Visible And Near-infrared Spectra Research Articles

Rapid quantitative characterization of tea seedlings under lead-containing aerosol particles stress using Vis-NIR spectra

The problem of excessive lead content in tea has become more and more serious with the development of society and industry. This paper investigated the ability of visible and near-infrared (Vis-NIR) spectroscopy to evaluate foliar lead uptake by tea plants through simulating real air pollution. Lead content of tea leaves in different treatment groups during stress time was measured by inductively coupled plasma mass spectrometry (ICP-MS). It was determined that stomata can be a channel for lead particles in the air and most of the lead entering through the stomata accumulates in the leaves. The spectral variation of treated samples was measured, and it was found that a combination of partial least squares-discriminant analysis (PLS-DA) and spectral responses can perfectly classify the tea samples under different lead concentrations stress with an overall accuracy of 0.979. Then the Vis-NIR spectra were used for fast monitoring physiological and biochemical indicators in tea leaves under atmospheric deposition. Relevant spectra pretreatment methods and characteristic wavelength selection approaches were evaluated for quantitative analysis and then optimal prediction models to instantly detect quality indicators in tea samples were built. Among predictive models, PLS had the best results (RMSE = 0.139 mg/g, 0.663 mmol/g, and 1.494 μmol/g) for the prediction of chlorophyll a (Chl-a), ascorbic acid (ASA), and glutathione (GSH), respectively. Also, principal component regression (PCR) gave the best results (RMSE = 0.053 mg/g, 0.024 mg/g, and 0.011%) for prediction of chlorophyll b (Chl-b), carotenoid (Car) and moisture content (MC), respectively. Results of this study can be applied for developing an effective and reliable approach for monitoring atmospheric deposition in plants.

Estimation of komatsuna freshness using visible and near-infrared spectroscopy based on the interpretation of NMR metabolomics analysis

The objective of this study was to explore the potentiality and mechanism of visible and near-infrared (Vis-NIR) spectroscopy in estimating the freshness of komatsuna. We monitored the cumulative CO2 production of komatsuna stored under different conditions as a freshness indicator and measured the Vis-NIR spectra of komatsuna as the predictor. Using the informative wavelengths (IW) selected using the stepwise selectivity ratio method, we constructed an accurate freshness prediction model through PLSR analysis. The IW in the visible region were attributed to pigments such as chlorophyll. In the NIR region, ten amino acids were identified as directly or indirectly contributing to the IW and were highly related to freshness. They were confirmed on the basis of the strong correlations between the informative NIR signals and NMR signals, which were determined using statistical heterospectroscopy. The results demonstrate the feasibility of Vis-NIR spectroscopy in estimating the freshness of komatsuna using the IW.

Nondestructive monitoring of polyphenols and caffeine during green tea processing using Vis-NIR spectroscopy.

Increasing consumption of green tea is attributed to the beneficial effects of its constituents, especially polyphenols, on human health, which can be varied during leaf processing. Processing technology has the most important effect on green tea quality. This study investigated the system dynamics of eight catechins, gallic acid, and caffeine in the processing of two varieties of tea, from fresh leaves to finished tea. It was found that complex biochemical changes can occur through hydrolysis under different humidity and heating conditions during the tea processing. This process had a significant effect on catechin composition in the finished tea. The potential application of visible and near‐infrared (Vis‐NIR) spectroscopy for fast monitoring polyphenol and caffeine contents in tea leaves during the processing procedure has been investigated. It was found that a combination of PCA (principal component analysis) and Vis‐NIR spectroscopy can successfully classify the two varieties of tea samples and the five tea processing procedures, while quantitative determination of the constituents was realized by combined regression analysis and Vis‐NIR spectra. Furthermore, successive projections algorithm (SPA) was proposed to extract and optimize spectral variables that reflected the molecular characteristics of the constituents for the development of determination models. Modeling results showed that the models had good predictability and robustness based on the extracted spectral characteristics. The coefficients of determination for all calibration sets and prediction sets were higher than 0.862 and 0.834, respectively, which indicated high capability of Vis‐NIR spectroscopy for the determination of the constituents during the leaf processing. Meanwhile, this analytical method could quickly monitor quality characteristics and provide feedback for real‐time controlling of tea processing machines. Furthermore, the study on complex biochemical changes that occurred during the tea processing would provide a theoretical basis for improving the content of quality components and effective controlling processes.

