气候变化领域集成服务门户(CCPortal): Predicting long-term dynamics of soil salinity and sodicity on a global scale

CCPortal

DOI	10.1073/PNAS.2013771117
	Predicting long-term dynamics of soil salinity and sodicity on a global scale
	Hassani A.; Azapagic A.; Shokri N.
发表日期	2021
ISSN	00278424
起始页码	33017
结束页码	33027
卷号	117 期号:52
英文摘要	Knowledge of spatiotemporal distribution and likelihood of (re)occurrence of salt-affected soils is crucial to our understanding of land degradation and for planning effective remediation strategies in face of future climatic uncertainties. However, conventional methods used for tracking the variability of soil salinity/sodicity are extensively localized, making predictions on a global scale difficult. Here, we employ machine-learning techniques and a comprehensive set of climatic, topographic, soil, and remote sensing data to develop models capable of making predictions of soil salinity (expressed as electrical conductivity of saturated soil extract) and sodicity (measured as soil exchangeable sodium percentage) at different longitudes, latitudes, soil depths, and time periods. Using these predictive models, we provide a global-scale quantitative and gridded dataset characterizing different spatiotemporal facets of soil salinity and sodicity variability over the past four decades at a ∼1-km resolution. Analysis of this dataset reveals that a soil area of 11.73 Mkm2 located in nonfrigid zones has been salt-affected with a frequency of reoccurrence in at least three-fourths of the years between 1980 and 2018, with 0.16 Mkm2 of this area being croplands. Although the net changes in soil salinity/sodicity and the total area of salt-affected soils have been geographically highly variable, the continents with the highest salt-affected areas are Asia (particularly China, Kazakhstan, and Iran), Africa, and Australia. The proposed method can also be applied for quantifying the spatiotemporal variability of other dynamic soil properties, such as soil nutrients, organic carbon content, and pH. © 2020 National Academy of Sciences. All rights reserved.
英文关键词	Global scale modeling; Machine learning; Soil salinity; Soil salinization; Soil sodicity
语种	英语
scopus关键词	sodium; Africa; Article; Australia; China; climate; cropland; electric conductivity; Iran; Kazakhstan; machine learning; priority journal; quantitative analysis; remote sensing; salinity; soil depth; soil quality; soil salinity; soil sodicity; topography; validation process
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179659
作者单位	Department of Chemical Engineering and Analytical Science, University of Manchester, Manchester, M13 9PL, United Kingdom; Institute of Geo-Hydroinformatics, Hamburg University of Technology, Hamburg, 21073, Germany
推荐引用方式 GB/T 7714	Hassani A.,Azapagic A.,Shokri N.. Predicting long-term dynamics of soil salinity and sodicity on a global scale[J],2021,117(52).
APA	Hassani A.,Azapagic A.,&Shokri N..(2021).Predicting long-term dynamics of soil salinity and sodicity on a global scale.Proceedings of the National Academy of Sciences of the United States of America,117(52).
MLA	Hassani A.,et al."Predicting long-term dynamics of soil salinity and sodicity on a global scale".Proceedings of the National Academy of Sciences of the United States of America 117.52(2021).