Spectral pedology: A new perspective on evaluation of soils along pedogenetic alterations

Demattê, José Alexandre Melo; Terra, Fabrício da Silva

doi:10.1016/j.geoderma.2013.11.012

Cited by 120 publications

(51 citation statements)

References 29 publications

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“…The spectroscopic features of CX ( Figure 8) were similar to those of other Cambissolos described by several authors (Formaggio et al, 1996;Bellinaso et al, 2010;Demattê and Terra, 2014). The maximum RFs of the Gleissolo Háplico (GX) spectrum were 0.5, with a positive trend ( Figure 10).…”

Section: Vissupporting

confidence: 78%

“…Some of these 2:1 clay minerals have additional absorption features centered at approximately 2,450 nm (Grove et al, 1992). Similar features of these clay minerals at 2,355 and 2,448 nm were also observed by Demattê and Terra (2014). Moreover, Grove et al (1992) showed that the presence of 2:1 minerals contribute to a greater intensity of the RF of soil.…”

Section: Interpretation Of Surface Spectrasupporting

confidence: 63%

See 1 more Smart Citation

Surface Spectroscopy of Oxisols, Entisols and Inceptisol and Relationships with Selected Soil Properties

Poppiel

Lacerda

Oliveira

et al. 2018

Rev. Bras. Ciênc. Solo

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Traditional method of soil survey is expensive, slow, and must be carried out by experienced researchers. Thus, advances in soil observation technologies and the need to obtain information quickly by modern techniques have intensified the use of proximal sensing. This study characterized surface reflectance spectra (A horizon) and related them to traditional soil classification, based on morphological, physical, and chemical properties of representative pedogenetic profiles, developed in two toposequences of the Distrito Federal, Brazil. In the toposequences, 15 soil profiles were selected for a complete morphological description and sampling for laboratory analyses. Soil-landscape relationships were established, and profiles were classified to the fourth level of the Brazilian Soil Classification System (SiBCS). Classes of similar soils were grouped based on their surface spectra, resulting in spectral curves of 10 representative soils in the studied area. The Morphological Interpretation of Reflectance Spectrum (MIRS) and second derivative of the Kubelka-Munk (KM) function were applied to the soil spectra. The clustered soils were similar, mainly in terms of color, textural class, and organic matter content. Groups based on soil physical and chemical properties and on surface and subsurface colors were similar to those determined by surface reflectance. The identification of soil-landscape relationships was fundamental to understand the genesis and distribution of the soils, which had similar chemical and physical properties to their parent materials. The analysis of clusters based on soil surface reflectance proved efficient in determining groups of soil classes with similar properties. Surface reflectance data were related to the soil surface and subsurface properties determined by traditional soil sample analyses, since the two approaches formed similar groups. The simultaneous interaction of soil properties was assessed by MIRS analysis, while the second derivative of the KM function adequately quantified the mineralogy of the spectra.

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Section: Vissupporting

confidence: 78%

Section: Interpretation Of Surface Spectrasupporting

confidence: 63%

Surface Spectroscopy of Oxisols, Entisols and Inceptisol and Relationships with Selected Soil Properties

Poppiel

Lacerda

Oliveira

et al. 2018

Rev. Bras. Ciênc. Solo

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“…This will give support to the definition of Spectral Pedology, as proposed by Demattê and Terra (2014).…”

Section: Introductionsupporting

confidence: 71%

Morphological Interpretation of Reflectance Spectrum (MIRS) using libraries looking towards soil classification

Demattê

Bellinaso²,

Romero

et al. 2014

Sci. agric. (Piracicaba, Braz.)

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The search for tools to perform soil surveying faster and cheaper has led to the development of technological innovations such as remote sensing (RS) and the so-called spectral libraries in recent years. However, there are no studies which collate all the RS background to demonstrate how to use this technology for soil classification. The present study aims to describe a simple method of how to classify soils by the morphology of spectra associated with a quantitative view (400-2,500 nm). For this, we constructed three spectral libraries: (i) one for quantitative model performance; (ii) a second to function as the spectral patterns; and (iii) a third to serve as a validation stage. All samples had their chemical and granulometric attributes determined by laboratory analysis and prediction models were created based on soil spectra. The system is based on seven steps summarized as follows: i) interpretation of the spectral curve intensity; ii) observation of the general shape of curves; iii) evaluation of absorption features; iv) comparison of spectral curves between the same profile horizons; v) quantification of soil attributes by spectral library models; vi) comparison of a pre-existent spectral library with unknown profile spectra; vii) most probable soil classification. A soil cannot be classified from one spectral curve alone. The behavior between the horizons of a profile, however, was correlated with its classification. In fact, the validation showed 85 % accuracy between the Morphological Interpretation of Reflectance Spectrum (MIRS) method and the traditional classification, showing the importance and potential of a combination of descriptive and quantitative evaluations.

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“…Diffuse reflectance spectroscopy (DRS) is a promising new option for soil analysis, with advantages such as ease of data acquisition from large amounts of samples, within less time and without involving environmentally hazardous chemicals (Viscarra Rossel et al, 2010;Sousa Junior et al, 2011;Ramirez-Lopez et al, 2014). DRS can be an alternative to current methods of routine analyses, since particle size is one of the most relevant properties for soil spectral reflectance (Dalmolin et al, 2005;Demattê and Terra, 2014). Improvements are needed, mainly in terms of model accuracy (requiring model training and validation variables) and of spatial dependence relations, to establish relationships with the environment.…”

Section: Introductionmentioning

confidence: 99%

Potential of Spectroradiometry to Classify Soil Clay Content

Dotto¹,

Dalmolin²,

Caten

et al. 2016

Rev. Bras. Ciênc. Solo

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Spectral pedology: A new perspective on evaluation of soils along pedogenetic alterations

Cited by 120 publications

References 29 publications

Surface Spectroscopy of Oxisols, Entisols and Inceptisol and Relationships with Selected Soil Properties

Surface Spectroscopy of Oxisols, Entisols and Inceptisol and Relationships with Selected Soil Properties

Morphological Interpretation of Reflectance Spectrum (MIRS) using libraries looking towards soil classification

Potential of Spectroradiometry to Classify Soil Clay Content

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