Assessment of total antioxidant capacity of altiplano colored quinoa (Chenopodium quinoa willd) by visible and near-infrared diffuse reflectance spectroscopy and chemometrics

The aim of this study was to predict the total antioxidant capacity (TAC) of altiplano colored quinoa using visible and near-infrared (Vis-NIR) spectral data and chemometrics. In colored quinoa, at least part of the phenolic compounds are included in the non-extractable fraction and therefore the TAC should be measured by a non-conventional QUick, Easy, New, CHEap and Reproducible procedure (QUENCHER). In this work, QUENCHER using 2,2-diphenyl-1-picrylhydrazyl (DPPH) returned a value 1.03–7.03 times higher than the extractable antioxidant capacity, and it represented 2.8%–85.8% of the TAC of altiplano colored quinoa. We applied partial least squares regression and ten-fold cross-validation to the raw and pre-processed NIR spectra for the calibration and validation of models, to predict the TAC of altiplano colored quinoa. The best model was obtained for whole quinoa seeds as opposed to ground samples (calibration: R2 = 0.97, RSME = 2.9; validation: R2 = 0.73, RSME = 8.6) using Savitzky-Golay smoothing, the D-trend method and multiplicative scatter correction procedures. The QUENCHER-DPPH result was also positively correlated with extractable antioxidant capacity (EAC-DPPH), total free phenolics, total betalain content and L values (p ≤ 0.001). Therefore, we demonstrate that the TAC of colored quinoa seeds, including both non-extractable and extractable antioxidants, can be predicted by Vis-NIR spectra coupled to chemometrics.

Exploring relevant wavelength regions for estimating soil total carbon contents of rice fields in Madagascar from Vis-NIR spectra with sequential application of backward interval PLS

ABSTRACT Laboratory visible and near-infrared (Vis-NIR) spectroscopy with partial least squares (PLS) regression can be used to determine the soil carbon (C) content, and the waveband selection procedures can refine the predictive ability. However, individually selected wavebands are not always the same depending on the location, scale, and approach. To simplify the variable selection issue, some methods for selecting wavelength regions instead of individual wavebands have been proposed. In this study, we explore relevant wavelength regions for predicting the total carbon (TC) content of lowland and upland soils in Madagascar from Vis-NIR spectroscopy using a dynamic version of backward interval PLS (biPLS) regression. The predictive ability of dynamic biPLS was compared with that of standard full-spectrum PLS (FS-PLS) using the cross-validated coefficient of determination (R 2), root mean squared error (RMSE), and ratio of performance to interquartile distance (RPIQ). The biPLS models using reflectance (R 2 = 0.877, RMSE = 0.690) and first derivative reflectance (FDR) (R 2 = 0.940, RMSE = 0.494) data sets showed better predictive accuracy than the FS-PLS models using reflectance (R 2 = 0.826, RMSE = 0.809) and FDR (R 2 = 0.933, RMSE = 0.518) data sets, the spectral efficiency was improved. By using biPLS to predict soil TC, the model was simplified by using only four selected wavelength regions in the reflectance (400–490, 1402–1440, 1846–1980 and 2151–2283 nm) and FDR (652–687, 1322–1443, 1856–1985, and 2290–2400 nm) data sets, which yielded reliable (RPIQ > 2.5) predictions.

Lifting wavelet transform for Vis-NIR spectral data optimization to predict wood density.

Visible and near infrared (Vis-NIR) spectroscopy is a mature analytical tool for qualitative and quantitative analysis in various sectors. However, in the face of "curse of dimensionality" due to thousands of wavelengths for a Vis-NIR spectrum of a sample, the complexity of computation and memory will be increased. Additionally, variable optimization technique can be used to improve prediction accuracy through removing some irrelevant information or noise. Wood density is a critical parameter of wood quality because it determines other important traits. Accurate estimation of wood density is becoming increasingly important for forest management and end uses of wood. In this study, the performance of two-dimensional (2D) correlation spectroscopy between wavelengths of various spectral transformations, i.e., reflectance spectra (R), reciprocal (1/R), and logarithm spectra (log (1/R)), were analyzed before optimizing spectral variable. The spectra of optimal transformation were decomposed using biorthogonal wavelet family from 3rd to 8th decomposition level based on lifting wavelet transform (LWT). The optimal wavelet coefficients of LWT were selected based on the performance of calibration set using partial least squares (PLS). Two frequent variable selection methods including uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS) were also compared. The results showed that the dimensionality of spectral matrix was reduced from 2048 to 16 and the best density prediction results of Siberian elm (Ulmus pumila L.) were obtained (Rp2R=0.899, RMSEP=0.016) based on LWT.

