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Open AccessArticle

The Spatial Prediction of Soil Texture Fractions in Arid Regions of Iran

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Department of Soil Science, College of Agriculture, Lorestan University, Khorramabad 6815144316, Iran
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Department of Soil Science, College of Agriculture, Lorestan University, Khorramabad 6815144316, Iran
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Department of Soil Science, Agriculture Faculty, Shahid Bahonar University of Kerman, Kerman 76169-14111, Iran
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Soil Science and Geomorphology, Institute of Geography, University of Tübingen, 72070 Tübingen, Germany
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Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan 8951656767, Iran
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School of Biological, Earth and Environmental Sciences, Faculty of Science, The University of New South Wales, NSW 2052, Australia
*
Author to whom correspondence should be addressed.
Soil Syst. 2019, 3(4), 65; https://doi.org/10.3390/soilsystems3040065
Received: 31 May 2019 / Revised: 10 August 2019 / Accepted: 24 September 2019 / Published: 26 September 2019
(This article belongs to the Special Issue Digital Soil Mapping of Soil Functions)
To predict the soil texture fractions, 115 profiles were identified based on the Latin hypercube sampling technique, the horizons were sampled, and the clay, sand, and silt contents (in percentages) of soil samples were measured. Then equal-area quadratic spline depth functions were used to derive clay, sand, and silt contents at five standard soil depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm). Auxiliary variables used in this study include the terrain attributes (derived from a digital elevation model), Landsat 8 image data (acquired in 2015), geomorphological map, and spectrometric data (laboratory data). Artificial neural network (ANN), regression tree (RT), and neuro-fuzzy (ANFIS) models were used to make a correlation between soil data (clay, sand, and silt) and auxiliary variables. The results of this study showed that the ANFIS model was more accurate in the prediction of the three parameters of clay, silt, and sand than ANN and RT. Moreover, the ability of ANFIS model to estimate the soil texture fractions in the surface layers was higher than the lower layers. The mean coefficient of determination (R2) values calculated by 10-fold cross validation suggested the higher prediction performance in the upper depth intervals and higher prediction error in the lower depth intervals (e.g., R2 = 0.91, concordance correlation coefficient (CCC) = 0.90, RMSE = 4.00 g kg−1 for sand of 0–5 cm depth, and R2 = 0.68, CCC = 0.60, RMSE = 8.03 g kg−1 for 60–100 cm depth). The results also showed that the most important auxiliary variables are spectrometric data, multi-resolution, valley-bottom flatness index and wetness index. Overall, it is recommended to use ANFIS models for the digital mapping of soil texture fractions in other arid regions of Iran. View Full-Text
Keywords: artificial neural network; regression tree; neuro-fuzzy; spectrometry data artificial neural network; regression tree; neuro-fuzzy; spectrometry data
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Mehrabi-Gohari, E.; Matinfar, H.R.; Jafari, A.; Taghizadeh-Mehrjardi, R.; Triantafilis, J. The Spatial Prediction of Soil Texture Fractions in Arid Regions of Iran. Soil Syst. 2019, 3, 65.

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