干旱区生态环境知识资源中心(Arid): Estimation of soil salt content using machine learning techniques based on remote-sensing fractional derivatives, a case study in the Ebinur Lake Wetland National Nature Reserve, Northwest China

Arid

DOI	10.1016/j.ecolind.2020.106869
	Estimation of soil salt content using machine learning techniques based on remote-sensing fractional derivatives, a case study in the Ebinur Lake Wetland National Nature Reserve, Northwest China
	Wang, Zheng; Zhang, Xianlong; Zhang, Fei; Ngai Weng Chan; Kung, Hsiang-te; Liu, Suhong; Deng, Laifei
通讯作者	Zhang, F
来源期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
EISSN	1872-7034
出版年	2020
卷号	119
英文摘要	Soil salinity is a common global environmental problem that severely restricts industrial, agricultural and human development. In Northwest China, soil salinity is a problem affecting the Lake Ebinur area and needs to be monitored and addressed. The use of optical remote sensing technology to for timely and accurate soil salinity monitoring has great potentials and can be crucial for industrial and agricultural development. Optical remote sensing technology is an important data source for monitoring of soil salinization because of its rich spectral information and high-resolution. Based on the HJ-1/HSI data of fractional derivative transformation, this paper calculated the parameters, such as the difference index (DI), normalized difference index (NDI), ratio index (RI) and salt index (SI), that can invert the spatial distribution of salinization and then calculates a particle swarm optimization support vector machine (PSO-SVM). These index parameters were used to invert the spatial distribution information of salinized soil to explore the influence of fractional derivatives on the estimation accuracy of the spatial distribution of salinized soil. The results showed the following: (1) The optimal model based on different fractional derivatives and exponential transformations was the DI transformation under the fractional derivative of 1.2-order. R-2, RMSE and RPD were 0.66, 13.81 g/kg and 2.59, respectively. These data show that the model had a good prediction effect. (2) The application of fractional derivatives in the processing of hyperspectral images could highlight the spectral details of the images and enrich their pretreatment methods, which is of great significance for the inversion of the distribution of salinized soil by hyperspectral images. (3) In the lakeside area of Lake Ebinur, the soil is mainly saline to severely saline. In the northern mountainous region of Lake Ebinur, the soil is mainly non-saline soil to mildly saline soil. Finally, in the Kuitun riverbank area in the southeast of Lake Ebinur, the soils are mainly non-saline soils to mildly saline soils. The results of this study demonstrate the potential of spectral index optimization and prediction models to monitor soil salinization based on fractional derivatives. This technique highlights the spectral detail information and enriches the preprocessing methods of hyperspectral imagery, resulting in the mapping of the spatial distribution of the degree of soil salinization. This has practical importance for the prevention and control of soil salinization and its spatial extent in arid and semi-arid regions.
英文关键词	Remote sensing Particle swarm optimization Support vector machine (SVM) Fractional derivative Soil salt content Hyperspectral index
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000579817600073
WOS关键词	HYPERSPECTRAL DATA ; SALINITY ; SALINIZATION ; OPTIMIZATION ; THREAT ; OASIS
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
来源机构	北京师范大学 ; 新疆大学 ; Commonwealth Scientific and Industrial Research Organisation
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326891
作者单位	[Wang, Zheng; Zhang, Xianlong; Zhang, Fei; Liu, Suhong; Deng, Laifei] Xinjiang Univ, Coll Resources & Environm Sci, Key Lab Wisdom City & Environm Modeling, Higher Educ Inst, Urumqi 830046, Peoples R China; [Wang, Zheng; Zhang, Xianlong; Zhang, Fei; Deng, Laifei] Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China; [Zhang, Fei] Commonwealth Sci & Ind Res Org Land & Water, Canberra, ACT 2601, Australia; [Ngai Weng Chan] Univ Sains Malaysia, Sch Humanities, George Town 11800, Penang, Malaysia; [Kung, Hsiang-te] Univ Memphis, Dept Earth Sci, Memphis, TN 38152 USA; [Liu, Suhong] Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China
推荐引用方式 GB/T 7714	Wang, Zheng,Zhang, Xianlong,Zhang, Fei,et al. Estimation of soil salt content using machine learning techniques based on remote-sensing fractional derivatives, a case study in the Ebinur Lake Wetland National Nature Reserve, Northwest China[J]. 北京师范大学, 新疆大学, Commonwealth Scientific and Industrial Research Organisation,2020,119.
APA	Wang, Zheng.,Zhang, Xianlong.,Zhang, Fei.,Ngai Weng Chan.,Kung, Hsiang-te.,...&Deng, Laifei.(2020).Estimation of soil salt content using machine learning techniques based on remote-sensing fractional derivatives, a case study in the Ebinur Lake Wetland National Nature Reserve, Northwest China.ECOLOGICAL INDICATORS,119.
MLA	Wang, Zheng,et al."Estimation of soil salt content using machine learning techniques based on remote-sensing fractional derivatives, a case study in the Ebinur Lake Wetland National Nature Reserve, Northwest China".ECOLOGICAL INDICATORS 119(2020).