VisNIR integrated multi-sensing penetrometer for in situ high-resolution vertical soil sensing

An in situ penetrometer system that can measure profile soil properties rapidly, cost-effectively, and at high vertical resolution would benefit the soil science and agriculture communities. A visible and near infrared (VisNIR) integrated multi-sensing penetrometer system was developed to automatically measure in situ soil VisNIR reflectance spectra, penetration resistance, and insertion depth along a soil profile. The system was tested in 11 agricultural fields in Nebraska, Illinois, Iowa, and South Dakota of the U.S. An independent soil VisNIR spectral library was used to build calibration models for soil property prediction. External Parameter Orthogonalization (EPO) was used to correct for the field intactness of in situ VisNIR spectra. The results showed that EPO was effective in correcting for the spectral disparity between in situ and dry-ground VisNIR spectra. The EPO correction showed an improvement of prediction accuracy of soil total carbon (R2 and RMSE improved from 0.29 and 3.06 % to 0.5 and 0.79 %, respectively) and total nitrogen (R2 and RMSE improved from 0.51 and 0.36 % to 0.62 and 0.06 %, respectively). The system also predicted soil bulk density with an RMSE of 0.12 g cm−3 and R2 of 0.80. It is concluded that the VisNIR multi-sensing penetrometer, along with the use of external soil spectral libraries and the spectral correction algorithm EPO, can lead to a rapid, robust and cost-effective system for in situ high resolution vertical soil sensing.

Laboratory Raman and VNIR spectroscopic studies of jarosite and other secondary mineral mixtures relevant to Mars

AbstractJarosite is an indicator of ephemeral, acidic fluids in Martian history, which often occurs with some other secondary minerals based on Mars in situ and remote sensing detections. However, detailed Raman and visible and near‐infrared (VNIR) spectroscopic studies of jarosite and other secondary mineral mixtures relevant to Mars still lack, which are essential for jarosite identifications and hence a better understanding of Martian aqueous history. In this study, we prepared 44 binary mixtures of jarosite with four other secondary minerals (alunite, gypsum, kaolinite, and montmorillonite), and characterized their detailed Raman and VNIR spectral features. We find the Raman spectral patterns of jarosite, alunite and gypsum are distinctive and ready for their identification in mixtures, although mixtures with montmorillonite and kaolinite show high fluorescence background and their Raman spectral features are obscured by strong and sharp peaks of jarosite. The normalizing strengths of their characteristic Raman spectral features (e.g., ν1 for jarosite) change with jarosite mass fractions, and mineral distribution maps and volume percentages of endmembers are acquired using Raman imaging method. The band depths of absorption features in VNIR spectra are also correlated with jarosite mass proportions, and four models are proposed to evaluate jarosite mass fractions in the mixtures using a partial least squares algorithm. These detailed spectral properties of mixtures and models would provide valuable information for in situ Raman investigations on Mars (e.g., ExoMars and Mars 2020) and orbital VNIR remote sensing detections (e.g., Compact Reconnaissance Imaging Spectrometer for Mars).

EXPLORE THE CHARACTER OF SOIL SPECTRAL REFLECTANCE RELATE TO THE SOIL ORGANIC MATTER CONTENT

ABSTRACTThe visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy has emerged as a rapid and low-cost tool for extensive investigation of soil properties. The objective of this research was to explore how significant the relationship between the soil spectral reflectance and soil organic matter (SOM) content. Some soil samples in Yogyakarta were taken for SOM content and spectroscopy measurement. The SOM was analyzed using Walkley and Black method, while the spectral reflectance was determined using ASD Field-spectrophotometer by scanned the sample with Vis-NIR spectrum. Pearson’s coefficient showed that there was a strong negative correlation between SOM and soil spectral of certain wavelengths. Soil with less organic matter content performed high reflectance. Keywords: Soil organic matter; Vis-NIR spectroscopy; soil reflectance; Pearson’s correlation coefficient.

