High resolution 3D mapping of soil organic carbon in a heterogeneous agricultural landscape

Lacoste, Marine; Minasny, Budiman; McBratney, Alex B.; Michot, Didier; Viaud, Valérie; Walter, Christian

doi:10.1016/j.geoderma.2013.07.002

Cited by 145 publications

(93 citation statements)

References 50 publications

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“…Assim, grande parte dos trabalhos propostos por Zinn et al (2005), Malone et al (2009, 2011), Lacoste et al (2014, Wiese et al (2016) utilizam funções para predição de propriedades do solo até cerca de 1 m de profundidade, em estudos focando estoques de carbono. Já no que se refere aos objetivos deste estudo (caracterização da composição granulométrica e diferenciação entre solos hidromórficos), é justificável que a análise considere dados para profundidade superiores à camada de 1 m de solo, quando disponíveis.…”

Section: Resultsunclassified

“…No entanto, no que tange à variação vertical da composição granulométrica, mais estudos são necessários para melhor entendimento desta propriedade dos solos. Nesse sentido, os esforços empregados na pedometria propõem a aplicação de funções de profundidade para representação da variabilidade de propriedades do solo em perfil, conforme demonstrado por Ponce-Hernandez et al (1986), Bishop et al (1999), , Malone et al (2009, 2011), Mishra et al (2009), Lacoste et al (2014, Liu et al (2016) e Wiese et al (2016). A grande maioria desses estudos aplicam variações da função "spline" e tratam da avaliação do estoque de carbono orgânico em função da profundidade do solo.…”

Section: Introductionunclassified

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Ferramentas de pedometria para caracterização da composição granulométrica de perfis de solos hidromórficos

Pinheiro

Chagas

Júnior

et al. 2016

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar ferramentas pedométricas para caracterização da composição granulométrica (areia, silte e argila) e comparação de perfis de solos hidromórficos. O estudo foi realizado na bacia hidrográfica do rio Guapi-Macacu, RJ. Foram aplicados o algoritmo "slice-wise" para o fatiamento do perfil em intervalos de 1 cm, e a função "spline" para harmonizar o conjunto de dados segundo seis intervalos de profundidade predefinidos. A análise do conjunto de perfis, por meio das funções de profundidade (slice-wise e spline) e de dendrograma de dissimilaridade, revelou que Planossolos e Gleissolos são relativamente rasos e apresentam camadas arenosas nos horizontes superficiais e teores de argila e silte irregulares em subsuperficie. Essas duas ordens ocorrem na área da bacia hidrográfica geralmente em planícies aluviais, em vales e em sopés de elevação. Os Neossolos Flúvicos apresentaram maiores quantidades de argila e silte em superfície, as quais decrescem em profundidade, e são mais profundos e ocorrem nos terraços aluviais. A abordagem permitiu descrever a variabilidade da textura dos solos hidromórficos em perfil e realizar a análise de similaridade entre as ordens, o que pode apoiar a distinção de fases em unidades de mapeamento e a identificação de critérios quantitativos para a classificação dos solos.Termos para indexação: algoritmo para pedologia quantitativa, banco de dados pedológicos, classificação do solo, funções de profundidade, harmonização de dados, mapeamento do solo. Pedometric tools to characterize soil granulometric composition from hydromorphical soil profilesAbstract -The objective of this work was to evaluate pedometric tools to characterize the particle size distribution (sand, silt, and clay) and to compare the profiles of hydromorphic soils. The study was carried out in the Guapi-Macacu watershed, in the state of Rio de Janeiro, Brazil. The slice-wise algorithm was applied to slice the profile data at 1-cm intervals, and the equal-area spline function for database harmonization, according to six predefined depth intervals. The analysis of the soil profile collection, using the soil-depth functions (slice-wise and spline) and the similarity analysis, revealed that Alfisols and Entisols are relatively shallow and have coarse texture in the topsoil layer and a more irregular distribution of clay and silt along the soil profile. These two orders usually occur in the watershed area related to floodplains, valleys, and footslopes. Fluvisols had higher amounts of clay and silt in the topsoil layer, which decrease with soil depth, and they are deeper and occur in alluvial terraces. The approach allowed to characterize the variability of texture along hydromorphical soil profiles and to perform a similarity analysis between soil orders, which can support the differentiation of soil mapping units and the identification of quantitative criteria for soil classification.Index terms: algorithm for quantitative pedology, pedological database, soil classification, soil-depth ...

