yongsheng, LI Xin, MA Mingguo
A land surface temperature observation system was set up in apple orchard near by the No.17 eddy covariance system of the MUlti-Scale Observation experiment on Evapotranspiration over heterogeneous land surfaces 2012 (MUSOEXE-12). This observation site can offer in situ calibration data of apple trees for TASI, WiDAS and L band sensor used in aerospace experiment. Observation Site: This point is located in a large and homogeneous apple orchard in Zhangye Experiment Field, Gansu Academy of Agricultural Sciences. It’s 4 meters away from southwest of No.17 eddy covariance system, and observation height is 4.55 m. Crown size of observed apple tree is 4 m × 4 m. Underlying surface of observation site is mainly apple trees. The coordinates of this site: 38°50′41.70" N，100°22′11.40" E. Observation Instrument: The observation system consists of one SI-111 infrared radiometers (Campbell, USA) installed vertically downward to apple tree. Observation Time: This site operates from 3 August, 2012 to 27 September, 2012. Observation data laagered by every 1 minute uninterrupted. Output data contained sample data of every 1 minute. Accessory data: Land surface (apple tree) infrared temperature (by SI-111) can be obtained. Dataset is stored in *.dat file, which can be read by Microsoft excel or other text processing software (UltraEdit, et. al). Table heads meaning: Target_C_Avg, apple tree temperature @ 4.55 m (℃); SBT_C_Avg, body temperature of SI-111 sensor (℃). Dataset is stored day by day, named as: data format + site name + interval time + date + time. The detailed information about data item showed in data header introduction in dataset.
MA Mingguo
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by vehicle borne microwave radiometer from November 22 to 24, 2013 in Desert Park desert, Ganzhou District, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 22-24, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 7.4M 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
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
Jia Shuzhen
This data includes the coverage data set of vegetation in one growth cycle in five stations of Daman super station, wetland, desert, desert and Gobi, and the biomass data set of maize and wetland reed in one growth cycle in Daman super station. The observation time starts from May 10, 2014 and ends on September 11, 2014. 1 coverage observation 1.1 observation time 1.1.1 super station: the observation period is from May 10 to September 11, 2014. Before July 20, the observation is once every five days. After July 20, the observation is once every 10 days. A total of 17 observations are made. The specific observation time is as follows:; Super stations: May 10, 15, 20, 25, 30, 10, 15, 20, 20, 30, 30, 30, 30, 30, 7, 10, 10, 10, 10, 10, 15 1.1.2 other four stations: the observation period is from May 20 to September 15, 2014, once every 10 days, and 11 observations have been made in total. The specific observation time is as follows:; Other four stations: May 10, 2014, May 20, 2014, May 30, 2014, June 10, 2014, June 20, 2014, June 30, July 10, 2014, July 20, August 5, 2014, August 17, 2014, September 11, 2014 1.2 observation method 1.2.1 measuring instruments and principles: The digital camera is placed on the instrument platform at the front end of the simple support pole to keep the shooting vertical and downward and remotely control the camera measurement data. The observation frame can be used to change the shooting height of the camera and realize targeted measurement for different types of vegetation. 1.2.2 design of sample Super station: take 3 plots in total, the sample size of each plot is 10 × 10 meters, take photos along two diagonal lines in turn each time, take 9-10 photos in total; Wetland station: take 2 sample plots, each plot is 10 × 10 meters in size, and take 9-10 photos for each survey; 3 other stations: select 1 sample plot, each sample plot is 10 × 10 meters in size, and take 9-10 photos for each survey; 1.2.3 shooting method For the super station corn and wetland station reed, the observation frame is directly used to ensure that the camera on the observation frame is far higher than the vegetation crown height. Samples are taken along the diagonal in the square quadrat, and then the arithmetic average is made. In the case of a small field angle (< 30 °), the field of view includes more than 2 ridges with a full cycle, and the side length of the photo is parallel to the ridge; in the other three sites, due to the relatively low vegetation, the camera is directly used to take pictures vertically downward (without using the bracket). 1.2.4 coverage calculation The coverage calculation is completed by Beijing Normal University, and an automatic classification method is adopted. For details, see article 1 of "recommended references". By transforming RGB color space to lab space which is easier to distinguish green vegetation, the histogram of green component A is clustered to separate green vegetation and non green background, and the vegetation coverage of a single photo is obtained. The advantage of this method lies in its simple algorithm, easy to implement and high degree of automation and precision. In the future, more rapid, automatic and accurate classification methods are needed to maximize the advantages of digital camera methods. 2 biomass observation 2.1 observation time 2.1.1 corn: the observation period is from May 10 to September 11, 2014, once every 5 days before July 20, and once every 10 days after July 20. A total of 17 observations have been made. The specific observation time is as follows:; Super stations: May 10, 15, 20, 25, 30, 10, 15, 20, 20, 30, 30, 30, 30, 30, 7, 10, 10, 10, 10, 10, 15 2.1.2 Reed: the observation period is from May 20 to September 15, 2014, once every 10 days, and 11 observations have been made in total. The specific observation time is as follows:; 2014-5-10、2014-5-20、2014-5-30、2014-6-10、2014-6-20、2014-6-30、2014-7-10、2014-7-20、2014-8-5、2014-8-17、2014-9-11 2.2 observation method Corn: select three sample plots, and select three corn plants that represent the average level of each sample plot for each observation, respectively weigh the fresh weight (aboveground biomass + underground biomass) and the corresponding dry weight (85 ℃ constant temperature drying), and calculate the biomass of unit area corn according to the plant spacing and row spacing; Reed: set two 0.5m × 0.5m quadrats, cut them in the same place, and weigh the fresh weight (stem and leaf) and dry weight (constant temperature drying at 85 ℃) of reed respectively. 2.3 observation instruments Balance (accuracy 0.01g), oven. 3 data storage All the observation data were recorded in the excel table first, and then stored in the excel table. At the same time, the data of corn planting structure was sorted out, including the plant spacing, row spacing, planting time, irrigation time, except for the parent time, harvesting time and other relevant information.