Estimation of andic properties from Vis-NIR diffuse reflectance spectroscopy for volcanic soil classification

Volcanic soils show peculiar characteristics related to the presence of poorly ordered crystalline minerals. These minerals are highly sensitive to specific spectral bands in the visible and near-infrared (Vis-NIR) regions and could be used to establish important relationships between andic properties and soil classification. In order to overcome the expense of traditional soil laboratory analysis and the limitations of pedotransfer functions, this research was designed to test i) the possibility of using diffuse reflectance spectra (DRS) for a supervised chemometric classification of soils with different degrees of andicity, ii) the possibility of using diffuse reflectance spectra (DRS) to estimate the andic properties needed for soil classification (iron and aluminum forms, allophane, phosphate retention, vitric content etc.), and iii) the different multivariate statistical approaches for the analysis of spectra. Diffuse reflectance spectra were measured between 200 and 2500 nm in the laboratory with a Jasco spectrophotometer equipped with an integrated sphere.Chemometric analysis was carried out by discriminant analysis that correctly divided 86% of the horizon samples in 5 classes, representing different levels of expression of the andic properties. Chemometric calibration was obtained by pre-processing Vis-NIR spectra with the application of partial least-squares regression (PLSR). Andic soil attributes were then predicted using calibrated models through PLSR methods and compared with the findings from the Supported Vector Machine (SVM). SVM generally outperformed PLSR on predicting andic properties by Vis-NIR. The most accurate predictive models were obtained for Al and Fe dithionite extracted, both with PLSR (ratio of performance to deviation, RPD 1.9) and SVM (1.7 < RPD < 1.9). The total amount of i) non-crystalline Al and Fe forms (oxalate extractable, Alo, Feo), ii) Alo + 1/2Feo (1.7 < RPD < 1.8), iii) non-crystalline Si (Sio) (RPD 1.5), and iv) Al associated with organic matter (sodium pyrophosphate extractable, Alp; RPD 1.5), Alo-Alp (RPD 1.5), allowed a first soil Andosol qualification, when obtained through SVM. Therefore, the results of this research highlight that Vis-NIR can be used as a preliminary analysis to estimate diagnostic parameters of Andosols. Results indicate that Vis-NIR is a promising technique for volcanic soil classification according to soil properties (especially Fe and Al forms) and horizons, which can be used in lieu of complex chemical and physical analyses involved in routine soil profile classification. Further studies should confirm Vis-NIR ability to discriminate Andosols at more detailed classification levels.

Laboratory synthesis and spectroscopic studies of hydrated Al-sulfates relevant to Mars

Orbital remote sensing has recently identified alunite as one of the types of Al-sulfate on Mars. As other types of hydrated Al-sulfates may also exist abundantly in the soil and rocks on Mars, it is important to perform systematic experimental investigations of the spectral characterization of hydrous Al-sulfates to identify and determine their potential distribution on Mars. We successfully used the humidity buffer technique to synthesize five alunogen series of Al-sulfates, Al2(SO4)3·xH2O (x = 0, 4, 8, 12, 14) and AlH(SO4)2·4H2O, with different degrees of hydration. X-ray diffraction (XRD) was used to identify them from the PDF 2004 database, except for the specie with 12 structural water molecules; the quantity of structural water in the latter was confirmed by thermogravimetry and differential scanning calorimetry measurements after heating to >500 °C. Raman, mid-infrared (MIR), and visible and near-infrared (VNIR) spectra were acquired to evaluate vibrational spectroscopic properties related to crystal structure. The prominent ν1 modes of SO4 tetrahedra of six Al-sulfates from Raman and MIR spectra have obvious shifts to higher wavenumbers (from 993.4 to 1133.6 cm−1) with decrease in hydration states. With no absorption bands from 250 to 1000 nm, all absorption features of the VNIR spectra of alunogen series of Al-sulfates are derived from overtones and combinations of fundamental vibrational modes from OH/H2O and SO4 groups, generally showing a red shift toward longer wavelengths with increasing hydration states. The XRD, Raman, MIR, and VNIR spectroscopic data of these Al-sulfates can provide crucial data supporting their identification for future remote sensing and in situ detection on Mars.

