干旱区生态环境知识资源中心(Arid): Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China

Arid

DOI	10.3390/land13060877
	Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China
	Zhang, Sheshu; Zhao, Jun; Yang, Jianxia; Xie, Jinfeng; Sun, Ziyun
通讯作者	Zhao, J
来源期刊	LAND
EISSN	2073-445X
出版年	2024
卷号	13 期号:6
英文摘要	(1) Monitoring salinized soil in saline-alkali land is essential, requiring regional-scale soil salinity inversion. This study aims to identify sensitive variables for predicting electrical conductivity (EC) in soil, focusing on effective feature selection methods. (2) The study systematically selects a feature subset from Sentinel-1 C SAR, Sentinel-2 MSI, and SRTM DEM data. Various feature selection methods (correlation analysis, LASSO, RFE, and GRA) are employed on 79 variables. Regression models using random forest regression (RF) and partial least squares regression (PLSR) algorithms are constructed and compared. (3) The results highlight the effectiveness of the RFE algorithm in reducing model complexity. The model incorporates significant environmental factors like soil moisture, topography, and soil texture, which play an important role in modeling. Combining the method with RF improved soil salinity prediction (R2 = 0.71, RMSE = 1.47, RPD = 1.84). Overall, salinization in Minqin oasis soils was evident, especially in the unutilized land at the edge of the oasis. (4) Integrating data from different sources to construct characterization variables overcomes the limitations of a single data source. Variable selection is an effective means to address the redundancy of variable information, providing insights into feature engineering and variable selection for soil salinity estimation in arid and semi-arid regions.
英文关键词	remote sensing soil salinization feature subset selection environmental covariate red edge texture feature
类型	Article
语种	英语
开放获取类型	gold
收录类别	SSCI
WOS记录号	WOS:001256115300001
WOS关键词	GREY RELATIONAL ANALYSIS ; VEGETATION ; SALINIZATION ; XINJIANG ; REGION ; SPECTRA ; SEASONS ; COUNTY ; SALT ; LAKE
WOS类目	Environmental Studies
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404807
推荐引用方式 GB/T 7714	Zhang, Sheshu,Zhao, Jun,Yang, Jianxia,et al. Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China[J],2024,13(6).
APA	Zhang, Sheshu,Zhao, Jun,Yang, Jianxia,Xie, Jinfeng,&Sun, Ziyun.(2024).Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China.LAND,13(6).
MLA	Zhang, Sheshu,et al."Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China".LAND 13.6(2024).