Dataset of ground truth land surface evapotranspiration at the satellite pixel scale in the Heihe River Basin (from the single station observation to satellite pixel scale) Version 1.0

The evapotranspiration (ET) is an important variable connecting land energy balance, water cycle and carbon cycle. Accurate monitoring and estimations of ET are essential not only for water resources management but also for simulating regional, global climate, and hydrological cycles. Remote sensing technology is an effective method to monitor ET. At present, a variety of ET remote sensing products have been produced and released. However, in the process of validation, there is a problem of spatial scale mismatch between ET remote sensing estimation value and station observation value, especially on heterogeneous surface. Therefore, it is very important to obtain the ground truth ET values at the satellite pixel scale by upscaling method on heterogeneous surface. In this study, using the station observation data and multi-source remote sensing information, the ET observed at a single ground station is upscaled to the satellite pixel scale, and the ground truth ET values at the satellite pixel scale in Heihe River Basin is obtained. Based on the ET data observed by the eddy covariance (EC) at 15 stations (3 superstations and 12 ordinary stations) in the Heihe integrated observatory network, combined with the fused high-resolution remote sensing data (surface temperature, vegetation index, net radiation, etc.) and atmospheric reanalysis data, the upscaling is carried out to obtain the ground truth ET at the satellite pixel scale. The distribution diagram is shown in Figure 1. Specifically, firstly, the spatial heterogeneity of the spatial heterogeneity of the land surface hydrothermal conditions was evaluated; Secondly, nine upscaling methods (the integrated Priestley-Taylor equation method, the Penman-Monteith equation combined with EnKF method, the Penman-Monteith equation combined with SCE_UA method, EC observation value, artificial neural network, Bayesian linear regression, deep belief network, Gaussian process regression, and random fores and directly taking the EC observation value as the ground truth ET) were compared and analyzed through direct validation and cross-validation; Finally, a comprehensive method (directly using the EC observation value on the homogeneous underlying surface; using the Gaussian process regression method for upscaling on the moderately heterogeneous underlying surface and highly heterogeneous underlying surface) was optimized to obtain the groud truth ET at the satellite pixel scale at 15 typical underlying surfaces in Heihe River Basin (2010-2016, spatial resolution of 1km). The results showed that the ground truth ET at the satellite pixel scale is relatively reliable. Compared with the pixel scale reference value (LAS observation value), the MAPE of the ground turth ET at the satellite pixel scale at the three superstations are 1.57%, 3.23% and 4.59% respectively, which can meet the needs of the validation of ET remote sensing products. For all site information and data processing, please refer to Liu et al. (2018), and for upscaling methods, please refer to Li et al. (2021).

0 2022-06-10

Dataset of ground truth land surface evapotranspiration at the satellite pixel scale in the Heihe River Basin (from multi-station observations to satellite pixel scale) 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 2022-06-06

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Cosmic-ray observation system of soil moisture of Daman Superstation, 2021)

This dataset includes data recorded by the Heihe integrated observatory network obtained from a Cosmic-ray Soil Moisture Observing System of soil moisture of Daman Superstation from January 1 to December 31, 2021. 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 bottom of the probe was 0.5 m above the ground; the sampling interval was 1 hour. The raw COSMOS data include the following: battery (Batt, V), temperature (T, ℃), 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.

0 2022-06-05

Water index in the Qilian Mountain Area (2021)

This dataset contains the ground surface water (including liquid water, glacier and perennial snow) distribution in Qilian Mountain Area in 2021. The dataset was produced based on classical Normalized Difference Water Index (NDWI) extraction criterion and manual editing. Landsat images collected in 2021 were used as basic data for water index extraction. Sentinel-2 images and Google images were employed as reference data for adjusting the extraction threshold. The dataset was stored in SHP format and attached with the attributions of coordinates and water area. Consisting of 1 season, the dataset has a temporal resolution of 1 year and a spatial resolution of 30 meters. The accuracy is about 1 pixel (±30 meter). The dataset directly reflects the distribution of water bodies within the Qilian Mountain in 2021, and can be used for quantitative estimation of water resource.

0 2022-06-05

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Large aperture scintillometer of Sidaoqiao Superstation, 2021)

This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Sidaoqiao Superstation in the Heihe integrated observatory network from January 1 to December 31 in 2021. There were BLS900 and BLS450 at Sidaoqiao Superstation. The north towers were set up with these instruments’ receivers and the south towers were transmitters. The site (north: 101.137° E, 42.008° N; south: 101.131° E, 41.987 N) was located in Ejinaqi, Inner Mongolia. The underlying surfaces between the two towers were tamarisk, populus, bare land and farmland. The elevation is 873 m. The effective height of the LAS was 25.5 m, and the path length was 2350 m. The data were sampled 1 minute. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (BLS900&BLS450:Cn2>7.25E-14). (2) The data were rejected when the demodulation signal was small (BLS900&BLS450:Average X Intensity<1000). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl (1992) were selected for BLS900 and BLS450. Detailed can refer to Liu et al. (2011, 2013). Due to instrument adjustment and inadequate power supply, the date of missing data for the large aperture scintillator is: 2021.03.15-2021.03.18;2021.9.12-2021.9.18. Several instructions were included with the released data. (1) The las data are firstly from BLS900, followed by BLS450, and finally the final missing data was marked with-6999. (2) The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) (for data processing) in the Citation section.

