The observational data of photosynthetic physiological and moisture physiology of desert dominant species from Jun to Jul, 2014

In the late June and early July of 2014, the dominant species of desert plants in the lower reaches of Heihe River, Lycium barbarum and Sophora alopecuroides, were selected. Using the LI-6400 portable photosynthesis system (LI-COR, USA), the photosynthetic and water physiological characteristics of desert plants were measured and analyzed.

0 2020-01-10

Spatial layout of characteristic agriculture

The data includes the county-level data of characteristic agriculture distribution in the Qinghai Tibet Plateau, which lays the foundation for the study of the spatial distribution and development of characteristic agriculture in the Qinghai Tibet Plateau. The data are from the development plan of Tibet Plateau characteristic agricultural products base (2015-2020), Qinghai province's 13th five year plan, Sichuan Province's 13th five year plan for agricultural and rural economic development, Xinjiang Uygur Autonomous Region's 13th five year plan for targeted poverty alleviation of agricultural characteristic industries (2016-2020), Yunnan Province's overall plan for plateau characteristic agricultural modernization( 2016-2020), implementation opinions on fostering and strengthening characteristic agricultural industries in Gansu Province to boost poverty alleviation, China National Geographic Indication product network (http://www.cgi.gov.cn/home/default/), regional layout planning of characteristic agricultural products (2013-2020). The data is the distribution of county-level characteristic agriculture, realizing the spatialization of county-level characteristic agriculture. The data can be applied to the research on the spatial distribution of characteristic agriculture and the development of characteristic agriculture in the future.

0 2020-01-09

WATER: Dataset of airborne imaging spectrometer (OMIS-II) mission in the Linze station-Linze grassland flight zone on Jun. 6, 2008

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

0 2019-12-27

Observation dataset of maize photosynthesis in the irrigating areas of the midstream of the Heihe River Basin (2012)

This data is based on the observation of corn in the middle reaches of heihe river irrigated area. The observation instrument is licor-6400 XTR and the site is selected near the HiWATER combined test superstation.The photosynthesis parameters of maize were observed through uncontrolled experiments and controlled experiments (controlling carbon dioxide and light intensity) from June 22, 2012 to August 24, 2012.

0 2019-12-27

HiWATER: Dataset of hydrometeorological observation network (cosmic-ray soil moisture of Daman Superstation, 2017)

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.

0 2019-11-06

The Landsat MSS image Datasets over Heihe River Basin (1972-1978)

On July 23, 1972, the United States launched the world's first resource satellite "Landsat 1" , and Landsat 2 and Landsat 3 were launched in the following 10 years. These three satellites were the first generation of resource satellites. They were equipped withreturn-beam vidicon cameras and multi-spectral scanners (MSS) with 3 and 4 spectral segments respectively, a resolution of 79m and a width of 185Km. There are 28 scenes of MSS data in Heihe River Basin currently which were obtained on the following dates: 1972-10-14, 1972-10-30, 1973-01-10, 1973-01-31, 1973-02-16, 1973-06-04, 1973. -10-07, 1973-10-28 (2 scenes), 1973-12-22, 1974-01-05, 1975-10-07, 1975-10-09, 1976-07-04, 1976-10-18 , 1976-11-07, 1976-11-27, 1976-12-30, 1977-01-19, 1977-02-07, 1977-04-20, 1977-05-06 (2 scenes), 1977-05 -08, 1977-06-10, 1977-06-29, 1977-07-18, 1978-10-09. Ortho rectification was performed on the images.