Combining Environmental Factors and Lab VNIR Spectral Data to Predict SOM by Geospatial Techniques

Soil organic matter (SOM) is an important parameter related to soil nutrient and miscellaneous ecosystem services. This paper attempts to improve the performance of traditional partial least square regression (PLSR) model by considering the spatial autocorrelation and soil forming factors. Surface soil samples (n = 180) were collected from Honghu City located in the middle of Jianghan Plain, China. The visible and near infrared (VNIR) spectra and six environmental factors (elevation, land use types, roughness, relief amplitude, enhanced vegetation index, and land surface water index) were used as the auxiliary variables to construct the multiple linear regression (MLR), PLSR and geographically weighted regression (GWR) models. Results showed that: 1) the VNIR spectra can increase about 39.62% prediction accuracy than the environmental factors in predicting SOM; 2) the comprehensive variables of VNIR spectra and the environmental factors can improve about 5.78% and 44.90% relative to soil spectral models and soil environmental models, respectively; 3) the spatial model (GWR) can improve about 3.28% accuracy than MLR and PLSR. Our results suggest that the combination of spectral reflectance and the environmental variables can be used as the suitable auxiliary variables in predicting SOM, and GWR is a promising model for predicting soil properties.

Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe.

Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343–2222 nm), apparent electrical conductivity (ECa), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, ECa, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties.

Hyperspectral sensing of soil pH, total carbon and total nitrogen content based on linear and non-linear calibration methods

Soil properties can be estimated non-destructively by visible and near infrared (VNIR) reflectance spectroscopy. However, results of calibration models differ in dependence of measurement precision, spectral range, variability of soil properties and calibration methods used for prediction. The objective of research was to estimate the ability of hyperspectral VNIR sensing for field-scale prediction of soil pH, total carbon (TC %) and total nitrogen (TN %) content in arable Stagnosols. Total of 200 soil samples taken from field experiment (soil depth: 30 cm; sampling grid: 15x15 m; 2016) were scanned in laboratory using portable spectroradiometer (FieldSpec®3, 350-1,050 nm). Partial least squares regression (PLSR) and artificial neural networks (ANN) were used to build prediction models of selected soil properties based on VNIR spectra. Very strong to complete correlation and low root mean squared error were obtained between predicted and measured values for the calibration and validation dataset, and both prediction methods. ANN models were more efficient in capturing the complex link between selected soil properties and soil reflectance spectra than PLSR. Calibrations defined in this research should help to support site-specific soil survey as addition to standard laboratory analysis, and represent valuable input for spectral database that should be built for Croatian soils.

A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra

Soil organic carbon (SOC) plays a crucial role as an ecosystems indicator. Its quantification requires an affordable, and less time-consuming method. Visible and near infrared (Vis-NIR) reflectance spectroscopy has demonstrated its applicability to predict SOC over the years. The aims of this study were to i) to compare the influence of preprocessing techniques on prediction performance, ii) assess the modeling performance of a wide range of multivariate methods, and iii) evaluate the potential of Vis-NIR spectroscopy to predict SOC. Soil sampling was conducted over an area of approximately 1800km2 in the Southern region of Brazil, where a total of 595 soil samples were collected. Oxisols are predominant in the area following by Entisols and Inceptisols.The seven preprocessing techniques that were employed can be divided into two categories based on their SOC prediction performance: scatter-correction and spectral-derivatives. A total of nine different methods were evaluated to predict SOC from Vis-NIR spectra. The models that use scatter-corrective preprocessing exhibited superior prediction compared to the spectral-derivatives group. In the scatter-correction group, continuum removal was the most suitable preprocessing method for SOC prediction. Except for random forest (RF), all the multivariate methods presented robust predictions. The best fit and highest model accuracy for SOC models in validation mode were achieved when applying the weighted average partial least-squares (WAPLS) method and normalization by range (NBR) preprocessing (R2=0.82, root mean square error=0.48%, and ratio of the performance to the interquartile range=3.18). Findings from this systematic methodology study identified the reliability of SOC determinations by examining how preprocessing techniques and multivariate methods affect spectral analyses. It also guides future studies to select the most appropriate methods on similar soils.

