Regional modelling of soil carbon at multiple depths within a subtropical watershed

Vasques, Gustavo M.; Grunwald, Sabine; Comerford, N. B.; Sickman, J.O.

doi:10.1016/j.geoderma.2010.03.002

Cited by 92 publications

(49 citation statements)

References 48 publications

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“…Kravchenko & Robertson (2007), mapping total carbon using topographic and crop yield as covariates, observed little or no improvement with RK in comparison to OK in 12 sites of 0.36 ha in Michigan, in the United States. In the same country, in Florida, RK outperformed OK for total carbon in three out of five depth intervals (Vasques et al, 2010). As reported in previous studies, preference for RK over OK is not granted nor expected, and there is no clear indication of when to use OK, RK, or any other geostatistical method for more accurate results.…”

Section: Introductionmentioning

confidence: 52%

“…bras., Brasília, v.51, n.9, p.1371-1385, set. 2016 DOI: 10.1590/S0100-204X2016000900036 Previous studies have also shown little or no improvement of RK over OK, at plot (Kravchenko & Robertson, 2007), farm/catchment (Zhu & Lin, 2010), or watershed scale (Vasques et al, 2010). Kravchenko & Robertson (2007) found that RK produced only a modest improvement in accuracy compared to OK and performed poorly in data sets with strong spatial correlation in the target variable, even when the regression model was relatively strong.…”

Section: Resultsmentioning

confidence: 98%

“…In two contrasting landscapes (agricultural versus forested) in the United States, RK was superior to OK only when the spatial structure could not be well captured by point-based observations or when a strong relationship existed between the target soil property and the covariates (Zhu & Lin, 2010). In the same country, in Florida, Vasques et al (2010) observed that the preference for RK over OK depended on the depth of the measurement and on the regression method used to estimate soil total carbon in a 3,585-km 2 watershed. Studies in tropical dry forest with similar extents or sampling densities were not found to compare with the results obtained in the present study.…”

Section: Resultsmentioning

confidence: 99%

“…One of its variants -regression kriging (RK) -has received special attention as a means to incorporate the variation of soil-forming environmental factors (so-called environmental covariates) into the variation of the target soil property (Knotters et al, 1995;Kravchenko & Robertson, 2007;Vasques et al, 2010). Regression kriging integrates remote, field, and laboratory data, and statistical methods in a quantitative estimation framework for soil property or class mappingdigital soil mapping -, with applications ranging from precision agriculture (Grunwald et al, 2015) to global mapping (Hengl et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil

Vasques

Coelho

Dart

et al. 2016

Pesq. agropec. bras.

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-The objective of this work was to compare ordinary kriging with regression kriging to map soil properties at different depths in a tropical dry forest area in Brazil. The 11 soil properties evaluated were: organic carbon content and stock; bulk density; clay, sand, and silt contents; cation exchange capacity; pH; water retention at field capacity and at permanent wilting point; and available water. Samples were taken from 327 sites at 0.0-0.10, 0.10-0.20, and 0.20-0.40-m depths, in a tropical dry forest area of 102 km 2 .Stepwise linear regression models for particle-size fractions and water retention properties had the best fit. Relief and parent material covariates were selected in 31 of the 33 models (11 properties at three depths) and vegetation covariates in 29 models. Based on external validation, ordinary kriging obtained higher accuracy for 21 out of 33 property x depth combinations, indicating that the inclusion of a linear trend model before kriging does not necessarily improve predictions. Therefore, for similar studies, the geostatistical methods employed should be compared on a case-by-case basis.Index terms: caatinga, digital soil mapping, gamma radiometric survey, geostatistics, pedometrics. Mapeamento de carbono, frações granulométricas e água do solo em Floresta Tropical Seca no BrasilResumo -O objetivo deste trabalho foi comparar krigagem ordinária com regressão-krigagem para mapear atributos do solo, em diferentes profundidades, em área de Floresta Tropical Seca no Brasil. Os 11 atributos do solo avaliados foram: conteúdo e estoque de carbono orgânico; densidade do solo; conteúdos de argila, areia e silte; capacidade de troca catiônica; pH; retenção de água na capacidade de campo e no ponto de murcha permanente; e água disponível. As amostras foram retiradas de 327 locais a 0,0-0,10, 0,10-0,20 e 0,20-0,40 m de profundidade, em área de Floresta Tropical Seca de 102 km 2 . Os modelos de regressão linear "stepwise" tiveram o melhor ajuste para as frações granulométricas e as propriedades de retenção de água. Foram selecionadas covariáveis de relevo e material de origem em 31 dos 33 modelos (11 atributos em três profundidades) e de vegetação em 29 modelos. Com base na validação externa, a krigagem ordinária obteve maior acurácia para 21 das 33 combinações atributo vs. profundidade, o que é indicativo de que a inclusão de um modelo linear de tendência antes da krigagem não necessariamente melhora as predições. Portanto, para estudos semelhantes, os métodos geoestatísticos empregados devem ser comparados caso a caso.Termos para indexação: caatinga, mapeamento digital de solos, levantamento gamarradiométrico, geoestatística, pedometria.

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Section: Introductionmentioning

confidence: 52%

Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil

Vasques

Coelho

Dart

et al. 2016

Pesq. agropec. bras.

View full text Add to dashboard Cite

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“…The clay content at the subsurface was better predicted by RK than OK, with the regression model selecting relief, multispectral, and radar variables. Although this was the only soil attribute better predicted by RK, the preference of OK over RK is not unusual, and has been reported elsewhere [37][38][39][40]. Thus, there is potential to improve soil attribute predictions by adding remote sensing covariates.…”

Section: Potential Of Using Multispectral and Radar Data As Covariatesmentioning

confidence: 52%

Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data

et al. 2017

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Soils from the remote areas of the Amazon Rainforest in Brazil are poorly mapped due to the presence of dense forest and lack of access routes. The use of covariates derived from multispectral and radar remote sensors allows mapping large areas and has the potential to improve the accuracy of soil attribute maps. The objectives of this study were to: (a) evaluate the addition of relief, and vegetation covariates derived from multispectral images with distinct spatial and spectral resolutions (Landsat 8 and RapidEye) and L-band radar (ALOS PALSAR) for the prediction of soil organic carbon stock (CS) and particle size fractions; and (b) evaluate the performance of four geostatistical methods to map these soil properties. Overall, the results show that, even under forest coverage, the Normalized Difference Vegetation Index (NDVI) and ALOS PALSAR backscattering coefficient improved the accuracy of CS and subsurface clay content predictions. The NDVI derived from RapidEye sensor improved the prediction of CS using isotopic cokriging, while the NDVI derived from Landsat 8 and backscattering coefficient were selected to predict clay content at the subsurface using regression kriging (RK). The relative improvement of applying cokriging and RK over ordinary kriging were lower than 10%, indicating that further analyses are necessary to connect soil proxies (vegetation and relief types) with soil attributes.

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Hot Spots and Hot Moments for Greenhouse Gas Emissions from Soils

Hagedorn

Bellamy

2011

Soil Carbon in Sensitive European Ecosystems

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Regional modelling of soil carbon at multiple depths within a subtropical watershed

Cited by 92 publications

References 48 publications

Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil

Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil

Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data

Hot Spots and Hot Moments for Greenhouse Gas Emissions from Soils

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