Cation exchange capacity (CEC) is an important soil physicochemical property that has great effect on fertility and soil quality management. Measurement of CEC is difficult, time-consuming and expensive. The objective of this study was to assess whether inclusion of soil spectral data as a unique set of the predictors and alternative to soil basic properties would improve CEC predictions. Consequently, a total of 120 soil samples were collected from surface soil layer. The CEC and easily-determined soil properties were measured by standard laboratory methods. The spectral reflectance of soils over 350 to 2500 nm range were also determined using a handheld spectroradiometer apparatus. Different pre-processing techniques were evaluated after recording the spectra. Stepwise multiple linear regression (SMLR) was used to estimate some soil properties and CEC. Three scenarios including spectrotransfer functions (STF), pedotransfer functions (PTF) and spectropedotransfer functions (SPTF) were investigated. Results showed that STF had higher accuracy (RPD=1. 50; RMSE=2. 57 cmolc/kg) than the others in predicting soil CEC. PTF (RPD=1. 09; RMSE=3. 55 cmolc/kg) and SPTF (RPD=0. 95; RMSR=4. 06 cmolc/kg) provided poor predictions accuracy. These results suggest the efficacy of the spectral data, which can be used as an indirect, simple, and fast method to predict soil cation exchange capacity.

In order to predict the behavior of soil-related phenomena, it is necessary to have knowledge about unsaturated flow and using models that provide optimal estimates of the retention curve and hydraulic conductivity of soils. Despite the widespread use of the classic van Genuchten-Mualem model (VGM), this model usually performs poorly in predicting hydraulic conductivity and modification of some of its parameters seems necessary. In this research, 283 soils from different textures of the UNSODA bank were selected and divided into two sections of calibration and validation and their soil parameters were exported and categorized. Then, by defining the modified unsaturated hydraulic conductivity (Ksc) instead of the saturated hydraulic conductivity (Ks) and determining the limits for l and n parameters, the hydraulic conductivity-moisture function of VGM were solved using 24600 pairs of points li and nj for each soil of the three main soil texture classes. In the following, the optimal l value (l̂) of each texture class was selected based on the minimum value of the hydraulic conductivity estimation error using the root mean square error (RMSE) index and the n values that had created the minimum errors, were selected as the optimal pore size distribution coefficients of the hydraulic conductivity-moisture function (n̂opt). In order to create pedotransfer functions for estimating n̂opt, we ran stepwise regression in MATLAB software considering the condition of statistical significance (P-value=0.05) for independent variables and functions for each soil texture class. After creating pedotransfer functions, the results of the proposed method of this research (MVGM) were compared with the VGM results using RMSE and Nash-Sutcliffe (NSE) indices. The results showed that in both sections of creation and validation functions, the MVGM performed better in estimating hydraulic conductivity and had a higher efficiency index in all textural classes of soil.

Quantification of soil water retention curve (WRC) is essential to study soil water movement in unsaturated soils. Direct measurement of soil moisture and matrix potential is too costly and time consuming. Moreover, due to the phenomenon of hysteresis, moisture in the drying branch cannot be used on wetting branch. Therefore, using the indirect method to find the relationship between the two branches of the WRC is needed. Development and use of the pedotransfer function (PTF) is one of the indirect methods. The purpose of this study was to provide a function for the parameters of van Genuchten model estimation in wetting branch using drying branch data. Required information in this research was collected from soil data base (Unsuda). These data include moisture retention curve data of drying and wetting and soil bulk density of 21 soil samples classified in sandy (10 samples), clay loam and loam (7samples), and silty clay loam (4samples) texture. To provide the functions, all samples were used. Results showed that nw in van Genuchten model was estimated with good accuracy, while the introduced function for the parameter aw had lower performance. The model obtained for estimation of the parameters of van Genuchten model in the wetting branch of WRC had a higher accuracy in low potentials. Results show that PTF are more powerful than Parlange hysteresis model for estimation of WRC in the wetting branch.

Quantitative description of infiltration process is crucial for many applications in hydrologic cycle. The direct measurement of infiltration is time consuming, expensive and often impractical because of the large spatial and temporal variability. Any indirect parametric estimation of this process would be quite useful. Although, the so-called pedotransfer functions (PTFs) are widely used as an indirect method to predict the soil hydraulic properties, no attention has made to indirect estimation of infiltration. The objective of this study was to develop and verify some parametric PTFs to predict the infiltration process under three different land uses; namely pasture, wheat and fallow. For this purpose, 123 double ring infiltration data were collected. The parameters of four infiltration models were then obtained, using sum of least squares error method. Basic soil properties of the two upper pedogenic layers such as initial water content, bulk density, particle-size distributions, organic carbon and gravel contents, CaC03 percent, field moisture capacity and penpanent wilting point water contents were measured for each sampling location. The parametric PTFs were then developed to predict the parameters of the infiltration models, using the step wise regression method. The accuracy of the derived PTFs was evaluated using MAMD, MRMSD, SDMRMSD and MPearson statistics. The results indicated that the PTFs derived for the land under fallow have the best performance on cumulative infiltration prediction. Under pasture, wheat and fallow land uses the derived PTFs for Philip, Horton and Kostiakov-Lewis models were the best predictor of infiltration, respectively.

