جستجوی مقالات مرتبط با کلیدواژه « نقشه های رقومی » در نشریات گروه « آب و خاک »

Soil properties play a crucial role as they determine the soil's suitability for different types of plant growth, ecosystems, and biota functioning. They have a significant impact on nutrient cycling, carbon sequestration, and soil management. Digital Soil Mapping (DSM) is a process aimed at delineating soil properties. Soil sampling for DSM serves as  a fundamental  step in improving prediction accuracy and is crucial for incorporating variability in terms of environmental covariates. Conditioned Latin Hypercube (CLH) sampling is a technique utilized to generate a sample of points from a multivariate distribution conditioned on one or more covariates. Numerous researchers (Ramirez-Lopez et al., 2014; Adhikari et al., 2017; Zhang et al., 2022) have endorsed this approach in their studies, following its inception by Minasny and McBratney in 2006. However, there has been limited research to date on the impact of the Latin hypercube method's random sample selection process on the accuracy of resulting maps. Hence, the central question remains: Is the Latin hypercube sampling method, which is currently widely adopted, always a dependable approach in this field?

The study area covers longitudes 50°35'47'' to 51°29'' east and latitudes 31°36''31'' to 32°15'48'' north in Borujen city, Chaharmahal, and Bakhtiari Province. The region, with an average elevation of 2338 meters above sea level, receives an annual rainfall of 250 millimeters and maintains an average temperature of 11.5 degrees centigrade. In this investigation, inherited data from soil studies were utilized, consisting of 250 samples distributed across the study area. In this research, the studied characteristics included percentage of equivalent calcium carbonate, clay, and soil organic carbon at a depth of 0 to 30 cm. Land component variables were extracted using the Alus Palsar digital elevation model with a spatial resolution of 12.5 meters. In the initial stage, digital maps of equivalent calcium carbonate, clay, and soil organic carbon were generated using the support vector machine method. The modeling process proceeded until a highly accurate model was achieved, with the root mean square error percentage (RMSE%) being less than 40. The Latin hypercube approach was utilized for sample design, with 500 repetitions in this study. After selecting sampling points for each run using the Latin hypercube method, these points were mapped onto a detailed map, and the corresponding feature values were retrieved. The final map was created based on the extracted points. Subsequently, the latin hypercube approach was employed to generate soil property maps for each selected dataset. Validation was conducted using criteria such as the coefficient of explanation, root mean square error, and root mean square error in multiple iterations to ensure the accuracy of the generated maps.

The results distinctly illustrates the varied selection of sampling positions with each implementation of the Latin hypercube method. It is important to note that there may be some overlaps in different implementations. Consequently, the primary question arises: Is a one-time execution of the Latin hypercube sufficient for selecting study points? The findings indicate that the support vector machine model achieves satisfactory accuracy for all the examined characteristics. In the studied area, the environmental factors such as slope and elevation were identified as a significant predictors for estimating percentage of equivalent calcium carbonate.

In the present study, the accuracy of the latin hypercube method was assessed for selecting sampling location for digital soil mapping endeavors in Chaharmahal and Bakhtiari Province. Given the impracticality of collecting numerous field samples to evaluate the soil sampling method, this research aimed to employ simulation methods based on highly accurate maps for this purpose. The results indicate that the different outputs of the Latin hypercube method influence the accuracy of modeling, although this effect is also influenced by the specific feature under investigation and the extent of its variability within the study area.  Considering that the Latin hypercube method is based on the principle that samples are randomly selected in each class of environmental parameters, it is suggested that future studies using this method should account for this principle. Adequate consideration should be given, and the selection of sampling locations should rely on multiple implementations of the Bhattacharya distance method to ensure robustness and reliability.

