干旱区生态环境知识资源中心(Arid): Soil salinization monitoring in the Werigan-Kuqa Oasis, China, based on a Three-Dimensional Feature Space Model with Machine Learning Algorithm.

Arid

DOI	10.1080/2150704X.2020.1868601
	Soil salinization monitoring in the Werigan-Kuqa Oasis, China, based on a Three-Dimensional Feature Space Model with Machine Learning Algorithm.
	Yao, Yuan; Ding, Jianli; Wang, Shuang
通讯作者	Ding, JL (corresponding author), Xinjiang Univ, Coll Resources & Environm Sci, Urumqi 830046, Peoples R China.
来源期刊	REMOTE SENSING LETTERS
ISSN	2150-704X
EISSN	2150-7058
出版年	2021
卷号	12 期号:3 页码:269-277
英文摘要	Land surface temperature (LST) is an important indicator for monitoring soil salinization in arid and semiarid regions. However, traditional salinity index (SI)-vegetation index (VI) feature space, and other single feature space often ignore the LST information, which has largely constrained the soil salinity detecting quantitatively and effectively. In this study, random forest (RF) as one of the machine learning algorithms was utilized to downscale the LST retrieved from Landsat 8 thermal-infrared band from 100 m to 30 m. Then, we developed a new three-dimensional feature space model that combines downscaled LST, SI and normalized difference vegetation index (NDVI) to assess the soil salinity in the Werigan-Kuqa Oasis, a typical delta oasis in an arid region, China. The experiment results are further compared with 20 different feature spaces and spectral indices. The result shows that the proposed model produces higher accuracy than other method, which can provide a rapid and relatively accurate monitoring results of soil salinization in the study area.
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000618274500001
WOS关键词	LAND-SURFACE TEMPERATURE ; SALINITY
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
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/2XILL650/348143
作者单位	[Yao, Yuan] Chengdu Univ, Sch Architecture & Civil Engn, Chengdu, Peoples R China; [Yao, Yuan] Chengdu Univ, Inst Higher Educ Sichuan Prov, Key Lab Pattern Recognit & Intelligent Informat P, Chengdu, Peoples R China; [Ding, Jianli] Xinjiang Univ, Coll Resources & Environm Sci, Urumqi 830046, Peoples R China; [Wang, Shuang] Xinjiang Agr Univ, Sch Management, Urumqi, Peoples R China
推荐引用方式 GB/T 7714	Yao, Yuan,Ding, Jianli,Wang, Shuang. Soil salinization monitoring in the Werigan-Kuqa Oasis, China, based on a Three-Dimensional Feature Space Model with Machine Learning Algorithm.[J]. 新疆大学,2021,12(3):269-277.
APA	Yao, Yuan,Ding, Jianli,&Wang, Shuang.(2021).Soil salinization monitoring in the Werigan-Kuqa Oasis, China, based on a Three-Dimensional Feature Space Model with Machine Learning Algorithm..REMOTE SENSING LETTERS,12(3),269-277.
MLA	Yao, Yuan,et al."Soil salinization monitoring in the Werigan-Kuqa Oasis, China, based on a Three-Dimensional Feature Space Model with Machine Learning Algorithm.".REMOTE SENSING LETTERS 12.3(2021):269-277.