Basic dataset of soil over the Great Lakes in Central Asia - Soil (2015)

Soil is mineral particles of different sizes formed by weathering of rocks. Soil not only provides nutrients and water for crops, but also has a transforming effect on various nutrients. In addition, the soil also has a self-cleaning function, which can improve organic matter content, soil temperature and humidity, pH value, anion and cation. The soil pollution causes several environmental problems: industrial sewage, acid rain, exhaust emissions, accumulations, agricultural pollution. After the land is polluted, the contaminated tops with high concentration of heavy metals are easily entered under the action of wind and water. Other secondary ecological and environmental problems such as air pollution, surface water pollution, groundwater pollution and ecosystem degradation in the atmosphere and water.he data set comes from the World Soil Database (Harmonized World Soil Database version 1.1) (HWSD) UN Food and Agriculture (FAO) and the Vienna International Institute for Applied Systems Research Institute (IIASA) constructed, which provides data model input parameters for the modeler, At the same time, it provides a basis for research on ecological agriculture, food security and climate change.

0 2020-04-06

Soil texture dataset of hwsd in Qaidam River basin (2009)

The dataset is the HWSD Soil texture data set of the qaidam basin. The data is from the Harmonized World Soil Database (HWSD) constructed by the United Nations food and agriculture organization (FAO) and Vienna institute for international applied systems (IIASA), which was released in version 1.1 on March 26, 2009.The data resolution is 1km.The main soil classification system adopted is fao-90.The main fields in the soil property list include SU_SYM90 (soil name in the FAO90 soil classification system) SU_SYM85(FAO85 classification) T_TEXTURE(top layer soil texture) (19.5);ROOTS: String(deep classification of obstacles to the bottom of the soil);SWR: String (soil moisture content characteristics);ADD_PROP: Real (specific type of soil in a soil unit related to an agricultural use);T_GRAVEL: Real (percent by volume);T_SAND: Real;T_SILT: Real (silt content);T_CLAY: Real;T_USDA_TEX: Real (USDA soil texture classification);T_REF_BULK: Real (soil bulk density);T_OC: Real (organic carbon content);T_PH_H2O: Real T_CEC_CLAY: Real;T_CEC_SOIL: Real (cation exchange capacity of soil) T_BS: Real (basic saturation);T_TEB: Real (commutative base);T_CACO3: Real (carbonate or lime content) T_CASO4: Real (sulfate content);T_ESP: Real (exchangeable sodium);T_ECE: Real.The attribute field beginning with T_ represents the upper soil attribute (0-30cm), and the attribute field beginning with S_ represents the lower soil attribute (30-100cm) (FAO 2009).This data can provide model input parameters for earth system modelers, and agricultural perspectives can be used to study eco-agricultural zoning, food security and climate change.

0 2020-04-06

Soil texture dataset of hwsd in Qaidam River basin (2009)

The dataset is the HWSD Soil texture data set of the qaidam basin. The data is from the Harmonized World Soil Database (HWSD) constructed by the United Nations food and agriculture organization (FAO) and Vienna institute for international applied systems (IIASA), which was released in version 1.1 on March 26, 2009.The data resolution is 1km.The main soil classification system adopted is fao-90.The main fields in the soil property list include SU_SYM90 (soil name in the FAO90 soil classification system) SU_SYM85(FAO85 classification) T_TEXTURE(top layer soil texture) (19.5);ROOTS: String(deep classification of obstacles to the bottom of the soil);SWR: String (soil moisture content characteristics);ADD_PROP: Real (specific type of soil in a soil unit related to an agricultural use);T_GRAVEL: Real (percent by volume);T_SAND: Real;T_SILT: Real (silt content);T_CLAY: Real;T_USDA_TEX: Real (USDA soil texture classification);T_REF_BULK: Real (soil bulk density);T_OC: Real (organic carbon content);T_PH_H2O: Real T_CEC_CLAY: Real;T_CEC_SOIL: Real (cation exchange capacity of soil) T_BS: Real (basic saturation);T_TEB: Real (commutative base);T_CACO3: Real (carbonate or lime content) T_CASO4: Real (sulfate content);T_ESP: Real (exchangeable sodium);T_ECE: Real.The attribute field beginning with T_ represents the upper soil attribute (0-30cm), and the attribute field beginning with S_ represents the lower soil attribute (30-100cm) (FAO 2009).This data can provide model input parameters for earth system modelers, and agricultural perspectives can be used to study eco-agricultural zoning, food security and climate change.

0 2020-04-06

1:100000 desert sand distribution data set of Qaidam River Basin (2000)

Data for 100000 desert map qaidam river basin, cutting since China 1:100000 desert sand data set, the data of TM images in 2000 data sources, to interpret, extraction, revision, using remote sensing and geographic information system technology combining 1:100000 scale mapping, the desert, sand and gravel gobi for thematic mapping.The desert codes are as follows: mobile sandy land 2341010, semi-mobile sandy land 2341020, semi-fixed sandy land 2341030, gobi desert 2342000, saline alkaline land 2343000.

