Search

Soil petroleum hydrocarbon contamination in the wetlands could cause ecological risk, especially through leakage into water reservoirs. So, the detection of the spatial variability of total petroleum hydrocarbons (TPH) in these soils is very crucial. The variability of TPH and its associations with magnetic susceptibility (χ

Background As one of the main components of land-use change, deforestation is considered the greatest threat to global environmental diversity with possible irreversible environmental consequences. Specifically, one example could be the impacts of land-use changes from oak forests into agricultural ecosystems, which may have detrimental impacts on soil mobilization across hillslopes. However, to date, scarce studies are assessing these impacts at different slope positions and soil depths, shedding light on key geomorphological processes. Methods In this research, the Caesium-137 (137Cs) technique was applied to evaluate soil redistribution and soil erosion rates due to the effects of these above-mentioned land-use changes. To achieve this goal, we select a representative area in the Lordegan district, central Iran. 137Cs depth distribution profiles were established in four different hillslope positions after converting natural oak forests to rainfed farming. In each hillslope, soil samples from three depths (0–10, 10–20, and 20–50 cm) and in four different slope positions (summit, shoulder, backslope, and footslope) were taken in three transects of about 20 m away from each other. The activity of 137Cs was determined in all the soil samples (72 soil samples) by a gamma spectrometer. In addition, some physicochemical properties and the magnetic susceptibility (MS) of soil samples were measured. Results Erosion rates reached 51.1 t·ha− 1·yr− 1 in rainfed farming, whereas in the natural forest, the erosion rate was 9.3 t·ha− 1·yr− 1. Magnetic susceptibility was considerably lower in the cultivated land (χhf = 43.5 × 10− 8 m3·kg− 1) than in the natural forest (χhf = 55.1 × 10− 8 m3·kg− 1). The lower soil erosion rate in the natural forest land indicated significantly higher MS in all landform positions except at the summit one, compared to that in the rainfed farming land. The shoulder and summit positions were the most erodible hillslope positions in the natural forest and rainfed farming, respectively. Conclusions We concluded that land-use change and hillslope positions played a key role in eroding the surface soils in this area. Moreover, land management can influence soil erosion intensity and may both mitigate and amplify soil loss.

A b s t r a c t. The potential use of optimized support vector machines with simulated annealing algorithm in developing prediction functions for estimating soil aggregate stability and soil shear strength was evaluated. The predictive capabilities of support vector machines in comparison with traditional regression prediction functions were also studied. In results, the support vector machines achieved greater accuracy in predicting both soil shear strength and soil aggregate stability properties comparing to traditional multiple-linear regression. The coefficient of correlation (R) between the measured and predicted soil shear strength values using the support vector machine model was 0.98 while it was 0.52 using the multiple-linear regression model. Furthermore, a lower mean square error value of 0.06 obtained using the support vector machine model in prediction of soil shear strength as compared to the multiple-linear regression model. The ERROR% value for soil aggregate stability prediction using the multiple-linear regression model was 14.59% while a lower ERROR% value of 4.29% was observed for the support vector machine model. The mean square error values for soil aggregate stability prediction using the multiplelinear regression and support vector machine models were 0.001 and 0.012, respectively. It appears that utilization of optimized support vector machine approach with simulated annealing algorithm in developing soil property prediction functions could be a suitable alternative to commonly used regression methods. K e y w o r d s: soft computing, support vector machines, simulated annealing algorithm, soil shear strength, aggregate stability

The cuurent study was performed to predict spatial distribution of some heavy metals (Ni, Fe, Cu, Mn) in western Iran, using environmental covariates and applying two machine learning methods comprised Random forest (RF), and Cubist. In this respect, a combination of different input environmental variables (remote sensing data, topographic attributes, thematic maps and soil properties) were used in modeling under four scenarios (I: remote sensing data (RS); II: RS + topographic attributes resulted from digital elevation model (DEM); III: RS + topographic attributes + thematic maps; IV: RS + topographic attributes + thematic maps +soil properties). The maps of Euclidean distance from mines and roads as well as the geology map have been used as thematic maps. A total of 346 soil samples were taken using stratified random sampling from the surface layers (0–20 cm depth) of the studied area and selected heavy metals (Ni, Fe, Cu, Mn), and soil properties were measured in the laboratory. RF and Cubist models were used to predict soil heavy metals in four scenarios. The results indicated that the best prediction accuracy was achieved for the fourth scenario (IV) when all input variables were combined to predict selected heavy metals. Moreover, two models showed different capability for various metals. According to our results, the random forest model had a high accuracy in predicting Ni (R2 = 0.67) and Cu (R2 = 0.60), In contrast, the Cubist model had a higher accuracy in predicting Mn (R2 = 0.55). For predicting Fe, both models provided a similar accuracy (R2 = 0.73). This study proved the high capability of machine learning methods to use easily available environmental data to predict studied heavy metals in the large scale that are essential for decision making in sustainable management in agricultural and environmental concerns.