YU Wenping, GENG Liying, Li Yimeng, TAN Junlei, MA Mingguo
The dataset contains vegetation type in the middle reaches of the Heihe River Basin, which was used to validate products from remote sensing. It was generated from investigating the land cover strips of CASI during 2012. Instruments: High-precision handheld GPS (2-3 m) and digital camera were used as main tools in the survey. Measurement method: Hierarchical classification is applied based on CASI data. According to various land types, pixel classifications is used for forest, grassland, bare land and building lands; in-situ observations and investigations are used for different crops. Dataset contains: land types, including maize, leek, poplar trees, cauliflower, bell pepper, potatoes, endive sprout, orchard, watermelon, kidney bean, pear orchard, shadow, and non-vegetation, except for 14 others which are not classified. Observation site: core experimental areas with 5*5 matrix structure in the middle reaches of the Heihe river basin Date: From 25 June in 2012 (UTC+8) on.
Zhang Miao
On 19 July 2012 (UTC+8), Leica ALS70 airborne laser scanner carried by the Harbin Y-12 aircraft was used in a LiDAR airborne optical remote sensing experiment. The relative flight altitude is 1500 m (the elevation of 2700 m). Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm with the point cloud density 4 points per square meter. Based on the original Airborne LiDAR-DEM data production were obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
During the 2012 aerial remote sensing experiment conducted midstream, Li-Cor8100 was used to measure soil respiration every five days in the EC matrix area. Instrument: LI-Cor8100 Measuring Method: Soil respiration ring was made using PVC pipe with length of 10 cm. Before measuring soil respiration, soil respiration ring was inserted into the soil, 4 cm in soil and 6 cm above soil. Soil respiration measurement should be taken after standing for at least 24 hours science ring was inserted in soil. Sample measurement time is during 9-12 in the morning. Set of three replicates per plot. Marked according to EC site name. Data content: Data content includes header information, and once every five days repeated three times observations value and the average value. Measuring location: EC sites within the matrix core experiment area (No. EC01 to EC17), each plot set three repeat samples. For the superstation (EC15) plot set nine repeat samples. Measuring time: From 6 June to 20 August, 2012, once every five days for site EC01, EC03, EC05, EC10, EC11, EC12, EC13, EC14, and EC17; from 1 July to 20 August, 2012, once every five days for site EC02, EC04, EC06, EC07, EC08, EC09 and EC16. The time used in this dataset is in UTC+8 Time. Part of the observation points during the observation just irrigation, these times are not observable.
LI Yuan, SHI Weiyu, SONG Yi
On June 26, 2012, the satellite transit ground synchronous observation was carried out in the TerraSAR-X sample near the super station in the dense observation area of Daman. TerraSAR-X satellite carries X-band synthetic aperture radar (SAR). The daily transit image is HH / VV polarized, with a nominal resolution of 3 m, an incidence angle of 22-24 ° and a transit time of 19:03 (Beijing time), which mainly covers the ecological and hydrological experimental area of the middle reaches artificial oasis. The local synchronous data set can provide the basic ground data set for the development and verification of active microwave remote sensing soil moisture retrieval algorithm. Quadrat and sampling strategy: Six natural blocks are selected in the southeast of the super station, with an area of about 100 m × 100 m. One plot in the northwest corner of the sample plot is watermelon field, others are corn. The basis of sample selection is: (1) considering different vegetation types, i.e. watermelon and corn; (2) considering the visible light pixel, the sample size of 100m square can guarantee at least 4 30 M-pixel is located in the sample; (3) the location of the sample is near the super station, with convenient transportation. The observation of the super station is in the north, and there is a water net node on both sides of the East and the west, which makes it possible to integrate these observations in the future; (4) in addition, there are some obvious points around the sample, which can ensure that the geometric correction of the SAR image is more accurate in the future. Considering the resolution of the image, 21 splines (distributed from east to West) are collected at 5m intervals. Each line has 21 points (north-south direction) at 5m intervals. Three hydroprobe data acquisition systems (HDAS, reference 2) are used to measure at the same time. The sampling interval is controlled by the scale and moving splines on the measuring line to make up for the lack of using hand-held GPS. Measurement content: About 440 points on the quadrat were obtained, and each point was observed twice, i.e. two times in each sampling point, one time inside the film (marked as a in the data record) and one time outside the film (marked as B in the data record); although the watermelon land was also covered with film, considering that it was not laid horizontally, only the soil moisture at the non covered position was measured (marked as B in the two data records). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and imaginary part of soil complex dielectric are observed. Because the vegetation in this area has been sampled and observed once every five days, no special vegetation synchronous sampling has been carried out on that day. Data: The data format of this data set is vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file.
WANG Shuguo, MA Mingguo, LI Xin
On August 2, 2012, airborne ground synchronous observation was carried out in plmr quadrats of Yingke oasis and huazhaizi desert. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The flight mainly covers the middle reaches of the artificial oasis eco hydrological experimental area. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: The observation area is located in the transition zone between the southern edge of Zhangye Oasis and anyangtan desert, on the west side of Zhangye Daman highway, and across the trunk canal of Longqu in the north and the south, which is divided into two parts. In the southwest, there is a 1 km × 1 km desert quadrat. Because the desert is relatively homogeneous, here 1 The soil moisture of 5 points (1 point and center point around each side, and several more points can be measured during walking along the road in the actual measurement process) is collected in KM quadrat. The four corner points are 600 m apart from each other except the diagonal direction. The southwest corner point is huazhaizi desert station, which is convenient to compare with the data of meteorological station. On the northeast side, a large sample with an area of 1.6km × 1.6km was selected to carry out synchronous observation on the underlying surface of oasis. The selection of quadrat is mainly based on the consideration of the representativeness of surface coverage, avoiding residential buildings and greenhouses as much as possible, crossing oasis farmland and some deserts in the south, accessibility, and observation (road consumption) time, so as to obtain the comparison of brightness and temperature with plmr observation. Considering the resolution of plmr observation, 11 splines (east-west distribution) were collected at the interval of 160 m in the east-west direction. Each line has 21 points (north-south direction) at the interval of 80 M. four hydraprobe data acquisition systems (HDAS, reference 2) were used for simultaneous measurement. Measurement content: About 230 points on the quadrat were obtained, each point was observed twice, that is to say, two times were observed at each sampling point, one time was inside the film (marked as a in the data record) and one time was outside the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and virtual part of soil complex dielectric are observed. No synchronous vegetation sampling was carried out on that day. Data: This data set consists of two parts: soil moisture observation and vegetation observation. The former saves data in vector file format, and the spatial location is the location of each sampling point (WGS84 + UTM 47N). Soil moisture and other measurement information are recorded in attribute file.
