干旱区生态环境知识资源中心(Arid): Monitoring Soil Salinity Using Machine Learning and the Polarimetric Scattering Features of PALSAR-2 Data

Arid

DOI	10.3390/su15097452
	Monitoring Soil Salinity Using Machine Learning and the Polarimetric Scattering Features of PALSAR-2 Data
	Zhao, Jing; Nurmemet, Ilyas; Muhetaer, Nuerbiye; Xiao, Sentian; Abulaiti, Adilai
通讯作者	Nurmemet, I
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2023
卷号	15 期号:9
英文摘要	Soil salinization is one of the major problems affecting arid regions, restricting the sustainable development of agriculture and ecological protection in the Keriya Oasis in Xinjiang, China. This study aims to capture the distribution of soil salinity with polarimetric parameters and various classification methods based on the Advanced Land Observing Satellite-2(ALOS-2) with the Phased Array Type L-Band Synthetic Aperture Radar-2 (PALSAR-2) and Landsat-8 OLI (OLI) images of the Keriya Oasis. Eleven polarization target decomposition methods were employed to extract the polarimetric scattering features. Furthermore, the features with the highest signal-to-noise ratio value were used and combined with the OLI optimal components to form a comprehensive dataset named OLI + PALSAR2. Next, two machine learning algorithms, Support Vector Machine (SVM) and Random Forest, were applied to classify the surface characteristics. The results showed that better outcomes were achieved with the SVM classifier for OLI + PALSAR2 data, with the overall accuracy, Kappa coefficient, and F1 scores being 91.57%, 0.89, and 0.94, respectively. The results indicate the potential of using PALSAR-2 data coupled with the classification in machine learning to monitor different degrees of soil salinity in the Keriya Oasis.
英文关键词	soil salinization polarized feature component SVM classification oasis in arid areas ALOS PALSAR-2
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000988153500001
WOS关键词	TARGET DECOMPOSITION-THEOREMS ; SAR DATA ; RADARSAT-2 ; INVERSION ; MODEL
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398790
推荐引用方式 GB/T 7714	Zhao, Jing,Nurmemet, Ilyas,Muhetaer, Nuerbiye,et al. Monitoring Soil Salinity Using Machine Learning and the Polarimetric Scattering Features of PALSAR-2 Data[J],2023,15(9).
APA	Zhao, Jing,Nurmemet, Ilyas,Muhetaer, Nuerbiye,Xiao, Sentian,&Abulaiti, Adilai.(2023).Monitoring Soil Salinity Using Machine Learning and the Polarimetric Scattering Features of PALSAR-2 Data.SUSTAINABILITY,15(9).
MLA	Zhao, Jing,et al."Monitoring Soil Salinity Using Machine Learning and the Polarimetric Scattering Features of PALSAR-2 Data".SUSTAINABILITY 15.9(2023).