Accurate mapping of soil organic carbon (SOC) and inorganic carbon (SIC) contents at regional scales can be very important for sustainable agriculture and soil management. Low variation in terrain attributes (classically used for digital soil mapping) at low relief areas calls for additional spatial data to explain soil variability. The main objective of this study was to evaluate the predictive capability of Sentinel-1 (radar) and Sentinel-2 (optical) time series and statistics, summarized as multi-temporal features (MTF) to improve the spatial predictions of SOC and SIC in Ghorveh plain, located in Kurdistan Province, Western Iran. A systematic grid sampling was then employed to collect 150 soil surface samples (0–30 cm) for SOC and SIC measurements. We applied boosted regression trees (BRT) and random forest (RF) to predict SOC and SIC contents by using covariate sets compiled from radar and optical time series and topographic attributes. Model performance, evaluated by 10-fold cross-validation, showed that RF using the covariate set containing time series of Sentinel-1, Sentinel-2 and topographic attributes performed the best in predicting SOC (RMSE = 0.23, ME = 0.005, R2 = 0.29). On the other hand, for SIC, the covariate set containing MTF of Sentinel-1, Sentinel-2 and topographic attributes ranked the best with BRT (RMSE = 0.77, ME= −0.001, R2 = 0.48). The study indicates that using the time series and MTF from multiple dates of remote sensing data with topographic attributes results in improved predictions. However, model performance for SIC and SOC was moderate to poor, respectively. Therefore more substantial studies would be required to verify if the computational effort is likely justified by an increase in accuracy in general.

The main goal of this study was to consider and compare the effects of different spatial resolutions of covariates from different sources on predicting SOC in a semi-arid region located in the west of Iran. For this purpose, 67 topsoil samples (0–30 cm) with the measured SOC contents were used as the dependent variable. The covariates controlling the SOC content from different sources were provided in two scenarios. For the first scenario (scenario I), six covariate sets with spatial resolution ranging from 2 to 30 m, and original and aggregated pixel sizes were prepared using the digital elevation models (DEMs) and remote sensing data to predict SOC. In the second scenario (scenario II), the available legacy data, including geology, land use and soil texture maps, were prepared with compatible spatial resolution and added to each covariate set provided for scenario I. After feature selection analysis, the modelling processes were performed using two machine learning models, namely, Random Forest (RF) and Support Vector Machine (SVM). The results of performance analysis, as obtained by leave one out cross validation (LOOCV), showed that the RF and covariate set B (with 10 m spatial resolution) in scenario I, with R2 = 0.21, CCC = 0.41, MAE = 0.26 and RMSE = 0.34%, and also, in scenario II, with R2 = 0.32, CCC = 0.51, MAE = 0.24, and RMSE = 0.32%, had a better performance in predicting SOC. In addition, the remote sensing data were identified as the most important variables controlling the spatial distribution of SOC. Finally, by using the RF model as the superior model, the SOC map provided by the covariate set B in scenario II, which was the combination of the three types of covariates (DEM, remote sensing data and legacy data), was shown to have the lowest uncertainty in comparison to the SOC provided by the covariate set B in scenario I. In general, our results showed that the model type, source, resolution and the combination of these variables could greatly influence the prediction outputs. In fact, the SOC map provided with the combination of parsimonious variables at the optimal pixel size could help decision-making in environmental resources management.

The present study was conducted to evaluate the effectiveness of combining proximal, and remote sensing with environmental variables for predicting USDA (United States Department of Agriculture) so...

Near-saturated hydraulic properties are the key parameters for water transport models in the unsaturated zone and essential for management practices. This study was conducted to compare efficacy of multiple linear regression (MLR) and hybrid method of genetic algorithm with artificial neural network (GA-ANN) for prediction of near-saturated soil hydraulic properties in Moghan plain, north-western Iran. The results of MLR analysis indicated that this method had low potential to predict near-saturated soil hydraulic properties in the study area, which only could explain 14–38% of variability in the studied properties. Otherwise, GA-ANN was much higher powerful that could explain about 35–80% of total variability in the mentioned properties in the study area. The results of sensitivity analysis suggest that soil particle size distribution, organic matter, electrical conductivity and relative bulk density were the most crucial with variety of priorities for explaining variability of the near-saturated soil hydraulic properties in the study area in the semiarid region. In overall, it was concluded that application of intelligent system using the easily available soil properties as predictors could provide reliable estimates of near-saturated soil hydraulic properties at the filed scale.

