干旱区生态环境知识资源中心(Arid): Soil salinity prediction using Machine Learning and Sentinel-2 Remote Sensing Data in Hyper-Arid areas

Arid

DOI	10.1016/j.pce.2023.103400
	Soil salinity prediction using Machine Learning and Sentinel-2 Remote Sensing Data in Hyper-Arid areas
	Kaplan, Gordana; Gasparovic, Mateo; Alqasemi, Abduldaem S.; Aldhaheri, Alya; Abuelgasim, Abdelgadir; Ibrahim, Majed
通讯作者	Alqasemi, AS
来源期刊	PHYSICS AND CHEMISTRY OF THE EARTH
ISSN	1474-7065
EISSN	1873-5193
出版年	2023
卷号	130
英文摘要	We are experiencing a considerable increase in soil salinity as a result of the influence of climate change or environmental contamination produced by excessive industry and agriculture. To be able to cope with this issue, reliable and up-to-date soil salinity measurements are required. The use of remote sensing data allows for faster and more efficient soil salinity mapping. This paper investigates several Machine Learning approaches and modeling methodologies for predicting soil salinity in hyper-arid environments using Sentinel-2 satellite imag-ery. Thus, 393 soil samples collected and used for modeling and testing in the study area, United Arab Emirates. Also, the paper benefits from open-source data and programs, such as Google Earth Engine and Weka. Different modeling strategies have been applied over the data. The results of the modeling show a strong correlation (0.84) with the test results. This study also shows interesting findings that will be examined further in future studies at other sites. As machine learning methods are evolving on a daily basis, new approaches needs to be considered in future studies for the demands of more precise modeling and mapping of soil salinity.
英文关键词	Soil salinity Google earth engine Sentinel-2 Remote sensing Machine learning Modeling
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000984903900001
WOS关键词	LANDSAT 8 ; XINJIANG ; PERFORMANCE ; RESOLUTION ; REGION
WOS类目	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398002
推荐引用方式 GB/T 7714	Kaplan, Gordana,Gasparovic, Mateo,Alqasemi, Abduldaem S.,et al. Soil salinity prediction using Machine Learning and Sentinel-2 Remote Sensing Data in Hyper-Arid areas[J],2023,130.
APA	Kaplan, Gordana,Gasparovic, Mateo,Alqasemi, Abduldaem S.,Aldhaheri, Alya,Abuelgasim, Abdelgadir,&Ibrahim, Majed.(2023).Soil salinity prediction using Machine Learning and Sentinel-2 Remote Sensing Data in Hyper-Arid areas.PHYSICS AND CHEMISTRY OF THE EARTH,130.
MLA	Kaplan, Gordana,et al."Soil salinity prediction using Machine Learning and Sentinel-2 Remote Sensing Data in Hyper-Arid areas".PHYSICS AND CHEMISTRY OF THE EARTH 130(2023).