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1.
Diffuse reflectance spectroscopy using visible (vis), near‐infrared (NIR) and mid‐infrared (mid‐IR) energy can be a powerful tool to assess and monitor soil quality and function. Mathematical pre‐processing techniques and multivariate calibrations are commonly used to develop spectroscopic models to predict soil properties. These models contain many predictor variables that are collinear and redundant by nature. Partial least squares regression (PLSR) is often used for their analysis. Wavelets can be used to smooth signals and to reduce large data sets to parsimonious representations for more efficient data storage, computation and transmission. Our aim was to investigate their potential for the analyses of soil diffuse reflectance spectra. Specifically we wished to: (i) show how wavelets can be used to represent the multi‐scale nature of soil diffuse reflectance spectra, (ii) produce parsimonious representations of the spectra using selected wavelet coefficients and (iii) improve the regression analysis for prediction of soil organic carbon (SOC) and clay content. We decomposed soil vis‐NIR and mid‐IR spectra using the discrete wavelet transform (DWT) using a Daubechies’s wavelet with two vanishing moments. A multiresolution analysis (MRA) revealed their multi‐scale nature. The MRA identified local features in the spectra that contain information on soil composition. We illustrated a technique for the selection of wavelet coefficients, which were used to produce parsimonious multivariate calibrations for SOC and clay content. Both vis‐NIR and mid‐IR data were reduced to less than 7% of their original size. The selected coefficients were also back‐transformed. Multivariate calibrations were performed by PLSR, multiple linear regression (MLR) and MLR with quadratic polynomials (MLR‐QP) using the spectra, all wavelet coefficients, the selected coefficients and their back transformations. Calibrations by MLR‐QP using the selected wavelet coefficients produced the best predictions of SOC and clay content. MLR‐QP accounted for any nonlinearity in the data. Transforming soil spectra into the wavelet domain and producing a smaller representation of the data improved the efficiency of the calibrations. The models were computed with reduced, parsimonious data sets using simpler regressions.  相似文献   

2.
Effective agricultural planning requires basic soil information. In recent decades visible near‐infrared diffuse reflectance spectroscopy (vis‐NIR) has been shown to be a viable alternative for rapidly analysing soil properties. We studied 7172 samples of seven different soil types collected from several regions of Brazil and varying in organic matter (OM) (0.2–10.3%) and clay content (0.2–99.0%). The aim was to explore the possibility of enhancing the performance of vis‐NIR data in predicting organic matter and clay content in this library by dividing it into smaller sub‐libraries on the basis of their vis‐NIR spectra. We used partial least square regression (PLSR) models on the sub‐libraries and compared the results with PLSR and two non‐linear calibration techniques, boosted regression trees (BT) and support vector machines (SVM) applied to the whole library. The whole library calibrations for clay performed well (ME (modelling efficiency) > 0.82; RMSE (root mean squared error) < 10.9%), reflecting the influence of the direct spectral responses of this property in the vis‐NIR range. Calibrations for OM were reasonably good, especially in view of the very small variation in this property (ME > 0.60; RMSE < 0.55%). The best results were, however, found when dividing the large library into smaller subsets by using variation in the mean‐normalized or first derivative spectra. This divided the global data set into clusters that were more uniform in mineralogy, regardless of geographical origin, and improved predictive performance. The best clustering method improved the RMSE in the validation to 8.6% clay and 0.47% OM, which corresponds to a 21% and 15% reduction, respectively, as compared with whole library PLSR. For the whole library, SVM performed almost equally well, reducing RMSE to 8.9% clay and 0.48% OM.  相似文献   

