SOIL hydraulic properties have important effects on describing water flow, solute and gass transports and also are important in hydrological studies. Although SPECTRAL information over visible near-infrared and shortwave infrared range, as a rapid, cost-effective and non-destructive method, has been recently applied to predict a number of SOIL properties, only few attempts have been conducted to predict SOIL hydraulic properties. The objective of this study was to assess whether inclusion of SOIL SPECTRAL data as a uniqe set of the predictors and alternative to basic SOIL properties would improve water retention predictions. Consequently, a number of 174 SOIL samples were taken and the SPECTRAL reflectances of the SOILs over 350 to 2500 nm range were measured, using a handheld spectroradiometer apparatus. The water retention at six matric potentials of -330, -1000, -3000, -5000, -10000 and -15000 cm were also measured by using preassure plate apparatus. Four scenarios including spectrotransfer functions (STFs), pedotransfer functions (PTFs), spectropedotransfer functions (SPTFs) and Rosetta PTFs were investigated. The transfer functions were first derived and compared with each other as well as with Rosetta PTFs afterwards. Based on the obtained results, basic SOIL properties and water retention parameters indicated high and significant (1% significancancy level) correlations with SPECTRAL reflectance values particularly in near and shortwave infrared ranges. The STFs indicated higher accuracy (R2>0.60; RMSR<0.011 cm3 cm-3) than the others especially at mid and dry end of retention curve. Although SPTFs and PTFs provided similar predictions, but PTFs were estimated narrowly better predictions at wet-end part of retention curve (-330 and -1000 cm). Weak predictions were obtained by Rosetta PTFs for all water contents particularly at the wet part of retention curve. These results suggest the efficacy of the SPECTRAL data, which can be used as an indirect method to predict SOIL water retention status.

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.

Background and Objectives: SOIL organic carbon (SOC), as a great constitute of SOIL organic matter (SOM), has an important role in chemical, physical and biological processes of SOIL. SOM or SOC is a key parameter of SOIL quality and a SOIL fertility indicator. SOM has an essential role in formation of SOIL aggregate and its stability, water and nutrients adsorption, water holding capacity, infiltration of air and water, hydraulic conductivity, SOIL water repellency and carbon sequestration. Various studies have shown that the quantity and quality of SOM can be affected by anthropogenic activities such as farming practices and other economic development activities. It has also been reported a high rate of SOM loss on eroded lands. Hence, monitoring temporal and spatial variation of SOM is essential for evaluating long-term SOIL productivity management. However, conventional SOIL sampling and chemical measurement of SOC, especially in large geographic scale, is tedious, time consuming and expensive. Therefore, rapid and precise assessment of SOC content can be useful in long-term management of SOIL. The objective of this study was to investigate the ability of SOIL visible-near infrared (vis-NIR) spectroscopy for estimating SOC in Zrebar lake watershed of Marivan, Kurdistan province, Iran. Materials and Methods: A total of 100 SOIL samples were collected from the studied region, with an area about 10718 hectares. The SPECTRAL reflectance and physicochemical properties of all SOIL samples were measured under laboratory controlled conditions. After recording of the spectra, different pre-processing methods were applied and compared. Then, pedo-transfer functions (PTFs) and SPECTRAL transfer functions (STFs) were developed to estimate SOC content using STEPWISE MULTIPLE LINEAR REGRESSION (SMLR). The accuracy and reliability of the derived PTFs and STFs were evaluated using coefficient of determination (R2), normalized root mean square error (NRMSE), mean error (ME), index of agreement (d), and ratio of performance to deviation (RPD) statistics. Results: Based on the results, SOIL organic carbon showed high and significant (significance level of 1%) correlations with SPECTRAL reflectance values at wavelengths 858 and 1916 nm. The results indicated that the derived PTFs had the higher accuracy (R2avg=0. 83, NRMSEavg = 24. 55%) to estimate SOC in comparison with the STFs (R2avg=0. 44, NRMSEavg= 44. 31%). However, SOC could be also fairly estimated by the derived SPECTRAL transfer functions (Ravg2=0. 52, RPDavg= 1. 44). The results also revealed that the Savitzky– Golay smoothing filter with 1st order derivative was the best SPECTRAL pre-processing method to reduce the effect of random noise and improve the calibration models. Conclusion: Overall, the results indicated that although the performance of STFs was not superior to the corresponding PTFs for estimating SOC, but this approach can be used as a reasonable indirect method in case of unavailability of PTFs.