0 2020-04-06

Dataset of vegetation regulation mechanism of soil water cycle in arid desert area (2002-2005)

The vegetation regulation mechanism project of soil water cycle in arid desert areas belongs to the national natural science foundation "environment and ecological science in western China" major research plan, led by li xinrong, a researcher of the institute of environment and engineering in dry and cold areas, Chinese academy of sciences, with the running time of 2003.1-2005.12. Remittance data of the project: 1. Dataset of observation field of shapotou railway vegetation sand fixation protection system (excel) Plant and soil information in the vegetation-sand fixation zone established in 1956, 1964, 1981 and 1987.Since the establishment of the observation field, long-term soil moisture and vegetation surveys have been conducted. This database records the soil moisture data after the neutron tube installation in August 2002, the vegetation data from 2003 to 2005 (vegetation structure, herb structure, shrub structure, etc.), and the soil physical and chemical properties data (particle size, total N,P2O5,K2O, hydrolyzed N) of the irregular surveys. 2. Physiological data set of desert plant stress (excel) From 2003 to 2005, the physiological and biochemical characteristics of typical plant communities and their dominant species in steppe desert under natural and simulated environmental conditions were analyzed.(including photosynthetic transpiration, fluorescence, biochemistry and other indicators) 3. Soil infiltration and evapotranspiration data set (excel) Precipitation infiltration process, soil water dynamics and evapotranspiration of fixed sand dunes monitored by desert artificial vegetation using TDR and Lysimeters from 2002 to 2005. 4. Data set of comprehensive survey on soil and vegetation in the southeastern margin of tengger desert (excel) In 2003-2004, silver (sichuan), yan (latour) highway, silver (sichuan) (state) highway through the tengger desert area, set up along the road of eight samples, 449 samples of soil conductivity, Ph, organic matter, total nitrogen (content) and vegetation (plants, coverage, average height, biomass, strains, coverage, high average, biomass).

0 2020-04-04

The impact of agricultural development on watershed scale water cycle and eco-environmental effect in Northwest Oasis projects collection data

The project on the impact of agricultural development in northwest Lvzhou on watershed scale water cycle and eco-environmental effects belongs to the major research program of "Environmental and Ecological Science in Western China" sponsored by the National Natural Science Foundation. The person in charge is Professor Kang Shaozhong of Northwest China Agriculture and Forestry University. The project runs from January 2003 to December 2005. Data collected from this project: soil experimental data of Shiyang River Basin, including: 1. Saturated hydraulic conductivity (excel table): includes four fields: number, sampling point, measured value and saturated hydraulic conductivity. 2. Conductivity (excel table): including number, sampling point, measured value, temperature, temperature correction value and conductivity. 3. Original indoor infiltration data (excel table): including number, time, cumulative value and reading. 4. Field Infiltration Data (excel Form): Including Number, Time, Cumulative Value and Reading. 5. Sampling point of horizontal infiltration data (excel form): including time, measuring cylinder (ml), wetting peak (ml), wet weight, dry weight, box weight and distance. 6. soil particle analysis (excel form): including numbers, > 0.25 mm, < 0.05 mm, < 0.01 mm, < 0.005 mm, < 0.001 mm. 7. Soil moisture characteristic curve (excel table): including soil weight and drying weight when the pressure of pressure membrane instrument is 0,0.05,0.1,0.3,0.5,0.8,1.5,3,5,14.4. 8. Organic matter (excel form): including number, sampling point, amount of soil taken (G), titration amount (ml) 9. Sampling Point Coordinates (excel Form)

0 2020-04-02

Soil moisture rate and soil nutrient dataset in the downstream of the Tarim River

In the ecosystem, soil and vegetation are two interdependent factors. Plants affect soil and soil restricts vegetation. On the one hand, there are a lot of nutrients such as carbon, nitrogen and phosphorus in the soil. On the other hand, the availability of soil nutrients plays a key role in the growth and development of plants, directly affecting the composition and physiological activity of plant communities, and determining the structure, function and productivity level of ecosystems. Soil moisture content (or soil moisture content): In the 9 sections from Daxihaizi to taitema lake in the lower reaches of Tarim River, plant sample plots are set in the direction perpendicular to the river channel according to the arrangement of groundwater level monitoring wells. Dig one soil profile in each sample plot, collect one soil sample from 0-5 cm, 5-15 cm, 15-30 cm, 30-50 cm, 50-80 cm, 80-120 cm and 120-170cm soil layers from bottom to top in each profile layer, each soil sample is formed by multi-point sampling and mixing of corresponding soil layers, each soil layer uses aluminum boxes to collect soil samples, weighs wet weight on site, and measures soil moisture content (or soil moisture content) by drying method. Soil nutrient: the mixed soil sample is used for determining soil nutrient after removing plant root system, gravel and other impurities, air-drying indoors and sieving. Organic matter is heated by potassium dichromate, total nitrogen is treated by semi-micro-Kjeldahl method, total phosphorus is treated by sulfuric acid-perchloric acid-molybdenum antimony anti-colorimetric method, total potassium is treated by hydrofluoric acid-perchloric acid-flame photometer method, effective nitrogen is treated by alkaline hydrolysis diffusion method, effective phosphorus is treated by sodium bicarbonate leaching-molybdenum antimony anti-colorimetric method, effective potassium is treated by ammonium acetate leaching-flame photometer method, PH and conductivity are measured by acidimeter and conductivity meter respectively (water to soil ratio is 5: 1). Soil water-soluble total salt was determined by in-situ salinity meter.