3.
In soil mapping, combining information from conceptually different proximal soil sensors can increase the accuracy of prediction and robustness of the model when compared with using individual sensors. In this study the predictability of soil texture (clay, silt and sand fractions) and soil organic matter (SOM) content was tested with a commercial integrated soil profiling tool that included sensors for measuring apparent electrical conductivity (ECa), reflectance in the visible and near‐infrared (vis‐NIR) parts of the electromagnetic spectrum and insertion force (IF). The measurements were made at 20 locations on each of two Swedish farms. At every location, sensor measurements were made at 1.5‐cm intervals from the soil surface to a depth of 0.8 m. Soil samples were collected close to the sensor measurement points and analysed for texture and SOM content. Farm‐specific calibrations were developed for texture and SOM with each sensor separately and with combinations of all three sensors. The calibrations were made using both partial least squares regression (PLSR) and simple linear regression. The results for the two farms were quite consistent in terms of rank in prediction performance between the individual sensors and the sensor combinations. The vis‐NIR spectrometer was the best individual sensor for predicting the soil properties tested on both farms, with root mean square error of cross‐validation (RMSECV) of 0.3–0.5% for SOM, about 6% for clay and silt and 10–11% for sand. The inclusion of IF reduced the RMSECV for predictions of SOM content by about 10%. For soil texture, including ECa reduced the RMSECV on average for all particle size fractions by 5–10%. However, the small improvements obtained by combining sensors do not provide strong support for combining vis‐NIR sensor measurements with measurements of ECa and or IF.  相似文献   

4.
Infrared spectroscopy in the visible to near-infrared (vis–NIR) and mid-infrared (MIR) regions is a well-established approach for the prediction of soil properties. Different data fusion and training approaches exist, and the optimal procedures are yet undefined and may depend on the heterogeneity present in the set and on the considered scale. The objectives were to test the usefulness of partial least squares regressions (PLSRs) for soil organic carbon (SOC), total carbon (Ct), total nitrogen (Nt) and pH using vis–NIR and MIR spectroscopy for an independent validation after standard calibration (use of a general PLSR model) or using memory-based learning (MBL) with and without spiking for a national spectral database. Data fusion approaches were simple concatenation of spectra, outer product analysis (OPA) and model averaging. In total, 481 soils from an Austrian forest soil archive were measured in the vis–NIR and MIR regions, and regressions were calculated. Fivefold calibration-validation approaches were carried out with a region-related split of spectra to implement independent validations with n ranging from 47 to 99 soils in different folds. MIR predictions were generally superior over vis–NIR predictions. For all properties, optimal predictions were obtained with data fusion, with OPA and spectra concatenation outperforming model averaging. The greatest robustness of performance was found for OPA and MBL with spiking with R2 ≥ 0.77 (N), 0.85 (SOC), 0.86 (pH) and 0.88 (Ct) in the validations of all folds. Overall, the results indicate that the combination of OPA for vis–NIR and MIR spectra with MBL and spiking has a high potential to accurately estimate properties when using large-scale soil spectral libraries as reference data. However, the reduction of cost-effectiveness using two spectrometers needs to be weighed against the potential increase in accuracy compared to a single MIR spectroscopy approach.  相似文献   

5.
The capture and storage of soil organic carbon (OC) should improve the soil's quality and function and help to offset the emissions of greenhouse gases. However, to measure, model or monitor changes in OC caused by changes in land use, land management or climate, we need cheaper and more practical methods to measure it and its composition. Conventional methods are complex and prohibitively expensive. Spectroscopy in the visible and near infrared (vis–NIR) is a practical and affordable alternative. We used samples from Australia's Soil Carbon Research Program (SCaRP) to create a vis–NIR database with accompanying data on soil OC and its composition, expressed as the particulate, humic and resistant organic carbon fractions, POC, HOC and ROC, respectively. Using this database, we derived vis–NIR transfer functions with a decision‐tree algorithm to predict the total soil OC and carbon fractions, which we modelled in units that describe their concentrations and stocks (or densities). Predictions of both carbon concentrations and stocks were reliable and unbiased with imprecision being the main contributor to the models' errors. We could predict the stocks because of the correlation between OC and bulk density. Generally, the uncertainty in the estimates of the carbon concentrations was smaller than, but not significantly different to, that of the stocks. Approximately half of the discriminating wavelengths were in the visible region, and those in the near infrared could be attributed to functional groups that occur in each of the different fractions. Visible–NIR spectroscopy with decision‐tree modelling can fairly accurately, and with small to moderate uncertainty, predict soil OC, POC, HOC and ROC. The consistency between the decision tree's use of wavelengths that characterize absorptions due to the chemistry of soil OC and the different fractions provides confidence that the approach is feasible. Measurement in the vis–NIR range needs little sample preparation and so is rapid, practical and cheap. A further advantage is that the technique can be used directly in the field.  相似文献   

