Dataset of digital soil mapping products for the Qinghai-Tibet Plateau (2015-2024)

Based on the "second Qinghai Tibet Plateau comprehensive scientific investigation" and "China's soil series investigation and compilation of China's soil series" "The obtained soil survey profile data, using predictive Digital Soil Mapping paradigm, using geographic information and remote sensing technology for fine description and spatial analysis of the soil forming environment, developed adaptive depth function fitting methods, and integrated advanced ensemble machine learning methods to generate a series of soil attributes (soil organic carbon, pH value, total nitrogen, total phosphorus, total potassium, cation exchange capacity, gravel content (>2mm) in the Qinghai Tibet plateau region." , sand, silt, clay, soil texture type, unit weight, soil thickness, etc.) and quantify the spatial distribution of uncertainty. Compared with the existing soil maps, it better represents the spatial variation characteristics of soil properties in the Qinghai Tibet Plateau. The data set can provide soil information support for the study of soil, ecology, hydrology, environment, climate, biology, etc. in the Qinghai Tibet Plateau.

0 2022-06-03

Basic soil property dataset of high-resolution China Soil Information Grids (2010-2018)

Soil is the basis of human survival and development. Many United Nations Sustainable Development Goals (SDGs) are directly related to the utilization and management of soil resources. However, most of the existing soil information in the world and China comes from historical soil survey, which is coarse and out-of-date, and can not meet the needs of dealing with global and regional problems such as food security, water shortage, land degradation and climate change. China has a vast territory with complex and diverse soil landscape and strong human activities. The establishment of high-precision soil information grid is of great significance in scientific frontier breakthrough and has broad prospects in applications. Here, we adopted predictive soil mapping paradigm and developed adaptive depth function fitting method and integrated it with state-of-the-art ensemble machine learning in a high performance parallel computing environment to generate 90-m resolution national gridded maps of soil properties (soil organic carbon, pH value, total nitrogen, total phosphorus, total potassium, cation exchange capacity, coarse fragments (> 2mm), sand, silt, clay, soil texture classes, bulk density, soil thickness, etc.) at multiple depths across China. Their uncertainty in soil predictions is also estimated in a spatial way. This was based on more than 5000 representative soil profile samples obtained from the "project of National Soil Series Survey and Compilation of Soil Series of China" in recent years and a suite of detailed covariates to characterize soil-forming environments using geographical information and remote sensing techniques. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory. This work has constructed China's first version of high-resolution National Soil Information Grids, which is also a significant contribution to the GlobalSoilMap.net project. It is expected to have a wide application prospect in the fields of soil resources, agriculture, hydrology, ecology, climate, environment and so on, such as soil monitoring and management, soil function evaluation, land surface process modelling and forensic soil evidence provenance.

0 2021-11-30

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

Soil observation data of typical sample points in Heihe River Basin (2012-2014)

The data set contains soil observation data of typical sample points in Heihe River Basin: pH value and soil texture 1. Soil pH value: longitude, latitude and pH value of typical soil sample points. 2. Soil texture: including soil texture data of typical soil samples in Heihe River Basin from July 2012 to August 2013. The typical soil sampling method in Heihe River Basin is representative sampling, which means that the typical soil types in the landscape area can be collected, and the representative sample points should be collected as far as possible. According to the Chinese soil taxonomy, soil samples from each profile were taken based on the diagnostic layers and diagnostic characteristics.

