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• W4316253709 endingPage "106932" @default.
• W4316253709 startingPage "106932" @default.
• W4316253709 abstract "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 investigation 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." @default.
• W4316253709 created "2023-01-15" @default.
• W4316253709 creator A5027466575 @default.
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• W4316253709 date "2023-04-01" @default.
• W4316253709 modified "2023-10-16" @default.
• W4316253709 title "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" @default.
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• W4316253709 doi "https://doi.org/10.1016/j.catena.2023.106932" @default.
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