Visible Near-infrared Diffuse Reflectance Spectroscopy Research Articles

Multinational prediction of soil organic carbon and texture via proximal sensors

AbstractNovel technologies help to monitor the environmental impact of human activities, but tests involving datasets from several countries, encompassing a large variability of soil properties, are still scarce. This study utilized proximal sensors to predict soil organic carbon (OC) and soil texture of samples from Brazil, France, India, Mozambique, and United States. A total of 1749 samples were analyzed by portable X‐ray fluorescence (pXRF) spectrometry and visible near‐infrared diffuse reflectance spectroscopy. Sand (R2 = 0.89), silt (0.87), and clay (0.84) predictions were very accurate, despite contrasting climates, soil parent materials, and weathering degrees. Soil OC predictions were similarly successful (0.74) using samples from five countries. pXRF was the optimal sensor for soil texture predictions. The addition of international data may improve local models. Proximal soil sensing can be successfully used with a multinational soil database offering a clean, rapid, and accurate alternative to estimate soil texture and OC with international datasets.

Synthesis and Characterisation of Mg2+ and Al3+ Co-Doped CoCr2O4 Inorganic Pigments With High Near-Infrared Reflectance

A new class of near-infrared (NIR) reflectance pigments based on Co1−xMgxCr2−yAlyO4 (x = 0–1 and y = 0–2) was synthesised using the Pechini-type sol-gel process. The developed powders were characterised by thermogravimetry and differential scanning calorimetry, X-ray diffraction (XRD), ultraviolet–visible near-infrared diffuse reflectance spectroscopy, and colour Commission Internationale del'Eclairage (CIE) 1976 (L*a*b*) colour scales. The XRD patterns revealed characteristic peaks of the spinel structure with good crystallinity. Substituting Mg2+ for Co2+ ions in the tetrahedral positions and Al3+ for Cr3+ ions in the octahedral positions of CoCr2O4 reduces the cost and changes the colour of the pigment (green to yellow and then blue). Moreover, the synthesised pigments exhibited high NIR solar reflectance in the 780–2,500 nm wavelength range. The thermal and chemical stability of the pigments was also studied. Our findings demonstrate the potential for applying these pigments in cool colourants.

Modeling of Soil Sand Particles Using Spectroscopy Technology

ABSTRACT The advent of lab diffuse reflectance spectroscopy (LDRS) that exploits the fundamental vibration, overtones and combination of functional groups of soil components makes the soil study easier. The present research intends to predict sand content utilizing the proximal soil sensing (PSS) tech. Thus, in accord with the supplementary data layers and stratified randomized sampling (SRS) method, eventually, 128 samples were gathered from 20 cm of soil surface of Mazandaran Province, Iran. First of all, the sample set was subdivided into two subsets: calibration (96) and validation (32). Using the multivariate regression analysis-partial least squares regression (PLSR) algorithm with leave-one-out cross-validation (LOOCV) technique and some pre-processing algorithms, such as spectral averaging, smoothing and 1st derivative (1st-D), the definitive calibration model with two & four latent vectors (LVs/LFs) and correlation coefficient (RP), determination coefficient (R2 P), root mean square error (RMSEP), ratio of performance to deviation (RPDP) and ratio of performance to interquartile distance (RPIQP) respectively: 0.83&0.82, 0.68&0.67,8.68&8.83%,1.78&1.75,2.45&2.41, were validated and spotted as the most appropriate predictive model for the sand content prediction in the study region. Last, the potentiality of the visible-near infrared diffuse reflectance spectroscopy (VNIR-DRS) for sand content estimation in Mazandaran soils was proved. Also, it is feasible to upscale the sand prediction process utilizing the principal resulted model and the key spectral domains via airborne/satellite hyperspectral data, which emphatically shows the LDRS importance as a commencement point for characterizing the informative optical wavelengths. Likewise, that will be the infrastructure for spaceborne data modeling and upscaling process.

