干旱区生态环境知识资源中心(Arid): Comparison of machine learning algorithms for soil salinity predictions in three dryland oases located in Xinjiang Uyghur Autonomous Region (XJUAR) of China

Arid

DOI	10.1080/22797254.2019.1596756
	Comparison of machine learning algorithms for soil salinity predictions in three dryland oases located in Xinjiang Uyghur Autonomous Region (XJUAR) of China
	Wang, Fei; Yang, Shengtian; Yang, Wei; Yang, Xiaodong; Ding Jianli
通讯作者	Yang, W (corresponding author), Xinjiang Univ, Xinjiang Common Univ, Coll Resource & Environm Sci, Key Lab Smart City & Environm Stimulat, Urumqi, Peoples R China.
来源期刊	EUROPEAN JOURNAL OF REMOTE SENSING
EISSN	2279-7254
出版年	2019
卷号	52 期号:1 页码:256-276
英文摘要	Many different machine learning approaches have been applied for various purposes. However, there has been limited guidance regarding which, if any, machine learning models and covariate sets might be optimal for predicting soil salinity across different oases in the Xinjiang Uyghur Autonomous Region (XJUAR) of China. This study aimed to compare five machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), Multiple Adaptive Regression Splines (MARS), Classification and Regression Trees (CART), Random Forest tree ensembles (RF), and Stochastic Gradient Treeboost (SGT), to predict soil salinity in three geographically distinct areas (the Qitai, Kuqa, and Yutian oases). A total of 21 data sets from three oases were used to evaluate the performance of the algorithm and to screen the optimal variables. The results show the following indices are considered to be important indicators for quantitative assessment of soil salinity: EEVI, CSRI, EVI2, GDVI, SAIO, and SIT. Comparison results show that SGT is the most suitable algorithm for predicting soil salinity in arid areas. This study provides a comprehensive comparison of machine learning techniques for soil salinity prediction and may assist in the modeling and variable selection of digital soil mapping in the XJUAR of China.
英文关键词	Soil salinity machine learning oasis Landsat OLI digital elevation model Xinjiang Uyghur autonomous region
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000490746100001
WOS关键词	STOCHASTIC GRADIENT TREEBOOST ; KERIYA RIVER-BASIN ; RANDOM FOREST ; VEGETATION ; MODELS ; INDEX ; CLASSIFICATION ; PERFORMANCE ; SALINIZATION ; SELECTION
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/369475
作者单位	[Wang, Fei; Yang, Shengtian; Yang, Wei; Yang, Xiaodong; Ding Jianli] Xinjiang Univ, Xinjiang Common Univ, Coll Resource & Environm Sci, Key Lab Smart City & Environm Stimulat, Urumqi, Peoples R China; [Wang, Fei; Yang, Shengtian; Yang, Wei; Yang, Xiaodong; Ding Jianli] Minist Educ, Lab Oasis Ecosyst, Urumqi, Peoples R China
推荐引用方式 GB/T 7714	Wang, Fei,Yang, Shengtian,Yang, Wei,et al. Comparison of machine learning algorithms for soil salinity predictions in three dryland oases located in Xinjiang Uyghur Autonomous Region (XJUAR) of China[J]. 新疆大学,2019,52(1):256-276.
APA	Wang, Fei,Yang, Shengtian,Yang, Wei,Yang, Xiaodong,&Ding Jianli.(2019).Comparison of machine learning algorithms for soil salinity predictions in three dryland oases located in Xinjiang Uyghur Autonomous Region (XJUAR) of China.EUROPEAN JOURNAL OF REMOTE SENSING,52(1),256-276.
MLA	Wang, Fei,et al."Comparison of machine learning algorithms for soil salinity predictions in three dryland oases located in Xinjiang Uyghur Autonomous Region (XJUAR) of China".EUROPEAN JOURNAL OF REMOTE SENSING 52.1(2019):256-276.