干旱区生态环境知识资源中心(Arid): Soil salinity estimation based on machine learning using the GF-3 radar and Landsat-8 data in the Keriya Oasis, Southern Xinjiang, China

Arid

DOI	10.1007/s11104-023-06446-0
	Soil salinity estimation based on machine learning using the GF-3 radar and Landsat-8 data in the Keriya Oasis, Southern Xinjiang, China
	Xiao, Sentian; Nurmemet, Ilyas; Zhao, Jing
通讯作者	Nurmemet, I
来源期刊	PLANT AND SOIL
ISSN	0032-079X
EISSN	1573-5036
出版年	2024
卷号	498 期号:1-2 页码:451-469
英文摘要	AimsSoil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source data, combining radar and optical remote sensing data, and applying machine learning-based algorithms to these data could be beneficial for addressing the soil salinization problem.MethodsThis study combines the environmental covariates extracted from the Gaofen-3 (GF-3) radar data, Landsat-8 multispectral data, and digital elevation model (DEM) data to explore the advantages of radar remote sensing in detecting soil salinity. The soil salinity distribution degree in the Keriya Oasis is mapped using a machine-learning-based method, and the advantages of different sensor images in predicting soil salinity are evaluated. Three soil salinity inversion models are constructed using measured electrical conductivity (EC) data, the random forest (RF), gradient boosting tree (GDBT), and extreme gradient boosting (XGBoost) models.ResultsThe best accuracy corresponding to an R2 of 0.87, and a root mean square error (RMSE) of 6.02 is achieved by the RF model on the GF-3 + Landsat-8 data. Therefore, the use of multi-source data is a more effective method for mapping soil salinity in the study area. The mapping results of the optimal model demonstrate that natural factors significantly influence the distribution of soil salinity.ConclusionThe radar polarization decomposition characteristics are incorporated into the inversion of soil salinity modeling as an environmental covariate, providing an innovative and efficient method for soil salinity estimation in arid areas.
英文关键词	Soil salinization estimation GF-3 data Random forest model Polarization decomposition Multi-source data prediction
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001131653300002
WOS关键词	SYNTHETIC-APERTURE RADAR ; DIELECTRIC-PROPERTIES ; RANDOM FOREST ; CLIMATE ; DESERT ; BORUTA ; OASES ; RIVER
WOS类目	Agronomy ; Plant Sciences ; Soil Science
WOS研究方向	Agriculture ; Plant Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405085
推荐引用方式 GB/T 7714	Xiao, Sentian,Nurmemet, Ilyas,Zhao, Jing. Soil salinity estimation based on machine learning using the GF-3 radar and Landsat-8 data in the Keriya Oasis, Southern Xinjiang, China[J],2024,498(1-2):451-469.
APA	Xiao, Sentian,Nurmemet, Ilyas,&Zhao, Jing.(2024).Soil salinity estimation based on machine learning using the GF-3 radar and Landsat-8 data in the Keriya Oasis, Southern Xinjiang, China.PLANT AND SOIL,498(1-2),451-469.
MLA	Xiao, Sentian,et al."Soil salinity estimation based on machine learning using the GF-3 radar and Landsat-8 data in the Keriya Oasis, Southern Xinjiang, China".PLANT AND SOIL 498.1-2(2024):451-469.