WANG Shuguo, MA Mingguo, LI Xin
The data set include crop leaf chlorophyll content observed at four sample regions, that is the soil moisture control experimental field at Daman county, and the EC plots, the super station, and Shiqiao sample plots at Wuxing village in Zhangye city. 1) Objective Crop leaf chlorophyll content, a key biophysical parameter, was observed as model parameter or a priori knowledge for canopy radiative transfer model or eco-hydrological models. 2) Measuring instruments SPAD. 3) Measuring site a. the soil moisture control experimental field at Daman county, Twelve soil water treatments are set. The wheat leaf chlorophyll content for each treatment is measured on 17, 23 and 29 May, and 3, 9, 14 and 24 June, and 5 and 12 July. b. the EC site The maize leaf chlorophyll content at 14 EC site (EC-2,EC-3,EC-5,EC-6,EC-7,EC-8,EC-9, EC-10, EC-11, EC-12, EC-13, EC-14, EC-15, EC-16) are measured on 14, 21, 25 and 31 May, 7, 13, 23 and 28 June, 3, 13, 18 and 23 July, 3, 12 and 28 August. c. the Super Station The maize chlorophyll content at the super station is measured on 22 and 28 May, 5, 11, 18, and 25 June, and 1, 8, 15, 22 and 31 July, 9, 15 and 22 August, and 3 and 11 September. d. the Shiqiao sample site The maize chlorophyll content at the Shiqiao village is measured on 17, 22 and 28 May, 4, 11, 17 and 25 June, 1, 8, 15, 22, and 30 July, 8, 16 and 27 August, and 9 September. 4) Data processing The observational data was recorded in the sheets and reorganized in the EXCEL sheets. The time used in this dataset is in UTC+8 Time.
Xu Fengying, Wang Jing, Huang Yongsheng, LI Xin, MA Mingguo
On July 3, 2012, airborne ground synchronous observation was carried out in plmr sample belt near Linze station. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: According to the typical ground surface type represented by three points near Linze station and taking part of neutron tube observation into account, the three routes from northwest to southeast are designed, with an interval of 200 m, a design altitude of about 300 m and a plmr ground resolution of 100 m. According to the observation characteristics of the route and plmr, three observation transects are designed on both sides of the route, each of which is about 6 km long. From west to East are L1, L2 and L3 respectively. Among them, L1 and L2 are centered on the middle route, 80 m apart; L2 and L3 are 200 m apart. Four hydroprobe data acquisition systems (HDAS, ref. 2) were used to measure at the same time. Measurement content: About 4500 points on the sample belt were obtained, each point was observed twice, that is to say, in each sampling point, once in the film (marked as a in the data record) and once out of the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and virtual part of soil complex dielectric are observed. Vegetation parameter observation was carried out in some representative soil water sampling points, and the measurement of plant height and biomass (vegetation water content) was completed. Note: the observation date coincides with the irrigation of large area of farmland in this area, which makes it difficult for the observer to move forward, the field block is difficult to enter, and the observation point position deviates from the preset point position. Data: This data set includes two parts: soil moisture observation and vegetation observation. The former saves the data format as a vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file; the vegetation sampling information is recorded in the excel table.
WANG Shuguo, MA Mingguo, LI Xin
The first dataset of ground truth measurements synchronizing with TerraSAR-X was obtained in the Daman foci experimental area on 4 June, 2012. The satellite image was in StripMap mode and HH/VV polarization with an incidence angle of 22-24°, and the overpass time was approximately at 19:00 UTC+8. The second dataset of ground truth measurements synchronizing with TerraSAR-X was obtained in the Daman foci experimental area on 15 June, 2012. The satellite image was in StripMap mode and HH/VV polarization with an incidence angle of 22-24°, and the overpass time was approximately at 19:00 UTC+8. The third dataset of ground truth measurements synchronizing with TerraSAR-X was obtained in the Daman foci experimental area on 26 June, 2012. The satellite image was in StripMap mode and HH/VV polarization with an incidence angle of 22-24°, and the overpass time was approximately at 19:00 UTC+8. The measurements were conducted at a sampling plot southeast to the Daman Superstation with an area of around 100 m × 100 m, which was dominantly planted with maize. Steven Hydro probes were used to collect soil moisture and other measurements with an interval of 5 m. For each sampling point, two measurements were acquired within an area of 1 m2, with one for the soil covered by plastic film (point name was tagged as LXPXXA) and the other for exposed soil (point name was tagged as LXPXXB). Concurrently with soil moisture sampling, vegetation properties were measured at around 10 locations within this sampling plot. Observation items included: Soil parameters: volumetric soil moisture (inherently converted from measured soil dielectric constant), soil temperature, soil dielectric constant, soil electric conductivity. Vegetation parameters: biomass, LAI, vegetation water content, canopy height, row distance and leaf chlorophyll content. Data and data format: This dataset includes two parts of measurements, i.e. soil and vegetation parameters. The former is as shapefile, with measured items stored in its attribute table. The measured vegetation parameters are recorded in an Excel file.
WANG Shuguo, LI Xin
In July 19, 2012 (UTC+8), the airborne LIDAR data is acquired in the foci area in the Heihe,middle reaches, which can provide high spatial resolution (m) and high precision (20 cm) of the surface elevation information. Based on airborne LIDAR data processing, the land surface DEM, DSM and point cloud density map were generated. By subtracting DSM and DEM directly, a Vegetation height product in the middle reaches of the Heihe River Basin was obtained. The product overall accuracy is 88%.