Introduction Carbonates are an essential and prominent constituent of SOIL chemical properties particularly in arid and semiarid regions, in regards with SOIL productivity and conservation. The conventional techniques for assessing SOIL properties rely on direct laboratory measurements which are expensive, time consuming and labor intensive. Hence, it is required to develop fast and cost-efficient techniques for evaluation of mentioned parameters. The Koppen climatic classification generally categorizes Iran among the arid and semi-arid climates. About 90 % of its lands are arid or semiarid. According to SOIL Survey Staff (2014), calcareous SOILs contain 5% or more volumes of inorganic carbon (or carbonate calcium equivalent), which are the prevailing formation in arid and semi-arid areas. These SOILs are typical of areas where minerals cannot be leached away from the SOIL profile due to low precipitation. Based on the reports of FAO. UNDP (1972), approximately 12% of SOILs all over the world and 65% in Iran are calcareous. Therefore, carbonate is a key component that physically and chemically influences SOIL properties, as well as its fertility and productivity. One of the fast, easy-to-use, cost-effective and non-destructive methods of SOIL analysis is the visible to near-infrared (Vis-NIR) and mid-infrared (mid-IR) spectroscopy, that can partly be employed for the optimization of traditional techniques. Therefore, the reflectance spectroscopy is considered as one of relatively inexpensive and fast techniques to evaluate these features. The purpose of the present study was to evaluate the capability of the reflectance spectroscopy technique in Vis-NIR (250-2500 nm) and mid-IR (400-400 cm1-) ranges to estimate SOIL carbonates content as one of the key components affecting the physical and chemical properties of SOILs (especially in arid and semi-arid regions). Materials and Methods The study area is located in Juneqan District, Chaharmohal and Bakhtiari Province, southwest of Iran. 272 SOIL samples were collected from a depth of 0-10 cm, air dried and passed through a 2 mm sieve. The carbonates value of each sample was determined by standard laboratory method. The SPECTRAL reflectance of SOIL samples was extracted in the Vis-NIR (250-2500 nm) and mid-IR (400-400 cm1-) ranges using a spectroradiometer FieldSpec 3 (ASD-Analytical SPECTRAL Devices, Boulder Colorado, USA) and Nicolet 6700 Fourier Transform Infrared (FT-IR) (Thermo Fisher Scientific Inc., Waltham, MA), respectively. In the next step, seven preprocessing methods included absorbance transformation (log [1/reflectance]) (Abs), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), Savitzsky-Golay derivation (SGD), CONTINUUM removal transformation (CR), Normalization in range <-1, >1 (Nor) and Detrend (Det), were performed over original spectra for correcting light scattering in reflectance measurements and data improvement before using data in calibration models. Afterward, The dataset (272 samples) for each spectra range was randomly divided in calibration (70%) and validation (30%) datasets. Four different calibration models were fitted over Vis-NIR and mid-IR spectra to develop carbonates prediction models including: Partial Least Squares REGRESSION (PLSR), Support Vector Machine (SVM), Random Forest (RF) and Gaussian Process REGRESSION (GPR). The evaluation of SOIL predicting models was done according to the value of R2, RMSE and RPD. According to some researches, RPD values more than 2 shows that the models provide precise predictions, values of RPD between 1. 4 and 2 are considered to be reasonably representative, and values less than 1. 4 indicate poor predictive value. Results and Discussion The carbonates content in studied samples ranged from 1 to 76% with an average value of 24. 7%. Overall, carbonates content promoted increase of SPECTRAL reflectance intensity on several region of spectrum in both SPECTRAL ranges. The specific absorption wavelength in Vis-NIR spectra used to indicate the presence of SOIL carbonates was 2338 nm and in the mid-IR range were 714, 850, 870, 1796, and 2510 cm1. The results showed that the best performance of the used models in the Vis-NIR SPECTRAL range was related to the SVM model (R2=0. 81, RMSE=5. 36) and in the mid-IR range allocated to PLSR model (R2=0. 86, RMSE=4. 5). Both of these models showed great accuracy in carbonates estimating (RPD>2). Besides, the results showed that the mid-IR SPECTRAL range in the prediction of carbonates provided better performance than the Vis-NIR range. This can explained by the fact that the fundamental molecular vibrations of SOIL components occur in the mid-IR range, while only their overtones and combinations are detected in the Vis-NIR range. Conclusion It seems that the reflectance spectroscopy technique can be considered as a precise substitute for the conventional methods of measuring carbonates, which are sometimes costly, time consuming and destructive. However, due to the spatial and temporal variability of SOIL properties as well as the huge variety of models and SPECTRAL preprocessing methods, it is necessary to examine the capability of this technique in other areas with other preprocessing methods and REGRESSION models.