6.
This study investigated the potential for visible–near‐infrared (vis–NIR) spectroscopy to predict locally volumetric soil organic carbon (SOC) from spectra recorded from field‐moist soil cores. One hundred cores were collected from a 71‐ha arable field. The vis–NIR spectra were collected every centimetre along the side of the cores to a depth of 0.3 m. Cores were then divided into 0.1‐m increments for laboratory analysis. Reference SOC measurements were used to calibrate three partial least‐squares regression (PLSR) models for bulk density (ρb), gravimetric SOC (SOCg) and volumetric SOC (SOCv). Accurate predictions were obtained from averages of spectra from those 0.1‐m increments for SOCg (ratio of performance to inter‐quartile (RPIQ) = 5.15; root mean square error (RMSE) = 0.38%) and SOCv (RPIQ = 5.25; RMSE = 4.33 kg m?3). The PLSR model for ρb performed least well, but still produced accurate results (RPIQ = 3.76; RMSE = 0.11 Mg m?3). Predictions for ρb and SOCg were combined to compare indirect and direct predictions of SOCv. No statistical difference in accuracy between these approaches was detected, suggesting that the direct prediction of SOCv is possible. The PLSR models calibrated on the 10‐cm depth intervals were also applied to the spectra originally recorded on a 1‐cm depth increment. While a bigger bias was observed for 1‐cm than for 10‐cm predictions (1.13 and 0.19 kg m?3, respectively), the two populations of estimates were not distinguishable statistically. The study showed the potential for using vis–NIR spectroscopy on field‐moist soil cores to predict SOC at high depth resolutions (1 cm) with locally derived calibrations.  相似文献   

7.
We need to determine the best use of soil vis–NIR spectral libraries that are being developed at regional, national and global scales to predict soil properties from new spectral readings. To reduce the complexity of a calibration dataset derived from the Chinese vis–NIR soil spectral library (CSSL), we tested a local regression method that combined geographical sub‐setting with a local partial least squares regression (local‐PLSR) that uses a limited number of similar vis–NIR spectra (k‐nearest neighbours). The central idea of the local regression, and of other local statistical approaches, is to derive a local prediction model by identifying samples in the calibration dataset that are similar, in spectral variable space, to the samples used for prediction. Here, to derive our local regressions we used Euclidean distance in spectral space between the calibration dataset and prediction samples, and we also used soil geographical zoning to account for similarities in soil‐forming conditions. We tested this approach with the CSSL, which comprised 2732 soil samples collected from 20 provinces in the People's Republic of China to predict soil organic matter (SOM). Results showed that the prediction accuracy of our spatially constrained local‐PLSR method (R2 = 0.74, RPIQ = 2.6) was better than that from local‐PLSR (R2 = 0.69, RPIQ = 2.3) and PLSR alone (R2 = 0.50, RPIQ = 1.5). The coupling of a local‐PLSR regression with soil geographical zoning can improve the accuracy of local SOM predictions using large, complex soil spectral libraries. The approach might be embedded into vis–NIR sensors for laboratory analysis or field estimation.  相似文献   