This study aimed to investigate the spatial changes of soil salinity using RF model in a part of Eyvanekey Plain (Semnan Province 2018). Grid sampling with 100 m intervals (106 samples) was taken from 105 ha of soils developed on marl and gravely alluviums. The land uses were pistachio plantations with furrow irrigation and abandoned land. The maximum EC was (173.2 and 34 dS/m) in the abandoned and furrow irrigation pistachio plantations respectively. The main factors of salinization were saline marls, saline irrigation water, and high PET. The R2 for the salinity prediction map by RF model was 0.49, and the most important covariates were normalized difference salinity index (NDSI), topographic wetness index (TWI), Channel Network Base Level (CNBL), normalized difference vegetation index (NDVI), and modified soil vegetation index (SAVI). Spectral ratio indices derived from Landsat 8 contributed the most to the soil salinity prediction. Out of 5 main auxiliary variables, 3 variables are related to spectral ratio indices and the reason was the presence of salt on the soil in the studied area. Using NDVI with other salinity and moisture indices improved the salinity prediction model. Examining the results of covariates correlation and the implementation of recursive feature elimination showed that many covariates increase model complexity and prediction error. Recursive feature elimination helped to simplify the model by identifying the most important covariates. The salinity prediction map by random forest was consistent with the field observations and clearly defined the critical saline area.

Understanding the particle size distribution (PSD) is of great importance for plant growth and soil management. In recent years, the science of soil has witnessed a significant increase in digital soil mapping (DSM) activities. In this regard, machine learning models (ML) have emerged as an alternative and tool for DSM, which are mainly used for data mining and pattern recognition purposes, and are now widely used for regression and classification tasks in all fields of science. Hence, this study was undertaken to spatially model sand, silt, and clay particles utilizing machine learning models such as Random Forest (RF), Support Vector Regression (SVR), and the Co-Kriging geostatistical model. Additionally, auxiliary variables with high spatial resolution were incorporated into the analysis. This investigation was conducted in a section of the Marvdasht plain, located in Fars province.

The present study was conducted in a part of Marvdasht plain located between 35.82´41°52' to 1.07´57°52' east longitude and 35.02´48°29' to 14.72´2°30' north latitude, and 40 km north of Shiraz with an area of about 50,000 hectares. After determining the study area boundaries, the positions of 200 sampling points were determined using the R software and the conditioned Latin hypercube sampling method. In other words, for soil feature modeling, 200 samples were taken from two depths of zero to 30 and 30 to 60 centimeters in the study area. Then, the samples were transferred to the laboratory, dried, and passed through a 2 mm sieve. Finally, the soil texture components were measured by the hydrometer method. The environmental variables used in this study are a wide range of representatives of soil-forming factors that were prepared as much as possible from sources with minimum cost and high accessibility. In total, 75 environmental variables were prepared, and the raster format related to all environmental variables, including 39 elevation and altitude variables and 36 remote sensing measurement variables, was extracted. Finally, the factor-tuning inflation variance and Boruta algorithm were used to select the optimal variables.

The minimum amount of clay was measured at 10.21% and 10.45%, respectively, and the maximum amount was 32.65% and 36.35% at the surface and subsurface depths. The average amount of clay in all samples was 37.91% and 35.61%. The average amount of sand was measured at 25.65% and 26.02% at the surface and subsurface depths, respectively. The maximum amount of sand was observed in the northern and higher parts of the study area and was equal to 54.68% and the minimum amount was predicted in the low-lying areas of the study area. Low-lying areas and sedimentary plains in the central part of the study area contained high amounts of silt. Four depth variables valley depths (VD), texture (TE), topographic wetness index (TWI), and clay index (CI) related to geomorphometric parameters and the normalized difference vegetation index (NDVI) variable related to remote sensing indices were selected as optimal variables. The RF model with R2 of 54.0% and 36.0% for predicting sand, 48.0% and 64.0% for predicting silt, and 52.0% and 49.0% for predicting clay at both surface and subsurface depths performed better than the SVR and Co-Kriging models. The most effective variable in predicting the spatial distribution of soil particles was VD with relative importance of 60% and 65% for predicting sand at the surface and subsurface depths, 70% for predicting silt at the surface depth, and 70% and 65% for predicting clay at both surface and subsurface depths, respectively. Only TE and TWI variables were more important than VD for predicting silt at subsurface depth. These results show that topographic variables are effective in the spatial variation of soil particles. Unlike clay, the highest amount of sand in both depths was observed in the northern part and the highest part of the study area, and the lowest amount was predicted in the low-lying areas of the study area.

