The data set records the current situation of land use in Qinghai Province. The data is divided by cultivated land, garden land, woodland, grassland, residential land, industrial and mining land, transportation land, water conservancy facilities land and unused land. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 8 data tables Land use status 2002.xls Land use status in 2003.xls Land use status 2004.xls Land use status 2006.xls Land use status 2007.xls Land use status in 2008.xls Land use status in 2009.xls The structure of 2012. XLS data table is the same. For example, there are four fields in the data table of land use status in 2002 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
The data set records the statistical data of land use status in Huangnan Prefecture of Qinghai Province from 2003 to 2012, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Land use status in Huangnan Prefecture, 2003.xls Land use status of Huangnan Prefecture, 2006.xls Land use status in Huangnan Prefecture, 2008 1.xls Land use status in Huangnan Prefecture, 2008 2.xls Land use status of Huangnan Prefecture, 2012.xls Land use status in Huangnan Prefecture, 2004.xls Land use status of Huangnan Prefecture, 2006.xls Land use status of Huangnan Prefecture 2007.xls Current situation of land use in Huangnan Prefecture The data table structure is the same. For example, there are four fields in the data table of land use status in Huangnan Prefecture in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
The data set records the statistical data of land use status in Haixi Prefecture of Qinghai Province from 2003 to 2007, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 8 data tables Land use status of Haixi Prefecture, 2003.xls Land use status of Haixi Prefecture 2007.xls Land use status of Haixi Prefecture, 2008.xls Land use status of Haixi Prefecture in 2008 Land use status of Haixi Prefecture, 2012.xls Land use status of Haixi Prefecture, 2006.xls Land use status of Haixi Prefecture 2007.xls Land use status of Haixi Prefecture, 2004.xls The data table structure is the same. For example, there are four fields in the data table of land use status in Haixi Prefecture in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of land use status in Hainan prefecture of Qinghai Province from 2003 to 2007, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Land use status in Hainan Province, 2003.xls Land use status in Hainan Province, 2006-2007.xls Land use status in Hainan Province, 2008 1.xls Land use status in Hainan Province, 2008 2.xls Land use status in Hainan, 2012.xls Land use status in Hainan Province, 2004.xls Land use status in Hainan, 2006.xls Land use status in Hainan, 2007.xls Land use status in Hainan Province The data table structure is the same. For example, there are four fields in the data table of land use status in Hainan in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of land use status in Haidong area of Qinghai Province from 2003 to 2012, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Land use status in Haidong region, 2003.xls Land use status in Haidong area 2007.xls Land use status in Haidong region, 2008 1.xls Land use status in Haidong region, 2008 2.xls Land use status in Haidong region, 2004.xls Land use status in Haidong region, 2006.xls Land use status in Haidong area 2007.xls Land use status in Haidong region, 2008 3.xls Land use status of Haidong City, 2012.xls The data table structure is the same. For example, there are four fields in the 2003 data table of land use status in Haidong region Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of land use status in Haibei Prefecture of Qinghai Province from 2003 to 2007, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Land use status of Haibei Prefecture, 2003.xls Land use status of Haibei Prefecture 2006 2007.xls Land use status of Haibei Prefecture, 2008.xls Land use status of Haibei Prefecture 2008.xls Land use status of Haibei Prefecture, 2012.xls Land use status of Haibei Prefecture, 2004.xls Land use status of Haibei Prefecture, 2006.xls Land use status of Haibei Prefecture 2007.xls Land use status of Haibei Prefecture, 2008.xls The data table structure is the same. For example, there are four fields in the data table of land use status in Haibei Prefecture in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of land use status in Guoluo Prefecture of Qinghai Province from 2003 to 2007, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Present situation of land use in Guoluo Prefecture, 2003.xls Current situation of land use in Guoluo Prefecture, 2006-2007.xls Current situation of land use in Guoluo Prefecture, 2008.xls Present situation of land use in Guoluo Prefecture, 2008.xls Current situation of land use in Guoluo Prefecture, 2012.xls Present situation of land use in Guoluo Prefecture, 2004.xls Current situation of land use in Guoluo Prefecture, 2006.xls Present situation of land use in Guoluo Prefecture, 2007.xls Current situation of land use in Guoluo Prefecture, 2008.xls The data table structure is the same. For example, there are four fields in the data table of land use status in Luozhou in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data includes the soil microbial composition data in permafrost of different ages in Barrow area of the Arctic. It can be used to explore the response of soil microorganisms to the thawing in permafrost of different ages. This data is generated by high through-put sequencing using the earth microbiome project primers are 515f – 806r. The region amplified is the V4 hypervariable region, and the sequencing platform is Illumina hiseq PE250; This data is used in the articles published in cryosphere, Permafrost thawing exhibits a greater influence on bacterial richness and community structure than permafrost age in Arctic permafrost soils. The Cryosphere, 2020, 14, 3907–3916, https://doi.org/10.5194/tc-14-3907-2020https://doi.org/10.5194/tc-14-3907-2020 . This data can also be used for the comparative analysis of soil microorganisms across the three poles.