Soil texture prediction using portable X-ray fluorescence spectrometry and visible near-infrared diffuse reflectance spectroscopy

Portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared diffuse reflectance spectroscopy (Vis-NIR DRS), used separately or in tandem, have become important techniques for determination and prediction of soil attributes worldwide. However, there is little information available regarding the effectiveness of their combined use in tropical soils. This study aimed to predict soil texture using pXRF and Vis-NIR DRS, evaluating the efficiency of using these proximal sensors separately and in tandem. A total of 315 soil samples were collected from A and B horizons in Brazil. Soil samples were submitted to analyses of texture, pXRF and Vis-NIR DRS. Vis-NIR DRS spectral data pre-processing was evaluated by comparing results delivered by the derivative smoothing methods Savitzky-Golay (WT), Savitzky-Golay with Binning (WB), and data without the pre-processing treatment (WOT). Four algorithms were utilized for predictions: Gaussian Process (Gaussian), Support Vector Machine with linear (SVM-L) and radial (SVM-R) kernels, and Random Forest (RF). In general, models using only pXRF data slightly outperformed models using Vis-NIR DRS (WT, WB, WOT) data alone. Models combining data from both sensors achieved similar results to those obtained by pXRF alone. The best predictions of sand, silt, and clay contents were obtained via pXRF + RF using B horizon data, reaching R2 values of 0.91, 0.81, and 0.83, respectively. Although pXRF alone provided slightly better results, soil texture can be accurately predicted via both pXRF and Vis-NIR DRS data, separately and in tandem. These sensors can contribute to reduce costs and time required for tropical soil texture determination.

Lithologic discontinuity in Andisols on tea plantation area in West Java, Indonesia

Tea plantation areas are normally found on Andisols developed from volcanic eruptions. Volcanic eruptions can occur within several periods, resulting in a sequence of soil layers that can differ in every period and produce lithologic discontinuity in the soil profile. Lithologic discontinuities were investigated in Andisols on Ciater Tea Plantation in Ciater, West Java, Indonesia. Andisols in this area developed from the eruption of Mt. Tangkuban Parahu, Mt. Dano and Mt. Tampomas derived from two geological formation, the Qyd (Middle Holocene) and Qyt (Early Holocene). The objective of this research was to investigate the lithologic discontinuity of Andisols in Ciater Tea Plantation through macromorphological observations from four profiles in this location. The results showed that there were lithologic discontinuities in all profiles, indicated by unusual changing color detected by Munsell Soil Color Chart and increasing organic carbon content in certain depth. Some A buried horizons (Ab) were found, indicated that the underlying soils had developed from the parent material of the earlier eruption period than the overlying horizons. Detailed analyses of mineralogical and element in sand and silt fraction were needed to have the more accurate information of lithology discontinuities. Instruments like X-Ray Fluorescence Spectrometry and Visible Near-Infrared Diffuse Reflectance Spectroscopy can be explored to investigate the lithologic discontinuity.

Effect of Residual Na+ on the Combustion of Methane over Co3O4 Bulk Catalysts Prepared by Precipitation

The effect of the presence of residual sodium (0.4 %wt) over a Co3O4 bulk catalyst for methane combustion was studied. Two samples, with and without residual sodium, were synthesized by precipitation and thoroughly characterised by X-ray diffraction (XRD), N2 physisorption, Wavelength Dispersive X-ray Fluorescence (WDXRF), temperature-programmed reduction with hydrogen followed by temperature-programmed reduction with oxygen (H2-TPR/O2-TPO), temperature-programmed reaction with methane (CH4-TPRe), ultraviolet–visible–near-infrared diffuse reflectance spectroscopy (UV-vis-NIR DRS), Raman spectroscopy and X-ray photoelectron spectroscopy (XPS). It was found that during calcination, a fraction of the sodium atoms initially deposited on the surface diffused and migrated into the spinel lattice, inducing a distortion that improved its textural and structural properties. However, surface sodium had an overall negative impact on the catalytic activity. It led to a reduction of surface Co3+ ions in favour of Co2+, thus ultimately decreasing the Co3+/Co2+ molar ratio (from 1.96 to 1.20) and decreasing the amount and mobility of active lattice oxygen species. As a result, the catalyst with residual sodium (T90 = 545 °C) was notably less active than its clean counterpart (T90 = 500 °C). All of this outlined the significance of a proper washing when synthesizing Co3O4 catalyst using a sodium salt as the precipitating agent.

