干旱区生态环境知识资源中心(Arid): Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index

Arid

DOI	10.1016/j.scitotenv.2021.145807
	Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index
	Wang, Zheng; Zhang, Fei; Zhang, Xianlong; Chan, Ngai Weng; Kung, Hsiang-te; Ariken, Muhadaisi; Zhou, Xiaohong; Wang, Yishan
通讯作者	Zhang, F (corresponding author), Xinjiang Univ, Coll Resources & Environm Sci, Key Lab Wisdom City & Environm Modeling Higher Ed, Urumqi 830046, Peoples R China.
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2021
卷号	775
英文摘要	Soil salinization is an extremely serious land degradation problem in arid and semi-arid regions that hinders the sustainable development of agriculture and food security. Information and research on soil salinity using remote sensing (RS) technology provide a quick and accurate assessment and solutions to address this problem. This study aims to compare the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction and exploration of the potential application of derivatives to RS prediction of salinized soils. It explores the ability of derivatives to be used in the Landsat-8 OLI and Sentinel-2A MSI multispectral data, and it was used as a data source as well as to address the adaptability of salinity prediction on a regional scale. The two-dimensional (2D) and three-dimensional (3D) optimal spectral indices are used to screen the bands that are most sensitive to soil salinity (0-10 cm), and RS data and topographic factors are combined with machine learning to construct a comprehensive soil salinity estimation model based on gray correlation analysis. The results are as follows: (1) The optimal spectral index (2D, 3D) can effectively consider possible combinations of the bands between the interaction effects and responding to sensitive bands of soil properties to circumvent the problem of applicability of spectral indices in different regions; (2) Both the Landsat-8 OLI and Sentinel-2A MSI multispectral RS data sources, after the first-order derivative techniques are all processed, show improvements in the prediction accuracy of the model; (3) The best performance/accuracy of the predictive model is for sentinel data under first-order derivatives. This study compared the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction in finding the potential application of derivatives to RS prediction of salinized soils, with the results providing some theoretical basis and technical guidance for salinized soil prediction and environmental management planning. (c) 2021 Elsevier B.V. All rights reserved.
英文关键词	Gray correlation analysis Landsat-8 OLI Optimal spectral indices Sentinel-2A MSI Soil salinization
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000641611700006
WOS关键词	ORGANIC-MATTER CONTENT ; YELLOW-RIVER DELTA ; REFLECTANCE SPECTROSCOPY ; QUANTITATIVE METHODS ; SALINITY ASSESSMENT ; HYPERSPECTRAL DATA ; MODEL INVERSION ; SENTINEL-2 MSI ; CHLOROPHYLL-A ; LAND-USE
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
来源机构	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/2XILL650/351655
作者单位	[Wang, Zheng; Zhang, Fei; Ariken, Muhadaisi; Zhou, Xiaohong; Wang, Yishan] Xinjiang Univ, Coll Resources & Environm Sci, Key Lab Wisdom City & Environm Modeling Higher Ed, Urumqi 830046, Peoples R China; [Wang, Zheng; Zhang, Fei; Ariken, Muhadaisi; Zhou, Xiaohong; Wang, Yishan] Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China; [Zhang, Fei] Commonwealth Sci & Ind Res Org Land & Water, Canberra, ACT 2601, Australia; [Zhang, Xianlong] Wuhan Univ, Sch Remote Sensing Informat Engn, Dept Informat, 129 Luoyu Rd, Wuhan, Hubei, Peoples R China; [Chan, Ngai Weng] Univ Sains Malaysia, Sch Humanities, Geog Sect, George Town 11800, Penang, Malaysia; [Kung, Hsiang-te] Univ Memphis, Dept Earth Sci, Memphis, TN 38152 USA
推荐引用方式 GB/T 7714	Wang, Zheng,Zhang, Fei,Zhang, Xianlong,et al. Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index[J]. 新疆大学, Commonwealth Scientific and Industrial Research Organisation,2021,775.
APA	Wang, Zheng.,Zhang, Fei.,Zhang, Xianlong.,Chan, Ngai Weng.,Kung, Hsiang-te.,...&Wang, Yishan.(2021).Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index.SCIENCE OF THE TOTAL ENVIRONMENT,775.
MLA	Wang, Zheng,et al."Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index".SCIENCE OF THE TOTAL ENVIRONMENT 775(2021).