In general, with the aim of this research, maps of the spatial distribution of soil texture components were prepared at both surface and subsurface depths using machine learning and geostatistical approaches along with environmental covariates in a part of Marvdasht plain. Among the selected environmental covariates, topographic attributes, especially the valley depth (VD), had the highest effect in justifying the spatial prediction of soil texture components. Also, the results of comparing the performance of machine learning models supported the higher efficiency of the RF model than other models. Therefore, the approach used in this study to prepare a map of soil texture components can be useful as a guide for mapping useful soil features in areas with similar climatic and topographic conditions.

Soil salinity and its development are the main problems that should be prevented by correct management methods. Recognition of saline districts and the preparation of salinity maps are the first steps in this way. Nowadays, the application of auxiliary data in digital soil mapping is increasing due to the current associated problems in the preparation of traditional maps. The objectives of this study were to map soil salinity by the Regression Kriging (RK) method,  to identify areas with high salinity, and to investigate the relationship between soil salinity and soil-forming factors in Khuzestan Province. For this purpose, 291 surface soil samples (0-10 cm) were randomly collected in April 2014. Auxiliary variables or soil-forming factors were included in the land parameters such as slope, watershed and wetness index, OLI and TIRS images of Landsat 8, and the category maps (soil, land use, and geological maps). Also, kriging approaches were used to compare the precision of different mapping methods. The results indicated that the Regression Kriging method has a higher precision compared with other methods so that the coefficient of determination, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were estimated as 0.84, 0.41, and 6.21, respectively. The Decision Tree Regression method could also create a good relationship between soil salinity and auxiliary variables. The results showed that some auxiliary variables were more effective on the prediction of soil salinity including 2, 4, 5, and 7 bands of Landsat 8, Brightness Index, Wetness Index, Multiresolution index of Valley Bottom Flatness (MrVBF), Channel Network Base Level (CNBL), NDVI, SAVI and soil map. A Digital map of soil salinity was prepared by the obtained rules, and then it was assimilated with the map of error of variance to prepare the final soil salinity map. Accordingly, soil salinity was found to have an increasing trend from north to south in Khuzestan Province which indicates a salinity problem in the south of the Province. The main reasons for the high salinity in the south and southwestern parts of the area could be attributed to the high water table levels, differences in topography, capillary movement of salt to the soil surface, the difference in the type of land uses, and also groundwater quality and irrigation water which is altered by the frequent application of wastewaters and animal manures.

Soil salinity, as one of the most important indicators of soil quality, has crucial roles in land use planning and land management in arid and semi-arid regions. The aim of this study was to model soil salinity at five standard depth (0-5, 5-15, 15-30, 30-60, and 60-100 cm) of global digital soil mapping project in 60,000 hectares of Qazvin plain with spatial resolution of 15m. Field studies included a sampling of 278 soil profiles and then the EC was measured in the laboratory. The recursive feature elimination (RFE) method was employed to select environmental covariates including parameters extracted from Landsat 8 image (OLI/TIRS) data, topography, and climatic parameters. Four machine learning algorithms as random forest (RF), cubist (CB), decision tree regression (DTr), and k-nearest neighbors (k-NN) were applied for predicting and mapping soil salinity. According to RFE, 10 covariates were chosen for each standardized depth. The results of modeling showed that the CB model at the depth of 0-5 and 15-30 cm with R2 values of 0.92 and 0.85 and RMSE 4.77 and 7.90 dS/m and the RF model at depths of 5-15, 30-60, and 60-100 cm with R2 values of 0.93, 0.94, 0.96 and RMSE 6.65, 5.10 and 3.20 dS/m, respectively, had the highest accuracy compared to two other models i.e., DTr and k-NN. Furthermore, the covariates extracted from RS data had more impact on topsoil salinity prediction while the climate and topographic attributes influence subsurface soil salinity. Generally, The RF and CB models along with appropriate environmental covariates were able to present salinity variation of study standard depths.