Complete Soil Texture is Accurately Predicted by Visible Near-Infrared Spectroscopy

The particle-size curve (PSC) defines the continuous size distribution of mineral particles <2 mm. It is used for soil classification and to derive functional soil parameters such as the soil–water characteristic (SWC) curve, soil hydraulic properties, and gas transport properties. Conventional methods for measuring texture are time-consuming and most methods only provide discrete particle-size intervals. The Rosin–Rammler and Fredlund functions enable a continuous description of the size distribution of mineral particles using two and three fitting parameters, respectively. Visible near-infrared diffuse reflectance spectroscopy (vis-NIRS) is a time-saving and well-known alternative soil analysis method. In this study vis-NIRS was used to indirectly obtain PSCs by predicting the fitting parameters of the Rosin–Rammler (αR, βR) and Fredlund (αF, nF, and mF) functions. A total of 431 soil samples from 7 agricultural fields in Denmark and Greenland were analyzed for soil texture (clay: 0.028–0.426 kg kg⁻¹) and organic matter (OM) content (0.018–0.143 kg kg⁻¹). The Rosin–Rammler and Fredlund functions were fitted to the PSCs. Soil diffuse reflectance was measured from 400 to 2500 nm with a spectrometer. The important spectral regions for correlating αR, βR, αF, nF, mF, and OM to spectra were selected using forward interval partial least squares (iPLS) regression on a calibration set. The soil spectra showed high correlation to PSC function parameters and OM content for the validation set. Further, vis-NIRS cross-validation models for the fitting parameters of the Rosin–Rammler and Fredlund functions were built on all samples and used as input for the PSCs, generating RMSE values of 4.2 and 3.5%, respectively. Both PSC functions convincingly covered the PSC variation within fields, although the Fredlund function performed slightly better. From one vis-NIRS scanning the complete texture comprising the PSC and the OM content was successfully characterized.

Mapping evaporite minerals and associated sediments in Lake Magadi, Kenya, using Hyperspectral Hyperion data

Hyperion hyperspectral (VNIR-SWIR) satellite image data was used to map the spatial distribution of mineral precipitates at Lake Magadi area, in the southernmost part of the eastern Kenya rift, Kenya. Mapping was coupled with laboratory analysis, including visible near infrared diffuse reflectance spectroscopy (VNIR) measurements and X-ray diffraction for selected rock and soil samples. The VNIR spectral responses of 92 rock and soil samples including trona, chert, diatomite, basalt/trachyte, erionite, Green bed and High Magadi bed were studied and identified. The spectral signatures of Chert samples show the broad Si-OH absorption feature at 2.2 μm while, Green bed, High Magadi bed and diatomite exhibit carbonate absorption feature at 2.35 μm with broad Si-OH absorption feature at 2.2 μm. Trona exhibits six common absorption features at 1.50, 1.74, 1.94, 2.03, 2.22 and 2.39 μm. These characteristics spectral absorption features together with the general shape of the spectral curve are used to identify the surface minerals of the area. In the mapping of different stages of evaporites and other surface minerals using Hyperion data, various image processing techniques including, the Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and Mixture Tuned Matched Filtering (MTMF) were applied. These spectral mapping methods coupled with geochemical knowledge of the area substantially improved the existing geological knowledge and enhanced the capability to derive substantial information related to the distribution and formation of precipitates and evaporites in the area.

