In situ separation of soil types along transects employing Vis-NIR sensors: a new view of soil evaluation

Fiorio, Peterson Ricardo; Demattê, José Alexandre Melo; Nanni, Marcos Rafael; Genú, Aline Marques; Martins, Juliano Araujo

doi:10.1590/s1806-66902014000300002

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…However, they did not show, at that time, how to use the information to achieve classification. With the aim of analyzing spectra by shapes and features, Demattê (2002) identified, much later, the differences between tropical soils, that were subsequently used and ratified in case studies by Vasques et al (2014), SorianoDisla et al (2014) and Fiorio et al (2014). But they still did not have a method that integrated quantitative and descriptive analysis.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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“…In fact, this led Demattê et al (2004a) to use a spectroscopy information to aid in pedological mapping. Afterwards, Fiorio et al (2014) suggest the use of spectral sensors for the aid of field work in pedological maps. In addition to laboratory or field spectroscopy, we can also gather information from space (using Landsat data) as a valuable strategy (Demattê et al, 2009).…”

Section: Resumo: Múltiplas Ferramentas Tecnológicas No Mapeamento Digmentioning

confidence: 99%

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Alves

Demattê

Barros

2015

Rev. Bras. Ciênc. Solo

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in recent years, geotechnologies as remote and proximal sensing and attributes derived from digital terrain elevation models indicated to be very useful for the description of soil variability. however, these information sources are rarely used together. therefore, a methodology for assessing and specialize soil classes using the information obtained from remote/proximal sensing, Gis and technical knowledge has been applied and evaluated. two areas of study, in the state of são Paulo, Brazil, totaling approximately 28.000 ha were used for this work. first, in an area (area 1), conventional pedological mapping was done and from the soil classes found patterns were obtained with the following information: a) spectral information (forms of features and absorption intensity of spectral curves with 350 wavelengths -2,500 nm) of soil samples collected at specific points in the area (according to each soil type); b) obtaining equations for determining chemical and physical properties of the soil from the relationship between the results obtained in the laboratory by the conventional method, the levels of chemical and physical attributes with the spectral data; c) supervised classification of Landsat TM 5 images, in order to detect changes in the size of the soil particles (soil texture); d) relationship between classes relief soils and attributes. subsequently, the obtained patterns were applied in area 2 obtain pedological classification of soils, but in GIS (ArcGIS). finally, we developed a conventional pedological mapping in area 2 to which was compared with a digital map, ie the one obtained only with pre certain standards. the proposed methodology had a 79 % accuracy in the first categorical level of Soil Classification System, 60 % accuracy in the second category level and became less useful in the categorical level 3 (37 % accuracy).

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“…Nessas estratégias, estimam-se teores de atributos ou classes de solos em locais desconhecidos a partir amostras conhecidas que têm correlação com dados espectrais de satélite e/ou dados de relevo, via técnicas multivariadas, e assim obter mapas com classes ou atributos do solo. Observa-se que os trabalhos realizados nos estudos de predição se dividem entre estimativas de atributos (Moore et al, 1993;Odeh et al, 2006;Junior et al, 2008;Dematte et al, 2009;Mulder et al, 2011;Gerighausen et al, 2012;Genú et al, 2013;Franceschini et al, 2015) e estimativa de classes de solos (Demattê et al, 2004(Demattê et al, , 2016Ziadat, 2007;Figueiredo et al, 2008;ten Caten et al, 2013ten Caten et al, , 2009Genú e Demattê, 2012;Arruda et al, 2013;Giasson et al, 2013;Adhikari et al, 2014;Fiorio et al, 2014;Teske et al, 2015;Dias et al, 2015) via imagens de satélite e/ou dados de relevo. Além das estratégias de predição via imagens de satélite e/ou dados de relevo, também se apresenta como importante ferramenta, a biblioteca espectral.…”

Section: Introductionunclassified

Mapeamento pedológico digital via regressão geograficamente ponderada e lógica booleana: uma estratégia integrada entre dados espectrais terrestres e de satélite

Neto¹,

Gonzaga²

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In situ separation of soil types along transects employing Vis-NIR sensors: a new view of soil evaluation

Cited by 7 publications

References 16 publications

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

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

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Mapeamento pedológico digital via regressão geograficamente ponderada e lógica booleana: uma estratégia integrada entre dados espectrais terrestres e de satélite

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