The wind erosion occurs when wind speed exceeds the soil erosion threshold and plants or their residues,surface roughness, or other obstacles do not protect the soil surface. Also wind erodibility is oneof the most important determining parameters of wind erosion under certain climatic conditions. The main objective of this research was mapping of soil erodibility through empirical relationship between satellite imagery and physicochemical properties and estimation of soil erosion using a comprehensive assessment model, on the east shore of the Urmia Lake. For this research work soil sampling carried out in 153 points of three elevation classes(1271-1273, 1273-1275 and 1275-1278meters)and4supervised classification methods such as, support vector machine(SVM), Maximum likehood classification (MLC), Minimum distance and Artificial neural network(ANN)were used in classifying and mapping of soil erodibility. Soil samples physicochemical properties measured and 26 samples of them randomly were selected for wind erodibility measurement in wind tunnel laboratory. Wind tunnel experiments at a distance of 20 cm from the tunnel floor, revealed wind erodibility of 2.92 grm-2.min-1/ms-1. Also stepwise regression (SPSSv.25) results showed that among the physicochemical properties of soils, erodible fraction were the most important soil properties, which used in estimating erodibility and have a positive correlation with soil erodibility. The mean weight of aggregate diameter had negative correlation with soil erodibility and no relationship was found between soil chemical properties and erodibility. Of the4supervised classification methods, the artificial neural network has a higher capability in classifying and mapping erodibility and finally the results showed that the overall classification accuracy is57.1%.

Soils are considered as one of the most important parameters to be achieved the sustainable agriculture at any place in the world. Additionally, the digital environment needs to have a soil continuous maps at local and regional scales. However, such information are always not available at the required scale and mapping with high accuracy. Digital soil mapping (DSM) is a key for quantifying and assessing the variation of soil properties such as organic carbon (OC) especially in un-sampled and scarcely sampled areas. Using remotely sensed indices as an important auxiliary information relevant to the study area and data mining techniques were the pathway to create digital maps. Previous studies showed that digital elevation model (DEM) and remotely sensed data are the most commonly useful ancillary data for soil organic carbon prediction. the importance of DEM and derivative data in soil spatial modelling, it was not carried out in our research because there were no sharp differences in relief, and climate for that matter, across the study area. This research aims to investigate the spatial distribution of soil organic carbon (SOC) in a study area in north-western Iran using 21 remotely sensed indices as well as two data mining techniques namely Random Forests (RF) and Cubist.

This study was performed on the east shore of Urmia Lake located in the east Azerbaijan province, Iran. The area extension is about 500 km2. Based on the synoptic meteorological station report, the average annual precipitation and temperature of the study area is 345.37 mm and 10.83°C, respectively. Soil moisture and temperature regimes are Xeric and Mesic, respectively. Using stratified random soil sampling method, 131 soil samples (for the depth of 0-10 cm) were collected. Soil organic carbon (SOC) were then measured. The next step was to gather a suite of auxiliary data or environmental covariates thought to be useful (and available) for predicting SOC within a DSM framework for the region studied. Then, a number of remotely sensed imagery scenes from the Landsat 8-OLI acquired were collected in July 2017. The RF and Cubist models were applied to establish a relationship between soil organic carbon and auxiliary data. Both reflectance of the individual bands and indices derived from combinations of the individual bands were used. Fourteen spectral indices relevant to four types of data including: i) vegetation and soil; ii) water; iii) landscape; and iv) geology were gathered. Three different statistics was used for evaluating the performance of model in predicting SOC, namely the coefficient of determination (R2), mean error or bias (ME) and root mean square error (RMSE).

The results of the descriptive statistics of determined and calculated SOC for 131 soil samples showed that the mean and median values for SOC were 2.52% and 2.11%, respectively. Also, the CVs was recorded 57.94%. Minimum and maximum recorded values for SOC were 0.83% and 5.22%, respectively. The contents of SOC was left-skewed in the data set. The RF model prediction was quite good with calibration (R2= 0.89, MSE = 0.16 and ME = 0.01). While, in the Cubist calibration data set, the Valve of RMSE and ME were increased (R2= 0.85, MSE = 0.21 and ME = 0.03). In terms of R2, The RF model showed the higher value (0.89) compared with the Cubist model (0.85) for the validation dataset. Generally, the remote sensing (RS) spectral indices can successfully predict various SOC across the study area. The covariate importance rankings showed that VARI , NDVI , CRI2 and CRI1 were the four important covariates to predict SOC in the study area. Accordingly, the changes in SOC over space were not uniform across the study area and also it means that the study area is very dynamic and evolved over time.