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils

Using 108 petroleum contaminated soil samples, this pilot study proposed a new analytical approach of combining visible near-infrared diffuse reflectance spectroscopy (VisNIR DRS) and portable X-ray fluorescence spectrometry (PXRF) for rapid and improved quantification of soil petroleum contamination. Results indicated that an advanced fused model where VisNIR DRS spectra-based penalized spline regression (PSR) was used to predict total petroleum hydrocarbon followed by PXRF elemental data-based random forest regression was used to model the PSR residuals, it outperformed (R2=0.78, residual prediction deviation (RPD)=2.19) all other models tested, even producing better generalization than using VisNIR DRS alone (RPD's of 1.64, 1.86, and 1.96 for random forest, penalized spline regression, and partial least squares regression, respectively). Additionally, unsupervised principal component analysis using the PXRF+VisNIR DRS system qualitatively separated contaminated soils from control samples. CapsuleFusion of PXRF elemental data and VisNIR derivative spectra produced an optimized model for total petroleum hydrocarbon quantification in soils.

Soil classification using visible/near-infrared diffuse reflectance spectra from multiple depths

Visible/near-infrared diffuse reflectance spectroscopy (VNIRDRS) offers an alternative to conventional analytical methods to estimate various soil attributes. However, the use of VNIRDRS in soil survey and taxonomic classification is still underexplored. We investigated the potential use of VNIRDRS to classify soils in a region with variable soils, geology, and topography in southeastern Brazil. Soils were classified in the field according to the Brazilian Soil Classification System, and visible/near-infrared (400–2500nm) spectra were collected from three depth intervals (0–20, 40–60 and 80–100cm) and combined in sequence to compose a pseudo multi-depth spectral curve, which was used to derive the classification models. Principal component (PC) analysis and multinomial logistic regression were used to classify 291 soils (202 in calibration and 89 in validation mode) at the levels of order (highest), suborder (second highest) and suborder plus textural classification (STC). Based on the validation results, best classification was obtained at the order level (67% agreement rate), followed by suborder (48% agreement) and STC (24% agreement). The inherent complexity and variability within soil taxonomic groups and in contrast the strong similarity among different groups in terms of soil spectra and other attributes cause confusion in the classification model. This novel approach combining spectral data from different depths in multivariate classification can improve soil classification and survey in a cost-efficient manner, supporting sustainable use and management of tropical soils.

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation=3.2, validation r2=0.91 and RMSE=13.38μgg−1h−1) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky–Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.

Assessing spatial variability of soil petroleum contamination using visible near-infrared diffuse reflectance spectroscopy

Visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) is a rapid, non-destructive method for sensing the presence and amount of total petroleum hydrocarbon (TPH) contamination in soil. This study demonstrates the feasibility of VisNIR DRS to be used in the field to proximally sense and then map the areal extent of TPH contamination in soil. More specifically, we evaluated whether a combination of two methods, penalized spline regression and geostatistics could provide an efficient approach to assess spatial variability of soil TPH using VisNIR DRS data from soils collected from an 80 ha crude oil spill in central Louisiana, USA. Initially, a penalized spline model was calibrated to predict TPH contamination in soil by combining lab TPH values of 46 contaminated and uncontaminated soil samples and the first-derivative of VisNIR reflectance spectra of these samples. The r(2), RMSE, and bias of the calibrated penalized spline model were 0.81, 0.289 log(10) mg kg(-1), and 0.010 log(10) mg kg(-1), respectively. Subsequently, the penalized spline model was used to predict soil TPH content for 128 soil samples collected over the 80 ha study site. When assessed with a randomly chosen validation subset (n = 10) from the 128 samples, the penalized spline model performed satisfactorily (r(2) = 0.70; residual prediction deviation = 2.0). The same validation subset was used to assess point kriging interpolation after the remaining 118 predictions were used to produce an experimental semivariogram and map. The experimental semivariogram was fitted with an exponential model which revealed strong spatial dependence among soil TPH [r(2) = 0.76, nugget = 0.001 (log(10) mg kg(-1))(2), and sill 1.044 (log(10) mg kg(-1))(2)]. Kriging interpolation adequately interpolated TPH with r(2) and RMSE values of 0.88 and 0.312 log(10) mg kg(-1), respectively. Furthermore, in the kriged map, TPH distribution matched with the expected TPH variability of the study site. Since the combined use of VisNIR prediction and geostatistics was promising to identify the spatial patterns of TPH contamination in soils, future research is warranted to evaluate the approach for mapping spatial variability of petroleum contaminated soils.

