Data set of soil physical and chemical indexes of temperate grassland in Eurasia (1981-2019)

In the past 50 years, under the background of global climate change, with the increase of population and economic development, Eurasian grassland has been seriously degraded. One belt, one road surface, is a key indicator of grassland quality. Its spatial temporal pattern and distribution can directly reflect the degradation of grassland. Effective assessment of grassland quality is of great significance for the sustainable development of the countries along the border and the promotion of China's "one belt and one road" strategy. In previous studies, there is room for improvement in accuracy and accuracy of spatial and temporal distribution of soil properties. With the development of geographic information system, global positioning system, various sensors and soil mapping technology, digital soil mapping has gradually become an efficient method to express the spatial distribution of soil. Based on soil landscape science and spatial autocorrelation theory, this study combined multi-source sample data and environmental covariate data, and used machine learning model to predict the spatial distribution of surface soil attributes of warm grassland in Eurasia around 2000. In order to solve the problem of soil sample standardization, the equal area spline function was used to fit the soil properties of different profiles to the soil properties of 20 cm in the surface layer, and the soil particle distribution parameter model was used to transform the classification standards of different soil textures into the United States system. In order to solve the problem of insufficient number of soil samples, pseudo expert observation points were used to supplement soil organic matter and sand content samples in under sampling area; stepwise regression combined with support vector machine model was used, and effective soil bulk density simulation samples were screened by calculating threshold. According to the characteristics of complex terrain and climate conditions, combined with multi-source remote sensing data, ngboost model is applied to mine the relationship between soil attributes and environmental landscape factors (topography, climate, vegetation, soil type, etc.) and spatial location based on sample points, and to predict soil organic matter, sand content and bulk density in the study area from 1980 to 1999 and 2000 to 2019 respectively, and the uncertainty of corresponding indicators is given Spatial distribution of sex. The spatial distribution trend of the simulated soil attribute indexes is consistent with the actual situation. Before 2000, the soil organic matter content, bulk density and sand content were 0.64, 0.35 and 0.44 respectively, and the RMSE were 0.25, 0.07 and 13.94 respectively; after 2000, the RMSE were 0.79, 0.77 and 0.86 respectively, and the RMSE were 0.2, 0.13 and 6.61 respectively. The results show that this method can effectively retrieve the soil physical and chemical properties of temperate grassland in Eurasia, and provide a basis for the evaluation of grassland degradation and the construction of grassland quality evaluation system.

0 2021-01-26

Field soil survey and analysis data in the upper reaches of Heihe River Basin (2013-2014)

The dataset is the field soil measurement and analysis data of the upstream of Heihe River Basin from 2013 to 2014, including soil particle analysis, water characteristic curve, saturated water conductivity, soil porosity, infiltration analysis, and soil bulk density I. Soil particle analysis 1. The soil particle size data were measured in the particle size laboratory of the Key Laboratory of the Ministry of Education, West Ministry of Lanzhou University.The measuring instrument is Marvin laser particle size meter MS2000. 2. Particle size data were measured by laser particle size analyzer.As a result, sample points with large particles cannot be measured, such as D23 and D25 cannot be measured without data.Plus partial sample missing. Ii. Soil moisture characteristic curve 1. Centrifuge method: The unaltered soil of the ring-cutter collected in the field was put into the centrifuge, and the rotor weight of each time was measured with the rotation speed of 0, 310, 980, 1700, 2190, 2770, 3100, 5370, 6930, 8200 and 11600 respectively. 2. The ring cutter is numbered from 1 to the back according to the number. Since three groups are sampled at different places at the same time, in order to avoid repeated numbering, the first group is numbered from 1, the second group is numbered from 500, and the third group is numbered from 1000.It's consistent with the number of the sampling point.You can find the corresponding number in the two Excel. 3. The soil bulk density data in 2013 is supplementary to the sampling in 2012, so the data are not available at every point.At the same time, the soil layer of some sample points is not up to 70 cm thick, so the data of 5 layers cannot be taken. At the same time, a large part of data is missing due to transportation and recording problems.At the same time, only one layer of data is selected by random points. 4. Weight after drying: The drying weight of some samples was not measured due to problems with the oven during the experiment. 3. Saturated water conductivity of soil 1. Description of measurement method: The measurement method is based on the self-made instrument of Yiyanli (2009) for fixing water hair.The mariot bottle was used to keep the constant water head during the experiment.At the same time, the measured Ks was finally converted to the Ks value at 10℃ for analysis and calculation.Detailed measurement record table refer to saturation conductivity measurement description.K10℃ is the data of saturated water conductivity after conversion to 10℃.Unit: cm/min. 2. Data loss explanation: The data of saturated water conductivity is partly due to the lack of soil samples and the insufficient depth of the soil layer to obtain the data of the 4th or 5th layer 3. Sampling time: July 2014 4. Soil porosity 1. Use bulk density method to deduce: according to the relationship between soil bulk density and soil porosity. 2. The data in 2014 is supplementary to the sampling in 2012, so the data are not available at every point.At the same time, the soil layer of some sample points is not up to 70 cm thick, so the data of 5 layers cannot be taken. At the same time, a large part of data is missing due to transportation and recording problems.At the same time, only one layer of data is selected by random points. 5. Soil infiltration analysis 1. The infiltration data were measured by the "MINI DISK PORTABLE specific vector INFILTROMETER".The approximate saturation water conductivity under a certain negative pressure is obtained.The instrument is detailed in website: http://www.decagon.com/products/hydrology/hydraulic-conductivity/mini-disk-portable-tension-infiltrometer/ 2.D7 infiltration tests were not measured at that time because of rain. Vi. Soil bulk density 1. The bulk density of soil in 2014 refers to the undisturbed soil taken by ring cutter based on the basis of 2012. 2. The soil bulk density is dry soil bulk density, which is measured by drying method.The undisturbed ring-knife soil samples collected in the field were kept in an oven at 105℃ for 24 hours, and the dry weight of the soil was divided by the soil volume (100 cubic centimeters). 3. Unit: G /cm3