In the context of the continued increased global uptake of fingerprinting procedures to explore fluvial sediment sources, far less attention has been paid to dust source tracing and especially using different particle size fractions and low-cost tracers such as colour and magnetic susceptibility. The objective of this study, therefore, was to apportion local dust storm source contributions for the < 63-μm and 63–125-μm fractions of dust samples in a case study in central Iran. Colour and magnetic susceptibility properties were measured on 62 source samples and six dust storm samples. Statistical methods were used to select four different composite fingerprints for discriminating the dust sediment sources. These statistical approaches comprised (1) the Kruskal–Wallis H test (KW-H), (2) a combination of KW-H and discriminant function analysis (DFA), (3) a combination of KW-H and principal components and classification analysis (PCCA), and (4) a combination of KW-H and a general classification and regression tree model (GCRTM). Local dust source contributions were ascribed using a Bayesian un-mixing model using the final composite fingerprints. For both the < 63- and 63–125-μm fractions, the different composite signatures consistently suggested that alluvial fan material was the dominant source of the dust samples. The root mean square differences between the apportionment results using the different fingerprints ranged from 0.5 to 1.6% for the < 63-μm fraction and from 1.8 to 5.8% for the 63–125-μm fraction. The Wald-Wolfowitz runs test was used to compare the posterior distributions of the predicted source proportions created using the alternative final composite fingerprints and the results indicated that most of the pairwise comparisons were significantly different (p ≤ 0.05). For the < 63-μm fraction, the RMSE and MAE estimates of divergence between the modelled and known virtual source mixtures using the different final composite signatures ranged between 1.5 and 23.4% (with a corresponding mean value of 9.4%). The equivalent estimates for the 63–125-μm fraction were 1.2–20.1% (8.3%). The findings clearly demonstrate that colour and magnetic susceptibility tracers offer low-cost options for apportioning dust sources.

The wheat production variability is not well-understood in hilly region, especially in loess-derived soils of Golestan province in Iran with a sub-humid climate. Topography can greatly influence the production of agricultural crops by affecting soil quality. A study area located in Golestan province was selected in order to assess the spatial variability of wheat production and to develop regression models between the crop, soil properties, and topography attributes. The samples of wheat and soil were randomly taken from 100 points at different hillslope positions (i.e., shoulder, back-, foot-, and toe-slope). The soil physicochemical analysis and the measurement of wheat yield components were conducted. The digital elevation model (DEM; 10 m resolution) was used, and the topographic attributes (i.e., elevation, slope, wetness index, stream power index, curvature, erosivity factor, and watershed specific area) were calculated. The results showed that the greatest total yield and the highest grain yield were estimated to be 14.53 and 4.41 ton ha−1, respectively, in areas with a slope of less than 10%, which were significantly higher than those in the steep areas (slope classes of 10–30% and > 30%). The highest and the lowest total yields, with average values of 15.82 and 5.68 ton ha−1, were observed in the toeslope and shoulder slope positions, respectively. The greatest grain yields were obtained from the foot- and toeslope positions with the average values of 4.61 and 4.66 ton ha−1, respectively. The topographic curvature and wetness index had a significant correlation with the yield of wheat. According to the regression equations, topographic indexes can well justify the spatial variability of wheat yield, indicating the importance of these factors by influencing the distribution of moisture during the process of wheat production in the study region. The enhancements of wheat yield components in the lower slope positions could be attributed to an increase in soil depth and plant available water as well as to the accumulation of further soil organic matter and nutrient elements, including nitrogen and potassium, in such positions as a result of soil redistribution. Moreover, the results illustrated that by using easy accessable, cheap, and none destructive data (DEM derivatives and soil properties); it is possible to predict the production yield of wheat with a reliable estimation. We concluded that for better farming management and productivity in hilly regions, topographic attributes should be considered for plantation. Therefore, this study introduces the most suitable slope positions and topographic attributes for crop production with the least soil degradation. Shoulder and backslope positions are the most unsuitable slopes possibly better for orchards while toeslopes and footslopes could be used for intensive crop production.

The purpose of this study was to determine the relative contribution of sub-basin resources to sediment production by using magnetic susceptibility data as the tracer in Chehelgazi catchment, Sanandaj. For this purpose, 20 samples of the output 5 sub-basins were measured by harvesting and magnetic susceptibility. Kruskal-Wallis test results showed that in all three trackings, frequency high, low and dependent, at least two sources had the ability to differentiate. In the second step, the three tracers were tested on the discriminant analysis by the sub-basin source and two tracers with different power splitters showed the high frequency of 88% and the frequency dependence of 12%; power splitters both tracers together in the sub-basin splitters was 90%, so they were selected as the optimal combination; therefore, they have the capability to determine the relative contribution model of sediment. The results of Bayesian uncertainty model also indicated Todarsamadi sub-basin with 44.4% of the largest contribution and Doveyseh, Chatan and Cherendo sub-basins with 35.5, 7.9 and 4.5, respectively, and Madian Dol sub-basin with 4/4 percent had the lowest contribution to sediment production. Based on the available results, Todarsamadi and Doveyseh sub-basins had the highest amount of sediment production; so these results could be used in soil conservation and management planning.