0 2020-07-30

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 2020-06-08

1:100,000 soil database in the upper reaches of the Yellow River (1995)

一.An overview The 1:100,000 soil database in the upper reaches of the Yellow River was tailored from the 1:100,000 soil database in China.The 1:100,000 soil database of China is based on the 1:100,000 soil map of the People's Republic of China compiled and published by the national soil census office in 1995.The database adopts the traditional "soil genetic classification" system, and the basic mapping unit is subcategories, which are divided into 12 classes of soil, 61 classes of soil and 227 classes of soil, covering all kinds of soil and its main attribute data in China. 二. Data processing instructions The 1:1 million soil database of China was established by the soil resources and digital management innovation research team led by shi xuezheng of nanjing soil research institute, Chinese academy of sciences, after four years.The database consists of two parts: soil spatial database and soil attribute database.The establishment of the database was funded by the knowledge innovation program of the Chinese academy of sciences and completed under the leadership of liu jiyuan and zhuang dafang. 三. data content description The soil spatial database, 1:1 million digitized soil maps of the country, is based on the 1:1 million soil maps of the People's Republic of China compiled and published by the national census offices in 1995.The digitized soil map faithfully reflects the appearance of the original soil map and inherited the mapping unit when the original soil map was compiled. Most of the basic mapping units are soil genera, which are divided into 12 classes, 61 classes and 235 subclasses. It is the only and most detailed digitized soil map in China. The soil attribute database, whose attribute data is quoted from the soil species record of China, is divided into six volumes, and nearly 2,540 soil species are collected.Soil property data can be divided into soil physical properties, soil chemical properties and soil nutrients.Soil physical properties soil particle composition and soil texture, soil chemical properties such as PH value, organic matter, soil nutrients include all N, all P, all K and effective P and effective K. 四. Data usage instructions Soil types and soil properties are an important content in the study of physical geography. With the help of 1:100,000 soil database in the upper reaches of the Yellow River, the type, quantity and spatial distribution of soil resources in the upper reaches of the Yellow River as well as the soil environment and characteristics can be understood and analyzed.This data set is of great significance for the early warning of large-scale soil erosion and the prediction of natural disasters in the upper reaches of the Yellow River.

0 2020-03-28

Digital soil mapping dataset of soil texture (soil particle-size fractions) in the upstream of the Heihe river basin (2012-2016)

Select the soil mechanical composition data of 0-20cm depth of soil surface, select the optimal spatial prediction mapping method of soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The American system classification is used as the standard of soil particle classification. The source data of this data set comes from the soil sampling data integrated by the data center of cold and dry areas and the major research plan integration project of Heihe River Basin (spatial interpolation and dynamic simulation analysis of vegetation and environmental elements in the upper reaches of Heihe River basin / approval No. 91325204).

0 2020-03-28

Digital soil mapping dataset of soil texture (soil particle-size fractions)in the Tianlaochi basin (2012-2014)

Select the soil mechanical composition data with a depth of 0-20cm on the surface of the soil, select the optimal spatial prediction mapping method for soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The classification standard of soil particle size is American classification. The source data of this data set are from the data center of cold and drought regions, soil physical properties-soil bulk density and mechanical composition data set soil sampling profile data of Tianlaochi watershed in Qilian mountain.

0 2020-03-28

Digital soil mapping dataset of soil texture (soil particle-size fractions) in the Heihe river basin (2012-2016)

Select the soil mechanical composition data of 0-20cm depth of soil surface, select the optimal spatial prediction mapping method of soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The American system classification is used as the standard of soil particle classification. The source data of this data set comes from the soil sampling data integrated by the data center of cold and dry areas and the major research plan integration project of Heihe River Basin (spatial interpolation and dynamic simulation analysis of vegetation and environmental elements in the upper reaches of Heihe River basin / approval No. 91325204).

0 2020-03-27

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

The American system classification is used as the standard of soil particle classification. 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). The prediction method is mainly based on the soil landscape model. The basic theory of the model is the classic soil genesis theory. The model regards the soil as the product of the comprehensive effects of climate, topography, parent material, biology and time. Scope: Heihe River Basin; Projection: WGS · 1984 · Albers; Spatial resolution: 100M; Data format: TIFF; Data content: spatial distribution of soil clay, silt and sand content Prediction method: enhanced regression tree Environmental variables: main soil forming factors

0 2020-03-27