干旱区生态环境知识资源中心(Arid): Spatial prediction of soil micronutrients using machine learning algorithms integrated with multiple digital covariates

Arid

DOI	10.1007/s10705-023-10303-y
	Spatial prediction of soil micronutrients using machine learning algorithms integrated with multiple digital covariates
	Keshavarzi, Ali; Kaya, Fuat; Basayigit, Levent; Gyasi-Agyei, Yeboah; Rodrigo-Comino, Jesus; Caballero-Calvo, Andres
通讯作者	Keshavarzi, A
来源期刊	NUTRIENT CYCLING IN AGROECOSYSTEMS
ISSN	1385-1314
EISSN	1573-0867
出版年	2023
卷号	127 期号:1 页码:137-153
英文摘要	The design and application of multiple tools to map soil micronutrients is key to efficient land management. While collecting a representative number of soil samples is time consuming and expensive, digital soil mapping could provide maps of soil properties fast and reliably. The objective of this research was to predict the spatial distribution of soil micronutrients within the piedmont plain in northeastern Iran using random forest (RF) and support vector regression (SVR) algorithms. Sixty-eight locations with different land uses were sampled to determine the content of iron, manganese, zinc and copper in the topsoil (0-20 cm). Forty-one digital covariates were used as input to the models and were derived from a digital elevation model, open-source remote sensing (RS) data (Landsat 8 OLI and Sentinel 2A MSI images), WorldClim climate database and maps of soil properties. Covariates were grouped into 11 scenarios: I-III, based on RS data; IV-VI, including RS, topographic, climate and soil covariates; VII, VIII and IX, based only on topographic, climate and soil covariates, respectively; X and XI, based on recursive feature elimination and expert opinion, respectively. The RF algorithm gave 91, 94, 91 and 108% normalized root mean squared error values for iron, manganese, zinc and copper, respectively, for the validation dataset with scenario XI. The most important digital covariates for micronutrients prediction with both RF and SVR models were precipitation seasonality, mean annual temperature and the mean saturation index based on Sentinel 2A MSI data. Digital maps produced at 30 m spatial resolution using scenario XI could be used to effectively identify micronutrient deficiencies and excess hotspots.
英文关键词	Arid regions Digital soil mapping Expert opinion Feature selection Remote sensing
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:001048660400002
WOS类目	Soil Science
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/2XILL650/397928
推荐引用方式 GB/T 7714	Keshavarzi, Ali,Kaya, Fuat,Basayigit, Levent,et al. Spatial prediction of soil micronutrients using machine learning algorithms integrated with multiple digital covariates[J],2023,127(1):137-153.
APA	Keshavarzi, Ali,Kaya, Fuat,Basayigit, Levent,Gyasi-Agyei, Yeboah,Rodrigo-Comino, Jesus,&Caballero-Calvo, Andres.(2023).Spatial prediction of soil micronutrients using machine learning algorithms integrated with multiple digital covariates.NUTRIENT CYCLING IN AGROECOSYSTEMS,127(1),137-153.
MLA	Keshavarzi, Ali,et al."Spatial prediction of soil micronutrients using machine learning algorithms integrated with multiple digital covariates".NUTRIENT CYCLING IN AGROECOSYSTEMS 127.1(2023):137-153.