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, Feng; Wu, Huayong; Zhao, Yuguo; Li, Decheng; Yang, Jin-Ling; Song, Xiaodong; Shi, Zhou; Zhu, A-Xing; Zhang, Gan-Lin
发表日期	2022
ISSN	2095-9273
EISSN	2095-9281
起始页码	328
结束页码	340
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 highperformance 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. (c) 2021 Science China Press. Published by Elsevier B.V. and Science China Press. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
英文关键词	Predictive soil mapping; Soil-landscape model; Machine learning; Depth function; Large and complex areas; Soil spatial variation
语种	英语
WOS研究方向	Multidisciplinary Sciences
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000750754200015
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/281092
作者单位	Chinese Academy of Sciences; Institute of Soil Science, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Zhejiang University; Nanjing Normal University; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS; Chinese Academy of Sciences; Nanjing Institute of Geography & Limnology, CAS
推荐引用方式 GB/T 7714	Liu, Feng,Wu, Huayong,Zhao, Yuguo,et al. Mapping high resolution National Soil Information Grids of China[J],2022,67(3).
APA	Liu, Feng.,Wu, Huayong.,Zhao, Yuguo.,Li, Decheng.,Yang, Jin-Ling.,...&Zhang, Gan-Lin.(2022).Mapping high resolution National Soil Information Grids of China.SCIENCE BULLETIN,67(3).
MLA	Liu, Feng,et al."Mapping high resolution National Soil Information Grids of China".SCIENCE BULLETIN 67.3(2022).