The potential of NIR spectroscopy to predict soil texture and mineralogy in Cerrado Latosols

Vendrame, Pedro Rodolfo Siqueira; Marchão, Robélio Leandro; Brunet, Didier; Becquer, Thierry

doi:10.1111/j.1365-2389.2012.01483.x

Cited by 59 publications

(34 citation statements)

References 35 publications

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“…Individualization of soil textural classes by means of spectroscopy at VIS-NIR-SWIR has been observed and reported by various authors and in Brazilian soils, such as Demattê and Garcia [6], Nanni and Demattê [14], Ferraresi et al [45], Vendrame et al [46] and Franceschini et al [36], among others. This individualization was corroborated in our study.…”

Section: Spectroscopic Analyses Of Soil Samplesmentioning

confidence: 84%

“…Ferraresi et al [45] reached R 2 0.85 for clay content quantification and 0.87 for sand in São Paulo and Goiás States. In regional sampling of Latosols (Ferralsols) of the Brazilian Cerrado, Vendrame et al [46] found 0.83 and 0.74 of R 2 for calibration and validation to determine clay content. In Mato Grosso do Sul State, Franceschini et al [36] obtained satisfactory quantification for clay (R 2 = 0.92 and RPD = 3.59), silt (R 2 = 0.80 and RPD = 2.15) and sand (R 2 = 0.87 and RPD = 2.62).…”

Section: Prediction Models Of Soil Attributesmentioning

confidence: 99%

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Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

et al. 2016

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Abstract:The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil texture using a regional spectral library and its usefulness on classification. We collected 3750 soil samples covering 3 million ha within strong soil class variations in São Paulo State. The spectral analyses of soil samples from topsoil and subsoil were measured in laboratory (400-2500 nm). The potential of a regional soil spectral library was evaluated on the discrimination of soil texture. We considered two types of soil texture systems, one related with soil classification and another with soil managements. The soil line technique was used to assess differentiation between soil textural groups. Soil spectra were summarized by principal component analysis (PCA) to select relevant information on the spectra. Partial least squares regression (PLSR) was used to predict texture. Spectral curves indicated different shapes according to soil texture and discriminated particle size classes from clayey to sandy soils. In the visible region, differences were small because of the organic matter, while the short wave infrared (SWIR) region showed more differences; thus, soil texture variation could be differentiated by quartz. Angulation differences are on a spectral curve from NIR to SWIR. The statistical models predicted clay and sand levels with R 2 = 0.93 and 0.96, respectively. Indeed, we achieved a difference of 1.2% between laboratory and spectroscopy measurement for clay. The spectral information was useful to classify Ferralsols with different texture classification. In addition, the spectra differentiated Lixisols from Ferralsols and Arenosols. This work can help the development of computer programs that allow soil texture classification and subsequent digital soil mapping at detailed scales. In addition, it complies with requirements for sustainable land use and soil management.

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Section: Spectroscopic Analyses Of Soil Samplesmentioning

confidence: 84%

Section: Prediction Models Of Soil Attributesmentioning

confidence: 99%

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

et al. 2016

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“…A técnica de espectroscopia de reflectância vem sendo proposta como alternativa para auxiliar os métodos convencionais na determinação de atributos do solo (Madari et al, 2006;Viscarra Rossel et al, 2009;Vendrame et al, 2012). Sousa Junior et al (2011) observaram que a determinação da distribuição granulométrica de uma amostra de solo seco e peneirado, pelo método convencional proposto por Bouyoucos (1962), leva aproximadamente 48 horas, enquanto a mesma determinação feita a partir de informação espectral do solo pelo VIS-NIR-SWIR (do visível e infravermelho próximo ao infravermelho de ondas curtas, 400 a 2.500 nm) leva apenas cerca de 10 min.…”

Section: Introductionunclassified

Abordagens semiquantitativa e quantitativa na avaliação da textura do solo por espectroscopia de reflectância bidirecional no VIS-NIR-SWIR