The results of this study showed that although variables and auxiliary data had different importance in predicting the distribution of SOC, in general it can be found by modelling the relationship between them and SOC through the model. The results revealed that the RF model was suitable for the target variable. Accordingly, the auxiliary variables had different importance in predicting the spatial distribution of SOC. Remote sensing imagery, particularly those encompassing the combined indices played an important role in the prediction of SOC. The obtained results also indicated that the Visible Atmospherically Resistant Index (VARI) and Normalized Difference Vegetation Index (NDVI) were important to predict SOC. The current study revealed that DSM using important environmental covariates can be successfully used in Iran which there is no sufficient soil databases. This research also provided a pathway to start further works in the future such as DSM relevant to the soil erosion, soil ripening, trace elements and so on.

This study was conducted in order to digital mapping soil classes according to WRB system was conducted by boosted regression tree (BRT) method on about 60.000 hectares of Hirmand county lands. 108 soil profiles were dug and soil profiles were sampled and described based on WRB system. Twenty environmental covariates were used as estimators for soil mapping including terrain attributes and remote sensing covariates. Results showed that the young soil covered the study area and mostly influenced by flood sediment which classified as Fluvisol and Cambisol groups and Solonchak group in salt -affected area. The variable importance showed that the environmental attributes such as Multi-resolution Valley Bottom Flatness Index (MrVBF), Valley Depth, Convergence Index, Catchment Area and Salinity Index (NDSI) had the highest importance among all covariates for two levels of WRB prediction. The validation results showed that the BRT model could predict WRB1 and WRB2 levels with overall accuracy of 47 and 25%, respectively, and also from WRB1 to WRB2 levels the accuracy decreased. In low relief area and young soil that the low soils variability, digital soil mapping approach could be useful, efficient, and fast technique to produce and predict soil classes map.

Knowledge about the spatial distribution of particle-size fractions in different areas is required for various land management applications and resources, modeling, and monitoring practices. In recent years, with the advancement of data mining methods and the availability of cheap data from satellite imagery, digital soil mapping methods have been developed to predict the spatial distribution of primary soil particles. The objective of this study was to conduct a spatial prediction of particle-size fractionssuch as clay, sand and silt using digital soil mapping in agricultural lands in Semnan. To achieve these goals, a total of 84 soil samples were collected from 0 to 20 cm of soil surface. Also, the environmental variables were obtained from OLI Satellite Landsat to make dependence with soil particles. In this study a linear model such as Partial Least Squares (PLS) and two non-linear models, including Random Forest (RF) and Stochastic Gradient Boosting Machin (GBM) were used for spatial prediction of particle-size fractions. The models were calibrated and validated by the 10-fold cross-validation methods. Three statistics, such as Root Mean Squared Error (RMSE), Coefficient of determination (R2), and Mean Absolute Error (MAE) were used to determine the performance of the investigated models. Values of RMSE, R2, and MAE statics of RF model for prediction of sand, silt and clay were (15.6, 0.35, 12.62), (11.49, 0.33, and 9.34), and (8.42, 0.28, and 5.9), respectively. These results indicated that the most accurate model for the prediction of particle-size fractions was the RF model. Also, the results showed that the most important environmental covariates for predicting particle-size fractions were band 10 (B10), band 5 (B5), and the gypsum index (GI). This indicated that the variables containing the near-infrared and infrared thermal waves had a major contribution to explaining the spatial patterns of particle-size fractions.