8.
Measuring color is important when assessing grain and grain products as this has a major influence on the end‐product quality. To objectively measure color, grain processors and plant breeding programs use colorimeters to measure L*a*b* values as defined by the Commission Internationale de L'Eclairage (CIE). In addition to color, most laboratories undertake other tests, often utilizing NIR (near infrared reflectance) technology. It is possible to improve laboratory efficiencies and remove double handling of samples by using one instrument, a visible‐NIR spectrophotometer (400–2,500 nm), to measure color and other quality traits such as protein. In this study, we compared two techniques for measuring color of flour, barley, and lentils with a visible‐NIR instrument. The first technique involved calibrating the visible‐NIR instrument with colorimeter values using calibration models. However, calibrations are product‐specific and require the development and maintenance of specific equations for each product analyzed. For the second technique, we calculated the color values from the visible reflectance spectra using the standard practice (E308) based on the CIE system. Our study showed the most accurate, efficient method for measuring the color of products with a visible‐NIR instrument is to apply calculations using the standard practice based on the CIE system.  相似文献   

9.
Visible–near infrared (vis–NIR) spectroscopy can be used to estimate soil properties effectively using spectroscopic calibrations derived from data contained in spectroscopic databases. However, these calibrations cannot be used with proximally sensed (field) spectra because the spectra in these databases are recorded in the laboratory and are different to field spectra. Environmental factors, such as the amount of water in the soil, ambient light, temperature and the condition of the soil surface, cause the differences. Here, we investigated the use of direct standardization (DS) to remove those environmental factors from field spectra. We selected 104 sensing (sampling) sites from nine paddy fields in Zhejiang province, China. At each site, vis–NIR spectra were recorded with a portable spectrometer. The soils were also sampled to record their spectra under laboratory conditions and to measure their soil organic matter (SOM) content. The resulting data were divided into training and validation sets. A subset of the corresponding field and laboratory spectra in the training set (the transfer set) was used to derive the DS transfer matrix, which characterizes the differences between the field and laboratory spectra. Using DS, we transferred the field spectra of the validation samples so that they acquired the characteristics of spectra that were measured in the laboratory. A partial least squares regression (PLSR) of SOM on the laboratory spectra of the training set was then used to predict both the original field spectra and the DS‐transferred field spectra. The assessment statistics of the predictions were improved from R2 = 0.25 and RPD = 0.35 to R2 = 0.69 and RPD = 1.61. We also performed independent predictions of SOM on the DS‐transferred field spectra with a PLSR derived using the Chinese soil spectroscopic database (CSSD), which was developed in the laboratory. The R2 and RPD values of these predictions were 0.70 and 1.79, respectively. Predictions of SOM with the DS‐transferred field spectra were more accurate than those treated with external parameter orthogonalisation (EPO), and more accurate than predictions made by spiking. Our results show that DS can effectively account for the effects of water and environmental factors on field spectra and improve predictions of SOM. DS is conceptually straightforward and allows the use of calibrations made with laboratory‐measured spectra to predict soil properties from proximally sensed (field) spectra, without needing to recalibrate the models.  相似文献   

10.
This paper reports on the influence of the number of samples used for the development of farm‐scale calibration models for moisture content (MC), total nitrogen (TN) and organic carbon (OC) on the prediction error expressed as root mean square error of prediction (RMSEP) for visible and near infrared (vis‐NIR) spectroscopy. Fresh (wet) soil samples collected from four farms in the Czech Republic, Germany, Denmark and the UK were scanned with a fibre‐type vis‐NIR, AgroSpec spectrophotometer with a spectral range of 305–2200 nm. Spectra were divided into calibration (two thirds) and prediction (one third) sets and the calibration spectra were subjected to a partial least squares regression (PLSR) with leave‐one‐out cross‐validation using Unscrambler 7.8 software. The RMSEP values of models with a large sample number (46–84 samples from each farm) were compared with those of models developed with a small sample number (25 samples selected from the large sample set of each farm) for the same variation range. Both large‐set and small‐set models were validated by the same prediction set for each property. Further PLSR analysis was carried out on samples from the German farm, with different sample numbers of the calibration set of 25, 50, 75 and 100 samples. Results showed that the large‐size dataset models resulted in smaller RMSEP values than the small‐size dataset models for all the soil properties studied. The results also demonstrated that with the increase in sample number used in the calibration set, RMSEP decreased in almost linear fashion, although the largest decrease was between 25 and 50 samples. Therefore, it is recommended that the number of samples should be chosen according to the accuracy required, although 50 soil samples is considered appropriate in this study to establish calibration models of TN, OC and MC with smaller expected prediction errors as compared with smaller sample numbers.  相似文献   

