جستجوی مقالات مرتبط با کلیدواژه « spectroscopy » در نشریات گروه « آب و خاک »

The present research performed to estimate soil texture using visible near-infrared spectrometry in Semirom, Isfahan. A total number of 200 soil samples (0-10 cm) were collected from the Semirom area (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan. The samples were air dried and passed through a 2 mm sieve, and soil particles percentage was determined in the laboratory using hydrometry method. Reflectance spectra of all samples were measured using an ASD field spectrometer. Different pre-processing methods i.e., First Derivatives and Savitzky-Golay Filter, Multiplicative Scatter Correction and Standard Normal Variable were applied and performed on spectral data. The Partial Least Squares Regression, Support Vector Machine Regression and Artificial Neural Network models were used to estimate soil texture. The best result was obtained for Silt estimation, with excellent values of RPD >2, R2 =0.98 and RMSE=1.08 using Artificial Neural Network model with MSC pre-processing technique. The results indicated the desirable capability of Artificial Neural Network model with MSC and SNV pre-processing techniques in estimating the Clay (RPD >2, R2=0.94 and RMSE=1.21) and Sand (RPD >2, R2=0.84 and RMSE=6.24) contents of the soils, respectively. In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil particles percentage and showed its potential for substituting laboratory analyses.

 Estimating soil properties on large scales using experimental methods requires specialized equipments and can be extremely time-consuming and expensive, especially when dealing with a high spatial sampling density. Soil Visible and Near-InfraRed (V-NIR) reflectance spectroscopy has proven to be a fast, cost-effective, non-destructive, environmental-friendly, repeatable, and reproducible analytical technique. V-NIR reflectance spectroscopy has been used for more than 30 years to predict an extensive variety of soil properties like organic and inorganic carbon, nitrogen, organic carbon, moisture, texture and salinity. The objectives of this study were to estimate soil properties (carbonate calcium equivalent (CCE), electrical conductivity (EC), pH, and organic carbon (OC)) using visible near-infrared and short-wave Infrared (SWIR) reflectance spectroscopy (350-2500 nm). In this study, the best predictions of all the soil properties, model and pre-processing technique were also determined. The Partial Least Squares Regression (PLSR), Artificial Neural Network, Support Vector Machine Regression and Principal Component Regression (PCR) models were also compared to estimate soil properties.

 A total number of 200 surface soil samples (0-10 cm) were collected from the Semirom region (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan, Iran. The samples were air dried and passed through a 2 mm sieve, and using standard procedures soil properties were determined in the laboratory. Accordingly, soil pH and the EC contents of soil samples were determined in saturated pastes and extracts, respectively. The CCE content of the soils were measured using back titration, and the OC contents of the samples were measured using Walkley-Black method. The Reflectance spectra of all samples were measured using an ASD field spectrometer. The selection of the best model was done according to the value of the Ratio of Performance to Deviation (RPD), the coefficient of determination (R2), and the Root Mean Square Eerror (RMSE).

 Once the models were constructed using PLSR, ANN, SVMR and PCR approaches, descriptive analysis was carried out for each property, for the data measured in the laboratory. The parameters calculated for the properties were mean, coefficient of variation (CV), minimum and maximum, standard deviation and range. Coefficient of variation for the organic carbon, CCE, pH, and EC values were 21.7, 12.4, 1.34, and 28.74, respectively. Wilding (1985) proposed low, medium, and high variability for the CV values less than 15%, 15-35%, and greater than 35%, respectively. Accordingly, the organic carbon and EC of soils could be classified in the group with moderate variability. However, the calcium carbonate equivalent and pH are in the group with low variability. Since spectral data preprocessing has an effective role on improving the calibration, in order to perform spectral preprocessing, two first nodes at the first (350-400 nm) and the end (2450-2500 nm) of each spectrum were removed. In addition, two interruptions were eliminated, due to the change in the detector in the range of 900 to 1700 nm. Different preprocessing methods i.e., Standard Normal Variable (SNV) and First (FD) and Second Derivatives (SD) and Savitzky-Golay preprocessing techniques were performed on spectral data. Then, using PLSR, the cross‐validation method was used to evaluate soil properties calibration and validation. According to Stenberg (2002), for agricultural applications, The values of RPD greater than 2 indicate that the models provide precise predictions, the values of RPD between 1.5 and 2 are considered to be reasonably representative, and the values of RPD less than 1.5 indicate poor predictive performance. The results indicated the desirable capability of the PLSR method in estimating the EC (RPD > 2, R2 = 0.94), CCE (RPD > 2, R2 = 0.88), and OC (RPD > 2, R2 = 0.89). The best results of the pH (RPD > 2, R2 = 0.79) were estimated by the SVMR method. In this study the best methods of preprocessing techniques were First (FD) and Second Derivatives (SD) and Savitzky-Golay filter.

