干旱区生态环境知识资源中心(Arid): Mapping clay mineral types using easily accessible data and machine learning techniques in a scarce data region: A case study in a semi-arid area in Iran

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DOI	10.1016/j.catena.2023.106932
	Mapping clay mineral types using easily accessible data and machine learning techniques in a scarce data region: A case study in a semi-arid area in Iran
	Shahrokh, Vajihe; Khademi, Hossein; Zeraatpisheh, Mojtaba
通讯作者	Shahrokh, V
来源期刊	CATENA
ISSN	0341-8162
EISSN	1872-6887
出版年	2023
卷号	223
英文摘要	Understanding the abundance variability of clay minerals, as fundamental soil components, will help the users to improve land management and address concerns over climate change and soil fertility. Therefore, this investi-gation aimed to model the abundance and spatial distribution of clay types, including palygorskite, illite, and kaolinite, and identify the most significant variables affecting their variability using a digital soil mapping (DSM) approach in Darab district, southern Iran. Multiple Linear Regression (MLR) and Random Forest (RF) techniques were applied to link clay types and environmental attributes that were obtained from a Landsat-8 operational land imager (OLI) and digital elevation model (DEM). A ten-fold cross-validation approach was applied to calibrate and validate the models, and 50 bootstrap models were used to quantify the prediction uncertainty. The models accuracy was defined by the coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to the interquartile range (RPIQ). Findings denoted that the RF model better predicts the abundance and variability of clay minerals in the study area (R2 = 0.56, 0.47, and 0.48, RMSE = 5.3, 1.91 and 0.63 % and RPIQ = 2.82, 3.28 and 2.62 for palygorskite, illite and kaolinite, respectively). Based on the feature selection analysis, topographic covariates and soil properties determined palygorskite and kaolinite content variations, while for illite, only soil properties could explain the spatial distribution. Besides, the RF produced a lower uncertainty for palygorskite compared to the other clay types. The present research can provide new insight into the spatial variability of clay minerals in arid and semi-arid regions of Iran that could be extended to other similar environments. Moreover, the results showed that the easily available environmental variables could provide reliable predictions. However, other environmental covariates, such as XRF analysis, Vis-NIR, and MIR spectroscopy, are also recommended as input variables for further studies.
英文关键词	Spatial modeling Uncertainty Environmental covariates Palygorskite Illite Kaolinite
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000924894700001
WOS关键词	SOIL PROPERTIES ; RANDOM FORESTS ; SPATIAL VARIABILITY ; PREDICTION ; PALYGORSKITE ; RHIZOSPHERE ; FRACTIONS ; KAOLINITE ; SEPIOLITE ; MAP
WOS类目	Geosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS研究方向	Geology ; Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395672
推荐引用方式 GB/T 7714	Shahrokh, Vajihe,Khademi, Hossein,Zeraatpisheh, Mojtaba. Mapping clay mineral types using easily accessible data and machine learning techniques in a scarce data region: A case study in a semi-arid area in Iran[J],2023,223.
APA	Shahrokh, Vajihe,Khademi, Hossein,&Zeraatpisheh, Mojtaba.(2023).Mapping clay mineral types using easily accessible data and machine learning techniques in a scarce data region: A case study in a semi-arid area in Iran.CATENA,223.
MLA	Shahrokh, Vajihe,et al."Mapping clay mineral types using easily accessible data and machine learning techniques in a scarce data region: A case study in a semi-arid area in Iran".CATENA 223(2023).