11.
We investigated the use of piecewise direct standardization (PDS) to remove the effects of water and other environmental factors from proximally sensed (field) visible–near infrared (vis–NIR) spectra. Our hypothesis was that the PDS‐standardized field spectra can be used to predict soil carbon effectively with calibrations derived from existing spectroscopic databases of spectra recorded in the laboratory on dried, ground and sieved samples. In our experiments we used field spectra recorded in situ with a portable spectrometer at 124 sites in 11 paddy fields in Zhejiang Province, China. We sampled the soil at these same sites, recorded their spectra in the laboratory and measured their soil organic carbon (SOC) contents with a conventional laboratory technique. Two‐thirds of the samples were used to relate the laboratory spectra to SOC by partial least squares regression (PLSR), and the remaining one‐third was used as an independent validation dataset. We selected a representative set of samples from corresponding field and laboratory spectra that we could use as the PDS transfer set. Piecewise direct standardization was used to relate each wavelength in the laboratory spectra to the corresponding wavelength and its neighbours in the field spectra. The field spectra of the validation samples were then corrected with PDS so that they acquired the characteristics of the spectra measured under laboratory conditions. The approach was evaluated by (i) quantifying the similarity between the PDS‐standardized spectra and their corresponding laboratory spectra, (ii) measuring the accuracy of their SOC predictions on the independent validation dataset and (iii) comparing these results with those of direct standardization (DS). Both PDS and DS led to considerable improvements in the predictions of SOC (R2 = 0.71, R2 = 0.60, respectively), compared with those with original field spectra (R2 = 0.03). However, fewer transfer samples were needed with PDS to obtain similar results.  相似文献   

12.
Modification of an existing single kernel wheat characterization system allowed collection of visible and near-infrared (NIR) reflectance spectra (450–1,688 nm) at a rate of 1 kernel/4 sec. The spectral information was used to classify red and white wheats in an attempt to remove subjectivity from class determinations. Calibration, validation, and prediction results showed that calibrations using partial least squares regression and derived from the full wavelength profile correctly classed more kernels than either the visible region (450–700 nm) or the NIR region (700–1,688 nm). Most results showed >99% correct classification for single kernels when using the visible and NIR regions. Averaging of single kernel classifications resulted in 100% correct classification of bulk samples.  相似文献   

13.
Arid soil is common worldwide and has unique properties that often limit agronomic productivity, specifically, salinity expressed as soluble salts and large amounts of calcium carbonate and gypsum. Currently, the most common methods for evaluating these properties in soil are laboratory‐based techniques such as titration, gasometry and electrical conductivity. In this research, we used two proximal sensors (portable X‐ray fluorescence (PXRF) and visible near‐infrared diffuse reflectance spectroscopy (Vis–NIR DRS)) to scan a diverse set (n = 116) of samples from arid soil in Spain. Then, samples were processed by standard laboratory procedures and the two datasets were compared with advanced statistical techniques. The latter included penalized spline regression (PSR), support vector regression (SVR) and random forest (RF) analysis, which were applied to Vis–NIR DRS data, PXRF data and PXRF and Vis–NIR DRS data, respectively. Independent validation (30% of the data) of the calibration equations showed that PSR + RF predicted gypsum with a ratio of performance to interquartile distance (RPIQ) of 5.90 and residual prediction deviation (RPD) of 4.60, electrical conductivity (1:5 soil : water) with RPIQ of 3.14 and RPD of 2.10, Ca content with RPIQ of 2.92 and RPD of 2.07 and calcium carbonate equivalent with RPIQ of 2.13 and RPD of 1.74. The combined PXRF and Vis–NIR DRS approach was superior to those that use data from a single proximal sensor, enabling the prediction of several properties from two simple, rapid, non‐destructive scans.  相似文献   

