Major Issues of Diffuse Reflectance NIR Spectroscopy in the Specific Context of Soil Carbon Content Estimation

Gobrecht, Alexia; Roger, Jean‐Michel; Maurel, Véronique Bellon

doi:10.1016/b978-0-12-420225-2.00004-2

Cited by 39 publications

(22 citation statements)

References 74 publications

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“…In addition, due to the scattering effect, the useful part of the absorption spectra (related to elemental concentration) is relatively small when compared to the scattering effect (Gobrecht et al, 2014). Multivariate calibration techniques are used to calibrate the spectra to known measured soil properties.…”

Section: Infrared Spectroscopy For Measuring Soil Contaminantsmentioning

confidence: 99%

“…field condition and air dried condition respectively. The spectral influence of soil moisture is then removed from the total spectral space by orthogonal projection (Gobrecht et al, 2014). Minasny et al (2011) demonstrated an improved calibration and validation in soil carbon concentration using EPO-transformed spectra.…”

Section: Taking Ir and Pxrf To The Field And The Role Of Spectral Libmentioning

confidence: 99%

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Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review

et al. 2015

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a b s t r a c tThere are tens of millions of contaminated soil sites in the world, and with an increasing population and associated risk there is a growing pressure to remediate them. A barrier to remediation is the lack of cost-effective approaches to assessment. Soil contaminants include a wide range of natural and synthetic metallic and organic compounds and minerals thus making analytical costs potentially very large. Further, soil contaminants show a large degree of spatial variation which increases the burden on sampling costs. This paper reviews potentially cost-effective methods for measurement, sampling design, and assessment. Current tiered investigation approaches and sampling strategies can be improved by using new technologies such as proximal sensing. Design of sampling can be aided by on-the-go proximal soil sensing; and expedited by subsequent adaptive spatially optimal sampling and prediction procedures enabled by field spectroscopic methods and advanced geostatistics. Field deployment of portable Visible & Near Infrared [wavelength 400-2500 nm] (Vis-NIR) and X-ray fluorescence (PXRF) spectroscopies will require special calibration approaches but show huge potential for synergistic use. The use of mid-infrared spectroscopy [wavelength 2500-25,000 nm, wavenumber 4000-400 cm −1 ] (MIR) for field implementation requires further adaptive research. We propose an integrated field-deployable methodology as a basis for further developments. Crown

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Section: Infrared Spectroscopy For Measuring Soil Contaminantsmentioning

confidence: 99%

Section: Taking Ir and Pxrf To The Field And The Role Of Spectral Libmentioning

confidence: 99%

Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review

et al. 2015

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“…Near infrared (NIR) spectroscopy (NIRS, 800–2500 nm, vis‐NIRS, 400–2500 nm) is a promising approach for simultaneous estimations of a large number of physical and chemical soil properties [for reviews see Reeves III (), Bellon‐Maurel and McBratney (), Gobrecht et al (), and Soriano‐Disla et al ()]. In general, vis‐NIR estimations will not accurately reproduce physical and wet‐chemical laboratory analyses (where, depending on the analysis procedure, precision and accuracy may also vary), but accuracies of vis‐NIR estimations may still be sufficient for studies, which require large data sets, such as, e.g ., long‐term monitoring studies or large scale inventories.…”

Section: Introductionmentioning

confidence: 99%

Use of different chemometric approaches for an estimation of soil properties at field scale with near infrared spectroscopy

Ludwig

Murugan

Parama

et al. 2018

J. Plant Nutr. Soil Sci.

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Visible and near infrared spectroscopy (vis‐NIRS) may be useful for an estimation of soil properties in arable fields, but the quality of results are often variable depending on the applied chemometric approach. Partial least squares regression (PLSR) may be replaced by approaches which employ supervised learning methods or variable selection procedures in order to increase the proportion of informative wavelengths used in the estimation procedure, to reduce the noise of the spectra and to find the best fitting solution. Objectives were (1) to compare the usefulness of PLSR with either PLSR combined with a genetic algorithm (GA‐PLSR) or support vector machine regression (SVMR) for an estimation of soil organic carbon (SOC), total nitrogen (N), pH, cation exchange capacity (CEC) and soil texture for surface soils (0–5 cm, n = 144) of an arable field in Bangalore (India) and (2) to test and optimize different calibration strategies for GA‐PLSR for an improved estimation of soil properties. PLSR was useful for an estimation of SOC, N, sand and clay. In the cross‐validation (n = 96), accuracies of estimated soil properties generally decreased in the order GA‐PLSR > SVMR > PLSR. However, the order of estimation accuracies for the random validation sample (n = 48) changed to SVMR > GA‐PLSR > PLSR for SOC, N, pH, and CEC, whereas for clay the order changed to SVMR > PLSR > GA‐PLSR. A sequential procedure, which used the most frequently selected wavelengths of the GA‐PLSR runs, proved to be useful for an improved estimation of SOC and N. Overall, SVMR especially improved estimations of SOC and clay, whereas GA‐PLSR was particularly useful for SOC and N and it was the only approach which successfully estimated CEC in cross‐validation and validation.

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“…Spectroscopy, covering the optical (reflected solar radiation) and thermal infrared (earth tsurface emitted radiation) regions across the 0.4-15 µm spectral range, can be used to determine a wide range of soil properties [1] such as organic carbon (OC) [2], texture [3], cationic exchange capacity (CEC) [4], total phosphorus (P) [5], exchangeable potassium (K) [6], electrical conductivity (EC) [7][8][9], total concentration of potential pollutant metals/metalloids [10] and mineral content [11,12]. Aside from the fundamental interaction of electromagnetic radiation with matter, indirect interaction can be found and provide additional quantitative information of the soil in question [13].…”

Section: Introductionmentioning

confidence: 99%

Modelling Diverse Soil Attributes with Visible to Longwave Infrared Spectroscopy Using PLSR Employed by an Automatic Modelling Engine

et al. 2017

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Abstract:The study tested a data mining engine (PARACUDA ® ) to predict various soil attributes (BC, CEC, BS, pH, C org , Pb, Hg, As, Zn and Cu) using reflectance data acquired for both optical and thermal infrared regions. The engine was designed to utilize large data in parallel and automatic processing to build and process hundreds of diverse models in a unified manner while avoiding bias and deviations caused by the operator(s). The system is able to systematically assess the effect of diverse preprocessing techniques; additionally, it analyses other parameters, such as different spectral resolutions and spectral coverages that affect soil properties. Accordingly, the system was used to extract models across both optical and thermal infrared spectral regions, which holds significant chromophores. In total, 2880 models were evaluated where each model was generated with a different preprocessing scheme of the input spectral data. The models were assessed using statistical parameters such as coefficient of determination (R 2 ), square error of prediction (SEP), relative percentage difference (RPD) and by physical explanation (spectral assignments). It was found that the smoothing procedure is the most beneficial preprocessing stage, especially when combined with spectral derivation (1st or 2nd derivatives). Automatically and without the need of an operator, the data mining engine enabled the best prediction models to be found from all the combinations tested. Furthermore, the data mining approach used in this study and its processing scheme proved to be efficient tools for getting a better understanding of the geochemical properties of the samples studied (e.g., mineral associations).

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Major Issues of Diffuse Reflectance NIR Spectroscopy in the Specific Context of Soil Carbon Content Estimation

Cited by 39 publications

References 74 publications

Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review

Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review

Use of different chemometric approaches for an estimation of soil properties at field scale with near infrared spectroscopy

Modelling Diverse Soil Attributes with Visible to Longwave Infrared Spectroscopy Using PLSR Employed by an Automatic Modelling Engine

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