0 2020-04-01

The HWSD soil texture dataset of the Qinghai Lake Basin (2009)

The dataset is the HWSD soil texture dataset of the Qinghai Lake Basin. The data comes from the Harmonized World Soil Database (HWSD) constructed by the Food and Agriculture Organization of the United Nations (FAO) and the Vienna International Institute for Applied Systems (IIASA). Version 1.1 was released on March 26, The data resolution is 1km. The soil classification system used is mainly FAO-90. The main fields of the soil attribute table include: SU_SYM90 (soil name in FAO90 soil classification system) SU_SYM85 (FAO85 classification) T_TEXTURE (top soil texture) DRAINAGE (19.5); ROOTS: String (depth classification of obstacles to the bottom of the soil); SWR: String (soil moisture characteristics); ADD_PROP: Real (a specific soil type related to agricultural use in the soil unit); T_GRAVEL: Real (gravel volume percentage); T_SAND: Real (sand content); T_SILT: Real (silt content); T_CLAY: Real (clay content); T_USDA_TEX: Real (USDA soil texture classification); T_REF_BULK: Real (soil bulk density); T_OC: Real (organic carbon content); T_PH_H2O: Real (pH) T_CEC_CLAY: Real (cation exchange capacity of cohesive layer soil); T_CEC_SOIL: Real (cation exchange capacity of soil) T_BS: Real (basic saturation); T_TEB: Real (exchangeable base); T_CACO3: Real (carbonate or lime content) T_CASO4: Real (sulfate content); T_ESP: Real (exchangeable sodium salt); T_ECE: Real (conductivity). The attribute field beginning with T_ indicates the upper soil attribute (0-30cm), and the attribute field beginning with S_ indicates the lower soil attribute (30-100cm) (FAO 2009). The data can provide model input parameters for modelers of the Earth system, and the agricultural perspective can be used to study eco-agricultural zoning, food security, and climate change.

0 2020-04-01

A China Dataset of soil hydraulic parameters pedotransfer functions for land surface modeling (1980)

This data uses soil conversion functions to take sand, silt, clay, organic matter, and bulk density as inputs to estimate soil hydrological parameters, including parameters of the Clapp and Hornberger function and van Genuchten and Mualem function, field water holding capacity, and withering coefficient. Median and coefficient of variation (CV) provide estimates. The data set is in a raster format with a resolution of 30 arc seconds, and the soil is layered vertically into 7 layers with a maximum thickness of 1.38 meters (ie 0-0.045, 0.045--0.091, 0.091--0.166, 0.166--0.289, 0.289-- 0.493, 0.493--0.829, 0.829--1.383 meters). The data is stored in NetCDF format. The data file name and its description are as follows: 1. THSCH.nc: Saturated water content of FCH 2. PSI_S.nc: Saturated capillary potential of FCH 3. LAMBDA.nc: Pore size distribution index of FCH 4. K_SCH.nc: Saturate hydraulic conductivity of FCH 5. THR.nc: Residual moisture content of FGM 6. THSGM.nc: Saturated water content of FGM 7. ALPHA.nc: The inverse of the air-entry value of FGM 8. N.nc: The shape parameter of FGM 9. L.nc: The pore-connectivity parameter of FGM 10. K_SVG.nc: Saturated hydraulic conductivity of FGM 11. TH33.nc: Water content at -33 kPa of suction pressure, or field capacity 12. TH1500.nc: Water content at -1500 kPa of suction pressure, or permanent wilting point

0 2020-04-01

1:100,000 land use dataset of Gansu Province (1980s)

This data is from "China 1:100,000 land use data".China 1:100,000 land use data was constructed in three years based on Landsat MSS, TM and ETM remote sensing data by using satellite remote sensing as a means to organize remote sensing science and technology teams from 19 institutes affiliated to the Chinese academy of sciences (cas) in the "eighth five-year plan" major application project "national macro survey and dynamic research on remote sensing of resources and environment". The 1:100,000 land use data set in gansu province adopts a hierarchical land cover classification system, which divides the whole country into 6 primary categories (arable land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 secondary categories.It is the most accurate land use data product in China and has played an important role in national land resource survey, hydrological and ecological research.

0 2020-04-01