The simultaneous effect of the environmental factors as determinants of soil invertebrate community structures is little understood. This study was carried out at two depths (0–10, 10–20 cm) in the forest ecosystem located in Golestan province, north of Iran. The abundance, diversity, and richness of twelve taxonomic groups of soil invertebrates were modeled using three groups of environmental attributes consisted of topographic attributes and vegetation indices, soil properties and tree diversity indices as predictors. Genetic algorithm was applied to identify and select the effective environmental attributes; the non-linear machine learning, random forest (RF), was employed to predict abundance, diversity, and richness of invertebrates and to determine the most important variables controlling the horizontal and vertical distribution of invertebrates in the forest ecosystem. Results showed that RF was an accurate model for predicting abundance (RMSE = 0.15), diversity (RMSE = 0.18) and richness (overall accuracy = 0.51, kappa = 0.34) of soil invertebrates at the first depth. Similarly, at the second depth, RF showed a good prediction for abundance (RMSE = 0.20), diversity (RMSE = 0.18) and richness (overall accuracy = 0.23, kappa = 0.12) of soil invertebrates. Horizontal distribution of invertebrates' abundance was affected by soil microbial respiration (Resp), land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Shannon index of trees and soil moisture, while vertical distribution of invertebrates' abundance was more related to soil organic carbon (SOC) and soil moisture. Variable importance analysis confirmed that horizontal and vertical types of distribution of Shannon index of soil invertebrates were affected by the same factors, including land temperature, NDVI, soil respiration and moisture. Also, soil moisture, soil respiration, NDVI and richness of trees had significant effects on horizontal distribution of soil invertebrates' richness, but silt, Normalized Ratio Vegetation Index (NRVI), soil respiration and moisture had positive effects on vertical distribution of richness. In conclusion, that digital soil mapping (DSM) model could be apparently considered as a powerful and economical method in biodiversity modeling. Other supplementary data such as invertebrates feeding web are also suggested as the input dataset for further investigations.

Soil salinization is a severe danger to agricultural activity in arid and semi-arid areas, reducing crop production and contributing to land destruction. This investigation aimed to utilize machine learning algorithms to predict spatial soil salinity (dS m−1) by combining environmental covariates derived from remotely sensed (RS) data, a digital elevation model (DEM), and proximal sensing (PS). The study is located in an arid region, southern Iran (52°51′–53°02′E; 28°16′–28°29′N), in which we collected 300 surface soil samples and acquired the spectral data with RS (Sentinel-2) and PS (electromagnetic induction instrument (EMI) and portable X-ray fluorescence (pXRF)). Afterward, we analyzed the data using five machine learning methods as follows: random forest—RF, k-nearest neighbors—kNN, support vector machines—SVM, partial least squares regression—PLSR, artificial neural networks—ANN, and the ensemble of individual models. To estimate the electrical conductivity of the saturated paste extract (ECe), we built three scenarios, including Scenario (1): Synthetic Soil Image (SySI) bands and salinity indices derived from it; Scenario (2): RS data, PS data, topographic attributes, and geology and geomorphology maps; and Scenario (3): the combination of Scenarios (1) and (2). The best prediction accuracy was obtained for the RF model in Scenario (3) (R2 = 0.48 and RMSE = 2.49), followed by Scenario (2) (RF model, R2 = 0.47 and RMSE = 2.50) and Scenario (1) for the SVM model (R2 = 0.26 and RMSE = 2.97). According to ensemble modeling, a combined strategy with the five models exceeded the performance of all the single ones and predicted soil salinity in all scenarios. The results revealed that the ensemble modeling method had higher reliability and more accurate predictive soil salinity than the individual approach. Relative improvement (RI%) showed that the R2 index in the ensemble model improved compared to the most precise prediction for the Scenarios (1), (2), and (3) with 120.95%, 56.82%, and 66.71%, respectively. We applied the best model in each scenario for mapping the soil salinity in the selected area, which indicated that ECe tended to increase from the northwestern to south and southeastern regions. The area with high ECe was located in the regions that mainly had low elevations and playa. The areas with low ECe were located in the higher elevations with steeper slopes and alluvial fans, and thus, relief had great importance. This study provides a precise, cost-effective, and scientific base prediction for decision-making purposes to map soil salinity in arid regions.

The present study was conducted to explore the relationships between magnetic measures and some heavy metals in some igneous rocks in north-western Iran. For this purpose, four major rocks including ultrabasic, basalt, granite and andesite were selected and totally 60 samples were collected. The collected samples were analyzed for magnetic measurements ( $$ \chi_{\text{lf}} $$ , $$ \chi_{\text{hf}} $$ , $$ \chi_{\text{fd}} $$ ) and heavy metals concentration (Fe, Cr, Cu, Zn, Co, Mn and Ni) by atomic absorption spectroscopy. Some samples were analyzed by XRD for iron mineral characterization. The results indicated that the highest and lowest $$ \chi_{\text{lf}} $$ and all measured heavy metals were found in ultrabasic (as a basic rock) and in granite (as an acidic rock), respectively. X-ray analysis confirmed the higher presence of magnetite/maghemite in basic rocks. Positive and significant correlations were found between magnetic susceptibility at low frequency and all heavy metals’ concentration except for Ni. Overall, magnetic susceptibility serves as a preliminary assessment of rock samples, providing rapid, non-destructive, economical and easy information about heavy metal concentration in igneous rocks in the study area.

