干旱区生态环境知识资源中心(Arid): Mapping the Levels of Soil Salination and Alkalization by Integrating Machining Learning Methods and Soil-Forming Factors

Arid

DOI	10.3390/rs14133020
	Mapping the Levels of Soil Salination and Alkalization by Integrating Machining Learning Methods and Soil-Forming Factors
	Yan, Yang; Kayem, Kader; Hao, Ye; Shi, Zhou; Zhang, Chao; Peng, Jie; Liu, Weiyang; Zuo, Qiang; Ji, Wenjun; Li, Baoguo
通讯作者	Ji, WJ
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:13
英文摘要	Accurate updating of soil salination and alkalization maps based on remote sensing images and machining learning methods plays an essential role in food security, biodiversity, and desertification. However, there is still a lack of research on using machine learning, especially one-dimensional convolutional neural networks (CNN)s, and soil-forming factors to classify the salinization and alkalization degree. As a case study, the study estimated the soil salination and alkalization by Random forests (RF) and CNN based on the 88 observations and 16 environmental covariates in Da'an city, China. The results show that: the RF model (accuracy = 0.67, precision = 0.67 for soil salination) with the synthetic minority oversampling technique performed better than CNN. Salinity and vegetation spectral indexes played the most crucial roles in soil salinization and alkalinization estimation in Songnen Plain. The spatial distribution derived from the RF model shows that from the 1980s to 2021, soil salinization and alkalization areas increased at an annual rate of 1.40% and 0.86%, respectively, and the size of very high salinization and alkalization was expanding. The degree and change rate of soil salinization and alkalization under various land-use types followed mash > salinate soil > grassland > dry land and forest. This study provides a reference for rapid mapping, evaluating, and managing soil salinization and alkalization in arid areas.
英文关键词	soil salination soil alkalization remote sensing convolutional neural network classification
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000824454200001
WOS关键词	AGRICULTURAL LANDS ; SENTINEL-2 MSI ; WET SEASONS ; SALINITY ; CHINA ; MESOPOTAMIA ; PREDICTION ; VEGETATION ; MOISTURE ; CARBON
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394162
推荐引用方式 GB/T 7714	Yan, Yang,Kayem, Kader,Hao, Ye,et al. Mapping the Levels of Soil Salination and Alkalization by Integrating Machining Learning Methods and Soil-Forming Factors[J],2022,14(13).
APA	Yan, Yang.,Kayem, Kader.,Hao, Ye.,Shi, Zhou.,Zhang, Chao.,...&Li, Baoguo.(2022).Mapping the Levels of Soil Salination and Alkalization by Integrating Machining Learning Methods and Soil-Forming Factors.REMOTE SENSING,14(13).
MLA	Yan, Yang,et al."Mapping the Levels of Soil Salination and Alkalization by Integrating Machining Learning Methods and Soil-Forming Factors".REMOTE SENSING 14.13(2022).