Using Visible-Near Infrared Spectroscopy to Predict Soil Properties of Mugan Plain, Azerbaijan (original) (raw)
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2020
This study aimed to predict soil properties using visible–near infrared (VIS-NIR) spectroscopy combined with partial least square regression (PLSR) modeling. Special emphasis was given to evaluating effect of pre-processing methods on prediction accuracy and important wavelengths. A total of 114 samples were collected and involved in chemical and spectral analyzes. PLSR model of each soil property was calibrated for all pre-processing methods using all samples, and leave-one-out cross-validation was used to make comparisons between them. Then, PLSR model of each best pre-processing method was calibrated using a 75% of all samples and correspondingly validated with the remaining a 25%. Model accuracy was evaluated based on coefficient of determination (R2), root mean-squared errors (RMSE), and residual prediction deviations (RPD). The high correlation coefficients were found between the tested soil properties and reflectance spectra. The pre-processing methods considerably improved pr...
Evaluating near infrared spectroscopy for field prediction of soil properties
This paper demonstrates the application of near infrared diffuse reflectance spectroscopy (NIR-DRS) measurements as part of digital soil mapping. We also investigate whether calibration functions developed from a spectral library can be used for rapid characterisation of soil properties in the field. Soil samples were collected along 24 toposequences in the Pokolbin irrigation district,~7 km 2 of predominantly agricultural land in the Hunter Valley, NSW, Australia. Soil samples at 2 depths: 0-0.10 and 0.40-0.50 m were collected. The soil samples were scanned using NIR under 3 different conditions: field condition, dried unground, and dried ground. A separate spectral library containing soil laboratory measurements was used to develop functions to predict 3 main soil properties from NIR spectra (total C content, clay content, and sum of exchangeable cations). The absorbance spectra were found to be different for the 3 soil conditions. The field spectra appear to have higher absorbance, followed by dried unground samples and then dried ground samples. Although most spectral signatures or peaks were similar for the 3 soil conditions, field samples appear to have higher absorbance, particularly at 1400 nm and 1900 nm. The convex hull of the first 2 principal components of the soil spectra is an easy tool to evaluate the similarity of spectra from a calibration set to an observation. For field prediction, samples need to be calibrated using field samples. Finally, this study shows that NIR-DRS measurement is a useful part of digital soil mapping.
Journal of Arid Environments, 2010
Reflectance spectroscopy can be used to nondestructively characterize materials for a wide range of applications. In this study, visible-near infrared reflectance spectroscopy (VNIR) was evaluated for prediction of diverse soil properties related to four different soil series of the Entisol soil group within a single field in northern Turkey. Soil samples were collected from 512 locations in a 25 Â 25 m sampling grid over a 32 ha (800 Â 400 m) area. Air-dried soil samples were scanned at 1 nm resolution from 350 to 2500 nm, and calibrations between soil physical and chemical properties and reflectance spectra were developed using cross-validation under partial least squares regression (PLSR) and multivariate adaptive regression splines (MARS). Raw reflectance and first derivative reflectance data were used separately and combined for all samples in the data set. Data were additionally divided into two random subsets of 70 and 30% of the full data, which were each used for calibration and validation. Overall, MARS provided better predictions when under cross-validation. However, PLSR and MARS results were comparable in terms of prediction accuracy when using separate data sets for calibration and validation. No improvement was obtained by combining first derivative and raw data. Strongest correlations were obtained with exchangeable Ca and Mg, cation exchange capacity, and organic matter, clay, sand, and CaCO 3 contents. When soil data were classified into groups, VNIR spectroscopy estimated class memberships well, especially for soil texture. In conclusion, VNIR spectroscopy was variably successful in estimating soil properties at the field scale, and showed potential for substituting laboratory analyses or providing inexpensive co-variable data.
Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties
2011
Reflectance spectroscopy can be used to nondestructively characterize materials for a wide range of applications. In this study, visible-near infrared reflectance spectroscopy (VNIR) was evaluated for prediction of diverse soil properties related to four different soil series of the Entisol soil group within a single field in northern Turkey. Soil samples were collected from 512 locations in a 25 Â 25 m sampling grid over a 32 ha (800 Â 400 m) area. Air-dried soil samples were scanned at 1 nm resolution from 350 to 2500 nm, and calibrations between soil physical and chemical properties and reflectance spectra were developed using cross-validation under partial least squares regression (PLSR) and multivariate adaptive regression splines (MARS). Raw reflectance and first derivative reflectance data were used separately and combined for all samples in the data set. Data were additionally divided into two random subsets of 70 and 30% of the full data, which were each used for calibration and validation. Overall, MARS provided better predictions when under cross-validation. However, PLSR and MARS results were comparable in terms of prediction accuracy when using separate data sets for calibration and validation. No improvement was obtained by combining first derivative and raw data. Strongest correlations were obtained with exchangeable Ca and Mg, cation exchange capacity, and organic matter, clay, sand, and CaCO 3 contents. When soil data were classified into groups, VNIR spectroscopy estimated class memberships well, especially for soil texture. In conclusion, VNIR spectroscopy was variably successful in estimating soil properties at the field scale, and showed potential for substituting laboratory analyses or providing inexpensive co-variable data.