XIAO Qing, Wen Jianguang
A land surface temperature and upward/downward shortwave radiation observation system was set up on the roof, which locate on the edge of No.4 eddy covariance system (EC4) of the MUlti-Scale Observation EXperiment on Evapotranspiration over heterogeneous land surfaces 2012 (MUSOEXE-12). This observation site can offer in situ calibration data for TASI, WiDAS and L band sensor used in aerospace experiment. Observation Site: This point is located in a large and homogeneous adobe roof in Shiqiao Village, Xiaoman Town, Zhangye City. Land surface of observation site is relatively flat and uniform, and also not tall trees around. It’s about 20 meters away from southwest No.4 eddy covariance system (EC4) observation points. The coordinates of this site: 38°52′38.50″ N，100°21′27.00″ E。 Observation Instrument: Observation system is composed of a SI-111 infrared radiometer (Campbell, USA) installed vertically downward, two CMP3 pyranometer (Kipp&Zonen, Netherlands) one upward, another downward. Observation height is 1.0 m, data logging by a Campbell CR850 logger. Sensor orientation： Observation mounting arm has 3 m long, parallel to roof edge, azimuth angle: 156° (East by south 66°) Observation Time: This site operates from 23 June, 2012 to 20 September, 2012. Observation data laagered by every 5 seconds uninterrupted. Output data contained sample data of every 5 seconds and mean data of 1 minute. Accessory data: Land surface (adobe roof) temperature, downward/upward total solar radiation, surface albedo. Dataset is stored in *.dat file, which can be read by Microsoft excel or other text processing software (UltraEdit, et. al). Table heads meaning: Rs_downwell, downward shortwave radiation (W/m^2); Rs_upwell, upward (reflect) shortwave radiation (W/m^2); albedo, calculate by Rs_upwell/ Rs_downwell. SBT_C, body temperature of SI-111 sensor (℃); Target_C, Target of surface temperature (℃). Dataset is stored day by day, named as: data format + site name + interval time + date + time. The detailed information about data item showed in data header introduction in dataset.
MA Mingguo
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by vehicle borne microwave radiometer from November 17 to 18, 2013 in Wuxing village farmland, Ganzhou District, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 17-18, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 3.6m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by the vehicle borne microwave radiometer on November 15-16, 2013 in the farmland of jiushe, Kangning, Zhangye City, Gansu Province. The surface temperature includes the soil temperature data observed by the temperature sensor at the soil depth of 0 cm, 1 cm, 3 cm, 5 cm and 10 cm. The time frequency of conventional observation of soil temperature is 5 minutes. Data details: 1. Time: November 15-16, 2013 2. data: Bright temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz v-polarization and H-polarization data (10.65ghz band instrument damaged) Soil temperature: use the sensor installed on dt85 to measure the soil temperature of 0cm, 1cm, 3cm, 5cm and 10cm Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 4.8m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
On July 26, 2012, the airborne ground synchronous observation was carried out in the plmr quadrat in the dense observation area of Daman. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The flight mainly covers the middle reaches of the artificial oasis eco hydrological experimental area. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: The observation area is located in the matrix of the dense observation area of Daman, and the detailed plan with an area of 3.0KM × 2.4km is selected to carry out synchronous observation on the underlying surface of oasis. The selection of the sample is mainly based on the representativeness of the surface coverage, accessibility and observation (road consumption) time, so as to obtain the comparison of brightness and temperature with plmr observation. Considering the resolution of plmr observation, 5 splines (east-west distribution) were collected at an interval of 450 m in the east-west direction. Each line has 31 points (north-south direction) at an interval of 100 m, and 5 hydraprobe data acquisition systems (HDAS, reference 2) were used for simultaneous measurement. Measurement content: About 150 points on the quadrat were obtained, each point was observed twice, that is to say, two times were observed at each sampling point, one time was inside the film (marked as a in the data record) and one time was outside the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and imaginary part of soil complex dielectric are observed. Because the vegetation in this area has been sampled and observed once every five days, no special vegetation synchronous sampling has been carried out on that day. Data: This data set consists of two parts: soil moisture observation and vegetation observation. The former saves data in vector file format, and the spatial location is the location of each sampling point (WGS84 + UTM 47N). Soil moisture and other measurement information are recorded in attribute file.
WANG Shuguo, MA Mingguo, LI Xin
Er’ba Reservoir surface temperature of water body can offer in situ calibration data for TASI, WiDAS and L band sensor used in aerospace experiment. Observation Site: This site is 14 KM away from East of ZhangYe city. It’s located in Er’ba village, JianTan town, ZhangYe city. The coordinates of this site: 38°54′57.14" N, 100°36′57.39" E. Observation Instrument: The observation system consists of two SI-111 infrared radiometers (Campbell, USA) and two 109SS temperature probes (Campbell, USA). Two SI-111 sensors, one installed vertically downward to water surface, another face to south of zenith angle 35°. Temperature probes float under water surface at 0 cm. SI-111 sensor installed at 3.0 m height, 3.4 m away from water edge. Observation Time: This site operates from 27 May, 2012 to 27 September, 2012. Observation data laagered by every 5 seconds uninterrupted. Output data contained sample data of every 5 seconds and mean data of 1 minute. Accessory data： Water surface infrared temperature (by SI-111), sky infrared temperature (by SI-111), water surface temperature (by 109ss) can be obtained. Dataset is stored in *.dat file, which can be read by Microsoft excel or other text processing software (UltraEdit, et. al). Table heads meaning: TarT_Atm, Sky infrared temperature (℃) @ facing south of zenith angle 35°; SBT_Atm, body temperature of SI-111 sensor (℃) measured sky; TarT_Sur, water surface infrared temperature @ 3.0 m height; SBT_Sur, body temperature of SI-111 sensor (℃) measured water surface; WaterT_1, WaterT_2, water surface temperature (℃) measured by 109SS temperature probes. Dataset is stored day by day, named as: data format + site name + interval time + date + time. The detailed information about data item showed in data header introduction in dataset.