SOIL hydraulic properties are very important in hydrological cycle. The objective of this study was to explore the feasibility of estimating SOIL hydraulic parameters using diffused SPECTRAL reflectance data in visible, near-infrared and short-wave infra-red (350-2500 nm) ranges. Consequently, hyper-SPECTRAL reflectance of some SOIL samples was measured using a handheld spectroradiometer. After preprocessing the spectra, correlation between SPECTRAL data in each wavelength and Mualem van Genuchten hydraulic parameters (a*, n and Ks*) were explored. Using STEPWISE MULTIPLE REGRESSION method, parametric spectro-transfer functions (PSTFs) were derived. According to the results, the largest correlation coefficients were obtained for a* and n at wavelengths 550 and 2300 nm, while Ks* parameter showed maximum correlation at wavelength 1927 nm. The parametric STFs showed similar results for a* (R=0.54) and n (R=0.58). The best results were obtained for Ks* parameter with R values equal to 0.76. The parametric STFs provided mean RMSR values of 0.017 cm3 cm-3 for all the pressure heads. Although the performance for MvG parameters was not very high, this approach can be considered as a novel application of SOIL spectroscopy and might be used as a useful indirect method for estimating SOIL hydraulic properties.

Pedotransfer functions (PTFs) have been developed to indirectly predict SOIL hydraulic properties (SHPs) from easily measurable SOIL properties mainly including textural properties, SOIL organic matter and bulk density. In the last few decades, several studies have addressed the potential of SOIL SPECTRAL information in visible, near-infrared (350-2500 nm), to provide predictors to estimate elementary SOIL properties. Predicting SHPs by SOIL SPECTRAL data is a new approach that has not yet been explored. In this study, the feasibility to estimate the Mualem-van Genuchten (MvG) hydraulic parameters was investigated using Spectro Transfer Functions (STFs). Four scenarios of data affrication namely: ASD full spectrum (scenario I), EnMAP (scenario II), Sentinel-2 (scenario III) satellite-based SPECTRAL resolution and laboratory and SOIL map-based Rosetta and HYPRESPTFs (scenario IV) were investigated. A STEPWISE MULTIPLE LINEAR REGRESSION (SMLR) coupled with bootstrap method was employed to derive STFs. The most appropriate results for predicting MvG parameters were obtained for scenarios I and II. Compared with scenario IV, all the other three SPECTRAL scenarios performed reasonably well in terms of predicting SOIL water retention characteristics and unsaturated hydraulic conductivity. These findings suggest that SPECTRAL reflectance data at various SPECTRAL resolution levels is a promising indirect and quick method for large scale SOIL hydraulic parameter estimations.