Determination of land suitability is one of the land evaluation methods that can determine the best use of land in each area. The purpose of this research was to determine the land suitability of Mirza Kuchak Khan's cultivation and industry fields based on the soil quality indicators and a geographic information system (GIS), and compare the results with those obtained by methods of land evaluation and root strategies. For this purpose, information on soil profiles and the amount of organic matter, phosphorus, nitrogen, potassium, zinc, drainage, texture, depth, topography, surface rocks and gravel, impervious layer depth, hydraulic conductivity, water holding capacity, electrical conductivity, reaction PH), calcium carbonate, and exchangeable sodium percent of the study area were collected. Land suitability classes based on the quality indices of fertility, chemical quality, and physical quality of soil were defined. The results showed that 27.4% of the land belonged to the very good class (S1), 62.83% of the land could be assigned to the suitable class (S2), 11.7% of the land was put in the low proportion class (S3), and 2.66% the land was in the inappropriate class (N). Also, based on the comparison of the results of the method based on the soil quality with the square root method, Kappa coefficient was 0.82, while it was equal to 0.38 for the Storie method.

Soil organic carbon is one of the most important soil properties which its spatial variability is essential to crop management, land degradation and environmental studies. Investigation of variability of soil organic carbon using traditional methods is expensive and time consuming. Therefore, one of the ways to overcomethis challenge is using digital soil mapping whichcan predict soil characteristics using auxiliary data and data mining methods. Previous studies have shown that digital elevation model (DEM) and remotely sensed data are the most commonly useful ancillary data for soil organic carbon prediction. Artificial neural network (ANN) is a common technique of digital mapping. The region of Marivan in Kurdistan province is one of the forested areas inIran. In recent decades, due to population growth and the increased need for food, thisforested area has been threatened and some parts are now cultivated. Therefore, accurate mapping of soil organic carbon so as to improve land management and prevent land degradation is necessary. The purpose of this research wasusing ANN model and auxiliary data to mapsoil organic carbon. The study area is located in Kurdistan Province, Marivan (cover 20000 ha). Soil moisture and temperature regimes are Xeric and Mesic, respectively. Elevation also varies between 1280 and 1980 m. The main land use typesarecropland, forestland and wetland. The major physiographic units are piedmont plain, mountain and hills with flat to steep slopes. Using stratified random soil sampling method, 137 soil samples (for the depth of 0-30 cm) were collectedand soil organic carbon were measured. In the current study, auxiliary data were terrain attributes and ETM+ data of Landsat 7. Terrain parameters (including 15 factors), bands 1, 2, 3, 4, 5, 6, 7, brightness index (BI) and normalized difference vegetative index (NDVI) were computed and extracted using SAGA and ArcGIS software, respectively. ANN model was applied to establish a relationship between soil organic carbon and auxiliary data. Finally, soil organic carbon weremappedusing ANN and validated based oncross validation method. Three different statistics was used for evaluating the performance of model in predicting soil organic carbon, namely the coefficient of determination (R2), mean error (ME) and root mean square error (RMSE). Based on sensitive analysis of ANN model, auxiliary variables includingwetness index, index of valley bottom flatness (MrVBF), LS factor, NDVI index, and B3were the most important factors
for prediction of soil organic carbon. The quantities of R2, ME and RMSE calculated for ANN model were0.80,0.01 and 0.67, respectively.Soil organic carbon content ranged from0.26 to 8.45 % and the highest contentwasobserved in forestland with hill and mountain physiography and wetland around the lake. It is noteworthy that the differences fordifferent land uses were not statistically significant. Auxiliary data including wetness index, index of valley bottom flatness, LS factor, and B3 in different land uses had statistically significant difference (p<0.05) indicatinga closerelationship between auxiliary data and soil organic carbon. MrVBF and wetness index were lower and higher in forestland and wetland, respectively. Conversely, LS factor was higher and lower in forestland and wetland, respectively. Band 3 was lower in cropland and wetland compared to in forestland. NDVI index was also insignificantly higher in forestland compared to in cropland and wetland. In this research,ANN model was used to investigate the spatial variability of soil organic carbon in Marivan, Kurdistan province. The highest content of Soil organic carbon was foundin forestland and wetland. NDVI index was the most important auxiliary data to predict soil organic carbon within ourstudy area. According to the values of statistics, ANN accurately estimated the soil organic carbon. Therefore, employingpedometric techniques such as ANN model, auxiliary data of terrain attributes and satellite images to digitally mapsoil properties and updateold maps is recommendable. Further studies are needed to compare these results withdirect measurements of soil organic carbon.