 In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil properties and showed its potential for substituting laboratory analyses. Moreover, spectroscopy could be considered as a simple, fast, and low-cost method in predicting soil properties. The PLSR model with First and Second derivatives and Savitzky-Golay pre-processing techniques seems to be more robust algorithm for estimating EC, OC, and CCE. The best results of the pH were estimated by the SVMR method with First and Second derivatives and Savitzky-Golay pre-processing techniques.

Sand fraction is one of the most important soil textural segments which should be highlighted for environmental modeling operations and digital soil mapping projects. Identification, mapping and monitoring of sand content over wide scales using traditional procedures is time-consuming and costly. Also spaceborne and airborne spectroscopy(remote sensing) have some defects compared to the laboratory and field spectroscopy. Hence, to overpower the imperfection of RS technology in relation to investigating the factors with spatial changeability, an apt technology is required. With the advent of Lab Diffuse reflectance Spectroscopy(LDRS) which exploits the fundamental vibration, overtones and combination of functional groups, that became a promising tool related. The present research intends to hyperspectral modeling of sand fraction utilizing the proximal soil sensing tech in some parts of Mazandaran province.

In accord with supplementary data layers(geology,pedology,landuse,etc.) and stratified randomized sampling method, eventually, 128 samples from 20cm of soil surface of Mazandaran province (scattered parts), were gathered. First of all, sample-set subdivided into two subsets: calibration and validation. Afterwards, using the hyperspectral analyses, multivariate regression analysis-PLSR method with the leave-one-out-cross-validation technique and some preprocessing algorithms such as: spectral averaging,smoothing and 1stderivative(Savitzky-Golay-derivation algorithm), the definitive calibration model with two&four latent vectors according to indices such as R,R2,RMSE,RPD and RPIQ were made. Results&

During the present research based on the sand hyperspectral modeling in the calibration subset containing 96 samples as well as the validation subset composing of 32 standalone samples, it has been showed the first two and four LVs out of the seven LVs can provide the best estimate of the soils of the study province. Consequently, the calibration process of sand hyperspectral model was done based on the first four LVs and the full LOOCV procedure. Because these number of LVs are able to concentrate the info-variance of sand variable more than 60% and likewise, the info-variance of spectral variables more than of 98%. The best calibrated hyperspectral model predicting sand components resulted with these specs: Rc=0.76,R2C=0.57,RMSEc= 9.77 and SEc of about 9.82. The correlation coefficients(R) of sand contents with the effective spectral domains were calculated as: UV-390nm=0.46, Vis-510to540nm about 0.53, 680to690 about 0.55, NIR- 950to970 about 0.67 and 1100nm=0.70, SWIR-1410 nm=0.76, 1860to1900 about 0.76, 2180to2220 about 0.77; which the specified spectral bands(spectral ranges) with the maximum of R contents indicating their highly impact and influence as the independent predictors on the sand parameter hyperspectral modeling processes at the studied soils of Mazandaran province. Furthermore, the most influential spectral domains involved in the modeling process of sand particles were determined as follows: UV-390nm,Vis-440-540nm,NIR-740-990nm,SWIR-1430-1890,1930,2190-2240,2330-2440nm, which these results were in agreement with others. The quality of calibrated sand hyperspectral model via assays such as Hotelling, adjusted leverage and residual variances was also confirmed. The accuracy assessment specs were as: Rp=0.83,R2p=0.68,RMSEp=8.68,SEp=8.72 and bias=-1.26.

