Variable selection in near infrared spectra for the biological characterization of soil and earthworm casts

Cécillon, Lauric; Cassagne, Nathalie; Czarnes, Sonia; Gros, Raphaël; Brun, Jean‐Jacques

doi:10.1016/j.soilbio.2008.03.016

Cited by 64 publications

(59 citation statements)

References 26 publications

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“…The number of components is selected to minimize the root mean square error of cross-validation (RMSECV) and to maximize the Stone-Geisser criteria Q 2 obtained from a leave-one-out cross-validation of the models (Tenenhaus 1999, Cécillon et al 2008. For several models presented in this paper, the number of components could be reduced without decreasing significantly the accuracy as measured by the RMSECV.…”

Section: Multivariate Data Analysismentioning

confidence: 99%

Monitoring of moisture content and basic specific gravity in black spruce logs using a hand-held MEMS-based near-infrared spectrometer

HansGuillaume

LeblonBrigitte

Stirling

et al. 2013

The Forestry Chronicle

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Our study presents results of a test of a hand-held micro-electro-mechanical system (MEMS)-based near-infrared (NIR) spectrometer to estimate moisture content and basic specific gravity of black spruce frozen and unfrozen logs. The spectra were acquired on sapwood and heartwood as well as on tangential and transversal log sections. Partial least square regression was used to develop and validate the models that relate NIR spectral data to the log properties. Models were developed for the frozen and unfrozen logs separately (temperature-specific models) and for both kinds of logs together (generalized model). Both model types gave similar prediction accuracy and there were no temperature condition effects on the PLS model, for both moisture content and basic specific gravity estimation. This implies that the NIR spectrometer can be used whatever the log temperature conditions, even under freezing conditions. Keywords: near-infrared spectroscopy, black spruce, partial least square regression, moisture content, basic specific gravity, hand-held near-infrared spectrometer, MEMS, log, sapwood, heartwood résumé Notre étude présente le test d'un spectromètre proche infra-rouge portable basé sur un système micro-électro-mécanique (SMEM) pour mesurer la teneur en eau et la densité basale de grumes gelées et non gelées. Les spectres ont été acquis sur l'aubier et sur le duramen ainsi que sur les sections tangentielles et transversales de la grume. La régression des moindres carrés partiels (PLS) est utilisée pour calibrer et valider les modèles d' estimation des propriétés de la grume à partir des données proche infra-rouge. Des modèles ont été développés pour les grumes gelées et non gelées, séparément (modèles spécifiques à une température donnée) ainsi que pour les deux types de grumes ensemble (modèles généralisés). Les deux types de modèles ont donné des précisions d' estimation semblables et il n'y a pas eu d' effets des conditions de température sur les prédictions des modèles PLS. Ceci implique que les mesures du spectromètre proche infra-rouge portable peuvent être utilisées quel que soient les conditions de température des grumes, même avec des températures en-dessous du point de congélation.Mots-clés : spectroscopie proche infra-rouge, épinette noire, régression PLS, teneur en eau, densité basale, spectromètre portable, MEMS, grume, aubier, duramen

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Section: Multivariate Data Analysismentioning

confidence: 99%

Monitoring of moisture content and basic specific gravity in black spruce logs using a hand-held MEMS-based near-infrared spectrometer

HansGuillaume

LeblonBrigitte

Stirling

et al. 2013

The Forestry Chronicle

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“…Each PC explained a certain amount of variation (percent) in the total dataset, and this percentage provided the weight for the variables chosen under a given PC. In order to evaluate the relative importance of each waveband in each of the PLS-R models, the variable importance in projection (VIP) was computed to reveal the score for each wavelength [66].…”

Section: Soil Quality Index (Sqi)mentioning

confidence: 99%

“…Spectroscopy is a reliable tool for assessing soil properties; however, it is site-specific and related to the soil function and structure. Previous studies have shown the ability of spectroscopy to predict several soil properties, such as texture, SOM, AWC, NH4, NO3, EC, and pH, with different prediction levels [15,25,31,66]. These differences are probably associated with the energy of the absorbance and reflectance of molecular bonds in the NIR region due to the combination of C-H, N-H, C-O, C-N and O-H groups (chromophores) [77][78][79], minerals, water, and nutrients.…”