In order to investigate the development of forest soils formed on loess, six representative modern soil pedons were selected along a precipitation gradient extending from eastern Golestan (mean annual precipitation, MAP = 500 mm) to eastern Mazandaran Provinces (MAP = 800 mm). Physiochemical, micromorphological and magnetic properties, as well as clay mineralogy of soils were studied using standard methods. Soils are mainly classified as Alfisols and Mollisols. Downward decalcification and the subsequent clay illuviation were the main criteria of soil development in all study areas. Pedogenic magnetic susceptibility of pedons studied varied systematically across the precipitation gradient in Northern Iran, increasing from 14.66 × 10−8 m3 kg−1 at the eastern part to 83.75 × 10−8 m3 kg−1 at the western margin of this transect. The frequencydependent magnetic susceptibility showed an increasing trend with rainfall as well. The micromorphological study of soils indicated that there is a positive relationship between climate gradient (increasing rainfall) and the micromorphological index of soil development (MISECA). The area and thickness of clay coatings showed an increasing trend with rainfall. Grain size analysis indicates that pedogenic processes are responsible for changing original grain size distribution of loess in our soils. The correlation achieved among modern soil properties and precipitation could be applied to the buried paleosols in the whole study area to refer degree of paleosol development and to reconstruct the paleoclimate.

We examine the potential of magnetic susceptibility measurements to discriminate different soil drainage classes in the Gandoman region, central Iran. Four soil drainage classes, comprising poorly drained (PD), somewhat poorly drained (SPD), moderately well drained (MWD) and well drained (WD), were identified, and a total number of 48 soil profiles were excavated and studied. The soil samples were collected from all studied profiles from the genetic horizons individually. Magnetic susceptibility was measured at both low (0.46 kHz) and high (4.6 kHz) frequencies. The crystallized and amorphous iron forms were also measured using citrate-bicarbonate-dithionite solution and oxalate-ammonium extracts, respectively. The highest magnetic susceptibility was observed in WD soils, whereas the lowest susceptibility was observed in PD soils. The results of the predictor models developed by discriminate analysis showed that the use of magnetic susceptibility and iron forms could correctly predict about 90.9, 78.6, 85.7 and 88.9% of all profiles in WD, MWD, SPD and PD classes, respectively. Overall, the results indicate that magnetic susceptibility could be applied as a marker for the discrimination of drainage classes in the study area. Magnetic susceptibility is thus a quickly accessible and low-cost indicator for soil drainage classes for landownerships and subsequent analyses.

The objective of this study was to characterize the morphological and magnetic properties of Sari loess-paleosol section in northern Iran for paleopedologic and paleoenvironmental interpretation. The section consisted of a modern soil (MS) and three paleosols (PS1, PS2, PS3) separated by loess layers (LS1, LS2 and LS3). Based on particle size distribution, clay mineralogy, carbonates distribution and size of secondary carbonates, pedogenic development of the soils was in order of PS3>PS2>PS1=MS. Presence of redoximorphic features in PS3 was attributed to alternate stagnic saturation due to local water or high precipitation. Dominance of smectite and vermiculite as well as large carbonated dolls in PS3 indicated suitable environment and sufficient time for pedogenic development. Magnetic properties (χlf and χfd%) were distinctly higher in MS, PS1 and PS2 when compared to loess layers. The Lowest magnetic properties values were observed in PS3 which can be the result of ferrimagnetic minerals destruction under hydromorphic conditions. The highest Fed content occurred in PS3, however, low χlf/Fed ratio indicated that majority of the iron minerals in PS3 are not magnetic. In conclusion, the particle size distribution, clay mineralogy and carbonates features were indicative of pedogenesis intensity, whereas, magnetic properties were useful to characterize the pedogenic environment.

Viscosity, density, pour point, flash point, and oxidation stability are some quality properties of engine oil. The entrance of any contamination into the engine oil can change its properties. In the present study the effect of calcite and silica sand dust particles on the quality of engine oil, namely, Sepahan Oil Production API, SC-CC, SAE 40, was investigated. The presence, amount, and particle size of silica and calcite in the engine oil were determined by X-ray diffraction (XRD), inductively coupled plasma spectroscopy (ICP), and energy dispersive X-ray spectroscopy (EDX), respectively. The concentrations of the silica and calcite particles (0, 1, 2, and 3 g/L) were used according to the dust conditions in the east of Isfahan, Iran. The quality of the oil was measured after 20 hours of the engine operation. The results showed viscosity, density, and the pour point of the engine oil were significantly (P

Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most relevant to predicting soil properties at the catchment scale in semi-arid areas. Thus, this research aims to investigate the ability of multivariate statistical analyses to distinguish which soil properties follow a clear spatial pattern conditioned by specific environmental characteristics in a semi-arid region of Iran. To achieve this goal, we digitized parent materials and landforms by recent orthophotography. Also, we extracted ten topographical attributes and five remote sensing variables from a digital elevation model (DEM) and the Landsat Enhanced Thematic Mapper (ETM), respectively. These factors were contrasted for 334 soil samples (depth of 0–30 cm). Cluster analysis and soil maps reveal that Cluster 1 comprises of limestones, massive limestones and mixed deposits of conglomerates with low soil organic carbon (SOC) and clay contents, and Cluster 2 is composed of soils that originated from quaternary and early quaternary parent materials such as terraces, alluvial fans, lake deposits, and marls or conglomerates that register the highest SOC content and the lowest sand and silt contents. Further, it is confirmed that soils with the highest SOC and clay contents are located in wetlands, lagoons, alluvial fans and piedmonts, while soils with the lowest SOC and clay contents are located in dissected alluvial fans, eroded hills, rock outcrops and steep hills. The results of principal component analysis using the remote sensing data and topographical attributes identify five main components, which explain 73.3% of the total variability of soil properties. Environmental factors such as hillslope morphology and all of the remote sensing variables can largely explain SOC variability, but no significant correlation is found for soil texture and calcium carbonate equivalent contents. Therefore, we conclude that SOC can be considered as the best-predicted soil property in semi-arid regions.

The characterization of magnetic minerals and the relationship of these minerals to the magnetic susceptibility of soils that have developed on various parent materials can provide valuable information to various disciplines, such as soil evolution and environmental science. The aim of the study reported here was to investigate variations in the magnetic susceptibility (χ) of soils in western Iran due to differences in lithology and to examine the relationship of χ to ferrimagnetic minerals. Eighty samples were collected from eight parent materials taken from both intact rocks and associated soils. The soil parent materials included a range of igneous and sedimentary rocks, such as ultrabasic rocks (Eocene), basalt (Eocene), andesite (Eocene), limestone (Permian), shale (Cretaceous), marl (Cretaceous), and the Qom formation (partially consolidated fine evaporative materials, early Miocene). The 80 samples were analyzed for χ using a dual-frequency magnetic sensor and for mineralogy using X-ray diffraction (XRD). The highest χ values were found in the ultrabasic rocks and associated soils, while the lowest χ values were observed in the limestone rocks and associated soils. The pedogenic processes significantly enhanced the χ values of soils developed on the sedimentary rocks due to the formation of ferrimagnetic minerals. In contrast, χ values decreased as a result of pedogenic processes in soils developed on igneous rocks due to the dilution effects of diamagnetic materials, such as halite, calcite, phyllosilicates, and organic matter. The significant positive correlation between the XRD peak intensity of the maghemite/magnetite particles and χ values confirmed that χ values in soils are largely controlled by the distribution and content of ferrimagnetic minerals. These results show that χ measurements can be used to quantify low concentrations of ferrimagnetic minerals in the soils of semiarid regions.

Increasing demand for food production, global change, and growing population are the enormous challenges in recent decades. Accurate soil maps and adequate models are indispensable tools to assist managers, scientists, and decision-makers in addressing these challenges. Legacy soil polygon maps at national and regional scales are available widely, but lack detail, and therefore effective methods such as digital soil mapping (DSM) are needed to disaggregate these maps. The objective of this study was to disaggregate a legacy 1:1,000,000 soil map by three methods of disaggregation: a supervised classification method (DSMART algorithm) and two unsupervised classification methods including fuzzy c-means (FCM) and k-means (KM) clustering in Borujen region, Chaharmahal-Va-Bakhtiari Province, Central Iran for both great group and subgroup Taxonomic levels. Although field validation indicated that the accuracy of the disaggregated soil maps was lower than that of the conventional soil map at both levels of Soil Taxonomy, disaggregated approaches produced more detailed soil maps when compared with the first, second, and third most probable soil classes of the conventional soil map. The higher overall accuracy of the conventional soil map was due to soil association units which consist of more than one soil taxonomic class. FCM and DSMART methods produced more accurate and detailed disaggregated soil maps than KM clustering algorithm at the great group and subgroup levels, respectively. We concluded that the decision on what method to use depends on the map, the level of available information (map detail), available expert knowledge, and the availability of the soil unit composition percentages in the soil map legend.

Knowledge about distribution of soil properties over the landscape is required for a variety of land management applications and resources, modeling, and monitoring practices. The main aim of this research was to conduct a spatially prediction of the top soil properties such as soil organic carbon (SOC), calcium carbonate equivalent (CCE), and clay content using digital soil mapping (DSM) approaches in Borujen region, Chaharmahal-Va-Bakhtiari province, central Iran. To achieve this goal, a total of 334 soil samples were collected from 0 to 30 cm depth. Three non-linear models including Cubist (Cu), Random Forest (RF), Regression Tree (RT) and a Multiple Linear Regression (MLR) were used to link environmental covariates and the studied soil properties. The environmental covariates were obtained from a digital elevation model (DEM) and satellite imagery (Landsat Enhanced Thematic Mapper; ETM). The model was calibrated and validated by the 10-fold cross-validation approach. Root mean square error (RMSE) and coefficient of determination (R2) were used to determine the performance of the models, and relative RMSE (RMSE%) was used to define prediction accuracy. According to the RMSE and R2, Cu and RF resulted in the most accurate predictions for CCE (R2 = 0.30 and RMSE = 9.52) and clay contents (R2 = 0.15 and RMSE = 7.86), respectively, while both of RF and Cu models showed the highest performance to predict SOC content (R2 = 0.55). Results showed that remote sensing covariates (Ratio Vegetation Index and band 4) were the most important variables to explain the variability of SOC and CCE content, but only topographic attributes were responsible for clay content variation. According to RMSE% results, it could be concluded that the best model is not necessarily able to make the most accurate estimation. This study recommended that more observations and denser sampling should be carried out in the entire study area. Alternatively, stratified sampling by elevation in homogeneous sub-areas was recommended. The stratified sampling probably will increase the performance of models.