Results indicate the apt hyperspectral analyses to estimate the sand based on LV=2: RPDc=1.51,RPIQc=2.44,RPDp=1.78 and RPIQp=2.45, additionally for LV=4:RPDc=1.54,RPIQc=2.48,RPDp=1.75 and RPIQp=2.41 have been gained. On the basis of the RPIQ values which were more than 2, it can be concluded the models are able to estimate the sand contents of Mazandaran soils satisfactorily and showing the acceptable quality of the predicting models utilizing the hyperspectral data. Our results can be the starting point to accurate mapping of sand constituents of soil texture using the RS platforms. It is noteworthy, the characterization of key wavelengths in the hyperspectral modeling of sand components, the upscaling operation as well as constructing the new airborne/satellite hyperspectral sensors can be bettered using the principle wavebands affecting the hyperspectral process so that providing the more precise hyperspectral studying of soil texture constituents using the aerial or space platforms.

For sustainable land management, it is necessary to understand the functions and characteristics of soils, and their spatial and temporal changes. Visible and near-infrared spectroscopy has a specific capability to identify and determine soil properties due to high accuracy and high-performance speed. The purpose of this study is to evaluate the accuracy of visible and near-infrared spectroscopy method in estimating soil organic matter and total neutralizing value. Therefore, 110 soil samples were collected from Khuzestan, Yazd and Tehran provinces, and spectral reflectance was performed using ASD FieldSpec3. The spectra obtained from the spectrometer were pre-processed using five methods including Savitzky-Golay filter (SG), the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normal variate (SNV), and Multiplicative scatter correction (MSC). Also, the performance of PLSR and SVMR methods was compared in terms of soil organic carbon and total neutralizing value estimation. The results indicated that the PLSR model in estimating both organic carbon properties and total neutralizing value had higher accuracy compared to the SVR model. In estimation of soil organic carbon, PLSR method and MSC preprocessing method had the best performance (R2VAL=0.59, RMSEVAL=0.19 and PRDVAL=1.47) and the second derivative method had the least performance (R2VAL=0.15, RMSEVAL=0.27 and PRDVAL=0.52). Also for estimation of total neutralizing value, the first derivative preprocessing method had the best performance (R2VAL=0.78, RMSEVAL=5.70 and PRDVAL=2.01) and the second derivative method had the least performance (R2VAL=0.1, RMSEVAL=11.13, and PRDVAL=0.31). The key wavelengths were observed for soil organic matter in the range of 421- 612 nm and for total neutralizing value in the range of 2315- 2151 nm. This study showed that the Vis-NIR spectroscopy method, due to its physical basis and considering the influencing factors, as a large-scale model, makes it possible to evaluate and predict soil OC and TNV.

