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

Created Time:2019-04-27
Standard weather station monthly data of the Yellow River’s Upstream (1952-2011)
Standard weather station monthly data of the Yellow River’s Upstream (1952-2011)

I. Overview This dataset contains monthly meteorological data for the upper Yellow River and its surroundings from 1952 to 2011. The standard station data includes 30 elements: average station pressure, extreme maximum station pressure, date of extreme maximum station pressure, extreme minimum station pressure, date of extreme minimum station pressure, average temperature, and extreme maximum temperature. , Extreme high temperature appearance day, extreme minimum temperature, extreme minimum temperature appearance day, average temperature anomaly, average maximum temperature, average minimum temperature, average relative humidity, minimum relative humidity, minimum relative humidity occurrence date, precipitation, daily precipitation > = 0.1mm days, maximum daily precipitation, maximum daily precipitation occurrence day, percentage of precipitation anomaly, average wind speed, maximum wind speed, day of maximum wind speed, maximum wind speed, wind direction of maximum wind speed, wind direction of maximum wind speed , The day of maximum wind speed, the hours of sunshine, and the percentage of sunshine. Ⅱ. Data processing description The data is stored as integers, the temperature unit is (0.1 ° C) value, the precipitation unit is (0.1 mm), and it is stored as an ASCII text file. Ⅲ. Data content description Standard station data, all meteorological elements are stored in one text, each element is: average own station pressure (V10004), extreme highest station pressure (V10201), extreme highest station pressure (V10201_001), extreme lowest station Barometric pressure (V10202), the day when the extreme minimum atmospheric pressure appeared (V10202_002), the average temperature (V12001), the extreme maximum temperature (V12011), the extreme maximum temperature (V12011_001), the extreme minimum temperature (V12012), the extreme minimum temperature (V12012_002), average temperature anomaly (V12201), average maximum temperature (V12211), average minimum temperature (V12212), average relative humidity (V13003), minimum relative humidity (V13007), minimum relative humidity occurrence date (V13007_001), precipitation Amount (V13011), daily precipitation> = 0.1mm days (V13011_000), maximum daily precipitation (V13052), maximum daily precipitation (V13052_001), percentage of precipitation anomaly (V13212), average wind speed (V11002), polar High wind speed (V11041), the day when the maximum wind speed appears (V11041_001), the maximum wind speed (V11042), the wind direction of the maximum wind speed (V11043), the wind direction of the maximum wind speed (V11212), the maximum wind speed Today (V11212_001), hours of sunshine (V14032), percentage of sunshine (V14033). Ⅳ. Data usage description In terms of resources and environment, meteorological data is used to simulate the regional climate change and runoff, sediment, water and soil loss and vegetation change in the basin, and it is also a necessary input condition for remote sensing inversion.

Created Time:2015-05-29
China regional atmospheric driving dataset based on geostationary satellites and reanalysis data (2005-2010)
China regional atmospheric driving dataset based on geostationary satellites and reanalysis data (2005-2010)

Based on the geostationary satellites and reanalysis data, the China Regional Atmospheric Driving Dataset is a set of atmospheric driving data sets with high spatiotemporal resolution prepared by the China Meteorological Administration, with a spatial resolution of 0.1 ° × 0.1 ° and a temporal resolution of 1 Hours, covering a range of 75 ° -135 ° east longitude and 15 ° -55 ° north latitude, include 6 elements of near-surface temperature, relative humidity, ground pressure, near-surface wind speed, incident solar radiation on the ground, and ground precipitation rate. The preparation process of precipitation products is as follows: The 6-hour cumulative precipitation estimated from the multi-channel data of the China Fengyun-2 geostationary satellite is integrated with the 6-hour cumulative precipitation from conventional ground observations to obtain 6-hour cumulative precipitation spatial distribution data, and then use the high-resolution cloud classification information retrieved from the multi-channel inversion of the geostationary satellites determines the interpolation time weight of the cumulative precipitation and obtains an estimated one-hour cumulative precipitation. The preparation process of the radiation data is as follows: The surface incident solar radiation based on FY-2C, uses the radiation transmission model DISORT (Discrete Ordinates Radiative Transfer Program for a Multi-Layered Plane-parallel Medium) to calculate the radiation transmission and obtains the data of surface incident solar radiation in China. Preparation process of other elements: The space and time interpolation method is used for the NCEP reanalysis data of 1.0 ° × 1.0 ° to obtain driving factors such as near-surface air temperature, relative humidity, ground pressure, and near-surface wind speed of 0.1 ° × 0.1 ° per hour. Physical meaning of each variable: Meteorological Elements || Variable Name || Unit || Physical Meaning | Surface temperature || TBOT || K || Surface temperature (2m) | Surface pressure || PSRF || Pa || Surface pressure | Relative humidity on the ground || RH || kg / kg || Relative humidity near the ground (2m) | Wind speed on the ground || WIND || m / s || Wind speed near the ground (anemometer height) | Surface incident solar radiation || FSDS || W / m2 || Surface incident solar radiation | Precipitation Rate || PRECTmms || mm / hr || Precipitation Rate For more information, see the data documentation published with the data.

Created Time:2013-07-07

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