Background and Objectives: The SOIL water retention curve (SWRC), as an important hydraulic SOIL property, is used in modeling water flow and solute transport in the unsaturated zone of the SOIL. Direct measurements of SWRC are difficult, time-consuming and costly. Hence, researchers have proposed indirect methods such as pedotransfer functions to estimate SOIL water retention curve using readily available SOIL data. Over the last decades, SOIL SPECTRAL data as a rapid, low cost, and nondestructive method has been widely applied to estimate basic SOIL properties. Consequently, in this study, the feasibility of using SOIL SPECTRAL information in the visible and near-infrared region, as input variables for transfer functions, and evaluation its performance was investigated compared to basic SOIL properties in estimating SOIL water retention curve. Materials and Methods: A number of 100 SOIL samples were collected and their SPECTRAL reflectance over 350-2500 nm region were measured using a handheld spectroradiometer apparatus. Some basic SOIL properties such as particle size distribution, particle density, bulk density, organic carbon content and calcium carbonate equivalent, and SOIL moisture content at matric potentials of-10,-33,-50,-100,-300,-500,-1000, and-1500 kPa were also determined with pressure plate-membrane apparatus. SPECTRAL reflectance curves of the samples were recorded using RS3 1 2 software on a portable computer connected to a spectroradiometer with 5 readings per SOIL sample. After SPECTRAL preprocessing, the correlation coefficient between absorption features of SOIL in each wavelength with SOIL moisture content at different matric potentials were investigated. STEPWISE MULTIPLE LINEAR REGRESSION was applied to derive pedo-transfer functions (PTFs) and SPECTRAL transfer functions (STFs) that uses basic SOIL properties and SOIL SPECTRAL reflectance as input, respectively. The accuracy of the proposed functions were assessed by adjusted coefficient of determination (R2 adj), normalized root mean square error (NRMSE), mean error (ME), and the ratio of performance to deviation (RPD). Results: Pedo-transfer functions (PTFs) provided more accurate estimates at the dry-end of the SOIL moisture curve than the wet-end, due to the high correlation of SOIL moisture with SOIL particle size distribution at the dry-end of the SOIL moisture curve. The results of the statistical parameters showed that the derived PTFs for estimating SOIL water retention at 10 to 1500 kPa matric suctions have good prediction accuracy. However, STFs also had reasonable but poorer results than the proposed PTFs in estimating the studied characteristics. Conclusion: Overall, the results of this study revealed that, despite the relatively poorer results of STFs than PTF, due to lower costs, time and field data, SOIL SPECTRAL data can be used as an indirect and novel method for estimating volumetric SOIL moisture content at different matric potentials.

Background: This paper compared the QSAR modeling of anti-cancer activity of compounds 1, 4-Dihydro-4-oxo-1-(2-thiazolyl)-1, 8-naphthyridines and its derivatives using STEPWISE MULTIPLE LINEAR REGRESSION (S-MLR) and combined genetic algorithm-MULTIPLE LINEAR REGRESSION methods (GA-MLR(. Materials and methods: A set of 100 compounds with certain anticancer activity were selected from literature. All molecules were “ cleaned up” and the Allinger’ s MM2 force field was used for energy minimization, the semi-empirical quantum method Austin method 1 (AM1) was used for geometry optimization using the Polak-Ribiere algorithm. A large number of theoretical descriptors for each molecule were calculated using Dragon software. In order to select the best set of descriptors for QSAR modeling, GA-MLR and STEPWISE-MLR as two variable selection methods were used. First the random sampling of the training sets (80% of data) were randomly taken 20 times, and the remaining molecules (20 percent of the data) were used as prediction set for external validation. Among the random samples, one of the samples with high Q2CV, Q2cal, Q2test was selected as the best train and test set. Using this train set, QSAR modeling performed using GA-MLR and STEPWISE-MLR methods. Results: QSAR models by GA-MLR modeling had larger validated squared correlation coefficient than the obtained models by S-MLR. Conclusion: According to the results, it could be concluded that the activity of similar compounds will be predictable by the obtained model.