Soil texture is one of the majorphysical properties of soils thatplays important roles inwater holding capacity, soil fertility, environmental quality and agricultural developments. Measurement of soil texture elements in large scales is time consuming and costly due to the high volume of sampling and laboratory analysis. Therefore, assessing and using simple, quick, low-cost and advanced methods such as soil spectroscopy can be useful. The objectives of this study were to examine two statistical models of Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) to estimate soil texture elements using Visible and Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) reflectance spectroscopy (400-2450nm).
A total of 120 composite soil samples (0-10 cm) were collected from the Kafemoor basin (55º 15' - 55º 25' E; 28º 51' - 29º 11' N), Sirjan, Iran. The samples were air dried and passed through a 2 mm sieve and soil texture components were determined by the hydrometer method (Miller and Keeny 1992). Reflectance spectra of all samples were measured using an ASD field-portable spectrometer in the laboratory. Soil samples were divided into two random groups (80% and 20%) for calibration and validation of models. PLSR and PCR models and different pre-processing methods i.e.First (FD) and Second Derivatives (SD), Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) were applied and compared to estimate texture elements. The cross‐validation method was used to evaluate calibration and validation sets in the first part (80%) and coefficient of determination (R2), Root Mean Square Error (RMSE) and Residual Prediction Deviation (RPD) were also calculated. For testing predictive models, the second part of data (20%) was used and R2 and RMSE of predictive accuracy were calculated.
The results of applying two statistical models for estimatingLogClay (%) showed that R2of calibration (R2CV) and validation (R2VAL) datasetranged from 0.22 to 0.72 and 0.12 to 0.54, respectively. The lowest RMSE was computed for PLSR model with SD pre-processing. The highest RPD of calibration (RPDCV) and validation (RPDVAL) were obtained for PLSR with SD pre-processing technique which was classified as a very good and good model, respectively. The results indicated possible prediction of soil clay content by using PCR model with SD pre-processing techniques. In addition, the PCR predicted soil texture elements poorly according to RPD values while the PLSR model with SD pre-processing was the best model for predict¬ing soil clay content. The R2CV and R2VAL of PLSR models for LogSilt (%) varied from 0.34 to 0.73 and 0.27 to 0.58, respectively. The RMSECV varied from 0.14 for FD pre-processing to 0.23 for no-preprocessing and the RMSEVAL rangedbetween 0.18 and0.24. The highest RPDCV (2.07) and RPDVAL (1.59) were obtained for PLSR with FD pre-processing which were classified as very good and good models, respectively. The results of PCR model developments for estimating LogSilt (%) indicated that the highest RPDCV and RPDVAL were, respectively, 1.31 and 1.25 for MSC pre-processing techniques which were rated as poor models. On the contrary to PLSR models, PCR models were not reliable for predicting LogSilt (%).Theresultsof PLSR models for estimatingLogSand (%) revealedthat the highest R2CV and R2VAL were 0.56 and 0.47, respectively and the lowest RMSECV and RMSEVAL were 0.14 and 0.16, respectively which were obtained for SD pre-processing. The RPDCV and RPDVAL values for SD pre-processing in PLSR model were 1.59 and 1.39 which were rated as good and poor performance of predictions, respectively. The highest RPDCV and RPDVALfor PCR models were obtained with the MSC pre-processing indicating poor model. Therefore, PLSR model with SD pre-processing techniques was superior model for estimation of LogSand(%).Overall, PLSR model with SD pre-processing techniques performed better in estimatingclay and sand and PLSR model with FD pre-processing gave better estimate of silt content.
Our finding indicated thatclay and silt contentcan be estimated by using electromagnetic spectrum between VNIR-SWIR region. Further, spectroscopy could be considered as a simple, fast and low cost method in predicting soil texture and PLSR model with SD and FD pre-processing seems to be more robust algorithm to estimateLogClay and LogSilt, respectively.

Due to substantial impact of humic substances on a variety of environmental processes, knowledge of the chemical, spectroscopic and structural properties of these substances, are aspects that should always be considered in studies related to the management and remediation of impacted ecosystems. In this study, after from extraction and purification of humic acid (HA) from low grade coals of IRAN, was used from gravimetric technic in order to determination of degree of purity of HA, from non-destructive spectroscopies such as ICP-OES, UV-VIS, FT-IR and CHNOS for measuring of quality and quantity of HA as well as from microstructures of SEM in order to structural study of HA and obtained results were compared with standard Aldrich HA. Results of gravimetric analysis showed that degree of purification of produced HA is 98%. Also, In point of quantity, results of elemental analysis of ICP-OES showed that after from purification of HA, the concentration of various heavy metals in HA are lower from recommended safe limit and there have macro and micronutrients at ranges in HA structure. Also, quality results of FT-IR, UV-VIS and SEM analysis, shown well presence of an aromatic predominant structure and rich from carboxylic and phenolic functional groups with high humification and condensation degree and A type with a loose spongy and heterogeneous porous surfaces for HA. CHNOS analysis also confirmed the suitability of the coal used to extract HA, and showed that the final product is in accordance with the standard HA results.

Vis-NIR spectroscopy has been introduced as a non-destructive, fast, and cheap technique, with minimal sample preparation and no loss or damage to the environment. No investigation has yet been carried out to examine the ability of this method to estimate soil properties in Iran. The objective of this research was to investigate the capability of Vis-NIR spectroscopy to predict the amount of organic matter, carbonate and gypsum in surface soils of Isfahan province. A total of 248 surface soil samples were collected from the study area. Soil organic matter content, gypsum and carbonates percentages were measured by standard laboratory methods. Soil spectral analyses were performed by a field spectrometer using 350-2500 nm wavelength range. Different pre-processing methods were evaluated after recording the spectra. Partial least squares regression was used to predict soil parameters. R2 values for organic matter, carbonates and gypsum were 0.61, 0.45 and 0.8, respectively. Based on RPD (Ratio of Prediction to Deviation) values, the precision of prediction model for gypsum was quite good, and acceptable for organic matter, whereas the prediction of the model for soil carbonates was poor. Consequently, vis-NIR spectroscopy is capable of predicting some soil properties simultaneously and the model accuracy is acceptable.