KONG Weidong
The data set records the main economic indicators of key enterprise groups in Qinghai Province from 2001 to 2008, and the data is divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains eight data tables, each of which has the same structure. For example, the 2001 data table has 15 fields: Field 1: indicator name Field 2: number of groups Field 3: total assets Field 4: accumulated depreciation Field 5: R & D expenses Field 6: minority shareholders' equity at the end of the year Field 7: year end number of employees Field 8: employees on duty Field 9: total profit Field 10: Investment completion amount of fixed assets Field 11: investment income Field 12: Equity Field 13: main business income Field 14: operating cost Field 15: other practitioners
Qinghai Provincial Bureau of Statistics
The data set records the land use status of Yushu prefecture in Qinghai Province from 2003 to 2012, and the data is divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains eight data tables, each of which has the same structure. For example, there are four fields in the data table from 1978 to 2004 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
The data set records the land use status of Xining City in Qinghai Province from 2003 to 2012, and the data is divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains 9 data tables with the same structure. For example, the data table in 2003 has four fields: Field 1: area at the beginning of the year Field 2: area reduced during the year Add field area within 3 years Field 4: year end area
Qinghai Provincial Bureau of Statistics
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.
LI Zhenyu, ZHANG Na
Soil moisture is one of the core variables in the water cycle. Although its variation is very small, for a precipitation process, soil moisture directly determines the transformation of precipitation into evaporation, runoff and groundwater, which is very important to finely simulate spatial-temporal dynamics of various variables in hydrological process and to accurately estimate water inflow in the upper reaches of Heihe River. This dataset includes soil moisture and temperature data observed by 40 nodes from July 2013 to December 2017. Each node in Babao River Basin has soil moisture observation at depth of 4cm and 20cm; some nodes also include observations at depth of 10 cm. The data observation frequency is 1 hour. The dataset can provide ground -based observations for hydrological simulation, data assimilation and remote sensing verification.
JIN Rui, KANG Jian
According to the distribution of cultivated land in 18 districts and counties in the "One River and Two Tributaries" region of Tibet Autonomous Region, a 5km × 5km grid was adopted, covering all cultivated land and greenhouse land. A total of 1092 5km × 5km grids were set up, and each grid contains a number. Data processing method: the fishnet tool in ArcGIS 10.3 is used to generate the grid covering the administrative boundaries of 18 districts and counties in the "one river, two rivers" region of Tibet Autonomous Region, and then the intersect tool is used to generate the grid covering cultivated land. The data can be used to collect soil samples of cultivated land in "One River and Two Tributaries" area of Tibet Autonomous Region.