Evaluation of soil quality for agricultural production using visible–near-infrared spectroscopy
Soil quality (SQ) assessment has numerous applications for agricultural management. Conventional quantification of SQ is based on laboratory analysis and integrative indices that can be costly and time consuming to obtain. A rapid, quantitative method using soil spectra, following the successful process of soil characterization by visible (VIS)-near infrared (NIR) spectroscopy, can provide a robust approach for soil monitoring. Objective: To predict specific soil indicator properties and soil quality indices for the productive function of the soil using VIS-NIR spectroscopy, and to evaluate the suitability of spectral data for assessing and monitoring the impact of arable and grassland management in a temperate maritime climate. Methods: The study used 40 sites in Ireland under both arable (n = 20) and grassland (n = 20) management systems. Specific indicators and soil quality indices (SQIs) identified by Askari and Holden and Askari were used as the reference standard for estimation using VIS-NIR spectra. Partial least-squares regression was used to predict the indicators and SQIs. SQI was predicted from both spectrally derived indicator values and directly from the soil spectra, and accuracy was assessed by comparison with laboratory and field derived measurements. Results: The indicators of SQ could be predicted with excellent (soil organic carbon and carbon to nitrogen ratio in grassland soils; total nitrogen, carbon to nitrogen ratio, extractable magnesium and aggregate size distribution in arable soils), good (bulk density of b 2 mm fraction in grassland soils) and moderate (penetration resistance, soil respiration and bulk density in arable soils) accuracy. The SQIs were predicted directly with excellent accuracy under grassland (RPD = 3.04, R 2 = 0.92, RMSE = 0.03) and arable (RPD = 2.78, R 2 = 0.89, RMSE = 0.04) management. Soil structural quality class, management type and management intensity were differentiable by their characteristic reflectance. Conclusion: The reliability of SQI and key indicators of soil quality, and the ability to differentiate by management practices and soil structural quality confirmed the efficiency of VIS-NIR spectroscopy for monitoring and evaluating SQ as a reliable alternative to conventional laboratory methods. Practice: Spectroscopy has the potential to provide a reliable approach that will allow rapid, low cost, high frequency SQ monitoring for multiple purposes, and can play an important role in sustainable land management. Implications: VIS-NIR spectroscopy was shown to be suitable for quantitative assessment of soil quality, thus paving the way for development of an applied tool that can be used for agricultural management on the context of soil security, soil health, soil protection and soil quality.
Korean Journal of Soil Science and Fertilizer, 2014
This study investigates the prediction of soil chemical properties (organic matter (OM), pH, Ca, Mg, K, Na, total acidity, cation exchange capacity (CEC)) on 688 Korean soil samples using the visible-near infrared reflectance (VIS-NIR) spectroscopy. Reflectance from the visible to near-infrared spectrum (350 to 2500 nm) was acquired using the ASD Field Spec Pro. A total of 688 soil samples from 168 soil profiles were collected from 2009 to 2011. The spectra were resampled to 10 nm spacing and converted to the 1st derivative of absorbance (log (1/R)), which was used for predicting soil chemical properties. Principal components analysis (PCA), partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil chemical properties. The regression rules model (Cubist) showed the best results among these, with lower error on the calibration data. For quantitatively determining OM, total acidity, CEC, a VIS-NIR spectroscopy could be used as a routine method if the estimation quality is more improved.
Soil characterization by near-infrared spectroscopy and principal component analysis
REVISTA CIÊNCIA AGRONÔMICA, 2021
This research aimed to use principal component analysis (PCA) as an exploratory method for spectral data of soil absorbance from the Baturité Massif and Central Hinterland (Ceará State, Brazil) to verify the potential of the technique in soil characterization. We analyzed 46 soil samples from different areas (native and cultivated). Each sample was analyzed in two particle sizes: 2 and 0.2 mm. We obtained spectral data by near-infrared spectroscopy (NIR), selecting the 1,360-2,260 nm range (2,376 variables). We evaluated three data pretreatment methods: multiplicative scatter correction (MSC), first derivative, and second derivative of the Savitzky-Golay filter. The absorption bands observed were: 1,414 nm (C-H stretching and deformation combination), 1,450 nm (O-H associated with the carbon chain), 1,780 nm (second overtone of C-H), 1,928 nm (O-H associated with molecular water), and 2,208 nm (C-H stretch and C=O combination). The best pretreatment was verified using only the multiplicative scatter correction (MSC). Two principal components explained 98% of the data variability, being the first principal component (PC1) related to the characteristic band of moisture, with negative values in the 1,928 nm region, while the second principal component (PC2) was related to the total organic matter (OM) originating from the C-H, C=O, and N-H bonds, wavelength region 1,414 nm. The PCA allowed characterizing the samples in terms of moisture and OM contents, with emphasis on soils under irrigated agroforestry system with higher values of moisture and OM, while the soil in degradation process presented lower values for these attributes. The NIR spectroscopy, associated with data processing methods (PCA and MSC), allows identifying changes in soil attributes, such as moisture and OM.