In this study, pedotransfer functions were developed using magnetic measures and soil properties to evaluate the concentrations of some heavy metals (Cr, Co, Fe, Cu, Ni, Mn, and Zn) in soils developed on some igneous rocks (gabbro, diorite-gabbro, gabbro-diorite, monzodiorite, spilite basalt, granite, and porphyritic-granite) in Kurdistan province, western Iran. A total number of 105 samples from both rocks and the associated surface soils were taken; magnetic susceptibility at two frequencies, concentrations of selected metals, and physcio-chemical properties of soils were then measured. The highest concentrations of metals were obtained for Co, Ni, Fe, Mn, and Cu in soils developed on gabbro and for Zn and Cr in soils developed on porphyritic-granite and spilite basalt, respectively. Magnetic measurements in soils and associated rocks could explain 78, 74, 77, 72, 75, 68, and 69% of the total variability of Zn, Cu, Ni, Fe, Mn, Co, and Cr concentrations in the studied soils. Inclusion of soil physcio-chemical properties did not significantly improve the prediction. Therefore, it seems that magnetic measurements, as fast, non-destructive, and cost-effective tools, could solely be used to successfully predict heavy metals in natural ecosystems, especially in reconnaissance scale studies.

This research was conducted to evaluate the utilization of magnetic susceptibility measurements in the assessment of metal concentrations in soils developed on a range of parent materials in northwestern Iran. Eighty surface soil samples were collected from eight parent rocks including ultrabasic rocks, basalt, andesite, granite, marl, limestone, Qom formation, and shale. The collected samples were assessed to determine magnetic susceptibility at low frequency (χlf) and concentrations of some metals comprising chromium (Cr), iron (Fe), copper (Cu), nickel (Ni), zinc (Zn), cobalt (Co), and manganese (Mn). The results showed that the highest levels of metals and χlf were observed in basic and ultrabasic soils. Strong positive correlations (P 0.01) detected between χlf and Fe (0.87), Mn (0.78), Zn (0.74), Ni (0.90), Co (0.78), and Cr (0.90) in all samples indicated a potential for using magnetic susceptibility in semi-quantitative estimation of metal concentrations in soils of natural ecosystems. Multiple linear regression between metal contents and χ

Due to the heavy costs of wind erosion control measures, the correct selection of technical methods is indispensable for a sustainable land management in arid and semiarid regions. Thus, th...

Temperature is a key factor that can affect soil properties and processes. Surface tension-viscous flow (STVF) theory is widely used to explain the temperature effects on soil hydraulic properties. We hypothesized that one of the reasons for deviation of measured soil water retention data from the STVF theory predictions is due to the temperature effects on soil structural stability (i.e., near-saturated pore size distribution [PSD]). Therefore, the objective of this study was to evaluate the effect of temperature on soil structural stability as characterized using the high energy moisture characteristic (HEMC) method in a wide range of arid and semi-arid soils of Iran. Aggregates of 28 soil samples were fast-wetted to mimic the natural conditions of soils during rainfall and irrigation. Four ambient temperatures (5, 10, 15 and 30 °C) were applied using an incubator during the wetting of aggregates, and after that HEMCs in the matric suction (h) range of 2 to 50 hPa were measured. Both van Genuchten (VG) and modified van Genuchten (MVG) models were fitted to the HEMC data and structural stability indices were calculated. The results generally showed that ambient temperature significantly altered soil PSD and structural stability, and as temperature increased: (i) an expansion of entrapped air initiated by the formation of microbubbles, was enhanced, (ii) water content at near full saturation h = 2 hPa (θ2hPa) decreased, and at h = 50 hPa (θ50hPa) it increased, leading to a reduction in the volume of drainable pores [VDP], and thus (iii) the soil structural index [SI] and slope at the inflection point of HEMC [Si] decreased, and (iv) amount of macropores (h, 2–12 hPa) was markedly decreased, however, micropores (h, 12–50 hPa) were not notably affected or were increased at high temperature due to intensive shifting of macropores into micropores. These findings imply that soil structure was damaged due to loosening effects of high temperatures on inter-particles bonds and increased repulsive forces between clay particles. Cluster analysis showed that the effect of ambient temperature on the structural stability was greatest in carbonate-rich soils with low clay and organic carbon (OC) contents and was least in the soils rich in OC. The results of this study could be important for projecting the effect of global warming and climate change on soil structure and erosion.