14.
Raindrop energy disintegrates soil aggregates and rearranges soil particles to form a structural crust on the upper soil layer. The structural crust affects the physical properties of the soil, which can be observed by significant colour changes on the soil surface. Spectral differences observed in the structural crust are caused by rearrangement of the soil surface texture, mainly an increase in the clay fraction. Previous studies conducted on crusted soils using reflectance spectroscopy were limited to a certain soil type or area and seemed to be strongly dependent on the small range of soil types. In the current study, the influence of raindrop energy on the NIR‐SWIR spectral reflectance (1200–2400 nm) of heterogeneous soils was evaluated and used in combination with partial least squares (PLS) regression to construct a model that correlates the infiltration rate (IR) with its reflectance. Four soils from Israel and three soils from the USA were studied to provide a single data set. A relatively small root mean square error of cross‐validation (RMSECV) of 15.2% was found. A ratio of prediction to deviation (RPD) value of 1.98 indicates a promising generic model. Additionally, PLS models were run on different combinations of soil types (RPD values ranging between 2.4 and 3.2). For all models, whether all soils were run in one cross‐validation data set, or run for different combinations of soils, the best assessment of IR was achieved when using reduced wavelength range (selected wavelengths based on Martens’ significance test selection). These results allowed us to conclude that a generic approach aimed at assessing the structural crust for a variety of soils is feasible. A generic model using the suggested spectral approach has the potential to provide NIR‐SWIR spectral soil IR predictions with either a local or global data base of soils worldwide and may contribute to improved protection of crusted soils from erosion or water loss by runoff.  相似文献   

15.
The absorbance of near‐infrared (NIR) radiation by plants depends on the overtones of vibration, bending and stretching of the chemical bonds within their organic components. The wide variety of these bonds gives rise to characteristic NIR spectra, or ‘fingerprints’, of plant materials. Do these fingerprints remain in the soil when the plants decompose and allow discrimination between soils that have supported different types of vegetation? We have compared the NIR spectra of grassland soil with those of soil under forest in the Vosges mountains in France. Near‐infrared reflectances, R, from 370 samples of soil were recorded as A = log10(1/R) at wavelengths between 1100 and 2500 nm and averaged over 10‐nm intervals to give spectra each with 140 values. A canonical variate analysis (CVA) of the raw spectra discriminated well, but not perfectly, between grassland and forest soil, with Mahalanobis distance, D, of 4.87. Standardization along the spectra to remove effects of varied mineral composition achieved complete separation between the two sources with D = 9.81. Canonical variate analysis of first and second derivatives of the spectra distinguished the two groups even better, with D = 12.27 and D = 16.65, respectively. The results show much promise for inferring past vegetative cover from NIR spectra deriving from the organic matter in the soil. The next step will be to extend these studies to see how well we can distinguish other types of vegetation from such spectra.  相似文献   