Prediction of Soil Sand and Clay Contents via Visible and Near-Infrared (Vis-NIR) Spectroscopy
2017
Visible and near infrared (Vis-NIR) spectroscopy is a non-destructive analytical method that can be used to complement, enhance or potentially replace conventional methods of soil analysis. The aim of this research was to predict the particle size distribution (PSD) of soils using a Vis-NIR) spectrophotometry in one irrigate field having a vertisol clay texture in the Karacabey district of Bursa Province, Turkey. A total of 86 soil samples collected from the study area were subjected to optical scanning in the laboratory with a portable, fiber-type Vis-NIR spectrophotometer (AgroSpec, tec5 Technology for Spectroscopy, Germany). Before the partial least square regression (PLSR) analysis, the entire reflectance spectra were randomly split into calibration (80%) and validation (20%) sets. A leave-one-out cross-validation PLSR analysis was carried out using the calibration set with Unscrambler (R) software, whereas the model prediction ability was tested using the validation (prediction...
Rapid estimation of soil engineering properties using diffuse reflectance near infrared spectroscopy
Biosystems Engineering, 2014
Kenya Materials testing involve complex reference methods and several soil tests have been used for indexing material functional attributes for civil engineering applications. However, conventional laboratory methods are expensive, slow and often imprecise. The potential of soil diffuse reflectance near infrared (NIR) spectroscopy for the rapid estimation of selected key engineering soil properties was investigated. Two samples sets representing different soils from across the Lake Victoria basin of Kenya were used for the study: A model calibration set (n ¼ 136) was obtained using a conditioned Latin hypercube sampling, and a validation set (n ¼ 120) using a spatially stratified random sampling strategy. Spectral measurements were obtained for air-dried (<2 mm) soil sub-samples using a Fouriertransform diffuse reflectance near infrared (NIR) spectrometer. Soil laboratory reference data were also obtained for liquid limit (LL), plastic limit (PL), plasticity index (PI), linear shrinkage (LS), coefficient of linear extensibility (COLE), volumetric shrinkage (VS), clay activity number (Ac), total clay content, air-dried moisture content, and cation exchange capacity (CEC). Soil reference data were calibrated to smoothed first derivative NIR spectra using partial least squares (PLS) regression. At the calibration stage, coefficient of determination for full cross-validation (R 2 ) of !0.70 was obtained for CEC, mc, LL, PI, LS, COLE and VS. Further independent validation gave R 2 ! 0.70 and RPD (ratio of reference data SD and root mean square error of prediction) 1.7e2.2 for LL, PI, mc and CEC. The results suggested that NIRePLS has potential for the rapid estimation of several key soil mean square error of cross-validation; RMSEP, root mean square error of prediction; RPD, ratio of standard deviation of reference values to RMSECV/RMSEP; SOC, soil organic carbon. ScienceDirect journal homepa ge: www .e lsev ie r.com/locate/issn/153 75110 b i o s y s t e m s e n g i n e e r i n g 1 2 1 ( 2 0 1 4 ) 1 7 7 e1 8 5 http://dx.
Current Analytical Chemistry, 2012
This study demonstrated the use of visible-near infrared (vis-NIR) reflectance spectroscopy and partial least squares regression (PLSR) for the effective analysis of important properties of Mediterranean soils from southern Italy. Understanding soil properties is an essential pre-requisite for sustainable land management. Assessment of these properties has long been gained through conventional laboratory analysis, which is considered costly and time consuming. Therefore, there is a need to develop alternative cheaper and faster techniques for soil analysis. In recent years, special attention has been given to vis-NIR reflectance spectroscopy and chemometrics. In this study we evaluated the potential of vis-NIR spectroscopy and PLSR for prediction of chemical and physical properties [sand, silt and clay, organic carbon (OC), total nitrogen (N), cation exchange capacity (CEC), and calcium carbonate (CaCO 3 )] of soils representative of three Mediterranean agro-ecosystems from the Campania region, southern Italy. We performed the analysis for each agroecosystem separately (local predictions) and for the combined ones (regional prediction). PLSR is one of the most popular modelling techniques used in chemometrics and is commonly used for quantitative spectroscopic analysis. We derived PLSR models, which were validated using an independent subset of data that was not used in the modelling. The accuracy of the calibrations and validations for the different soil properties were assessed using the root mean squared error (RMSE) and the relative percent deviation (RPD). Our results showed that regional and local predictions are from very good to excellent for OC (RPD of validation = 2.36 ÷ 3.03) and clay content (RPD = 2.31 ÷ 2.95). For the remaining properties, RPD values ranged from 1.40 ÷ 2.07 (poor/fair-very good), for regional predictions, to 1.10 ÷ 2.33 (poor-very good), for local predictions.