Franceschini

Demattê

Sato

et al. 2013

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar o potencial da espectroscopia de reflectância no VIS-NIR-SWIR, para a caracterização granulométrica de amostras de solos de diferentes classes texturais, e obter modelos de predição dos teores de argila, silte e areia no solo. Utilizou-se um conjunto de amostras representativas de Latossolos e Argissolo de cinco locais do Estado do Mato Grosso do Sul. Os espectros do visível e do infravermelho próximo ao infravermelho de ondas curtas (de 350 a 2.500 nm) das amostras foram obtidos e analisados. Empregaram-se a análise de componentes principais (ACP), agrupamento por "fuzzy c-means", regressão logística multinomial (RLM) e regressão por mínimos quadrados parciais. Espectros característicos para as diferentes classes texturais e a segregação de amostras de classes texturais e de locais de coleta com características distintas, por meio da ACP, "fuzzy c-means" e RLM, mostram o potencial semiquantitativo dos dados de reflectância no VIS-NIR-SWIR. Obteve-se quantificação satisfatória quanto à argila (R²=0,92, RPD=3,59), ao silte (R²=0,80, RPD=2,15) e à areia (R²=0,87, RPD=2,62). As técnicas de espectroscopia de reflectância podem auxiliar na determinação da textura e da variabilidade espacial do solo com metodologias semiquantitativas ou quantitativas.Termos para indexação: granulometria do solo, espectroscopia de reflectância, estatística multivariada, pedometria, sensoriamento próximo. Semiquantitative and quantitative approaches for soil texture evaluation through VIS-NIR-SWIR bidirectional reflectance spectroscopyAbstract -The objective of this work was to evaluate the potential of VIS-NIR-SWIR reflectance spectroscopy for the characterization of soil particle-size distribution of samples from different textural classes, and to obtain models to predict clay, silt, and sand contents in the soil. A representative sample set of Oxisols and Ultisols from five locations in Mato Grosso do Sul state, Brazil, were used. Visible and near-infrared to short-wave infrared (from 350 to 2,500 nm) spectra of the samples were obtained and analyzed. Principal component analysis (PCA), fuzzy c-means cluster analysis, multinomial logistic regression (MLR), and partial least squares regression were used. Characteristic spectra for the different soil texture classes and segregation of samples from texture classes and from sampling sites with distinct characteristics, through PCA, fuzzy c-means, and RLM, show the semiquantitative potential of the VIS-NIR-SWIR reflectance data. Satisfactory quantification was obtained for clay (R²=0.92, RPD=3.59), silt (R²=0.80, RPD=2.15), and sand (R²=0.87, RPD=2.62). The reflectance spectroscopy techniques can help to assess soil texture and soil spacial variability with semiquantitative or quantitative methodologies.

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“…This study seems to confirm that soil type itself does not affect calibrations for total carbon concentrations, but the textural diversity within the sample population used for calibration influences it. The potential of NIRS to predict soil texture and mineralogy in tropical soils have been assessed by other workers (Ferraresi et al, 2012;Vendrame et al, 2012) who describe the development of the calibration models with NIRS and DRIFTS using samples from different soil textures and taxonomies. Such models indicated robustness, since the prediction of amounts of clay, silt and sand leads to a textural classification very similar to the classification obtained from the results of the classical method.…”

Section: Carbon Quantification Using Nirsmentioning

confidence: 99%

Quantifying soil carbon stocks and humification through spectroscopic methods: A scoping assessment in EMBU-Kenya

Segnini

Posadas²,

Silva

et al. 2019

Journal of Environmental Management

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A B S T R A C TA soil carbon assessment was performed comparing agricultural cropping systems with natural vegetation along a sampling transect spanning different agro-ecologies on the eastern foot slopes of Mount Kenya in Embu county, 125 km from Nairobi, Kenya. The aim was to determine differences in soil carbon stocks and carbon recalcitrance and relate these to soil textural class, altitude, climatic parameters and land use. Soils from main agricultural systems as tea, coffee and maize-based intercropping, as well as from natural vegetation cover were sampled in triplicates, in five layers from 0 to 30 cm in depth and processed for total carbon analysis. The whole soil samples were also analysed using Laser-Induced Fluorescence Spectroscopy (LIFS) to assess carbon humification. Prototype portable equipment intended for future in situ analysis was used in the lab to ascertain the structure of the most recalcitrant and stable carbon present in different agro-ecosystems. In addition, Near Infrared Spectroscopy (NIRS) was tested for the quantitative analysis of soil carbon, showing that it is a reproducible and low-cost method that provided satisfactory results under the processing conditions of the samples. Results showed wide variation in the level and quality of carbon stored in the soils, depending on soil texture, land use, elevation, climate, agricultural practices and land use history. Considering the heterogeneous nature of sampled soils and the performance of NIRS and LIFS, these results can be used as a basis for the development of fully portable systems able to provide rapid, clean and potentially cost-effective relevant information for soil management.

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The potential of NIR spectroscopy to predict soil texture and mineralogy in Cerrado Latosols

Cited by 59 publications

References 35 publications

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

Abordagens semiquantitativa e quantitativa na avaliação da textura do solo por espectroscopia de reflectância bidirecional no VIS-NIR-SWIR

Quantifying soil carbon stocks and humification through spectroscopic methods: A scoping assessment in EMBU-Kenya

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