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Section: Resultsunclassified

Section: Introductionunclassified

Ferramentas de pedometria para caracterização da composição granulométrica de perfis de solos hidromórficos

Pinheiro

Chagas

Júnior

et al. 2016

Pesq. agropec. bras.

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“…Below 15 cm depth, soils under pasture have a higher capacity to accumulate SOC which is commonly found (Guo and Gifford, 2002;Lacoste et al, 2014;Nieder and Benbi, 2008). At interval depth 30-60 cm, regardless of soil type or land use, the soil accumulates more carbon than if the soil was under forest.…”

Section: Soil C Changesmentioning

confidence: 98%

Digital mapping of soil carbon in a viticultural region of Southern Brazil

et al. 2016

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“…Also, the information of the depth distribution is summarized in only 4 parameters, one of which (surface SOC content) is directly measurable. In contrast to other methods (e.g., spline function, Lacoste et al, 2014), only three coefficients are required to map the full 3D SOC content distribution continuously in both the horizontal (i.e., spatial) and vertical (i.e., with depth) dimensions.…”

Section: External Validation With Dataset Amentioning

confidence: 99%

“…They concluded that patterns of soil redistribution greatly improved the prediction of subsoil SOC patterns. By using equal area quadratic smoothing spline functions at the regional scale, Lacoste et al, 2014;Malone et al, 2009 mapped the SOC content for individual soil layers. Although these approaches provide a first assessment of the three-dimensional patterns of SOC storage, they focus largely on the spatial (i.e., horizontal) patterns; the use of fixed and relatively large depth intervals (25-40 cm or topsoil versus subsoil) results in low-resolution characterization of the vertical SOC profile.…”

Section: Introductionmentioning

confidence: 99%

High resolution characterization of the soil organic carbon depth profile in a soil landscape affected by erosion

Aldana-Jague

Sommer

Saby

et al. 2016

Soil and Tillage Research

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A B S T R A C TThe identification of soil management strategies as well as the evaluation of their effectiveness requires detailed information on the spatial and temporal patterns of soil organic carbon storage. High-resolution SOC profile data are generally not available and traditional methods for collecting these are time consuming and costly. Recent studies use geo-statistical approaches to assess the three-dimensional patterns of SOC storage. However, there is still a large discrepancy between the continuous and high resolution mapping of the horizontal SOC variability on the one hand, and the coarse and discontinuous mapping of the vertical SOC profile on the other. In this study, we combine spectroscopic techniques with spatial modeling in a small, cultivated catchment in Germany and we evaluate the contribution of soil redistribution processes and topographical parameters to the observed spatial and vertical patterns. Using high-resolution data from soil cores, we evaluated the robustness of a third order polynomial function to model the vertical SOC profile. Using a crossvalidation, our results show that this approach results in a robust model (RSME = 0.24%) and performs better than the widely used exponential depth model (RMSE = 0.39%). In a next step, we evaluated the relationship between the parameters of the SOC depth model and co-variables including soil redistribution (inferred from 137 Cs data) and topographical indices using a multiple linear regression model. The performance was calculated by cross-validation and we found a low robustness of the models because of the low number of profiles (i.e. n = 19). A statistical evaluation of the co-variables highlighted two key factors influencing the SOC vertical distribution. Soil redistribution processes mainly influenced the surface SOC content (first centimeters) whereas the shape of the depth distribution was controlled by slope curvature alone. The mapping of polynomial parameters was validated using an external SOC profile dataset and showed a poor prediction of the surface content but a good prediction of the depth distribution once the surface SOC content is known (RMSE = 0.15-0.25%C). This suggests that estimating the vertical SOC profile from topsoil data by applying remote sensing data, in combination with our SOC profile model, is promising and can will result in an accurate mapping of 3D SOC patterns at a very high resolution.

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High resolution 3D mapping of soil organic carbon in a heterogeneous agricultural landscape

Cited by 145 publications

References 50 publications

Ferramentas de pedometria para caracterização da composição granulométrica de perfis de solos hidromórficos

Ferramentas de pedometria para caracterização da composição granulométrica de perfis de solos hidromórficos

Digital mapping of soil carbon in a viticultural region of Southern Brazil

High resolution characterization of the soil organic carbon depth profile in a soil landscape affected by erosion

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