干旱区生态环境知识资源中心(Arid): Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China

Arid

DOI	10.1016/j.geoderma.2018.08.006
	Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China
	Peng, Jie 1,2; Biswas, Asim 3; Jiang, Qingsong 1,4; Zhao, Ruiying 1; Hu, Jie 1; Hu, Bifeng 1; Shi, Zhou 1
通讯作者	Shi, Zhou
会议名称	Pedometrics Conference
会议日期	JUN 26-JUL 01, 2017
会议地点	Wageningen, NETHERLANDS
英文摘要	Soil salinization is one of the main reasons for soil health and ecosystem deterioration in most degraded arid and semiarid areas. To monitor its spatial variation as precise as possible over a large area, we collected 225 samples using traditional field experiment and laboratory analysis method from the southern part of the Xinjiang Province, China, affected by soil salinity under strong arid climate. Then, we constructed both Cubist and partial least square regression (PLSR) models on electrical conductivity (EC) (150 ground-based measurements as calibration set) using various related covariates (e.g. terrain attributes, remotely sensed spectral indices of vegetation and salinity from landsat8 OLI satellite) that are at the same time period corresponding to soil sampling. Two models were validated using remaining 75 independent ground based measurements and were then used to map the soil salinity over the study area. Finally, the validation results of two models were compared under different intervals of EC, soil moisture content and vegetation coverage. The results indicated that Cubist model could predict EC value with better accuracy and stability under variable environment than PLSR. The R-2, RMSE, MAE and RPD of the Cubist model were 0.91, 5.18 dS m(-1), 3.76 dS m(-1) and 3.15 while corresponding values of the PLSR model were 0.66, 10.46 dS m(-1), 8.21 dS m(-1) and 1.56 in validation dataset, respectively. Additionally, the map derived from Cubist model revealed more detailed variation information of the spatial distribution of EC than that from PLSR model across the study area. Thus, Cubist model was recommended for mapping soil salinity using indices derived from satellite and terrain in other arid areas.
英文关键词	Soil salinization Cubist Partial least squares regression Remote sensing Digital soil mapping
来源出版物	GEODERMA
ISSN	0016-7061
EISSN	1872-6259
出版年	2019
卷号	337
页码	1309-1319
出版者	ELSEVIER
类型	Article; Proceedings Paper
语种	英语
国家	Peoples R China;Canada
收录类别	CPCI-S
WOS记录号	WOS:000456761500129
WOS关键词	YELLOW-RIVER DELTA ; AGRICULTURAL SOILS ; METHODS PLSR ; VEGETATION ; REFLECTANCE ; INDEX ; SPECTRA ; CLIMATE ; SEASONS ; REGION
WOS类目	Soil Science
WOS研究方向	Agriculture
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/308288
作者单位	1.Zhejiang Univ, Coll Environm & Resource Sci, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Zhejiang, Peoples R China; 2.Tarim Univ, Coll Plant Sci, Alar 843300, Peoples R China; 3.Univ Guelph, Sch Environm Sci, 50 Stone Rd East, Guelph, ON N1G 2W1, Canada; 4.Tarim Univ, Coll Informat Engn, Alar 843300, Peoples R China
推荐引用方式 GB/T 7714	Peng, Jie,Biswas, Asim,Jiang, Qingsong,et al. Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China[C]:ELSEVIER,2019:1309-1319.