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

Sarkhot, D. V.; Grunwald, Sabine; Ge, Yao; Morgan, Cristine L.S.

doi:10.1016/j.geoderma.2011.05.006

Cited by 71 publications

(37 citation statements)

References 44 publications

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“…Moreover, the eight pre-processing methods shared several wavelengths, particularly near 800, 1000, 1400 and 1900−2450 nm, which confirmed that the wavelengths in the near-infrared spectral region were important for SOC content estimations [4,16,22,24,30,50]. Ge et al [13] estimated SOC contents with soil VIS/NIR spectra from different instruments/scanning environments.…”

Section: Wavelength Selectionmentioning

confidence: 87%

“…A rapid and economical method for estimating SOC content can improve environmental monitoring, modeling and precision agriculture [3][4][5]. However, the traditional laboratory analysis for estimating SOC content is time-consuming, relatively expensive and cannot describe the spatial and temporal dynamics of SOC contents over large areas with sufficient detail [6].…”

Section: Introductionmentioning

confidence: 99%

“…VIS/NIR spectroscopy has great potential for simultaneously estimating a variety of soil properties [1,4]. In recent years, various quantitative methods have been applied to VIS/NIR hyperspectral data to estimate SOC content, such as multiple linear regression (MLR) [9], principal component regression (PCR) [10] and partial least squares regression (PLSR) [9,11].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

et al. 2014

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With 298 heterogeneous soil samples from Yixing (Jiangsu Province), Zhongxiang and Honghu (Hubei Province), this study aimed to combine a successive projections algorithm (SPA) with a support vector machine regression (SVMR) model (SPA-SVMR model) to improve the estimation accuracy of soil organic carbon (SOC) contents using the laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The effects of eight spectra pre-processing methods, i.e., Log (1/R), Log (1/R) coupled with Savitzky-Golay (SG) smoothing (Log (1/R) + SG), first derivative with SG smoothing (FD), second derivative with SG smoothing (SD), SG, standard normal variate (SNV), mean center (MC) and multiplicative scatter correction (MSC), on SPA-based informative wavelength selection were explored. The SVMR model (i.e., SVMR without SPA) and SPA-PLSR model (i.e., SPA combined with partial least squares regression (PLSR)) were developed and compared with the SPA-SVMR model in order to evaluate the performance of SPA-SVMR. The results indicated that the variables selected by SPA and their distributions were strongly affected by different pre-processing methods, and SG was the optimal pre-processing method for SPA-SVMR model development; the SPA-SVMR model using SG pre-processing and 28 SPA-selected wavelengths obtained a better result (R 2 V = 0.73, RMSE V = 2.78 g·kg and RPD V = 1.63). Most of the spectral bands used by the SPA-SVMR model over the near-infrared region were important wavelengths for SOC content estimation. This study demonstrated that the combination of SPA and SVMR is feasible and reliable for estimating SOC content from the VIS/NIR spectra of soils in regions with multiple soil and land-use types.

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Section: Wavelength Selectionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

et al. 2014

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“…In addition, the RPIQ was computed as error measure. We made use of the RPD because it is an already established and well known error measure to which this study can be related to similar studies in the literature, but also made use of the RPIQ as RPIQ is a more robust and preferable way of standardizing the prediction error with respect to the spread of the population for soil analysis than RPD, as acknowledged by [53,54]. To produce the soil maps, the final PLS model was used to predict all non-masked pixels of the images, transformed with the automatically selected, best performing filters as given by the autoPLSR procedure.…”