0 2022-06-04

Dataset of discharge and meteorological data of Duodigou Alpine Runoff Experimental Basin on the Tibet Plateau (2018-2021)

This dataset provides the monitoring data of runoff, precipitation and temperature of the Duodigou Runoff Experimental Station located in the northern suburbs of Lhasa city. Among the dataset, there are two runoff monitoring stations, which provide discharge data from June to December 2019, with a data step of 10 minutes. There are five precipitation monitoring stations, which provide precipitation data from 2018 to 2021, with a data step of 1 day. There are eight air temperature monitoring stations, which provide air temperature data from 2018 to 2021 in 30 minute steps. The discharge, the precipitation and the temperature data are the measured values. The dataset can provide data support for the study of hydrological and meteorological processes in the Tibet Plateau.

0 2022-06-01

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Cosmic-ray observation system of soil moisture of Sidaoqiao Superstation, 2021)

This dataset includes data recorded by the Heihe integrated observatory network obtained from a Cosmic-ray Soil Moisture Observing System of soil moisture of Sidaoqiao Superstation from January 1 to December 31, 2021. The site (101.1374° E, 42.0012° N) was located in the Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 873 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: 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.

0 2022-05-30

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Cosmic-ray observation system of soil moisture of Arou Superstation, 2021)

This dataset includes data recorded by the Heihe integrated observatory network obtained from a Cosmic-ray Soil Moisture Observing System of soil moisture of Arou Superstation from January 1 to December 31, 2021. The site (100.4643° E, 38.0473° N) was located in the Caodaban Village, near Qilian County in Qinghai Province. The elevation is 3033 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: 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.

0 2022-05-30

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (automatic weather station of mixed forest station, 2021)

This dataset includes data recorded by the Heihe integrated observatory network obtained from the automatic weather station (AWS) at the Sidaoqiao mixed forest station from January 1 to December 31, 2021. The site (101.134° E, 41.990° N) was located on a tamarix and populous forest (Tamarix chinensis Lour. and Populus euphratica Olivier.) surface in the Sidaoqiao, Dalaihubu Town, Ejin Banner, Inner Mongolia Autonomous Region. The elevation is 874 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (28 m, north), wind speed and direction profile (28 m, north), air pressure (in tamper box), rain gauge (28 m, south), four-component radiometer (24 m, south), two infrared temperature sensors (24 m, south, vertically downward), two photosynthetically active radiation (24 m, south, one vertically upward and one vertically downward), soil heat flux (HFP01; 3 duplicates, -0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, -1.0, -1.6, -2.0, -2.4 m), and soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.6, -1.0, -1.6, -2.0, -2.4 m). The observations included the following: air temperature and humidity (Ta_28 m; RH_28 m) (℃ and %, respectively), wind speed (Ws_28 m) (m/s), wind direction (WD_28 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 of upward and downward (PAR_up and PAR_down) (μmol/ (s m^-2)), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_20 cm, Ts_40 cm, Ts_60 cm, Ts_100, Ts_160, Ts_200, Ts_240 cm) (℃), and soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, Ms_100, Ms_160, Ms_200, Ms_240 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 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: 2019-9-10 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.

0 2022-05-30

Datasets of streamflow, evapotranspiration and precipitation in the Upper Heihe River Basin (1992-2015)

This data is the runoff and evapotranspiration generated by the precipitation in the growing season of the upper reaches of Heihe River from 1992 to 2015. Temporal resolution: year (growingseason), spatial resolution: 0.00833°. The data include precipitation (mm), evapotranspiration (mm), runoff (mm) and soil water content (m3 / m3). The data are obtained by using meteorological, soil and vegetation parameters based on Eagleson eco hydrological model. The simulated rainfall runoff is verified by using the observed runoff data in the growing season of 6 sub basins in the upper reaches of Heihe River (Heihe main stream, Babao River, yeniugou, Liyuan River, Wafangcheng and Hongshui River). The variation range of correlation coefficient (R) is 0.53-0.74, RMSE is 32.46-233.18 mm, and the relative error range is -0.66-0.0005; The difference between simulated evapotranspiration and gleam et is − 115.36 mm to 44.1 mm. The simulation results can provide some reference for hydrological simulation in the upper reaches of Heihe River.

0 2022-05-26