气候变化领域集成服务门户(CCPortal): Mapping high resolution National Soil Information Grids of China

CCPortal

DOI	10.1016/j.scib.2021.10.013
	Mapping high resolution National Soil Information Grids of China
	Liu F.; Wu H.; Zhao Y.; Li D.; Yang J.-L.; Song X.; Shi Z.; Zhu A.-X.; Zhang G.-L.
发表日期	2022
卷号	67 期号:3
英文摘要	Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a high-performance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties (pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately 5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate (Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development. © 2021 Science China Press
英文关键词	Depth function; Large and complex areas; Machine learning; Predictive soil mapping; Soil spatial variation; Soil-landscape model
语种	英语
scopus关键词	Climate change; Food supply; Forecasting; Machine learning; Mapping; Organic carbon; Soils; Accurate prediction; Depth function; High resolution; Large and complex area; Predictive soil mapping; Soil mapping; Soil property; Soil spatial variation; Soil-landscape models; Spatial variations; Soil surveys
来源期刊	Science Bulletin
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/251205
作者单位	State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing, 210023, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Key Laboratory of Watershed Geographic Science, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China
推荐引用方式 GB/T 7714	Liu F.,Wu H.,Zhao Y.,et al. Mapping high resolution National Soil Information Grids of China[J],2022,67(3).
APA	Liu F..,Wu H..,Zhao Y..,Li D..,Yang J.-L..,...&Zhang G.-L..(2022).Mapping high resolution National Soil Information Grids of China.Science Bulletin,67(3).
MLA	Liu F.,et al."Mapping high resolution National Soil Information Grids of China".Science Bulletin 67.3(2022).