干旱区生态环境知识资源中心(Arid): Remote sensing-based prediction of organic carbon in agricultural and natural soils influenced by salt and sand mining using machine learning

Arid

DOI	10.1016/j.jenvman.2024.120107
	Remote sensing-based prediction of organic carbon in agricultural and natural soils influenced by salt and sand mining using machine learning
	Zhang, Tianqi; Li, Ye; Wang, Mingyou
通讯作者	Wang, MY
来源期刊	JOURNAL OF ENVIRONMENTAL MANAGEMENT
ISSN	0301-4797
EISSN	1095-8630
出版年	2024
卷号	352
英文摘要	It is important to keep soil organic carbon (SOC) in balance to ensure soil health and quality. In this manner, mining activities have crucial impacts on SOC stocks, especially in semi-arid and arid regions such as Iran. For this purpose, SOC was measured at 180 randomly selected points in both natural and agricultural soils in the central part of Iran. Machine learning methods, such as GEP (Genetic Expression Programming), SVR (Support Vector Regression), and ANNs (Artificial Neural Networks), were developed and employed to estimate SOC for all sampled points, including both natural and agricultural soils. Following that, topography and remotely sensed data were employed as input variables to improve SOC prediction influenced by mining. The remotely sensed data and topography factors were extracted from Landsat 9 images and Digital Elevation Models (DEMs), respectively. Input variables were considered in three scenarios, including the use of topography factors (scenario I), the use of remote sensing data (scenario II), and the use of both topography factors and remote sensing data (scenario III). The results of this study showed that the most effective model for predicting SOC across all sampled data was SVR (ME = -0.1539%, R2 = 0.642 and RMSE = 0.620%) when employing scenario III. Furthermore, the results indicated that the optimal method for both natural and agricultural soils was the SVR method when employing scenario III. Further analysis through mapping SOC contents showed that mining activities influenced the distribution of SOC in the studied region. Overall, the predicted maps of SOC contents indicated that lower SOC contents were predominantly distributed in the vicinity of salt and sand mines, particularly in salt-rich areas, for both natural and agricultural soils.
英文关键词	Agriculture soils Mining activities Natural soils Topography Remote sensing
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001169230900001
WOS关键词	MATTER ; VARIABILITY ; INDICATORS ; STOCKS
WOS类目	Environmental Sciences
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/2XILL650/404452
推荐引用方式 GB/T 7714	Zhang, Tianqi,Li, Ye,Wang, Mingyou. Remote sensing-based prediction of organic carbon in agricultural and natural soils influenced by salt and sand mining using machine learning[J],2024,352.
APA	Zhang, Tianqi,Li, Ye,&Wang, Mingyou.(2024).Remote sensing-based prediction of organic carbon in agricultural and natural soils influenced by salt and sand mining using machine learning.JOURNAL OF ENVIRONMENTAL MANAGEMENT,352.
MLA	Zhang, Tianqi,et al."Remote sensing-based prediction of organic carbon in agricultural and natural soils influenced by salt and sand mining using machine learning".JOURNAL OF ENVIRONMENTAL MANAGEMENT 352(2024).