WANG Zhaofeng, GONG Dianqing
The data of greenhouse land is based on Google Earth image interpretation in Lhasa city, 2018, with a spatial resolution of 0.52 meters. Most of the greenhouses in Lhasa are regular rectangles with high reflectivity, which is easy to identify. In the process of interpretation, the open fields with an area of more than 0.10 hectares and roads with a width of more than 7 meters in the greenhouse area of protected agriculture, as well as the greenhouse covered with black textile were removed, while the small empty fields and ridges between the farmland of protected agriculture were not removed. The accuracy of interpretation is 98%. The data well reflects the spatial pattern characteristics of greenhouse land in Lhasa city.
WANG Zhaofeng, GONG Dianqing
Soil mapping and attribution dataset of all nodes area in pan-third pole is an important information to provide extremely valuable reference for the weather process, drought and hydrological monitoring, which reflects the soil resources, soil fertility, soil environment, soil biology and other soil databases. The base of this data is from the world soil database (HWSD), which belongs to the FAO soil portal V1.2) authoritative public data are the main data, and some supplementary data such as ISRIC soil data published by ISRIC and other project data collected by the world soil data center (ISRIC). Finally the data of soil information in the expected 31 pan third pole key node regionsand 3 projects is obtained. As the research basis of soil, this data set provides the basic information of soil regionalization for the project. This data has the characteristics of grid visualization and attribute table visualization, which can be viewed through many ways. The grid visualization data provides general data on soil types and distribution. And the attribute information is included in the excel, including detailed soil parameters (including organic carbon, pH, water storage, soil depth, cation exchange capacity and clay content of soil, total exchangeable nutrients, lime and gypsum content, sodium exchange rate, salinity, structural grade and particle size, etc.).
SHANG Cheng, LING Feng
This dataset records The experiment of soil water content in the lower reaches of the Tarim River (Karl) was carried out by the members of the Xinjiang salt water Regiment (Karl) from September to September, 2020 In order to study the phenotypic characteristics of different plants under high salinity saline water irrigation, and to explore the feasibility of high salinity saline water for vegetation construction.
LI Xinrong, HE Mingzhu, ZHAO Zhenyong
This dataset contains 18 years (2002-2020) global spatio-temporal consistent surface soil moisture . The resolution is 36 km at daily scale, and the data unit is m3 / m3. This dataset adopts the soil moisture neural network retrieval algorithm developed by Yao et al. (2017). This study transfers the merits of SMAP to AMSR-E/2 through using an Artificial Neural Network (ANN) in which SMAP standard SSM products serve as training targets with AMSR-E/2 brightness temperature (TB) as input. Finally, long term soil moisture data are output. The accuracy is about 5% volumetric water content. (evaluation accuracy of 14 dense ground network globally.)
YAO Panpan, LU Hui
This dataset contains daily 0.01°×0.01° land surface soil moisture products in the Qinghai-Tibet Plateau in 2005, 2010, 2015, 2017, and 2018. The dataset was produced by utilizing the multivariate statistical regression model to downscale the “SMAP Time-Expanded 0.25°×0.25° Land Surface Soil Moisture Dataset in the Qinghai-Tibet Plateau (SMsmapTE, V1)”. The auxiliary datasets participating in the multivariate statistical regression include GLASS Albedo/LAI/FVC, 1km all-weather surface temperature data in western China by Ji Zhou, and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
This dataset contains land surface soil moisture products with SMAP time-expanded daily 0.25°×0.25°in Qinghai-Tibet Plateau Area. The dataset was produced based on the Random Forest method by utilizing passive microwave brightness temperature along with some auxiliary datasets. The temporal resolution of the product in 1980,1985,1990,1995 and 2000 is monthly, by using SMMR, SSM/I, and SSMIS brightness temperature from 19 GHz V/H and 37 GHz V channels. The temporal resolution of the product between June 20, 2002 and Dec 30, 2018 is daily, by utilizing AMSR-E and AMSR2 brightness temperature from 6.925 GHz V/H, 10.65 GHz V/H, and 36.5 GHz V channels. The auxiliary datasets participating in the Random Forest training include the IGBP land cover type, GTOPO30 DEM, and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