0 2019-11-06

Dataset of ground truth of land surface evapotranspiration at the satellite pixel in the Heihe River Basin (2012-2015) Version 1.0

Surface evapotranspiration (ET) is an important link of water cycle and energy transmission in the earth system. The accurate acquisition of ET is helpful to the study of global climate change, crop yield estimation, drought monitoring, and has important guiding significance for regional and even global water resources planning and management. With the development of remote sensing technology, remote sensing estimation of surface evapotranspiration has become an effective way to obtain regional and global evapotranspiration. At present, a variety of low and medium resolution surface evapotranspiration products have been produced and released in business. However, there are still many uncertainties in the model mechanism, input data, parameterization scheme of remote sensing estimation of surface evapotranspiration model. Therefore, it is necessary to use the real method. The accuracy of remote sensing estimation of evapotranspiration products was quantitatively evaluated by sex test. However, in the process of authenticity test, there is a problem of spatial scale mismatch between the remote sensing estimation value of surface evapotranspiration and the site observation value, so the key is to obtain the relative truth value of satellite pixel scale surface evapotranspiration. Based on the flux observation matrix of "multi-scale observation experiment of non-uniform underlying surface evaporation" in the middle reaches of Heihe River Basin from June to September 2012, the stations 4 (Village), 5 (corn), 6 (corn), 7 (corn), 8 (corn), 11 (corn), 12 (corn), 13 (corn), 14 (corn), 15 (corn), 17 (orchard) and the lower reaches of January to December 2014 Oasis Populus euphratica forest station (Populus euphratica forest), mixed forest station (Tamarix / Populus euphratica), bare land station (bare land), farmland station (melon), sidaoqiao station (Tamarix) observation data (automatic meteorological station, eddy correlator, large aperture scintillation meter, etc.) are used as auxiliary data, and the high-resolution remote sensing data (surface temperature, vegetation index, net radiation, etc.) are used as auxiliary data. See Fig. 1 for the distribution map. Considering the land Through direct test and cross test, six scale expansion methods (area weight method, scale expansion method based on Priestley Taylor formula, unequal weight surface to surface regression Kriging method, artificial neural network, random forest, depth belief network) were compared and analyzed, and finally a comprehensive method (on the underlying surface) was optimized. The area weight method is used when the underlying surface is moderately inhomogeneous; the unequal weight surface to surface regression Kriging method is used when the underlying surface is moderately inhomogeneous; the random forest method is used when the underlying surface is highly inhomogeneous) to obtain the relative true value (spatial resolution of 1km) of the surface evapotranspiration pixel scale of MODIS satellite transit instantaneous / day in the middle and lower reaches of the flux observation matrix area respectively, and to observe through the scintillation with large aperture. The results show that the overall accuracy of the data set is good. The average absolute percentage error (MAPE) of the pixel scale relative truth instantaneous and day-to-day is 2.6% and 4.5% for the midstream satellite, and 9.7% and 12.7% for the downstream satellite, respectively. It can be used to verify other remote sensing products. The evapotranspiration data of the pixel can not only solve the problem of spatial mismatch between the remote sensing estimation value and the station observation value, but also represent the uncertainty of the verification process. For all site information and scale expansion methods, please refer to Li et al. (2018) and Liu et al. (2016), and for observation data processing, please refer to Liu et al. (2016).

0 2019-10-24

1:100000 vegetation map of Heihe River Basin(2015) (version 3.0)

1:100000 vegetation map of Heihe River Basin, the regional scope is subject to the Heihe river boundary of Huangwei Committee, the area is about 14.29 × 104km2, the data format is GIS vector format, this version is version 3.0. The data is mainly based on ground observation data, integrated with all kinds of remote sensing data, 1:1 million vegetation map, climate, terrain, landform, soil data mapping, and compiled by cross validation. The classification standard, legend unit and system of vegetation map of the people's Republic of China (1:1000000), 2007 are adopted, including vegetation type group, vegetation type, formation and sub formation. The new version mainly unifies the codes of the new formation (74 codes in total, distinguishing the formation and the sub formation). 9 vegetation type groups, 22 vegetation types and 74 formations (sub formations) in version 2.0 were changed into 9 vegetation type groups, 22 vegetation types and 67 formations (7 sub formations).

0 2019-10-24