MA Mingguo
On July 10, 2012, the airborne flight and ground observation was synchronously carried out in the PLMR quadrat of Yingke Oasis and the Huazhaizi Desert. PLMR (Polarimetric L-band Multibeam Radiometer) is a dual-polarized (H/V) L-band microwave radiometer with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, and a resolution of 1 km (relative flight height of 3 km).The radiometer has 6 beams to observe synchronously, and the incident angles are ±7º，±21.5º，±38.5º, and the sensitivity is less than 1K. The flight observation mainly covers the artificial oasis eco-hydrological test area in the middle reaches. This ground-synchronized data set provides a basic ground dataset for developing and validating passive microwave remote sensing inversion soil moisture algorithms. Quadrat and sampling strategy: The observation area is located in the transition zone between the southern margin of Zhangye Oasis and Anyang beach desert, the west side of Zhang (Zhangye)-Da (Daman) highway. It is divided into two parts by the main canal of the Dragon Canal from North to South. The Southwest area is a desert quadrat with the size of 1 km×1 km. The desert is relatively homogeneous, so soil moisture of 5 points in the 1 km quadrat are collected (1 point of each corner and the center point, in the actual measurement process, several extra points can be measured along the road). The four corner points are 600 meters away from each other,except the diagonal direction. And the southwest corner point is Huazhaizi Desert Station, for the convenience of comparison with weather station data. On the northeast side, a large size quadrat of 6 km×1.6 km is selected for simultaneous observation of the oasis underlying surface.In order to obtain the brightness temperature comparison with the PLMR observation, the quadrat was chose based on the following factors :surface coverage representative, avoiding the residential and greenhouses, crossing the oasis farmland and part of the Southern desert, accessibility, and observation time(road consumption). Taking the resolution of PLMR observations into consideration, in the synchronous observation, 11 sampling lines (East-West distribution) were collected with an interval of 160 meters from the East to the West. Each line from the North to the South was separated by 21 points with an interval of 80 meters. And 4 Hydraprobe Data Acquisition System (HDAS, Reference 2) were used to measure simultaneously. Measurement contents: About 230 points of the quadrat were collected, 2 observations were performed on each point, that is, 2 observations were performed on each sampling point of the film mulched corn field, 1 inside the film (marked as a in the data record), 1 outside the film (marked as b in the data record). Since the HDAS system useed the POGO portable soil sensor, the soil temperature, soil moisture (volumetric water content), loss tangent, soil electrical conductivity, soil complex dielectric real part and imaginary part were obtained by observation. No special simultaneous sampling of vegetation was carried out on the same day. Data: The data set includes two parts: soil moisture observation and vegetation observation. The former saves the data as a vector file, the spatial position is the position of each sampling point (WGS84+UTM 47N), and the measurement information of soil moisture is recorded in the attribute file.
WANG Shuguo, LI Xin
On 30 June 2012 (UTC+8), TASI sensor carried by the Harbin Y-12 aircraft was used in a visible near Infrared hyperspectral airborne remote sensing experiment, which is located in the observation experimental area (30×30 km), Linze region and Heihe riverway. The relative flight altitude is 2500 meters. The wavelength of TASI is 8-11.5 μm with a spatial resolution of 3 meters. Through the ground sample points and atmospheric data, the data are recorded in surface radiance processed by geometric correction and atmospheric correction. Land surface temperature (LST) data was retrieved by temperature/emissivity separation algorithm.
XIAO Qing, Wen Jianguang
The aim of the simultaneous observation of land surface temperature is obtaining the land surface temperature of different kinds of underlying surface, including greenhouse film, the roof, road, ditch, concrete floor and so on, while the sensor of thermal infrared go into the experimental areas of artificial oases eco-hydrology on the middle stream. All the land surface temperature data will be used for validation of the retrieved land surface temperature from thermal infrared sensor and the analysis of the scale effect of the land surface temperature, and finally serve for the validation of the plausibility checks of the surface temperature product from remote sensing. 1. Observation time and other details On 25 June, 2012, ditch and asphalt road surface temperatures were observed once every five minutes using handheld infrared thermometers recorded. On 26 June, 2012, ditch and asphalt road surface temperatures were observed once every five minutes using handheld infrared thermometers while greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 29 June, 2012, concrete floor surface temperatures were observed continuously using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 30 June, 2012, asphalt road, ditch, bare soil, melonry and ridge of field surface temperatures were observed continuously using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 10 July, 2012, asphalt road, ditch, bare soil, melonry and ridge of field surface temperatures were observed once every one minute using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, concrete floor surface temperatures were observed once every six second using self-recording point thermometer. On 26 July, 2012, asphalt road, concrete floor, bare soil and melonry surface temperatures were observed once every one minute using handheld infrared thermometers during the sensor of WiDAS go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. On 2 August, 2012, corn field and concrete floor surface temperatures were observed using handheld infrared thermometers. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. For corn field, twelve sites were selected according to the flight strip of the WiDAS sensor, and for each site one plot surface temperatures were recorded continuously during the sensor of WiDAS go into the region. On 3 August, 2012, corn field and concrete floor surface temperatures were observed using handheld infrared thermometers. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. For corn field, fourteen sites were selected according to the flight strip of the WiDAS sensor, and for each site three plots surface temperatures were recorded continuously during the sensor of WiDAS go into the region. 2. Instrument parameters and calibration The field of view of the self-recording point thermometer and the handheld infrared thermometer are 10 and 1 degree, respectively. The emissivity of the latter was assumed to be 0.95. The observation heights of the self-recording point thermometer for the greenhouse film and the concrete floor were 0.5 m and 1 m, respectively. All instruments were calibrated three times (on 6 July, 5 August and 20 September, 2012) using black body during observation. 3. Data storage All the observation data were stored in excel.
GENG Liying, Jia Shuzhen, WANG Haibo, PENG Li, Dong Cunhui
Data content: precipitation data of the Aral Sea basin from 2015 to 2018. Data sources and processing methods: from the new generation of global precipitation measurement (GPM) of NASA (version 06, global precipitation observation program), the daily rainfall can be obtained by adding the three-hour rainfall data, and then the eight day rainfall can be obtained. Data quality: the spatial resolution is 0.1 ° x 0.1 ° and the temporal resolution is 8 days. The value of each pixel is the sum of rainfall in 8 days. Data application results: under the background of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics.
XIAO Qing, Wen Jianguang
On 4 July 2012 (UTC+8), a TASI sensor boarded on the Y-12 aircraft was used to obtain the thermal-infrared hyperspectral image, which is located in the observation experimental area, Linze region and Heihe riverway. The relative flight altitude is 1000 meters. The wavelength of TASI is 8-11.5 μm with a spatial resolution of 3 meters. Through the ground sample points and atmospheric data, the data are recorded in surface radiance processed by geometric correction and atmospheric correction. Land surface temperature (LST) data was retrieved by temperature/emissivity separation algorithm.
XIAO Qing, Wen Jianguang
Trough the select tasking, we obtained the WorldView-2 stereo image data in Dayekou Basin production in mid-May 2012. In the same year from July to August, 27 GPS ground control points (GCP) and checkpoints were measured based on the watershed differential GPS control network. Based on the full-field GCPs, the rational polynomial coefficients (RPC) files of WorldView-2 images were corrected in the digital photogrammetry software system. In the stereo model, 60 high-precision tie points evenly distributed were got through image matching technology, and the 1-m and 2-m resolution digital elevation model (DEM) were rapid extracted. Moreover, the DEM was edited in some key areas, such as the shady forest coverage and Dayekou reservoir. The terrain feature points and line data were added to improve the accuracy of the results in large variation of terrain feature. Check points were composed of GPS points and model confidential points, which used for quantitative validation. And they root mean square errors RMSE were 1.9 meters and 1.2 meters respectively, which achieve the requirements of two degree accuracy of 2.0 m at a scale of 1:2000 in high mountains.