0 2020-10-13

Soil bulk density of representative samples in the Heihe River Basin (2012-2013)

The aerosol optical thickness data of the Arctic Alaska station is based on the observation data products of the atmospheric radiation observation plan of the U.S. Department of energy at the Arctic Alaska station. The data coverage time is updated from 2017 to 2019, with the time resolution of hour by hour. The coverage site is the northern Alaska station, with the longitude and latitude coordinates of (71 ° 19 ′ 22.8 ″ n, 156 ° 36 ′ 32.4 ″ w). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is NC format. The aerosol optical thickness data of Qomolangma station and Namuco station in the Qinghai Tibet Plateau is based on the observation data products of Qomolangma station and Namuco station from the atmospheric radiation view of the Institute of Qinghai Tibet Plateau of the Chinese Academy of Sciences. The data coverage time is from 2017 to 2019, the time resolution is hour by hour, the coverage sites are Qomolangma station and Namuco station, the longitude and latitude coordinates are (Qomolangma station: 28.365n, 86.948e, Namuco station Mucuo station: 30.7725n, 90.9626e). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is TXT.

0 2020-05-25

Soil bulk density of representative samples in the Heihe River Basin

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.

0 2020-05-25

Digital soil mapping dataset of soil bulk density in the Heihe river basin (2012-2014)

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;

0 2020-03-27

Soil physical properties - soil bulk density and mechanical composition dataset of Tianlaochi Watershed in Qilian Mountains

A total of 137 soil samples of different vegetation types, different altitudes and different terrains were collected from June 2012 to August 2012. The soil layer of each sample point was divided into three layers of 0-10cm, 10-20cm and 20-30cm, with an altitude of 2700-3500m m. The vegetation types were divided into five types: Picea crassifolia forest, Sabina przewalskii, subalpine scrub meadow, grassland and dry grassland. At the same time of sampling, hand-held GPS is used to record the location information and environmental information of each sampling point, including longitude, latitude, altitude, slope, aspect, terrain curvature, vegetation type, soil thickness, maximum root depth, etc. Soil bulk density: The measurement method of soil bulk density is to put the sample into an envelope and dry it in an oven at 105℃ for 24 hours, then take it out and place it for 30 minutes to weigh. The ratio of the weighing result to the volume of the ring cutter is the soil bulk density, and the unit is g/cm3. Soil mechanical composition: hydrometer method is used to measure the soil mechanical composition, which includes the content of soil sand, silt and clay.

0 2020-03-15

HiWATER: Dataset of soil parameters in the midstream of the Heihe River Basin (2012)

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.

0 2019-09-15