16.
The usefulness and limitations of near‐infrared reflectance spectroscopy (NIRS) for the assessment of several soil characteristics are still not sufficiently explored. The objective of this study was to evaluate the ability of visible and near‐infrared reflectance (VIS‐NIR) spectroscopy to predict the composition of organic matter in soils and litter. Reflectance spectra of the VIS‐NIR region (400–2500 nm) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by 13C‐CPMAS‐NMR spectroscopy. A modified partial least‐square method and cross‐validation were used to develop equations for the different constituents over the whole spectrum (1st to 3rd derivation). Near‐infrared spectroscopy predicted well the C : N ratios, the percentages of O‐alkyl C and alkyl C, the ratio of alkyl C to O‐alkyl C, and the sum of phenolic oxidation products: the ratios of standard deviation of the laboratory results to standard error of cross‐validation (RSC) were greater than 2, the regression coefficients (a) of a linear regression (measured against predicted values) ranged from 0.9 to 1.1, and the correlation coefficients (r) were greater than 0.9. Satisfactorily (0.8 ≤ a ≤ 1.2, r ≥ 0.8, and 1.4 ≤ RSC ≤ 2.0) assessed were the contents of C, N, and production of DOC, the percentages of carbonyl C and aromatic C and the ratio of alkyl C to aromatic C. However, the N‐mineralization rate and the microbial biomass were predicted unsatisfactorily (RSC < 1.4). The good and satisfactory predictions reported above indicate a marked usefulness of NIRS in the assessment of biological and chemical characteristics of soils and litter.  相似文献   

17.
Abstract

The objective of this study was to compare mid‐infrared (MIR) an near‐infrared (NIR) spectroscopy (MIRS and NIRS, respectively) not only to measure soil carbon content, but also to measure key soil organic C (SOC) fractions and the δ13C in a highly diverse set of soils while also assessing the feasibility of establishing regional diffuse reflectance calibrations for these fractions. Two hundred and thirty‐seven soil samples were collected from 14 sites in 10 western states (CO, IA, MN, MO, MT, ND, NE, NM, OK, TX). Two subsets of these were examined for a variety of C measures by conventional assays and NIRS and MIRS. Biomass C and N, soil inorganic C (SIC), SOC, total C, identifiable plant material (IPM) (20× magnifying glass), the ratio of SOC to the silt+clay content, and total N were available for 185 samples. Mineral‐associated C fraction, δ13C of the mineral associated C, δ13C of SOC, percentage C in the mineral‐associated C fraction, particulate organic matter, and percentage C in the particulate organic matter were available for 114 samples. NIR spectra (64 co‐added scans) from 400 to 2498 nm (10‐nm resolution with data collected every 2 nm) were obtained using a rotating sample cup and an NIRSystems model 6500 scanning monochromator. MIR diffuse reflectance spectra from 4000 to 400 cm?1 (2500 to 25,000 nm) were obtained on non‐KBr diluted samples using a custom‐made sample transport and a Digilab FTS‐60 Fourier transform spectrometer (4‐cm?1 resolution with 64 co‐added scans). Partial least squares regression was used with a one‐out cross validation to develop calibrations for the various analytes using NIR and MIR spectra. Results demonstrated that accurate calibrations for a wide variety of soil C measures, including measures of δ13C, are feasible using MIR spectra. Similar efforts using NIR spectra indicated that although NIR spectrometers may be capable of scanning larger amounts of samples, the results are generally not as good as achieved using MIR spectra.  相似文献   