Zhang Yanli, MA Mingguo
Trough the select tasking, we obtained the WorldView-2 stereo image data in Dayekou Watershed production in mid-May 2012. In the same year from July to August, 27 GPS ground control points (GCP) and checkpoints were measured based on the watershed differential GPS control network. Based on the full-field GCPs, the rational polynomial coefficients (RPC) files of WorldView-2 images were corrected in the digital photogrammetry software system. In the stereo model, 60 high-precision tie points evenly distributed were got through image matching technology, and the 1-m and 2-m resolution digital elevation model (DEM) were rapid extracted. Based on collinearity equations, images at nadir were corrected to adjust relief displacements and geometric errors, and the 0.5-m resolution digital orthorectified images DOM were obtained with the principle of digital differential rectification in Dayekou Basin.
Zhang Yanli, MA Mingguo
The dataset include the planting structure and area information of major crops in 11 districts and counties of the Heihe River Basin from 2000 to 2012 (grain, wheat, corn, potato, soybean, cotton, oil, vegetables, etc.)
DENG XiangZheng
We produced surface photosynthetic effective radiation (PAR), solar radiation (SSR) and net radiation (NR) products with 1KM resolution in the heihe basin in 2012.The temporal resolution ranges from instantaneous to hourly and daily.Day-by-day ancillary data were also produced, including aerosol optical thickness, moisture content, NDVI, snow cover, and surface albedo.Among them, PAR and SSR use the method of lookup table to directly invert by combining the stationary weather satellite and polar orbit satellite MODIS product.NR was calculated by analyzing the relationship between net short-wave and net surface radiation.Hourly instantaneous products are weighted by average and integral to obtain hourly and daily cumulative products.
HUANG Guanghui
According to the global soil map. Net standard, the 0-1m soil depth is divided into 5 layers: 0-5cm, 5-15cm, 15-30cm, 30-60cm and 60-100cm. According to the principle of soil landscape model, the spatial distribution data products of soil organic carbon content in different layers are produced by using the digital soil mapping method. The source data of this data set comes from the soil profile data integrated by the major research plan integration project of Heihe River Basin (soil data integration and soil information product generation of Heihe River Basin, 91325301). Scope: Heihe River Basin; Projection: WGS · 1984 · Albers; Spatial resolution: 100M; Data format: TIFF; Dataset content: hh_soc_layer1.tif: 0-5cm soil organic carbon content; hh_soc_layer2.tif: 5-15cm soil organic carbon content; hh_soc_layer3.tif: 15-30cm soil organic carbon content; hh_soc_layer4.tif: 30-60cm soil organic carbon content; hh_soc_layer5.tif: 60-100cm soil organic carbon content;
ZHANG Ganlin
Using ETWatch model with the system complete the heihe river basin scale 1 km resolution 2014 surface evaporation data with middle oasis 30 meters resolution on scale data set, the surface evaporation raster image data of the data sets, it is the time resolution of scale from month to month, the spatial resolution of 1 km scale (covering the whole basin) and 30 meters scale (middle oasis area), the unit is mm.Data types include monthly, quarterly, and annual data. The projection information of the data is as follows: Albers equal-area cone projection, Central longitude: 110 degrees, First secant: 25 degrees, Second secant: 47 degrees, Coordinates by west: 4000000 meter. File naming rules are as follows: 1) 1 km resolution remote sensing data set Monthly cumulative ET value file name: heihe-1km_2014m01_eta.tif Heihe refers to heihe river basin, 1km means the resolution is 1km, 2014 means the year of 2014, m01 means the month of January, eta means the actual evapotranspiration data, and tif means the data is tif format. Name of quarterly cumulative ET value file: heihe-1km_2014s01_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2014 represents the year of 2014, s01 represents the period from January to march, and the first quarter, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Annual cumulative value file name: heihe-1km_2014y_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2014 represents the year of 2014, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format. 2) remote sensing data set with a resolution of 30 meters Monthly cumulative ET value file name: heihe-midoasa-30m_2014m01_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents 2014, m01 represents January, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Name of quarterly cumulative ET value file: heihe-midoasa-30m_2014s01_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents 2014, s01 represents january-march, and the first quarter, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Annual cumulative value file name: heihe-midoasa-30m_2014y_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents the year of 2014, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format.
WU Bingfang
1、 Data Description: data includes doc and DIC values of river water and groundwater in hulugou small watershed from July to September 2015. The sampling frequency is once every two weeks. 2、 Sampling location: (1) there are two river water sampling points. The first sampling point is located at the hydrological section at the outlet of hulugou Small Watershed at the upper reaches of Heihe River, with the longitude and latitude of 99 ° 52 ′ 47.7 ″ E and 38 ° 16 ′ 11 ″ n. The second sampling point of the river is located at the outlet of hulugou area II at the upper reaches of Heihe River, with the longitude and latitude of 99 ° 52 ′ 58.40 ″ E and 38 ° 14 ′ 36.85 ″ n. (2) Underground water spring and well water sampling points. The spring sampling point is located at 20 m to the east of the drainage basin outlet, with the longitude and latitude of 99 ° 52 ′ 50.9 ″ E and 38 ° 16 ′ 11.44 ″ n. The well water sampling point is located near the intersection of the East and West Branch ditches, with the longitude and latitude of 99 ° 52 ′ 45.38 ″ E and 38 ° 15 ′ 21.27 ″ n. 3、 Test method: Doc and DIC values of samples were measured by oiaurora 1030w TOC instrument, detection range: 2ppb c-30000ppm C.
MA Rui , HU Yalu
The data includes the county-level data of characteristic agriculture distribution in the Qinghai Tibet Plateau, which lays the foundation for the spatial distribution and development of characteristic agriculture in the Qinghai Tibet Plateau.
