The Slope Length and Stepness Factor (LS) dataset of Pan-third pole 20 country is calculated based on the free accessed 1 arc second resolution SRTM digital elevation data (Shuttle Radar Topography Mission, SRTM; the website is http://srtm.csi.cgiar.org）. After the pre-processing such as pseudo edge removal, filtering and noise removal, the LS factor with 7.5 arc second resolution was calculated with the LS factor algorithm in CSLE model and the LS calculation tool (LS_tool) developed in this project. The LS factor data of Pan-third pole 20 countries is the fundamental data for soil erosion rate calculation based on CSLE, and it also the fuandatmental data for analyzing the erosion topographic characteristics of Pan third pole 20 countries (such as macro distribution and micro pattern of elevation, slope and slope) . The dataset if of great importance for the analysis of geomorphic characteristics and geological disaster characteristics in this area.
The soil erodibility factor (K) dataset of 20 countries in the Pan-third pole area is calculated based on the soil properties downloaded from website of the international soil reference and Information Center (ISRIC)（https://files.isric.org/soilgrids/latest/data/） with 7.5 arc second resolution, and the dataset used in the calculation include soil clay content (%), silt content (%), and sand content (%), soil organic carbon content (g / kg) and soil texture class. The soil erodibility factor algorithm proposed by Wischmeier (1978) in the second edition of USLE manual was used, and the soil erodibility factor calculation tool (k_tool) was developed, to generat the 1 arc second (about 25m) soil erodibility factor map. The soil erodibility factor data of Pan third pole 20 countries is not only the fundamental input for soil erosion rate calculation based on CSLE, but also the basic data for analyzing soil characteristics.
1)The dataset includes the grid data of vegetation coverage and biological measure factor B of 20 countries in key regions, with a spatial resolution of 300 meters. 2）The basic data source is the MODIS MOD13Q1 product from 2014 to 2016 with a spatial resolution of 250 m. Based on this, a 24-half month average vegetation coverage raster data during a 3 year period was calculated, and then the soil loss ratio was calculated according to the land type. The, the 24- half months rainfall erosivity was further weighted and averaged to obtain a grid map of vegetation coverage and biological measures B factor. 3）MOD13Q1 remote sensing vegetation data was processed by cloud removal. The calculated B factor was statistically analyzed by landuse types and rationality analyzed. The final data quality is good. 4）The factor B of vegetation coverage and biological measures reflects the impact of surface land use/vegetation coverage on soil erosion, and is of great significance for soil erosion simulation and spatial pattern analysis in 20 key regions.
1)The data includes the raster data of soil erosion intensity in 20 countries in key regions in2015, with a spatial resolution of 300 meters. 2）Based on the data of 13,000 survey units in 20 countries in key regions, the Chinese soil erosion prediction model (CSLE) was used to calculate the factors of rainfall erosivity, soil erodibility, slope length, slope gradient, vegetation cover and biological measures, engineering measures and tillage measures. And then the amount of soil erosion was interpolated by soil types, and the a soil erosion intensity map of 20 countries in key regions was then obtained. 3）The rationality of spatial pattern of soil erosion intensity data was analyze, and the data quality was good . 4）Soil erosion intensity data is of great significance for understanding the spatial pattern of soil erosion in 20 countries in key regions and for carrying out soil erosion control.
The Northwest Institute of Ec-Environment and Resources of the Chinese Academy of Sciences organized a team of 9 and 5 people to carry out the research on "key technologies and demonstration for vegetation restoration and reconstruction in desertification land " from the middle and lower reaches of the Amu Darya River basin to the surrounding area of the Aral Sea from April 3, 2019 to April 30, 2019 and from September 16 to 28, 2019, respectively, and investigated the middle and lower reaches of the Amu Darya River basin to the surrounding area of the Aral Sea The site includes Tashkent, Samarkand, Navoi, Bukhara, Nukus, muinak, etc., with a total length of more than 4000 kilometers. It mainly conducts UAV low altitude remote sensing, plant community investigation, soil type, climate and soil moisture status comprehensive investigation in different degree of degradation desertification areas, and samples of plant, soil are taken. A total of 30 sample plots were investigated, and data sets of desertification degree and distribution characteristics, vegetation type and distribution, soil type and physical and chemical properties were obtained.
According to the soil map of the people's Republic of China (1:1 million) compiled and published by the national soil survey office in 1995, the spatial distribution data of soil types in the transportation corridor between Sichuan and Tibet are generated digitally. The traditional "soil genetic classification" system is adopted. The basic mapping unit is subclass, which is divided into 12 soil classes, 61 soil classes and 227 subclasses. There are 2647 records and 16 attribute data items in the soil attribute database, covering all kinds of soil and its main attribute characteristics in China.
This data set contains experimentally measured soil nutrient data collected in typical small watersheds in Sichuan Province, Tibet Autonomous Region and Qinghai Province. The data comes from the survey of grassland, cultivated land, and woodland in Minhe County, Menyuan County and the east area of Qinghai Lake in the second Qinghai-Tibet Plateau scientific expedition, and recorded detailed soil parameters (including organic carbon, ph, soil Cation exchange capacity, water content, etc.) can provide important values for tracing the source of soil water erosion in small watershed areas and understanding the soil environment.
The dataset contains the distribution of soil heavy metal in the upper and middle areas of Heihe River Basin. In August 2020, 49 soil samples were obtained from a field investigation in the Heihe River Basin. The soil samples were brought back to the laboratory for preliminary classification and removal of impurities. Then the soil samples were naturally air-dried, mixed evenly, ground with a ball mill, and screened to obtain the test samples. Next, the samples were heated and digested in the ST-60 automatic digestion instrument. Finally, the content of heavy metals in the soil, including Cd, Zn, Cu, Ni, Cr, As, and Pb were determined (mg/kg as unit) by ICP-AES spectrometer after making up to volume. The detection limit of Cd was 0.0002, Zn and Cu were 0.001, Ni and Cr were 0.001, As and Pb were 0.003. Our dataset is reliable and can be used to analyze the distribution patterns of heavy metal elements in the soil in the urbanized area of Tibetan plateau.
Soil profiles in this dataset were surveyed in the western and central Qinghai-Tibet Plateau in July 2019, including Ali, Xigaze and Naqu of the Tibet and Kashgar and Hotan of the Xinjiang. Information on the profile ID, longitude, latitude, soil types was provided. Soil types were referenced according to the Chinese Soil Taxonomy. The Chinese Soil Taxonomy is a hierarchical system, in which 6 categories were defined: Order, Suborder, Group, Subgroup, Family and Series. The sampling location was recorded by a handheld GPS receiver. Especially, these soil types were initially determined based on the diagnostic horizons and diagnostic properties identified in field. Due to the effect of epidemic, physicochemical properties of some soil samples have not been achieved and thus some soil types need to be updated in the following months.
This data set contains the biological property data of soil samples from several scientific research routes in the Qinghai Tibet Plateau from 2019 to 2021, including the information of the collector, collection time, collection location, longitude and latitude, altitude, vegetation type, sampling depth, phosphatase activity, microbial respiration, nitrogen transformation characteristics, functional gene abundance, fungi, bacteria, protobiotic diversity, etc. The analysis of various soil properties refers to the requirements of "technical specification for soil environmental quality monitoring", and the first-hand data obtained through laboratory analysis. The data quality is controlled by determining blank samples, duplicate samples and standard samples. The data set can be used to evaluate soil quality and function under the influence of climate change and human activities.