Potential of visible and near-infrared reflectance spectroscopy for the determination of rare earth elements in soil

The simple and rapid determination of rare earth elements (REE) in soil is essential for the assessment of their environmental and health risks. Inductively coupled plasma-mass spectrometry (ICP-MS) and -emission spectrometry (ICP-ES) are most commonly used for the determination of REE. However, both require complex sample preparation. In this study, we investigated the potential of visible and near-infrared (Vis-NIR) reflectance spectroscopy for determining REE in soil. We collected 130 soil samples near REE mines. Their Vis-NIR spectra, which range from 350 to 2500nm, were measured after air-drying and grinding in the laboratory. Partial least squares regression (PLSR) was selected to calibrate the spectra and REE contents measured by ICP-MS. The results showed that the absorption features of REE were not distinct in the soil spectra, such as for Nd at 798nm, which were masked by soil components, particularly by iron oxides because they have overlapping spectral features with REE. However, when using the spectra in the 400–1000nm range, good or reasonable accuracy [R2>0.5 and residual prediction deviation (RPD)>1.4] was observed for La, Pr, Nd, Sm, and total light REE (∑La–Eu excluding Pm) because their spectral features could be identified by PLSR calibration. The spectral pretreatments of the first derivative, standard normal variate transformation, and multiplicative scatter correction did not improve the prediction accuracy of REE compared with Savitzky-Golay smoothing. This study provides a simple and rapid method for determining REE in soil, particularly for La, Pr, Nd, Sm, and total light REE.

Organic carbon prediction in soil cores using VNIR and MIR techniques in an alpine landscape

Diffuse reflectance spectroscopy (DRS), including visible and near-infrared (VNIR) and mid-infrared (MIR) radiation, is a rapid, accurate and cost-effective technique for estimating soil organic carbon (SOC). We examined 24 soil cores (0–100 cm) from the Sygera Mountains on the Qinghai–Tibet Plateau, considering field-moist intact VNIR, air-dried ground VNIR and air-dried ground MIR spectra at 5-cm intervals. Preprocessed spectra were used to predict the SOC in the soil cores using partial least squares regression (PLSR) and a support vector machine (SVM). The SVM models performed better with three predictors, with the ratio of performance to inter-quartile distance (RPIQ) and R2 values typically exceeding 1.74 and 0.73, respectively. The SVM using the DRS technique indicated accurate predictive results of SOC in each core. The RPIQ values of the shrub meadow, forest and total dataset prediction using air-dried ground VNIR were 1.97, 2.68 and 1.99, respectively; the values using field-moist intact VNIR were 1.95, 2.07 and 1.76 and those using air-dried ground MIR were 1.78, 1.96 and 1.74, respectively. We conclude that the DRS technique is an efficient and rapid method for SOC prediction and has the potential for dynamic monitoring of SOC stock density on the Qinghai–Tibet Plateau.

Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

The visible and near-infrared (VNIR) spectroscopy prediction model is an effective tool for the prediction of soil organic matter (SOM) content. The predictive accuracy of the VNIR model is highly dependent on the selection of the calibration set. However, conventional methods for selecting the calibration set for constructing the VNIR prediction model merely consider either the gradients of SOM or the soil VNIR spectra and neglect the influence of environmental variables. However, soil samples generally present a strong spatial variability, and, thus, the relationship between the SOM content and VNIR spectra may vary with respect to locations and surrounding environments. Hence, VNIR prediction models based on conventional calibration set selection methods would be biased, especially for estimating highly spatially variable soil content (e.g., SOM). To equip the calibration set selection method with the ability to consider SOM spatial variation and environmental influence, this paper proposes an improved method for selecting the calibration set. The proposed method combines the improved multi-variable association relationship clustering mining (MVARC) method and the Rank–Kennard–Stone (Rank-KS) method in order to synthetically consider the SOM gradient, spectral information, and environmental variables. In the proposed MVARC-R-KS method, MVARC integrates the Apriori algorithm, a density-based clustering algorithm, and the Delaunay triangulation. The MVARC method is first utilized to adaptively mine clustering distribution zones in which environmental variables exert a similar influence on soil samples. The feasibility of the MVARC method is proven by conducting an experiment on a simulated dataset. The calibration set is evenly selected from the clustering zones and the remaining zone by using the Rank-KS algorithm in order to avoid a single property in the selected calibration set. The proposed MVARC-R-KS approach is applied to select a calibration set in order to construct a VNIR prediction model of SOM content in the riparian areas of the Jianghan Plain in China. Results indicate that the calibration set selected using the MVARC-R-KS method is representative of the component concentration, spectral information, and environmental variables. The MVARC-R-KS method can also select the calibration set for constructing a VNIR model of SOM content with a relatively higher-fitting degree and accuracy by comparing it to classical calibration set selection methods.