MA Rui , HU Yalu
一. data description The data included the precipitation, river water and groundwater in the small calabash valley from July to September 2015 2H, 18O, with a sampling frequency of 2 weeks/time. 二. Sampling location (1) the precipitation sampling point is located in the ecological hydrology station of the institute of cold and dry regions, Chinese academy of sciences, with the latitude and longitude of 99 ° 53 '06.66 "E, 38 ° 16' 18.35" N. (2) the sampling point of the river is located at the outlet flow weir of haugugou small watershed in the upper reaches of the heihe river, with the latitude and longitude of 99 ° 52 '47.7 "E and 38 ° 16' 11" N.The water sampling point number 2 position for heihe river upstream hoist ditch Ⅱ area exports, latitude and longitude 99 ° 52 '58.40 "E, 38 ° 14' 36.85" N. (3) underground water spring and well water sampling points.The sampling point of spring water is located at 20m to the east of the outlet of the basin, with the latitude and longitude of 99°52 '50.9 "E, 38°16' 11.44" N. The well water sampling point is located near the intersection of east and west branches, with the latitude and longitude of 99 ° 52 '45.38 "E, 38 ° 15' 21.27" N. 三. Test method The δ2H and δ18O values of the samples were measured by PICARRO L2130-i ultra-high precision liquid water and water vapor isotope analyzer. The results were expressed by the test accuracy value of v-smow relative to the international standard substance, and the measurement accuracy was 0.038‰ and 0.011‰, respectively.
MA Rui , XING Wenle
1、 Data Description: the data includes the samples of anions and anions of river water and groundwater in hulugou small watershed from July to September 2015 for test and analysis. The sampling frequency is once every two weeks. 2、 Sampling location: (1) there are two river water sampling points. One is located at the outlet flow weir of hulugou small watershed in the upper reaches of Heihe River, with latitude and longitude of 99 ° 52 ′ 47.7 ″ E and 38 ° 16 ′ 11 ″ n. The second sampling point of the river is located at the outlet of hulugou area II at the upper reaches of Heihe River, with the longitude and latitude of 99 ° 52 ′ 58.40 ″ E and 38 ° 14 ′ 36.85 ″ n. (2) Underground water spring and well water sampling points are 20 m to the east of the drainage basin outlet, with longitude and latitude of 99 ° 52 ′ 50.9 ″ E and 38 ° 16 ′ 11.44 ″ n. The well water sampling point is located near the intersection of the East and West Branch ditches, with the longitude and latitude of 99 ° 52 ′ 45.38 ″ E and 38 ° 15 ′ 21.27 ″ n. 3、 Test method: the cation of sample is tested by inductively coupled plasma atomic emission spectrometer (ICP-AES), the test accuracy is 0.05mg/l, and the anion is tested by ion chromatograph (ics1100), the test accuracy is 0.002mg/l.
MA Rui , HU Yalu
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.
MA Rui , HU Yalu
The site No. 1 EC towers were used for the intercomparison field in the Yingke irrigation district (1552.75 m, 38°59′51.71″ N, 100°24′38.76″ E). The land surface is homogeneous and dominated by vegetables in the middle reaches of the Heihe River Basin. The precipitation comparison dataset was collected between 12 June, 2012, and 22 November, 2012. The dataset includes data for five different rain gauge types, i.e., pit gauge, Chinese standard manual precipitation gauge, siphon rain gauge, tipping bucket gauge, and weighting gauge. The mountain heights for these gauges were 0.0, 0.7, 1.2, 1.5, and 1.5 m, respectively. The data were recorded every 1 hour, 1 day, 10 minutes, 10 minutes, and 10 minutes, respectively. The main objective of the data collection was to perform an intercomparison of in situ rainfall measurements. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.
ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
The No. 8 hydrological section is located at Gaotai Heihe River Bridge (39 ° 23′22 .93 ″ N, 99 ° 49′37 .29″ E, 1347 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.8 hydrological section from 17 June, 2012, to 24 November, 2012. The width of this section is 130 meters. The water level was measured using SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following sections: Water level (recorded every 30 minutes) and Discharge. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
This data set includes the observation data of 25 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River from January 2015 to December 2015. 4cm and 20cm soil moisture / temperature is the basic observation of each node; some nodes also include 10cm soil moisture / temperature, surface infrared radiation temperature, snow depth and precipitation observation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification. For details, please refer to "2015 data document 20160501. Docx of water net of Babao River in the upper reaches of Heihe River"
KANG Jian, LI Xin, MA Mingguo
The 1km / 5day vegetation index (NDVI / EVI) data set of Heihe River basin provides a 5-day resolution NDVI / EVI composite product in 2015. The data uses the characteristics of China's domestic FY-3 satellite data with high time resolution (1 day) and spatial resolution (1km) to construct a multi angle observation data set. Based on the analysis of the multi-source data set and the existing composite vegetation index products and algorithms A global synthetic vegetation index product algorithm system based on multi-source data set is proposed. The vegetation index synthesis algorithm of MODIS is basically adopted, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on the semi empirical walthal model. Using the algorithm system, the composite vegetation index is calculated for the first level data and the second level data, and the quality is identified. Multi-source data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm system first classifies the quality of multi-source data sets, which can be divided into primary data, secondary data and tertiary data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. In the middle reaches of Heihe River, the verification results of farmland and forest areas show that the NDVI / EVI composite results of combined multi temporal and multi angle observation data are in good agreement with the ground measured data (RMSE = 0.105). Compared with the time series of MODIS mod13a2 product, it fully shows that when the time resolution is increased from 16 days to 5 days, a stable and high-precision vegetation index can describe the details of vegetation growth in detail. In a word, the NDVI / EVI data set of Heihe River Basin, which is 1km / 5day, comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products and better serves the application of remote sensing data products.
LI Jing, LIU Qinhuo, ZHONG Bo, YANG Aixia
The 5-day Lai synthesis results in 2015 are provided by the 1 km / 5-day Lai data set of Heihe River Basin. The data set is constructed by using the data of Terra / MODIS, Aqua / MODIS, as well as the domestic satellites fy3a / MERSI and fy3b / MERSI to construct the multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. Multi-source remote sensing data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm first classifies the quality of multi-source data sets, which can be divided into first level data, second level data and third level data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. The purpose of quality evaluation and classification is to provide the basis for the selection of the optimal data set and the design of inversion algorithm flow. Leaf area index product inversion algorithm is designed to distinguish mountain land and vegetation type, using different neural network inversion model. Based on global DEM map and surface classification map, PROSAIL model is used for continuous vegetation such as grassland and crops, and gost model is used for forest and mountain vegetation. Using the reference map generated by the measured ground data of the forests in the upper reaches of Heihe River and the oasis in the middle reaches, and scaling up the corresponding high-resolution reference map to 1km resolution, compared with the Lai product, the product has a good correlation between the farmland and the forest area and the reference value, and the overall accuracy basically meets the accuracy threshold of 0.5%, 20% specified by GCOS. By cross comparing this product with Lais products such as MODIS, geov1 and glass, the accuracy of this Lai product is better than that of similar products compared with reference value. In a word, the synthetic Lai data set of 1km / 5 days in Heihe River Basin comprehensively uses multi-source remote sensing data to improve the estimation accuracy and time resolution of Lai parameter products, so as to better serve the application of remote sensing data products.
