干旱区生态环境知识资源中心(Arid): Mapping the Soil Salinity Distribution and Analyzing Its Spatial and Temporal Changes in Bachu County, Xinjiang, Based on Google Earth Engine and Machine Learning

Arid

DOI	10.3390/agriculture14040630
	Mapping the Soil Salinity Distribution and Analyzing Its Spatial and Temporal Changes in Bachu County, Xinjiang, Based on Google Earth Engine and Machine Learning
	Zhang, Yue; Wu, Hongqi; Kang, Yiliang; Fan, Yanmin; Wang, Shuaishuai; Liu, Zhuo; He, Feifan
通讯作者	Fan, YM
来源期刊	AGRICULTURE-BASEL
EISSN	2077-0472
出版年	2024
卷号	14 期号:4
英文摘要	Soil salinization has a significant impact on agricultural production and ecology. There is an urgent demand to establish an effective method that monitors the spatial and temporal distribution of soil salinity. In this study, a multi-indicator soil salinity monitoring model was proposed for monitoring soil salinity in Bachu County, Kashgar Region, Xinjiang, from 2002 to 2022. The model was established by combining multiple predictors (spectral, salinity, and composite indices and topographic factors) and the accuracy of the four models (Random Forest [RF], Partial Least Squares [PLS], Classification Regression Tree [CART], and Support Vector Machine [SVM]) was compared. The results reveal the high accuracy of the optimized prediction model, and the order of the accuracy is observed as RF > PLS > CART > SVM. The most accurate model, RF, exhibited an R-2 of 0.723, a root mean square error (RMSE) of 2.604 gkg(-1), and a mean absolute error (MAE) of 1.95 gkg(-1) at a 0-20 cm depth. At a 20-40 cm depth, RF had an R-2 value of 0.64, an RMSE of 3.62 gkg(-1), and an MAE of 2.728 gkg(-1). Spatial changes in soil salinity were observed throughout the study period, particularly increased salinization from 2002 to 2012 in the agricultural and mountainous areas within the central and western regions of the country. However, salinization declined from 2012 to 2022, with a decreasing trend in salinity observed in the top 0-20 cm of soil, followed by an increasing trend in salinity at a 20-40 cm depth. The proposed method can effectively extract large-scale soil salinity and provide a practical basis for simplifying the remote sensing monitoring and management of soil salinity. This study also provides constructive suggestions for the protection of agricultural areas and farmlands.
英文关键词	Google Earth Engine soil salinization vertical soil salinity machine learning spatial and temporal variability
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:001211313400001
WOS关键词	SALINIZATION ; IRRIGATION ; REGION ; OASIS ; INDICATORS ; VEGETATION ; DEPTH ; CHINA ; MODEL ; SCALE
WOS类目	Agronomy
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/402714
推荐引用方式 GB/T 7714	Zhang, Yue,Wu, Hongqi,Kang, Yiliang,et al. Mapping the Soil Salinity Distribution and Analyzing Its Spatial and Temporal Changes in Bachu County, Xinjiang, Based on Google Earth Engine and Machine Learning[J],2024,14(4).
APA	Zhang, Yue.,Wu, Hongqi.,Kang, Yiliang.,Fan, Yanmin.,Wang, Shuaishuai.,...&He, Feifan.(2024).Mapping the Soil Salinity Distribution and Analyzing Its Spatial and Temporal Changes in Bachu County, Xinjiang, Based on Google Earth Engine and Machine Learning.AGRICULTURE-BASEL,14(4).
MLA	Zhang, Yue,et al."Mapping the Soil Salinity Distribution and Analyzing Its Spatial and Temporal Changes in Bachu County, Xinjiang, Based on Google Earth Engine and Machine Learning".AGRICULTURE-BASEL 14.4(2024).