Section: Model Performance Assessmentmentioning

confidence: 99%

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

et al. 2016

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Abstract:With the upcoming availability of the next generation of high quality orbiting hyperspectral sensors, a major step toward improved regional soil mapping and monitoring and delivery of quantitative soil maps is expected. This study focuses on the determination of the prediction accuracy of spectral models for the mapping of common soil properties based on upcoming EnMAP (Environmental Mapping and Analysis Program) satellite data using semi-operational soil models. Iron oxide (Fe d ), clay, and soil organic carbon (SOC) content are predicted in test areas in Spain and Luxembourg based on a semi-automatic Partial-Least-Square (PLS) regression approach using airborne hyperspectral, simulated EnMAP, and soil chemical datasets. A variance contribution analysis, accounting for errors in the dependent variables, is used alongside classical error measurements. Results show that EnMAP allows predicting iron oxide, clay, and SOC with an R 2 between 0.53 and 0.67 compared to Hyperspectral Mapper (HyMap)/Airborne Hyperspectral System (AHS) imagery with an R 2 between 0.64 and 0.74. Although a slight decrease in soil prediction accuracy is observed at the spaceborne scale compared to the airborne scale, the decrease in accuracy is still reasonable. Furthermore, spatial distribution is coherent between the HyMap/AHS mapping and simulated EnMAP mapping as shown with a spatial structure analysis with a systematically lower semivariance at the EnMAP scale.

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“…IW-DOM was extracted with Rhizon samplers (Seeberg-Elverfeldt et al, 2005) from a sediment depth of down to 6 m below seafloor and compared to the organic matter (fraction <0.4 lM) that is extractable with hot water in a Soxhlet apparatus (water extractable organic matter WE-OM). Hot water extraction of organic matter has been previously applied to determine the labile organic matter fraction in soils (e.g., Sparling et al, 1998;Ghani et al, 2003;Gregorich et al, 2003;Bu et al, 2010;Xing et al, 2010;Sarkhot et al, 2011). However, to our knowledge, it has never been utilized for the molecular characterization of organic matter in marine sediments.…”

Section: Introductionmentioning

confidence: 99%

Extending the analytical window for water-soluble organic matter in sediments by aqueous Soxhlet extraction

Schmidt

Koch

Witt

et al. 2014

Geochimica et Cosmochimica Acta

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Dissolved organic matter (DOM) in marine sediments is a complex mixture of thousands of individual constituents that participate in biogeochemical reactions and serve as substrates for benthic microbes. Knowledge of the molecular composition of DOM is a prerequisite for a comprehensive understanding of the biogeochemical processes in sediments. In this study, interstitial water DOM was extracted with Rhizon samplers from a sediment core from the Black Sea and compared to the corresponding water-extractable organic matter fraction (<0.4 lm) obtained by Soxhlet extraction, which mobilizes labile particulate organic matter and DOM. After solid phase extraction (SPE) of DOM, samples were analyzed for the molecular composition by Fourier Transform Ion-Cyclotron Resonance Mass Spectrometry (FT-ICR MS) with electrospray ionization in negative ion mode. The average SPE extraction yield of the dissolved organic carbon (DOC) in interstitial water was 63%, whereas less than 30% of the DOC in Soxhlet-extracted organic matter was recovered. Nevertheless, Soxhlet extraction yielded up to 4.35% of the total sedimentary organic carbon, which is more than 30-times the organic carbon content of the interstitial water. While interstitial water DOM consisted primarily of carbon-, hydrogen-and oxygen-bearing compounds, Soxhlet extracts yielded more complex FT-ICR mass spectra with more peaks and higher abundances of nitrogenand sulfur-bearing compounds. The molecular composition of both sample types was affected by the geochemical conditions in the sediment; elevated concentrations of HS -promoted the early diagenetic sulfurization of organic matter. The Soxhlet extracts from shallow sediment contained specific three-and four-nitrogen-bearing molecular formulas that were also detected in bacterial cell extracts and presumably represent proteinaceous molecules. These compounds decreased with increasing sediment depth while one-and two-nitrogen-bearing molecules increased, resulting in a higher similarity of both sample types in the deep sediment. In summary, Soxhlet extraction of sediments accessed a larger and more complex pool of organic matter than present in interstitial water DOM.

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

Cited by 71 publications

References 44 publications

Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

Extending the analytical window for water-soluble organic matter in sediments by aqueous Soxhlet extraction

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