Section: Predictability Of Indicators and Indicesmentioning

confidence: 99%

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

et al. 2015

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Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and demonstrate the ability of IS to evaluate soil properties and quality across anthropogenically induced land-use changes. This aim was fulfilled by developing and implementing a spectral soil quality index (SSQI) using IS obtained by a laboratory and field spectrometer (point scale) as well as by airborne hyperspectral imaging (local scale), in two experimental sites located in Israel and Germany. In this regard, 13 soil physical, biological, and chemical properties and their derived soil quality index (SQI) were measured. Several mathematical/statistical procedures, consisting of a series of operations, including a principal component analysis OPEN ACCESSRemote Sens. 2015, 7 15749(PCA), a partial least squares-regression (PLS-R), and a partial least squares-discriminate analysis (PLS-DA), were used. Correlations between the laboratory spectral values and the calculated SQI coefficient of determination (R 2 ) and ratio of performance to deviation (RPD) were R 2 = 0.84; RPD = 2.43 and R 2 = 0.78; RPD = 2.10 in the Israeli and the German study sites, respectively. The PLS-DA model that was used to develop the SSQI showed high classification accuracy in both sites (from laboratory, field, and imaging spectroscopy). The correlations between the SSQI and the SQI were R 2 = 0.71 and R 2 = 0.7, in the Israeli and the German study sites, respectively. It is concluded that soil quality can be effectively monitored using the spectral-spatial information provided by the IS technology. IS-based classification of soils can provide the basis for a spatially explicit and quantitative approach for monitoring SQ and function at a local scale.

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“…These techniques rely on registration of reflected radiation from an analysed surface and they allow to obtain data for: soil monitoring, digital soil mapping, environmental modelling, and precision agriculture (Brown et al 2006). Also, it allows to measure quantitative information about soil parameters such as soil texture or soil organic matter content (Cécillon et al 2008). These techniques are used under field or laboratory conditions (Gholizadeh et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Influence of the Distance Between a Reflectance Sensor and Soil Samples with Different Roughness on Their Spectra

Herodowicz

2017

pjss

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The study assessed the influence of the distance between a reflectance sensor and soil samples with various roughness states (R1-the lowest, R2 -the medium, R3 -the highest roughness state) on their spectra level, under laboratory condition. Studied soil samples were illuminated at three light source zenith angles (θ s equal to 20°, 40°, 60°) and observed by the sensor to the nadir, from various distances (H s ) from 10 to 54 cm. These dark (the Mollic Gleyic Fluvisol) and light (the Cutanic Stagnic Luvisol) soil materials with their minimum roughness were characterized by diffused reflectance spectra. The relative differences (RD) between the spectra level of soil samples with R1, R2, R3 roughness states and the diffused reflectance level of soil materials were calculated with 1 nm interval in range of 420-2,300 nm. Higher roughness state and higher θ s , result in higher RD. Thus, for the dark and light soil samples with R3 roughness state and illuminated at θ s = 60°,the RD are the highest reached 63 and 39% (H s =54 cm) and reached 77 and 63% (H s =10 cm), respectively. The spectra level of the soil samples in R1 and R3 roughness states, illuminated at θ s =20° and soil samples with R1 roughness, illuminated at θ s =60°, reached a stable level, at a specific H s . It means, that a spectra does not significantly change with a further increase H s . However, the soil samples in R3 roughness, illuminated at θ s =60° have not reached the stability.

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Variable selection in near infrared spectra for the biological characterization of soil and earthworm casts

Cited by 64 publications

References 26 publications

Monitoring of moisture content and basic specific gravity in black spruce logs using a hand-held MEMS-based near-infrared spectrometer

Monitoring of moisture content and basic specific gravity in black spruce logs using a hand-held MEMS-based near-infrared spectrometer

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

Influence of the Distance Between a Reflectance Sensor and Soil Samples with Different Roughness on Their Spectra

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