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

Morgan, Cristine L.S.; Waiser, Travis H.; Brown, David J.; Hallmark, C. T.

doi:10.1016/j.geoderma.2009.04.010

Cited by 177 publications

(148 citation statements)

References 28 publications

(46 reference statements)

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“…All the outliers, reducing SOC prediction, were observed in the O and A-horizon (Figure 3). The presence of non-degraded or partly degraded organic material in the topsoil can increase the prediction uncertainty [30][31][32][33][34][35]. The model was more efficient to predict in the subsurface horizons due to more humified (humic acids) organic matter that has absorption in the VIS-NIR region.…”

Section: Predictions Of Soil Chemical Propertiesmentioning

confidence: 99%

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

et al. 2017

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Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization. The first step in this process is to develop a comprehensive VIS-NIR soil library of multiple key soil properties to be used in future soil surveys. This paper presents the first VIS-NIR soil library for a remote region in the Central Amazon. We evaluated the performance of VIS-NIR for the prediction of soil properties in the Central Amazon, Brazil. Soil properties measured and predicted were: pH, Ca, Mg, Al, H, H+Al, P, organic C (SOC), sum of bases, cation exchange capacity (CEC), percentage of base saturation (V), Al saturation (m), clay, sand, silt, silt/clay (S/C), and degree of flocculation. Soil samples were scanned in the laboratory in the VIS-NIR range (350-2500 nm), and forty-one pre-processing methods were tested to improve predictions. Clay content was predicted with the highest accuracy, followed by SOC. Sand, S/C, H, Al, H+Al, CEC, m and V predictions were reasonably good. The other soil properties were poorly predicted. Among the soil properties predicted well, SOC is one of the critical soil indicators in the global carbon cycle. Besides the soil property of interest, the landscape position, soil order and depth influenced in the model performance. For silt content, pH and S/C, the model performed better in well-drained soils, whereas for SOC best predictions were obtained in poorly drained soils. The association of VIS-NIR spectral data to landforms, vegetation classes, and soil types demonstrate potential for soil characterization.

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Section: Predictions Of Soil Chemical Propertiesmentioning

confidence: 99%

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

et al. 2017

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“…In recent years, a comprehensive literature review found that partial least squares regression (PLSR) is the most common VNIR calibration method (Dunn et al, 2002;Morgan et al, 2009;Escribano et al, 2010;Fontan et al, 2010). Although PLSR has many advantages, such as its simplicity, robustness, predictability, precision, and clearly quantitative explanations, the principal disadvantage is that PLSR does not provide a quantitative explanation for the relationship between predictor variables and response variables, and it does not support re-use of model algorithms between variable instrumentations.…”

Section: Introductionmentioning

confidence: 99%

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Chen

Peng

et al. 2012

Soil and Tillage Research

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“…Weindorf et al (2012bWeindorf et al ( , 2014 outlined the rationale for such differences with regard to PXRF as follows: (i) PXRF data align well with traditional morphological horizons, (ii) PXRF reveals more horizons than traditional morphological descriptions due to differences in elemental concentrations imperceptible to the human eye, and/or (iii) PXRF reveals fewer horizons than morphological descriptions based on differences undetectable to the PXRF (e.g., differences in soil structure, rooting, bulk density, soil organic C). Although VisNIR DRS should reasonably be able to detect differences in organic C (Morgan et al, 2009) (and by extension perhaps even differences in rooting density), soil characteristics such as bulk density, soil structure, and consistence probably remain elusive to these two proximal sensors. However, those characteristics seldom form the sole basis for LD designation.…”

Section: Proximal Sensor Approachesmentioning

confidence: 99%

“…Previously, PXRF and VisNIR DRS have been independently used to successfully predict a wide range of soil physicochemical properties, including organic C (Morgan et al, 2009;Chakraborty et al, 2013), gypsum content (Weindorf et al, 2009(Weindorf et al, , 2013a, salinity (Swanhart et al, 2014), pH (Sharma et al, 2014), texture (Zhu et al, 2011), cation exchange capacity , diagnostic subsurface horizons and features (Weindorf et al, 2012c), moisture , and organic and inorganic pollutants (Weindorf et al, 2012a(Weindorf et al, , 2013bChakraborty et al, 2010;Paulette et al, 2015). Most importantly, Weindorf et al (2012b) showed that PXRF could be used for enhanced soil horizonation whereby horizons could be differentiated using elemental data from PXRF in soil profiles where morphological differentia were unremarkable.…”

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confidence: 99%

Lithologic Discontinuity Assessment in Soils via Portable X-ray Fluorescence Spectrometry and Visible Near-Infrared Diffuse Reflectance Spectroscopy

Weindorf

Chakraborty

Abdalsatar

et al. 2015

Soil Science Society of America Journal

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Lithologic discontinuity identification can be arduous and erroneous in instances where distinct morphological differences between parent materials are absent. Often, investigators must wait for laboratory data to help differentiate parent materials via physicochemical properties. This study used visible near-infrared diffuse reflectance spectroscopy (VisNIR DRs) and portable X-ray fluorescence (PXRF) spectrometry for establishing parent material differentia more quickly. Ten pedons containing 135 samples were scanned in situ in the united states, Italy, and Hungary, morphologically described by trained pedologists, then sampled for standard laboratory characterization. Compared with laboratory data and/or morphologically described discontinuities, PXRF data were associated with large, abrupt changes in standardized PXRF differences of elements (DEs), noted in data plots as DE maxima and minima-areas of likely discontinuity. standardized VisNIR DRs calculated differences (CDs) in reflectance spectra (350-2500 nm) were also associated with discontinuities based on CD reflectance maxima and minima. Notably within both types of data plots, lithologic discontinuities were not well captured by the proximal sensors when CD or DE values fell in the data plot midsection (i.e., not at maxima or minima within the data plots). Across the pedons evaluated, PXRF was more useful for detecting discontinuities than VisNIR DRs. summarily, PXRF showed good alignment with morphologically established discontinuities in eight out of 10 pedons, while VisNIR DRs showed good alignment in only five pedons. Both PXRF and VisNIR DRs provided useful information for lithologic discontinuity recognition, especially in soils with nondescript morphology.Abbreviations: CD, calculated difference; DE, difference of elements; EC, electrical conductivity; LD, lithologic discontinuity; PCA, principal component analysis; PXRF, portable X-ray fluorescence; SOM, soil organic matter; VisNIR DRS, visible near-infrared diffuse reflectance spectroscopy.

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

Cited by 177 publications

References 28 publications

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Lithologic Discontinuity Assessment in Soils via Portable X-ray Fluorescence Spectrometry and Visible Near-Infrared Diffuse Reflectance Spectroscopy

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