Introduction: SOIL aggregate stability is a key factor in SOIL resistivity to mechanical stresses, including theimpacts of rainfall and surface runoff, and thus to water erosion (Canasveras et al., 2010). Various indicatorshave been proposed to characterize and quantify SOIL aggregate stability, for example percentage of water-stableaggregates (WSA), mean weight diameter (MWD), geometric mean diameter (GMD) of aggregates, and waterdispersible clay (WDC) content (Calero et al., 2008). Unfortunately, the experimental methods available todetermine these indicators are laborious, time-consuming and difficult to standardize (Canasveras et al., 2010).Therefore, it would be advantageous if aggregate stability could be predicted indirectly from more easilyavailable data (Besalatpour et al., 2014). The main objective of this study is to investigate the potential use ofsupport vector machines (SVMs) method for estimating SOIL aggregate stability (as quantified by GMD) ascompared to MULTIPLE LINEAR REGRESSION approach.Materials and Methods: The study area was part of the Bazoft watershed (31o37′ to 32o39′ N and 49o34′to 50o32′ E), which is located in the Northern part of the Karun river basin in central Iran. A total of 160 SOILsamples were collected from the top 5 cm of SOIL surface. Some easily available characteristics includingtopographic, vegetation, and SOIL properties were used as inputs. SOIL organic matter (SOM) content wasdetermined by the Walkley-Black method (Nelson & Sommers, 1986). Particle size distribution in the SOILsamples (clay, silt, sand, fine sand, and very fine sand) were measured using the procedure described by Gee & Bauder (1986) and calcium carbonate equivalent (CCE) content was determined by the back-titration method (Nelson, 1982). The modified Kemper & Rosenau (1986) method was used to determine wet-aggregate stability (GMD). The topographic attributes of elevation, slope, and aspect were characterized using a 20-m by 20-mdigital elevation model (DEM). The data set was divided into two subsets of training and testing. The trainingsubset was randomly chosen from 70% of the total set of the data and the remaining samples (30% of the data) were used as the testing set. The correlation coefficient (r), mean square error (MSE), and error percentage (ERROR%) between the measured and the predicted GMD values were used to evaluate the performance of themodels.Results and Discussion: The description statistics showed that there was little variability in the sampledistributions of the variables used in this study to develop the GMD prediction models, indicating that theirvalues were all normally distributed. The constructed SVM model had better performance in predicting GMDcompared to the traditional MULTIPLE LINEAR REGRESSION model. The obtained MSE and r values for the developedSVM model for SOIL aggregate stability prediction were 0.005 and 0.86, respectively. The obtained ERROR%value for SOIL aggregate stability prediction using the SVM model was 10.7% while it was 15.7% for theREGRESSION model. The scatter plot figures also showed that the SVM model was more accurate in GMDestimation than the MLR model, since the predicted GMD values were closer in agreement with the measuredvalues for most of the samples. The worse performance of the MLR model might be due to the larger amount ofdata that is required for developing a sustainable REGRESSION model compared to intelligent systems. Furthermore, only the LINEAR effects of the predictors on the dependent variable can be extracted by LINEAR models while inmany cases the effects may not be LINEAR in nature. Meanwhile, the SVM model is suitable for modellingnonLINEAR relationships and its major advantage is that the method can be developed without knowing the exactform of the analytical function on which the model should be built. All these indicate that the SVM approachwould be a better choice for predicting SOIL aggregate stability.Conclusion: The pixel-scale SOIL aggregate stability predicted that using the developed SVM and MLRmodels demonstrates the usefulness of incorporating topographic and vegetation information along with the SOILproperties as predictors. However, the SVM model achieved more accuracy in predicting SOIL aggregate stabilitycompared to the MLR model. Therefore, it appears that support vector machines can be used for prediction of some SOIL physical properties such as geometric mean diameter of SOIL aggregates in the study area. Furthermore, despite the high predictive accuracy of the SVM method compared to the MLR technique which was confirmedby the obtained results in the current study, the advantages of the SVM method such as its intrinsic effectivenesswith respect to traditional prediction methods, less effort in setting up the control parameters for architecture design, the possibility of solving the learning problem according to constrained quadratic programming methods, etc., should motivate SOIL scientists to work on it further in the future.

Clay minerals constitute a fundamental fraction of SOILs and their quantitative information is important in SOIL management. Therefore, the objectives of this research were to evaluate the ability of vis-NIR spectroscopy to quantify the dominant clay minerals of SOILs and to determine the limitations of this approach. One hundred surface SOIL samples were collected from the Isfahan province. Semi-quantitative mineralogical analyses were performed by XRD. SOIL SPECTRAL analyses were carried out by a field spectrometer using 350-2500 nm wavelength range. Partial least squares REGRESSION and CONTINUUM-REMOVED spectra were used for modeling. Modeling by CONTINUUM-REMOVED spectra could not precisely predict dominant clay minerals. Clay minerals estimation by partial least square REGRESSION was more accurate than CONTINUUM-REMOVED spectra. It appears that mixing the clay fraction with each mineral (palygorskite, smectite and illite) significantly influences the special absorption features of mineral and makes it difficult to estimate clay minerals accurately. In arid and semi-arid regions, mineralogical diversity is high and the presence of gypsum and carbonates increases the complexity of the SOIL system. Therefore, information from spectra is difficult to obtain and clay minerals could not be accurately estimated.