Soil maps are the major sources for management of lands، natural resources and environmental aspects. Digital soil mapping methods can decrease the cost of mapping and are not time-consuming. Conditioned Latin Hypercube Sampling for determining the sites of sampling and Random Forest to predict soil sub group classes were evaluated at an area of 85000 ha in Golestan province. 12 environmental variables including terrain attribute، geomorphology units and vegetation cover index were used as predictor parameters. There were11 sub group classes in the soils of study area. The results revealed that the lowest OOB estimate error rate in modeling was 52. 53 %. The soil classes with higher frequency had lower OOB error. Gypsic Aquisalids with 22 samples and Aquic Calcixerepts with 9 and Typic Haploxerepts with 22 samples had the OOB error rates 22، 25 and 37%، respectively. Gypsic Haploxerepts (6 samples)، Typic Aquisalids (5 samples) Typic Calcixerepts (4 samples)، Typic Calcixerolls (4 samples) and Typic Halaquepts (3 samples) had OOB error rate 100%. SAVI، geomorphology، elevation and aspect were the most importance parameters in prediction of soil map and increasing model accuracy. Results indicate that RF technique can be reliable and appropriate method to give satisfactory results with small samples.

xture is an important soil physical property that governs most physical, chemical, biological, and hydrological processes in soils. Detailed information on soil texture variability is crucial for proper crop and land management and environmental studies. Therefore, at present research, 103 soil profiles were dogged and then sampled in order to prepare digital map of soil texture in Bijar, Kurdistan. Auxiliary data used in this study to represent predictive soil forming factors were terrain attributes, Landsat 7 ETM+ data and a geomorphologic surfaces map. To make a relationship between the soil data set (i.e. Clay, sand and silt) and auxiliary data, regression tree (RT) and artificial neural network (ANN) were applied. Results showed that the RT had the higher accuracy than ANN for spatial prediction of three parameters. For the clay fraction, determination of coefficient (R2) and root mean square root (RMSE) calculated for two models were 0.46, 0.81 and 17.10, 12.50, based on validation data set (20%). Our results showed some auxiliary variables had more influence on predictive soil class model which included: geomorphology map, wetness index, multi-resolution index of valley bottom flatness, elevation, slope length, and B3. In general, results showed that decision tree models had higher accuracy than ANN models and also their results are more convenient for interpretation. Therefore, it is suggested using of decision tree models for spatial prediction of soil properties in future studies.

In response to the demand for soil spatial information، the acquisition of digital auxiliary data and matching it to field soil observation is increasing. With the harmonization of these data sets، through computer based methods، so-called Digital soil Maps are increasingly being found to be as reliable as traditional soil mapping practices but without the prohibitive costs. Therefore، at present research، we have attempted to develop decision tree (DTA) and artificial neural network (ANN) models for spatial prediction of soil taxonomic classes in an area covering 720 km2 located in arid region of central Iran where traditional soil survey methods are very difficult to undertake. In this area، using the conditioned Latin hypercube sampling method، location of 187 soil profiles were selected، which then described، sampled، analyzed and allocated in taxonomic classes according to soil taxonomy of America. Auxiliary data used in this study to represent predictive soil forming factors were terrain attributes، Landsat 7 ETM+ data and a geomorphologic surfaces map. Results showed that the DTA had the higher accuracy than ANN about 7% for prediction of soil classes. Determination of coefficient (R2)، overall accuracy and kappa coefficient calculated for two models were 0. 34، 0. 46، 48%، 52%، 0. 13 and 0. 25، respectively. Our results showed some auxiliary variables had more influence on predictive soil class model which included: wetness index، geomorphology map and multi-resolution index of valley bottom flatness. In general، results showed that decision tree models had higher accuracy than ANN models and also their results are more convenient for interpretation. Therefore، it is suggested using of decision tree models for spatial prediction of soil properties (category and continuous soil data) in future studies.