Measuring functional pedodiversity using spectroscopic information

Pedodiversity studies greatly depend on the discretization of the soil continuum via various soil classification standards. Soil taxonomic systems are constructed considering soil properties and their organization in the soil profile. Therefore, it seems logical that the pedodiversity of an area could be calculated by considering the simultaneous variation of multiple soil properties within that area. With the aim of creating a bridge between these two lines of thinking, this study presents the development of two new indices of pedodiversity (HULLdiv and EIGENdiv) which consider continuous variables as input. The soil input information tested was a) Visible and Near Infrared (Vis-NIR) reflectance values and b) values of five soil properties predicted from Vis-NIR spectra. Both indices were employed to measure the pedodiversity at different extents in two perpendicular transects containing 27 (North to South) and 22 (East to West) locations respectively in New South Wales (NSW), Australia. Each location considered natural and intervened land use. The indices successfully represented the pedodiversity of the area of study, however they were different depending on the input soil information i.e., raw Vis-NIR and predicted properties. HULLdiv was affected by extreme soil observations and as a result its discrimination power between areas with different soil diversity was inferior. On the other hand, EIGENdiv represented well the pedodiversity of an area, despite the extreme observations. The results showed good agreement with conventional methods for assessing pedodiversity i.e., Shannon's and Simpson's indices. However, the new indices were more discriminating by being able to better represent the landuse effect. This study represents the first attempt to measure pedodiversity in a continuous way using Vis-NIR information.

Comparisons of spatial and non-spatial models for predicting soil carbon content based on visible and near-infrared spectral technology

Visible and near-infrared (VNIR) reflectance spectroscopy is a rapid, non-destructive, and cost-effective method for predicting soil properties. Partial least squares regression (PLSR) is a common method used to predict soil properties based on VNIR reflectance spectra. However, PLSR ignores the spatial autocorrelation of soil properties and the assumption of linear regression models, in which explanatory variables and model residuals should be independently and identically distributed. In this study, PLSR, partial least squares–geographically weighted regression (PLS–GWR), partial least squares regression Kriging (PLSRK), and partial least squares–geographically weighted regression Kriging (PLS–GWRK) were constructed to predict soil organic matter (SOM) based on soil spectral reflectance. In addition, this study explores the influence of the spatial non-stationarity of explanatory variables on prediction accuracy. Among the aforementioned models, PLSR was used as a reference model; PLS–GWR considered the spatial autocorrelation of SOM and its auxiliary variables; PLSRK and PLS–GWRK considered the spatial dependence of the model residuals to ensure the usability of PLSR and PLS–GWR. A total of 256 topsoil samples (0–30cm) were collected from Chahe Town, located in Jianghan Plain, China, and the reflectance spectra (400–2350nm) of soil were used. The prediction capabilities of the models were evaluated using the coefficient of determination (R2), the root-mean-square error (RMSE), and the ratio of performance to inter-quartile range (RPIQ). The evaluation indices showed that PLS–GWRK was the optimal model for predicting SOM using VNIR spectra. PLS–GWRK has the lowest values of RMSEC [0.109 ln (g·kg−1)] and RMSEP [0.223 ln (g·kg−1)] and the highest values of R2C (0.933), R2P (0.653), and RPIQ (3.015). PLS–GWR result showed that the spatial dependence of SOM and principal components could improve prediction accuracy compared with the PLSR result. The result of PLSRK showed that the spatial dependence of the model residuals could influence the prediction accuracy of PLSR. The PLS–GWRK approach explicitly addressed the spatial dependency and spatial non-stationarity issues for interpolating SOM at regional scale.