Post-fire erosion is a main concern to society because it has inflicted serious damages in managed ecosystems. In this study, the impacts of fire and time after fire events on soil erosion (as predicted using 137Cs technique) and on some soil chemical and physical properties were investigated in steep rangelands of western Iran. Three sites in rangelands with similar slope gradients and parent materials were selected, and within each site, the burnt (1, 5, and 10 years after the fire events) and the unburnt treatments were studied and soil samples were collected from five depths (0–2.5, 2.5–5, 5–10, 10–20, and 20–40 cm) with three replicates. The results indicated that soil organic matter (SOM), total nitrogen (TN), available phosphorus (Pava), available potassium (Kava), electrical conductivity (EC), and bulk density (BD) were significantly different between burnt and unburnt treatments for two times (1 and 5 years) after fire events. No significant difference by Duncan’s test was obtained for these properties between the 10 and 5 years after fire events. In addition, clay and sand contents and magnetic measures (χlf, χhf) were significantly different between burnt and unburnt treatments for all the three times after fire. The results of soil erosion by the 137Cs technique showed that profile distribution model (PDM) estimated the mean soil erosion rate of 38.9 and 23.02 Mg ha−1 year−1 in the three studied years in the burnt and the unburnt rangelands, respectively. Fire events increased soil erosion rate and altered soil physical, chemical, and magnetic properties in the studied steep rangelands. Overall, the results confirmed that the 137Cs technique could be used as a rapid and efficient model to determine soil degradation in the rangelands. The fire diminished soil organic matter and, subsequently, reduced aggregate stability, and increased soil erosion and degradation in the burnt rangelands. Hence, understanding historical contexts of fire occurrences is paramount to increase our capacity for ecological transformations and management in the face of the critical situation.

In the digital soil mapping (DSM) framework, machine learning models quantify the relationship between soil observations and environmental covariates. Generally, the most commonly used covariates (MCC; e.g., topographic attributes and single-time remote sensing data, and legacy maps) were employed in DSM studies. Additionally, remote sensing time-series (RST) data can provide useful information for soil mapping. Therefore, the main aims of the study are to compare the MCC, the monthly Sentinel-2 time-series of vegetation indices dataset, and the combination of datasets (MCC + RST) for soil organic carbon (SOC) prediction in an arid agroecosystem in Iran. We used different machine learning algorithms, including random forest (RF), Cubist, support vector machine (SVM), and partial least square regression (PLSR). A total of 237 soil samples at 0–20 cm depths were collected. The 5-fold cross-validation technique was used to evaluate the modeling performance, and 50 bootstrap models were applied to quantify the prediction uncertainty. The results showed that the Cubist model performed the best with the MCC dataset (R2 = 0.35, RMSE = 0.26%) and the combined dataset of MCC and RST (R2 = 0.33, RMSE = 0.27%), while the RF model showed better results for the RST dataset (R2 = 0.10, RMSE = 0.31%). Soil properties could explain the SOC variation in MCC and combined datasets (66.35% and 50.82%, respectively), while NDVI was the most controlling factor in the RST (50.22%). Accordingly, results showed that time-series vegetation indices did not have enough potential to increase SOC prediction accuracy. However, the combination of MCC and RST datasets produced SOC spatial maps with lower uncertainty. Therefore, future studies are required to explicitly explain the efficiency of time-series remotely-sensed data and their interrelationship with environmental covariates to predict SOC in arid regions with low SOC content.

Clear-cutting of Oak forests and intensive cultivation on the hilly regions of the Zagros Mountains has led to plausible threats on the natural ecosystem and catastrophic floods in western Iran. This study was conducted to explore the effects of clear-cutting of natural forests in different slope positions on some soil chemical and physical properties as well as quantification of soil redistribution using magnetic susceptibility. Two adjacent sites, including natural forest and cultivated lands, were selected and a total of six transects at three soil depths (0–10, 10–20, and 20–30 cm) and a total of seventy-two soil samples were examined. Soil properties such as particle size distributions, bulk density (ρb), calcium carbonate equivalent (CCE), soil organic matter (SOM), and magnetic susceptibility at low and high frequencies were measured. Results indicated that clear-cutting and cultivation for 50 years significantly (p < 0.05) increased ρb and CCE and reduced SOM and TN. Moreover, magnetic susceptibility at four slope positions was significantly (p < 0.05) lower compared with the natural forest. Using a simple proportional model to estimate the soil loss or gain and comparing the mean χlf in any given point confirmed a high rate of soil loss in cultivated sites. In the forest soils, low soil loss in shoulder position and high rate of deposition in footslope and backslope positions were obtained. In general, it is revealed that magnetic measures could provide valuable estimates of mid-term soil erosion and sedimentation in the hilly region following land use changes.