LI Jing, Yin Gaofei, YIN Gaofei, ZHONG Bo, WU Junjun, WU Shanlong
The 1 km / 5-day FVC data set of Heihe River basin provides the 5-day FVC synthesis results in 2015. The data uses the data of Terra / MODIS, Aqua / MODIS, and domestic satellites fy3a / MERSI and fy3b / MERSI to build a multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. The whole country is divided into different vegetation divisions and land types, and the conversion coefficient of NDVI and FVC is calculated respectively. The conversion coefficient look-up table and 1km / 5-day synthetic NDVI product production area 1km / 5-day synthetic FVC product are used. In the Heihe River Basin, 1 km / 5-day synthetic FVC products can directly obtain vegetation coverage ratio through high-resolution data to reduce the impact of low-resolution data heterogeneity; in addition, select the typical period of vegetation growth and change, obtain the corresponding growth curve parameters of each pixel by fitting the vegetation index of each pixel time series; and then cooperate with land use map and vegetation classification map, To find the representative uniform pixel of the region to train the conversion coefficient of vegetation index. Compared with the results of high-resolution aster reference FVC in Heihe River Basin, the first step is to aggregate the aster products in Heihe River basin to 1km scale by combining the measured ground data and using the scale up method, and to obtain the aster aggregate FVC data, which is based on spot vegetation remote sensing data released by geoland 2 project (geov1 for short) The results show that the results of geov1 are higher than those of ASTER image combined with ground measurement, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin are between the two, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin in the experimental area are better than those of geov1 products. In a word, the comprehensive utilization of multi-source remote sensing data to improve the estimation accuracy and time resolution of FVC parameter products can better serve the application of remote sensing data products.
MU Xihan, RUAN Gaiyan, ZHONG Bo, LIU Qinhuo
The data set includes the river level observation data of point 2 in the dense runoff observation of the middle reaches of Heihe River from January 1, 2015 to December 31, 2015. The observation point is located in Heihe bridge, 312 National Road, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n38.996667 °, e100.427222 °, altitude 1485m, river width 70m and 20m. Sr50 ultrasonic range finder is used for water level observation, with acquisition frequency of 30 minutes. The data includes the following parts: Water level observation, observation frequency 30 minutes, unit (CM); In 2015, the section of bridge no.2-312 was frequently disturbed by human beings. The dam was built within 1km of the upstream and downstream of the section. The unstable area of the hydrological section led to the disorder of the water level and flow curve. During the measurement, the stable flow and water level curve could not be obtained. The observation of water level is based on the manual observation of water level at 0:00 on January 1, 2015. In the later stage, the hydrological section of river undercut changes. The result is that the datum water level changes and negative value appears; Refer to Li et al. (2013) for hydrometeorological network or station information, and he et al. (2016) for observation data processing
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The data set contains cosmic ray instrument (CRS) observations from January 1, 2015 to December 31, 2015.The station is located in dachman super station, dachman irrigation district, zhangye city, gansu province.The longitude and latitude of the observation point are 100.3722e, 38.8555n, and 1556m above sea level. The bottom of the instrument probe is 0.5m from the ground, and the sampling frequency is 1 hour. Original observations of cosmic ray instruments include: voltage Batt (V), temperature T (c), relative humidity RH (%), pressure P (hPa), fast neutron number N1C (hr), thermal neutron number N2C (hr), fast neutron sampling time N1ET (s) and thermal neutron sampling time N2ET (s).The data published are processed and calculated. The data headers include Date Time, P (pressure hPa), N1C (fast neutron number/hour), N1C_cor (fast neutron number/hour with revised pressure) and SW (soil volume moisture content %). The main processing steps include: 1) data filtering There are four criteria for data screening :(1) data with voltage less than and equal to 11.8 volts are excluded;(2) remove the data of air relative humidity greater than and equal to 80%;(3) data whose sampling interval is not within 60±1 minute are excluded;(4) the number of fast neutrons removed changed by more than 200 in one hour compared with that before and after.In addition, the missing data was supplemented by -6999. 2) air pressure correction According to the fast neutron pressure correction formula mentioned in the instrument instruction manual, the original data were revised to obtain the revised fast neutron number N1C_cor. 3) instrument calibration In the process of calculating soil moisture, N0 in the calculation formula should be calibrated.N0 is the number of fast neutrons under the condition of soil drying. The measured soil moisture (or through relatively dense soil moisture wireless sensor) m (Zreda et al. Here, according to Soilnet soil water data in the source area of the instrument, the instrument was calibrated to establish the relationship between soil volumetric water content v and fast neutrons.Selected dry wet condition are the obvious difference of June 26-27 and July 16-17, four days of data, including June 26-27 rate data showed that soil moisture is small, so the selection of 4 cm, 10 and 20 cm the three values of average as calibration data, the change range of 22% to 30%, and July 16-17 rate data showed that soil moisture is bigger, so select 4 cm and 10 cm as two value average rate data, the range of 28% - 39%, final N0 an average of 3597. 4) soil moisture calculation According to the formula, the hourly soil water content data were calculated. Please refer to Liu et al. (2018) for information of hydrometeorological network or site, and Zhu et al. (2015) for observation data processing.
LIU Shaomin, ZHU Zhongli, LI Xin, XU Ziwei
The No. 1 hydrological section is located at 213 Heihe River Bridge (100.345° E, 38.912° N, 1546 m) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. The dataset contains observations recorded by the No.1 hydrological section from 13 June, 2012, to 6 September, 2013. This section consists of two river sections, i.e., the east section,which is denoted as No. 1 and the west section, which is denoted as No. 2. The width of this section is 330 meters and consists of a gravel bed; the cross-sectional area is unstable because of human factors. The water level was measured using an SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
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