Field capacity (FC) and permanent wilting point (PWP) are efficacious in determining net irrigation water depth. However, direct measurement of these properties is tedious, time consuming and costly especially on large scale. Soil pedotransfer functions (PTFs) as the indirect methods can replace by the direct methods. In this study, performance of the six available pedotransfer functions on FC and PWP moisture content predicting was evaluated on 112 soil samples that were collected from the north and northeast regions of Iran. The Root Mean Square Error (RMSE) values of menioned available PTFs were changed between 0.05 to 0.17 and 0.03 to 0.13 in moisture prediction on FC and PWP points, respectively. Therefore new PTFs were developed by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques based on soil properties (90 samples) and the results were validated on different soils (22 samples). The results showed that both MLR technique with assigning the RMSE values approximately 0.035, 0.01, 0.027 and 0.024 to predict soil moisture content on FC and PWP, total available water and specific yield and ANN technique with assigning the values approximately 0.013, 0.007, 0.015 and 0.013 to the same properties, evaluated in appropriate performance. The results also showed that using variables such as geometric mean and geometric standard deviation particle diameter, fractal dimension and air-entry suction, for the first one on input variables of PTFs, improved the accuracy of the results significantly, although accepting of this theory requires more studies.

Parametric description of the soil water retention curve is crucial for many applications and mode ling water movement and solute transport in the unsaturated zone. Due to high spatial and temporal variability of this hydraulic characteristic, its direct measurement requires a large numbers of samples. The direct measurements, in the other hand, is rather time consuming and costly. Thus, the indirect methods such as pedotransfer functions (PTFs), as an alternative, are increasingly employed for practical and modeling purposes. The objective of this study was to derive and verify some pedotransfer functions based on the geometric soil particle size variables to estimate the van Genuchten parameters. Consequently, the geometric mean and geometric standard deviation of particle diameters as input parameters were used to describe the pore-size distribution in PTFs. Therefore, 34 soil samples were randomly collected from Karaj area. The particle-size distributions and in situ bulk densities were determined with the hydrometery and core methods, respectively. The soil water retention curves for the entire range of interest were obtained using the pressure plate apparatus. The so-called easily obtainable variables were separated into two groups: (i) particle size distribution and bulk density, (ii) bulk density, geometric mean diameter and the geometric standard deviation of soil particle diameters. The parametric PTFs of these two group variables were developed, using the stepwise regression method. The derived pedotransfer functions were also verified and compared with some other collected data. The results indicated that the second group of variables can better predict the van Genuchten parameters. The coefficient of determination of 94 and 69 percents were obtained for the saturation and a, respectively.

Investigation of soil hydraulic properties like soil moisture retention curve and unsaturated hydraulic conductivity plays an important role in study of environmental researches in which their spatial and temporal variability led to development of indirect methods in prediction of these soil characteristics. Therefore, in this study indirect methods have been used in order to estimate surface fractal dimension to predict soil moisture curve. One parameter linear and nonlinear regressions were developed and compared to artificial neural networks by using readily available parameters like soil clay content, water content at permanent wilting point, cation exchange capacity and soil porosity. In the training step of regression analysis and neural networks, 97 measured soil samples and in the testing step 24 rest of soil samples with Petersen et al. (1996) database were used. The calculated values of RSE and RMSE showed that neural networks with seven neurons in the hidden layer are able to estimate surface fractal dimension from the easily available parameters more accurate than the other models.

Pedotransfer function (PTF) is a new technique for predication of the soil physical properties (SPP). Generally, SPP such as dry density, porosity, void ratio, soil hydraulic conductivity are estimated by a semi-empirical equation. The objective of this research was developing some PTF for estimation of SPP in bank of the Yangtze River, in Nanjing city, Jiangsu province, China. The SPP that has been considered in this research were: wet density (rw), dry density (rd), void ratio (e), liquid limit (LL) and plastic limit (LP). All soil analysis carried out by the soil geotechnical analysis standard method. 650 series of data were used for calibration and more than 100 series data for verification. The result shows that most of SPP in the study area can be significantly estimated by wet density (rw). For instant rd=1.474+1.531 × rw and L1=142.766- 54.898 × rw. Base on the results a computer program has been developed to estimate the SPP.