Micro Side-Viewing Optical Probe for VNIR-DRS Soil Measurement

In this paper, we present the design, fabrication and testing of a new micro optical sensor probe with side-viewing function to enable in-field visible-near infrared diffuse reflectance spectroscopy (VNIR-DRS) for soil. The optical probe consists of two microfabricated source chips and two collection chips. The source chips are used to deliver oblique (45°) light incidence onto the soil surface, while the diffuse reflectance is received from the normal direction through the collector chips. Several design and fabrication considerations have been made to optimize the optical transmission efficiency of the probe and the signal-to-noise ratio (SNR) of the collected diffuse reflectance signal. The probe has a small outer dimension of 6 mm × 15 mm × 6 mm and thus can be easily fit into a typical soil penetrometer for in-filed VNIR-DRS measurements. Using the new optical probe, VNIR-DRS measurements have been successfully conducted on a number of soil samples, which shows the new optical probe offers better performance than the existing commercial ASD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> 135680 bifurcated reflectance soil probe.

Hyperspectral remote sensing of evaporate minerals and associated sediments in Lake Magadi area, Kenya

Pleistocene to present evaporitic lacustrine sediments in Lake Magadi, East African Rift Valley, Kenya were studied and mapped using spectral remote sensing methods. This approach incorporated surface mineral mapping using space-borne hyperspectral Hyperion imagery together with laboratory analysis, including visible, near-infrared diffuse reflectance spectroscopy (VNIR) measurements and X-ray diffraction for selected rock and soil samples of the study area. The spectral signatures of Magadiite and Kenyaite, which have not been previously reported, were established and the spectral signatures of trona, chert series, volcanic tuff and the High Magadi bed were also analyzed.Image processing techniques, MNF (Minimum Noise Fraction) and MTMF (Mixture Tuned Matched Filtering) using a stratified approach (image analysis with and without the lake area), were used to enhance the mapping of evaporates. High Magadi beds, chert series and volcanic tuff were identified from the Hyperion image with an overall mapping accuracy of 84.3%. Even though, the spatial distribution of evaporites and sediments in Lake Magadi area change in response to climate variations, the mineralogy of this area has not been mapped recently. The results of this study shows the usefulness of the hypersspectral remote sensing to map the surface geology of this kind of environment and to locate promising sites for industrial open-pit trona mining in a qualitative and quantitative manner.

Comparison and detection of total and available soil carbon fractions using visible/near infrared diffuse reflectance spectroscopy

Available carbon (C), measured as hot water extractable carbon (HC), has been suggested as an indicator to assess management effects on soil C fractions, transformations among C fractions, and carbon sequestration potential. Their upscaling to coarser scales has been hampered due to the cost and labor to measure C fractions. With the advent of visible/near-infrared-diffuse reflectance spectroscopy (VNIR-DRS) to infer on soil C fractions, new opportunities exist to combine sensor-based and geostatistical methods to estimate them across landscapes. Our research objectives were to (i) assess the accuracy of VNIR-DRS for estimating available C (HC) in soil, and (ii) compare interpolated HC estimates derived from laboratory measurements and VNIR-DRS derived estimates. The study site was within the Rio Grande floodplain in Quemado, Texas with mixed acreage of Arundo donax L. and Cynodon dactylon (L.) pers. Soil samples were taken in 10-cm depth increments from 0- to 50-cm depth, at 125 locations across a 34.5 ha field. Total C (TC, dry combustion), inorganic C (IC, modified pressure calcimeter), and HC (extraction at 80 °C) were measured and organic C (OC) was calculated by difference (TC − IC). Partial least squares regression (PLSR) and boosted regression tree (RT) were used to relate TC, HC, IC, and OC to VNIR-DRS spectra. Ordinary kriging was used to interpolate HC derived from analytical measurements and the values derived from the best spectral estimation model (PLSR). All four C pools were estimated well using VNIR-DRS and PLSR (R 2 > 0.77, residual prediction deviation > 2), ratio of performance to inter-quartile distance > 1.62. Overall, the maps estimating HC derived from VNIR-DRS estimates of HC were comparable to those from analytical measurements. In conclusion, VNIR-DRS reliably predicted soil C, as measured by HC.

