Knowledge Resource Center for Ecological Environment in Arid Area
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
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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). |
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