18.
The prediction accuracy of visible and near‐infrared (Vis‐NIR) spectroscopy for soil chemical and biological parameters has been variable and the reasons for this are not completely understood. Objectives were (1) to explore the predictability of a series of chemical and biological properties for three different soil populations and—based on these heterogeneous data sets—(2) to analyze possible predictive mechanisms statistically. A number of 422 samples from three arable soils in Germany (a sandy Haplic Cambisol and two silty Haplic Luvisols) of different long‐term experiments were sampled, their chemical and biological properties determined and their reflectance spectra in the Vis‐NIR region recorded after shock‐freezing followed by freeze‐drying. Cross‐validation was carried out for the entire population as well as for each population from the respective sites. For the entire population, excellent prediction accuracies were found for the contents of soil organic C (SOC) and total P. The contents of total N and microbial biomass C and pH were predicted with good accuracy. However, prediction accuracy for the other properties was less: content of total S was predicted approximately quantitatively, whereas Vis‐NIR spectroscopy could only differentiate between high and low values for the contents of microbial N, ergosterol, and the ratio of ergosterol to microbial biomass C. Contents of microbial biomass P and S, basal respiration, and qCO2 could not be predicted. Prediction accuracies were greatest for the entire population and the Luvisol at Garte, followed by the Luvisol at Hohes Feld, whereas the accuracy for the sandy Cambisol was poor. The poor accuracy for the sandy Cambisol may have been due to only smaller correlations between the measured properties and the SOC content compared to the Luvisols or due to a general poor prediction performance for sandy soils. Another reason for the poor accuracy may have been the smaller range of contents in the sandy soil. Overall, the data indicated that the accuracy of predictions of soil properties depends largely on the population investigated. For the entire population, the usefulness of Vis‐NIR for the number of chemical and biological soil properties was evident by markedly greater correlation coefficients (measured against Vis‐NIR predicted) compared to the Pearson correlation coefficients of the measured properties against the SOC content. However, the cross‐validation results are valid only for the closed population used in this study.  相似文献   

19.
We have investigated the speciation and distribution of iron (Fe) and aluminium (Al) between minerals and organic species in A and B horizons of two Aluandic Andosols with X‐ray diffraction, thermal analyses, visible diffuse reflectance and Fourier transform infrared (FTIR) spectroscopies, together with selective and total chemical extractions. The two Aluandic Andosols of the Nilgiri Highlands (south India) have formed at the expense of intensively weathered lateritic formations of the Eocene. Data revealed that Al and Fe were predominantly stored in end‐weathering products of laterites, mainly as gibbsite and Fe (hydr)oxides in B horizons of the Aluandic Andosols. These secondary minerals are gradually replaced by organo‐metallic complexes in the topsoil A horizons exhibiting andic properties. We then indicate that formation of the organo‐metallic complexes results from weathering of the dominant crystalline Al‐ and Fe‐(hydr)oxides mediated by the organic ligands and complexation of the polyvalent metals following the accumulation of organic matter. Such weathering and complexation mechanisms are therefore similar to those recently ascribed to deferralitization and the formation of freely drained Umbric Podzols (Humus‐Podzols) in the upper Amazon Basin. In the present case, large supplies of both Al‐ and Fe‐bearing minerals provide large metal:carbon ratios that prevent the mobility of the organo‐metallic complexes and induce the formation of Aluandic Andosols rather than Podzols.  相似文献   

20.
The development of accurate calibration models for selected soil properties is a crucial prerequisite for successful implementation of visible and near infrared (Vis‐NIR) spectroscopy for soil analysis. This paper compares the performance of calibration models developed for individual farms with that of general models valid for three farms in three European countries. Fresh soil samples collected from farms in the Czech Republic, Germany and Denmark were scanned with a fibre‐type Vis‐NIR spectrophotometer. After dividing spectra into calibration (70%) and validation (30%) sets, spectra in the calibration set were subjected to partial least squares regression (PLSR) with leave‐one‐out cross‐validation to establish calibration models of soil properties. Except for the Czech Republic farm, individual farm models provided successful calibration for total carbon (TC), total nitrogen (TN) and organic carbon (OC), with coefficients of determination (R2) of 0.85–0.93 and 0.74–0.96 and residual prediction deviations (RPD) of 2.61–3.96 and 2.00–4.95 for the cross‐validation and independent validation respectively. General calibration models gave improved prediction accuracies compared with models of farms in the Czech Republic and Germany, which was attributed to larger ranges in the variation of soil properties in general models compared with those in individual farm models. The results revealed that larger standard deviations (SDs) and wider variation ranges have resulted in larger R2 and RPD, but also larger root mean square errors of prediction (RMSEP). Therefore, a compromise solution, which also results in small RMSEP values, should be found when selecting soil samples for Vis‐NIR calibration to cover a wide variation range.  相似文献   

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