Spectroscopic Models of Soil Organic Carbon in Florida, USA

Soil organic carbon (SOC) is an indicator of ecosystem quality and plays a major role in the biogeochemical cycles of major nutrients and water. Shortcomings exist to estimate SOC across large regions using rapid and cheap soil sensing approaches. Our objective was to estimate SOC in 7120 mineral and organic soil horizons in Florida using visible/near-infrared diffuse reflectance spectroscopy (VNIRS) calibrated by committee trees and partial least squares regression (PLSR). The derived VNIRS models were validated using independent datasets and explained up to 71 and 38% of the variance of SOC in mineral and organic horizons, respectively. We stratified the mineral horizons into seven soil orders and derived PLSR models for each order, which explained from 32% (Histosols) to 75% (Ultisols) of the variance of SOC concentration in validation mode. Estimates of SOC from all models were highly scattered along the regression lines, especially for high SOC values, and the slopes of the regression lines were generally <1 because VNIRS models tended to underestimate high SOC values and overestimate low SOC. Despite the great scatter of estimates in the prediction plots, VNIRS models had reasonable explanatory power for mineral horizons, given the heterogeneity of soils and environmental conditions in Florida, and have potential for the rapid assessment of SOC, with implications for regional SOC assessments, modeling, and monitoring. However, VNIRS models for organic horizons were hampered by small sample size and had very limited explanatory power.

Rapid erosion modeling in a Western Kenya watershed using visible near infrared reflectance, classification tree analysis and 137Cesium

Human-induced soil erosion has severe economic and environmental impacts throughout the world. It is more severe in the tropics than elsewhere and results in diminished food production and security. Kenya has limited arable land and 30% of the country experiences severe to very severe human-induced soil degradation. The purpose of this research was to test visible near infrared diffuse reflectance spectroscopy (VNIR) as a tool for rapid assessment and benchmarking of soil condition and erosion severity class. The study was conducted in the Saiwa River watershed in the northern Rift Valley Province of western Kenya, a tropical highland area. Soil 137Cs concentration was measured to validate spectrally derived erosion classes and establish the background levels for different land use types . Results indicate VNIR could be used to accurately evaluate a large and diverse soil data set and predict soil erosion characteristics. Soil condition was spectrally assessed and modeled. Analysis of mean raw spectra indicated significant reflectance differences between soil erosion classes. The largest differences occurred between 1350 and 1950 nm with the largest separation occurring at 1920 nm. Classification and Regression Tree (CART) analysis indicated that the spectral model had practical predictive success (72%) with Receiver Operating Characteristic (ROC) of 0.74. The change in 137Cs concentrations supported the premise that VNIR is an effective tool for rapid screening of soil erosion condition .

Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy

Visible and near-infrared (VisNIR, 400–2500 nm) diffuse reflectance spectroscopy (DRS) is a rapid, inexpensive sensing method that has shown promise for lab-based soil characterization. However, little has been reported on how DRS will work in a field setting on intact soil cores. Seventy-two soil cores, representing 21 soil series and four parent materials, were extracted from six fields in Central Texas. Each soil core was scanned with a VisNIR spectrometer with a spectral range of 350–2500 nm in four combinations of moisture content and pre-treatment, including field-moist intact, air-dried intact, field-moist smeared intact, and air-dried ground. Visible near-infrared spectra were then used to predict soil organic and inorganic carbon (C) using partial least squares (PLS) regression. The PLS model was validated with 30% of the original soil cores that were randomly selected and withheld from the calibration model. The organic C validation had a root mean squared deviation (RMSD) of 5.4 g kg − 1 and 4.1 g kg − 1 for the field-moist and air-dried intact scans, respectively. The RMSD values for inorganic C were 8.7 g kg − 1 and 7.8 g kg − 1 for the field-moist and air-dried intact scans, respectively. Smearing the samples had minimal effects on prediction accuracies for organic and inorganic C. Variable soil moisture did reduce prediction accuracies. Soil color, pH, and soil reaction to 1 N HCL were added as auxiliary predictors. Soil color improved organic C predictions by 0.2 to 0.4 g kg − 1 . The field-moist intact inorganic C model improved with soil color (RMSD = 8.0 g kg − 1 ), soil pH (8.3 g kg − 1 ), and soil reaction to HCl (6.5 g kg − 1 ). These results show that in situ spectroscopy can measure organic and inorganic C with some loss of accuracy compared to dried ground samples. In inorganic C predictions, an easy-to-measure auxiliary variable, like soil reaction with 1 N HCl, can improve in situ predictions compared to dried ground predictions.

Modeling of Soil Organic Carbon Fractions Using Visible–Near‐Infrared Spectroscopy

There is a pressing need for rapid and cost‐effective tools to estimate soil C across larger landscapes. Visible–near‐infrared diffuse reflectance spectroscopy (VNIRS) offers comparable levels of accuracy to conventional laboratory methods for estimating various soil properties. We used VNIRS to estimate soil total organic C (TC) and four organic C fractions in 141 samples collected in the Santa Fe River watershed of Florida. The C fractions measured were (in order of decreasing potential residence time in soils): recalcitrant C (RC), hydrolyzable C (HC), hot‐water‐soluble C (SC), and mineralizable C (MC). Soil samples were scanned in the visible–near‐infrared spectral range. Six preprocessing transformations were applied to the soil reflectance, and five multivariate techniques were tested to model soil TC and the organic C fractions: stepwise multiple linear regression (SMLR), principal components regression, partial least squares regression (PLSR), regression tree, and committee trees. Total organic C was estimated with the highest accuracy, obtaining a coefficient of determination using a validation set (Rv2) of 0.86, followed by RC (Rv2 = 0.82), both using PLSR. The SC fraction was modeled best by SMLR (Rv2 = 0.70), while PLSR produced the best models of MC (Rv2 = 0.65) and HC (Rv2 = 0.40). The addition of TC as a predictor improved the VNIRS models of the soil organic C fractions. Our study indicates the suitability of VNIRS to quantify soil organic C pools with widely varying turnover times in soils, which are important in the context of C sequestration and climate change.

Determination of Ash Content in Solid Cattle Manure with Visible Near-Infrared Diffuse Reflectance Spectroscopy

Visible and near-infrared (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy (DRS) is increasingly being used to quantify constituents of organic matter both in the lab and in situ. However, it is unknown if DRS can be utilized as a tool for determining crude ash content of solid cattle manure. Ash content is a significant contributor to the suitability and value of manure for use both as a biofuel and soil fertilizer, but conventional ash analysis is time-consuming and labor-intensive. In this study, we explored the feasibility of VisNIR-DRS for the rapid prediction of ash content in solid manure from beef feedyards in the southern High Plains. Proportionally mixed samples of soil and manure (n = 201) were evaluated for ash content by conventional analysis and then used to calibrate a statistical model for prediction of ash content by VisNIR-DRS based on multivariate partial-least squares regression and random test-set validation. Two thirds of the samples were randomly selected to build a calibration model, and the remaining third was used for validation. The coefficient of determination (r2), root mean squared deviation (RMSD), and ratio of prediction to standard deviation (RPD) were calculated to assess the prediction model. The prediction model had an r2 of 0.94, an RMSD of 5% ash (d.b.), and an RPD of 4. The VisNIR-DRS model successfully predicted crude ash content within 5% of the observed ash content (d.b.) as determined by dry oxidation using the accepted ASTM standard E1755-01.