Visible–NIR reflectance: a new approach on soil evaluation

Demattê, José Alexandre Melo; Campos, Rogério Costa; Alves, Marcelo Corrêa; Fiorio, Peterson Ricardo; Nanni, Marcos Rafael

doi:10.1016/j.geoderma.2003.09.012

Cited by 191 publications

(114 citation statements)

References 15 publications

(23 reference statements)

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“…The curve concavity at 900 nm was sharp only for the subsurface horizons (BA, Bt 1 and Bt 2 ), which may be attributed to the iron content in the B horizons, as soil organic matter is masking the effect of the iron in the surface horizon [50][51][52]. According to Viscarra Rossel et al [27], peaks near 900 nm may represent absorption caused by electronic transitions in goethite.…”

Section: Relationships Between Spectral Reflectance and Soil Typementioning

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: Relationships Between Spectral Reflectance and Soil Typementioning

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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“…The application of remote sensing techniques in soil studies began in the 1960s (Bowers & Hanks, 1965) and expanded to various applications, including quick and nondestructive quantification of soil attributes (Janik et al, 1998;Shepherd & Walsh, 2002, Dunn et al, 2002ViscarraRossel, et al, 2006a;Demattê & Nanni, 2006;Brown, et al,. 2006), soil survey and classification (Demattê et al, 2004, Ben-Dor et al, 2008, mineralogical measurements (Madeira-Netto, 1996;Viscarra-Rossel et al, 2006b;Sellito et al, 2009), digital soil mapping (Viscarra-Rossel & McBractney, 2008), precision agriculture (Thomasson et al, 2001Mouazen et al, 2007) and quantification of heavy metals (Wu Zhao et al, 2005). Thus, a basic requirement for their successful application is the creation of a spectral database, or Spectral Library (SL) (ViscarraRossel et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Soil spectral library and its use in soil classification

Bellinaso

Demattê

Romeiro

2010

Rev. Bras. Ciênc. Solo

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SUMMARYSoil science has sought to develop better techniques for the classification of soils, one of which is the use of remote sensing applications. The use of ground sensors to obtain soil spectral data has enabled the characterization of these data and the advancement of techniques for the quantification of soil attributes. In order to do this, the creation of a soil spectral library is necessary. A spectral library should be representative of the variability of the soils in a region. The objective of this study was to create a spectral library of distinct soils from several agricultural regions of Brazil. Spectral data were collected (using a

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“…the effect of soc on spectra is well-known and was demonstrated by e.g. Demattê et al (2004). this is especially true in the 350-1,350 nm range for saturated samples.…”

Section: Introductionmentioning

confidence: 74%

Visible and Near-Infrared Spectroscopy as a Tool for Soil Classification and Soil Profile Description

Debaene

Bartmiński

Niedźwiecki

et al. 2017

pjss

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this paper presents preliminary results of the use of visible and near-infrared (Vis-nir) spectroscopy for soil classification and soil profile examination. three experiments involving (1) three different soil types (albic luvisol, Gleyic Phaeozem, brunic arenosol), (2) three artificial micro-plots with similar texture (loamy sand, Gleyic Phaeozem) but different soil organic carbon (soc) content and (3) a soil profile (Fluvisol) have been investigated using Vis-nir spectroscopy. results indicated that Vis-nir is a promising technique for preliminary soil description and can classify soils according to soil properties (especially soc) and horizons. instead of complex chemical and physical analyses involved in routine soil profile classification, Vis-nir spectroscopy is suggested as a useful, rapid, and inexpensive tool for soil profile investigation.

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Visible–NIR reflectance: a new approach on soil evaluation

Cited by 191 publications

References 15 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

Soil spectral library and its use in soil classification

Visible and Near-Infrared Spectroscopy as a Tool for Soil Classification and Soil Profile Description

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