干旱区生态环境知识资源中心(Arid): Spatial Prediction of Soil Salinity by Using Remote Sensing and Data Mining Algorithms at Watershed Scale, Northwest Iran

Arid

DOI	10.1007/s12524-024-01906-1
	Spatial Prediction of Soil Salinity by Using Remote Sensing and Data Mining Algorithms at Watershed Scale, Northwest Iran
	Honarbakhsh, Afshin; Mahmoudabadi, Ebrahim; Afzali, Sayed Fakhreddin; Khajehzadeh, Mohammad
通讯作者	Honarbakhsh, A
来源期刊	JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING
ISSN	0255-660X
EISSN	0974-3006
出版年	2024
卷号	52 期号:8 页码:1777-1785
英文摘要	Soil salinity plays an important role in agriculture production and land degradation, especially in semi-arid and arid regions. Accurate prediction of soil salinity requires evaluating crop yield, native vegetation situations, and irrigation command area management. In this study, MLR (multiple linear regression), SVMs (support vector machines) and ANNs (artificial neural networks) models were employed by using Landsat-8 OLI and GIS (Geographical Information Systems) techniques for predicting soil salinity in northwest Iran. Soil salinity was measured at 92 points (in a depth of 0-20 cm). The vegetation and soil salinity spectral indices, extracted from Landsat-8 OLI, were employed as input data. The results of this study indicated that the best-developed model for predicting soil salinity was the SVM-based model with R2 (0.874) and RPD (2.32) and the lowest RMSE (11.20 dS m-1). Moreover, the performance of developed models under different vegetation coverage showed that the SVM-based model yielded the best result. It was concluded that the SVM-based model is reliable for quantifying soil salinization.
英文关键词	Calcareous soils Landsat Salinity Spectral indices
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001251052100002
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; SEMIARID REGION ; LAKE ; VEGETATION ; INDEX ; TEXTURE ; MODEL ; SALT
WOS类目	Environmental Sciences ; Remote Sensing
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404732
推荐引用方式 GB/T 7714	Honarbakhsh, Afshin,Mahmoudabadi, Ebrahim,Afzali, Sayed Fakhreddin,et al. Spatial Prediction of Soil Salinity by Using Remote Sensing and Data Mining Algorithms at Watershed Scale, Northwest Iran[J],2024,52(8):1777-1785.
APA	Honarbakhsh, Afshin,Mahmoudabadi, Ebrahim,Afzali, Sayed Fakhreddin,&Khajehzadeh, Mohammad.(2024).Spatial Prediction of Soil Salinity by Using Remote Sensing and Data Mining Algorithms at Watershed Scale, Northwest Iran.JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING,52(8),1777-1785.
MLA	Honarbakhsh, Afshin,et al."Spatial Prediction of Soil Salinity by Using Remote Sensing and Data Mining Algorithms at Watershed Scale, Northwest Iran".JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 52.8(2024):1777-1785.