干旱区生态环境知识资源中心(Arid): Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China

Arid

DOI	10.1016/j.scitotenv.2019.135387
	Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China
	Wei, Yang 1; Shi, Zhou 2; Biswas, Asim 3; Yang, Shengtian 1,4; Ding, Jianli1 ; Wang, Fei 1
通讯作者	Wang, Fei
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2020
卷号	716
英文摘要	Precise and spatially explicit regional estimates of soil salinity are necessary to efficiently management and utilise limited land and water resources. Despite advances achieved in remote sensing over the past century, knowledge about the distribution and severity of soil salinization in economically important areas, such as oasis agroecosystems and desert-oasis ecotones (OADoE), is currently limited. An example of an area is southern Xinjiang, where the OADoE has a high anthropogenic influence. This study was conducted with the aim of mapping soil salinity in typical OADoE using remote sensing and machine learning techniques (Cubist and Random Forest, RF). A range of covariates was obtained from the multi-temporal Landsat-8 operational land imager (OLI) satellite for the period from 2013 to 2018. The values of coefficients of determination (R-2), Lin's concordance correlation coefficient, root mean square error, and relative root mean squared error values, were 0.78, 0.87, 9.59, and 0.76, respectively, for the Cubist and 0.78, 0.86, 9.79, and 0.78, respectively, for RF models. The slope of the linear fitting equation was higher for the Cubist model (0.75) than for RF (0.69). The explanatory power of Cubist and RF for soil salinity variation were 33.22% and 31.41% in the agroecosystem, and 72.25% and 71.66% in desert-oasis ecotone, respectively. For the agroecosystem, the range of the predicted values for 89.13% (Cubist) and 84.78% (RF) of sample was controlled within the same observational range at an interval of 0-5 dS m(-1). Compared to single-year data (from 2013 to( )2018), the ability to account for model spatial variability in soil salinity based on multi-year Landsat images was increased by 16%-35%. According to the variable importance evaluation, soil-related indices are the most important predictor variables, followed by vegetation, topography, landform, and land use, with relative importance values of 60%, 21%, 16%, and 3%, respectively. The predicted map was also broadly consistent with those obtained for Xinjiang in the Harmonized World Soil Database (HWSD) from the second national soil survey of China conducted from 1984 to 1997. The results also showed that the average value of the study area is 8.10 dS m(-1) based on the Cubist-based map whereas that of the HWSD is 10.60 dS m(-1), this implied that the overall salinity level has reduced by 23.58%. The methodological framework presented covers all prediction process steps and has considerable potential to be used in future soil salinity mapping at large scales for other similar region as OADoEs. The map derived from the Cubist/RF model revealed more detailed variation information about spatial distribution of the soil salinity compared to HWSD, and can further assist with decision-making when planning and utilising on existing soil and water resources in OADoEs. (C) 2019 Elsevier B.V. All rights reserved.
英文关键词	Soil salinity Machine learning Digital soil mapping Tarim Basin Harmonized world Soil Database
类型	Article
语种	英语
国家	Peoples R China ; Canada
收录类别	SCI-E
WOS记录号	WOS:000519987300146
WOS关键词	LAND-USE ; SPATIAL PREDICTION ; ORGANIC-CARBON ; RANDOM FOREST ; ECOSYSTEM SERVICES ; SOUTHERN XINJIANG ; VEGETATION INDEX ; SEMIARID REGION ; CLIMATE-CHANGE ; ARID REGION
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
来源机构	北京师范大学 ; 新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315540
作者单位	1.Xinjiang Univ, Xinjiang Common Univ, Coll Resource & Environm Sci, Key Lab Smart City & Environm Stimulat, Urumqi 830046, Peoples R China; 2.Zhejiang Univ, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Peoples R China; 3.Univ Guelph, Sch Environm Sci, Guelph, ON N1G2W1, Canada; 4.Beijing Normal Univ, Coll Water Sci, Beijing Key Lab Urban Hydrol Cycle & Sponge City, Beijing 100875, Peoples R China
推荐引用方式 GB/T 7714	Wei, Yang,Shi, Zhou,Biswas, Asim,et al. Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China[J]. 北京师范大学, 新疆大学,2020,716.
APA	Wei, Yang,Shi, Zhou,Biswas, Asim,Yang, Shengtian,Ding, Jianli,&Wang, Fei.(2020).Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China.SCIENCE OF THE TOTAL ENVIRONMENT,716.
MLA	Wei, Yang,et al."Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China".SCIENCE OF THE TOTAL ENVIRONMENT 716(2020).